jvaton/clinical-trials-xml-2018-2024 · Datasets at Hugging Face

[ { "type": "text", "content": "A Phase I Study of Pelabresib (CPI-0610), a Small-Molecule Inhibitor of BET Proteins, in Patients with Relapsed or Refractory Lymphoma" } ]

Purpose: NF-κB, a transcription factor essential for inflammatory responses, is constitutively activated in many lymphomas. In preclinical studies, pelabresib (CPI-0610), an investigational (BET) bromodomain inhibitor, downregulated NF-κB signaling and demonstrated antitumor activity in vitro . Here we report the safety, pharmacokinetics, pharmacodynamics, and preliminary clinical activity from the first-in-human phase I study of pelabresib in patients with relapsed/refractory lymphomas (NCT01949883). Experimental Design: Sixty-four patients with relapsed/refractory lymphoma (median of 4 prior lines of therapy) were treated with either capsule (6, 12, 24, 48, 80, 120, 170, 230, 300 mg) or tablet (125, 225 mg) doses of pelabresib orally once daily on a 14 days on, 7 days off schedule. Results: The MTD was determined as the 225 mg tablet daily. The most frequent adverse events were fatigue, nausea, and decreased appetite. Thrombocytopenia, a class effect for all BET inhibitors, was dose-dependent, reversible, and noncumulative. Pelabresib exhibited dose-proportional increases in systemic exposure, rapid absorption, and a half-life of approximately 15 hours (supporting once daily dosing). The bioavailability of the tablet formulation was 60% greater than the capsules. Pelabresib suppressed IL8 and CCR1 mRNA at doses above 120 and 170 mg, respectively. Four patients (6.2%) had an objective response (2 complete response and 2 partial response) and 5 patients had prolonged stable disease. Conclusions/Discussion: Pelabresib is capable of BET target gene suppression in an exposure-dependent manner with an acceptable safety profile leading to the recommended phase II dose of the 125 mg tablet once daily. Significance: BET proteins inhibition can potentially modify the pathogenic pathways which contribute to many diseases including malignancies. Pelabresib (CPI-0610), a potent and selective small molecule BET proteins inhibitor, has a MTD of 225 mg once daily for 14 days with a 7-day break, clear pharmacokinetic/pharmacodynamic relationship, and manageable clinical safety profile. These findings are part of the foundation for the ongoing pivotal study of pelabresib in patients with myelofibrosis.

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[ { "order": "1", "subsections": [], "section_title": "Introduction", "paragraphs": [ { "aggregated_text": "Bromodomain and extraterminal domain (BET) proteins include a family of four related proteins (BRD2, BRD3, BRD4, and BRDT), each containing two tandem amino-terminal bromodomains (BD1 and BD2) that regulate the expression of an array of genes ( 1, 2 ). BET inhibition has the potential to modify multiple critical components of lymphomagenesis, including cell proliferation, cell fate, and survival ( 3, 4 ). BET inhibition can attenuate Nuclear Factor-κB (NF-κB) pathway and IκB kinase signaling resulting in rapid reversal of expression of specific genes including B-cell lymphoma 2, IL6 , IL8 , and IL10 ( 5 ). Modulation of C-C motif chemokine receptor 1 (CCR1) by BET inhibition has been observed in a number of human hematologic and solid tumor cell lines, xenograft models, and ex vivo treated human peripheral blood mononuclear cells ( 6, 7 ). In addition, 50% suppression of CCR1 expression has been used as a pharmacodynamic (PD) marker of BET inhibition in clinical trials, where it was associated with clinical response in patients with relapsed or refractory lymphoma ( 8 ).", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "While BET proteins have a broad distribution across the genome and BET inhibition has widespread effects on gene expression, interference with BET protein binding to chromatin using small-molecule inhibitors of their bromodomains has more circumscribed and selective effects. Three groups ( 9–11 ) have independently confirmed that suppression of MYC transcription plays a dominant role in mediating the phenotypic effects of BET inhibition. Maximal inhibition of transcription is achieved within approximately 4 hours, and is accompanied by decreases in the level of MYC protein.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Pelabresib (CPI-0610) is a synthetic, small-molecule inhibitor of the tandem amino-terminal bromodomains (BD1 and BD2) of BET proteins ( 12 ) currently in clinical development.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "In preclinical studies, pelabresib treatment results in downregulation of NF-κB signaling activity, accompanied by loss of viability in ABC- diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL) cell lines. A study conducted in immunodeficient mice bearing subcutaneous xenografts of Raji Burkitt lymphoma demonstrated that pelabresib inhibits the expression of MYC in tumor tissue in a dose-dependent manner. Rearrangement of the MYC proto-oncogene serves as a defining feature for BL, but it also occurs in subsets of DLBCL and high-grade B-cell lymphomas. Following administration of a single 30 mg/kg oral dose of pelabresib, maximum inhibition of MYC expression (75%) was achieved 4 hours postdosing (the earliest postdosing time point assessed). Suppression of MYC expression persisted at 8 hours postdosing, but by 12 hours, MYC expression had returned to its baseline value, consistent with pelabresib's rapid clearance in mice ( 11 ).", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Here we present the safety, pharmacokinetics and pharmacodynamics (PK/PD), antitumor activity results, and maximum tolerated dose (MTD) from a first-in-human phase 1 study in patients with relapsed or refractory lymphoma to support the recommended phase 2 dose (RP2D) and dosing schedule of pelabresib.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2", "subsections": [ { "order": "2.1", "subsections": [ { "order": "2.1.1", "subsections": [], "section_title": "Endpoints", "paragraphs": [ { "aggregated_text": "The primary objective of the study was to determine the MTD of pelabresib with characterization of dose-limiting toxicities (DLT) as a primary endpoint. Secondary objectives included adverse events (AE) and serious adverse events (SAE); tumor response according to 2007 Revised Response Criteria for Malignant Lymphoma ( 13 ); plasma concentration of pelabresib; PK parameters; changes in expression of MYC and other genes sensitive to BET inhibition; and changes in levels of selected mRNAs that are expressed in PBMCs and are sensitive to BET inhibition.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "To characterize the plasma PK of pelabresib for the first dose and at steady state for multiple dosing, blood samples were taken before and at 0.5, 1, 1.5, 2, 3, 4, 6, 8, and 24 hours after the initial dose; before dosing on Day 8; before and at 0.5, 1, 1.5, 2, 3, 4, 6, 8, 24, 48, 72, 96, and 120 hours after the Day 14 dose; and prior to dosing on Day 1 of Cycle 2. Blood samples were centrifuged within 30 minutes of collection to harvest the plasma which was promptly stored in cryovials maintained at −80°C. The concentration of CPI-0610 in plasma samples was determined by reverse-phase high performance liquid chromatography with tandem mass spectrometric detection. The assay was validated and applied to the analysis of study samples according to the recommendations in the current FDA guidance. At the lowest concentration included in the calibration curves, which was 0.25 ng/mL, interday accuracy was within 0.3% of the nominal concentration and the precision was 3.0%. Interday accuracy ranged from 99.9 to 101.2% and the precision ranged from 4.0 to 6.0% for all other calibration standards. Actual time points were calculated as the difference between the blood sample collection time and the time that the drug was taken. The plasma concentration–time data were analyzed by noncompartmental methods using WinNonlin version 5.0.1 (Pharsight Corp). Area under the plasma concentration–time curve from the time of dosing to the time of last sample collected prior to administration of the next dose (AUC 0–24 ) was estimated using the linear-log trapezoidal method. Apparent oral clearance (CL/F) was calculated as the dose divided by the AUC 0–24 for the dose given on Day 14. Pharmacokinetic parameters are reported as the geometric mean [geometric coefficient of variation (CV%)] of the values for individual patients at each dose level unless otherwise indicated.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Whole (peripheral) blood samples were also collected at selected time points that coincided with PK sampling. Blood from patients was drawn into PAXgene blood RNA tubes (Becton Dickinson) at the clinical sites and shipped frozen to the Massachusetts General Hospital (MGH, Boston, MA) for processing. RNA was purified using a PAXgene Blood RNA Kit IVD (Qiagen) according to the manufacturer's protocol and quantitated using a Nanodrop 8000 (Thermo Scientific). RNA isolated from PBMCs was used to synthesize cDNA for qPCR analysis and gene expression was assessed by qPCR using a CCR1 or IL8 TaqMan assay. Housekeeping genes were assessed using B2M TaqMan assay, PPIB Universal Probe Library (UPL) or TaqMan assay. Relative gene expression for CCR1 and IL8 was determined after normalization to the geometric mean of the two housekeeping genes ( B2M and PPIB ).", "non_text_elements": { "figures": [], "tables": [] } } ] } ], "section_title": "Study Design", "paragraphs": [ { "aggregated_text": "Study 0610–01 was a first-in-human phase 1, multi-center, open-label, dose-escalation study of pelabresib (CPI-0610) in patients with relapsed or refractory lymphoma (NCT01949883).", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Eligible patients for treatment with pelabresib were adults with relapsed non-Hodgkin or Hodgkin lymphoma after at least one prior therapy or who were not eligible for high-dose chemotherapy and autologous stem cell transplantation or had refused high-dose chemotherapy and autologous stem cell transplantation. Patients were required to have platelet count ≥75 × 10 9 /L, absolute neutrophil count ≥1.0 × 10 9 /L, and Eastern Cooperative Oncology Group performance status of 2 or less.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Pelabresib was formulated as both capsules and tablets for oral administration. The capsule formulation consisted of CPI-0610 monohydrate (2, 10, or 25 mg), microcrystalline cellulose PH101, and magnesium stearate in hydroxypropyl methyl cellulose capsules. A tablet formulation was introduced after the study had been initiated which consisted of micronized CPI-0610 monohydrate (25 mg) and inactive excipients that included microcrystalline cellulose, lactose monohydrate, hydroxypropyl cellulose, croscarmellose sodium, sodium lauryl sulfate, colloidal silicon dioxide, and magnesium stearate.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Pelabresib was administered once daily (QD) for 14 days followed by a 7-day break, in continuous 21-day cycles to prevent/ameliorate thrombocytopenia. Cohorts of 3 to 6 patients were sequentially enrolled with increasing doses of pelabresib (6, 12, 24, 48, 80, 120, 170, 230, and 300 mg with the capsule formulation and 125 mg and 225 mg with the micronized tablet formulation) until the MTD was determined. Pelabresib was administered under fasting conditions (no food intake 2 hours before until 1 hour after study drug administration).", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.2", "subsections": [], "section_title": "Statistical Analysis", "paragraphs": [ { "aggregated_text": "The MTD of pelabresib was defined as the highest dose that could be given without causing DLT in 33% or more of the patients treated at that dose (capsules or tablets). Dose-escalation followed a traditional “3 + 3” design based on the frequency of DLT. All patients who were treated with at least 1 dose of pelabresib were included in the Safety Population and used for all tabulations of demographic information, baseline characteristics, safety, PK, pharmacodynamic, and efficacy analyses.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.3", "subsections": [], "section_title": "Data Availability Statement", "paragraphs": [ { "aggregated_text": "The data generated in this study are not publicly available due to patient privacy but are available upon reasonable request from the corresponding author.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.4", "subsections": [], "section_title": "Study Conduct", "paragraphs": [ { "aggregated_text": "The study was approved by the institutional review board at each participating site and was conducted in accordance with the principles of the Declaration of Helsinki. The study was overseen by an independent safety review committee. Written informed consent was obtained from each patient or from the patient's legally authorized representative prior to study entry.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Data were collected by the study investigators and analyzed by the study sponsor, Constellation Pharmaceuticals. All of the authors, including authors employed by the study sponsor, contributed to authoring the manuscript for publication.", "non_text_elements": { "figures": [], "tables": [] } } ] } ], "section_title": "Materials and Methods", "paragraphs": [] }, { "order": "3", "subsections": [ { "order": "3.1", "subsections": [], "section_title": "Patient Disposition and Demographics", "paragraphs": [ { "aggregated_text": "Between September 2013 and December 2017, 64 patients were enrolled and treated with pelabresib. Forty-seven patients enrolled in the study were treated with the capsule formulation of pelabresib at 9 dose levels ranging from 6 mg to 300 mg QD ( Supplementary Fig. S1 ). Seventeen patients received the tablet formulation at 125 mg ( n = 7) and 225 mg ( n = 10) daily. As 1 of the initial 3 patients experienced a DLT, additional patients were dosed in the 6, 48, 80, 170, and 230 mg capsule and 125 mg tablet cohorts. None of these additional patients experienced DLTs and dose escalation was able to continue until the MTD definition was met.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "All patients discontinued pelabresib treatment and progressive disease was the most commonly reported reason for treatment discontinuation (50 patients, 78.1%). The remaining 14 pts discontinued pelabresib treatment either due to AE, PI decision, patient consent withdrawal or death.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "In total, 5 (7.8%) patients discontinued from the study, 1 each due to no active lymphoma, alternative treatment, death from progressive disease, AEs or laboratory abnormalities (grade 3 AEs of hyponatremia, confusional state, and hypertension) and disease progression, respectively.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The median age was 63 years, and the most common lymphoma subtype was diffuse large B-cell lymphoma (56.3%), followed by follicular lymphoma (12.5%), and Hodgkin lymphoma (7.8%). Patients had received a median of 4 prior therapies, and the mean time from diagnosis to study entry was 42.62 months ( Table 1 ).", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "[TABLE] TABLE 1 Demographic characteristics and disease characteristics Characteristic Overall N = 64 Sex, n (%) Male 48 (75.0) Female 16 (25.0) Race, n (%) White/Caucasian 60 (93.8) Asian 2 (3.1) Other 2 (3.1) Ethnicity, n (%) Latino/Hispanic 4 (6.3) Not Latino/Hispanic 59 (92.2) Declined 1 (1.6) Age (years) Median 63.0 Min, Max 23, 92 Lymphoma type, n (%) Diffuse large B-cell lymphoma 36 (56.3) Follicular lymphoma 8 (12.5) Hodgkin lymphoma 5 (7.8) Mantle cell lymphoma 3 (4.7) Other 3 (4.7) Burkitt lymphoma 2 (3.1) Mycosis Fungoides 2 (3.1) Nodal marginal zone B-cell lymphoma 2 (3.1) Lymphoplasmacytic lymphoma 1 (1.6) Peripheral T-cell lymphoma (not otherwise specified) 1 (1.6) Small lymphocytic lymphoma 1 (1.6) Time since initial diagnosis (months) Mean (STD) 42.62 (42.416) Median 29.45 Min, Max 4.4, 240.8 Number of lines of prior systemic therapy (for lymphoma) Median 4.0 Min, Max 1, 12 1 2 (3.1) 2 14 (21.9) 3 14 (21.9) ≥4 34 (53.1) Abbreviations: Max, maximum; Min, minimum; STD, standard deviation.", "non_text_elements": { "figures": [], "tables": [ "tables/PMC10010313/table_1.md" ] } } ] }, { "order": "3.2", "subsections": [], "section_title": "Safety", "paragraphs": [ { "aggregated_text": "All 64 patients in the safety analysis set received at least one dose of pelabresib. Thirty-seven of 64 (58%) patients completed 1 cycle of treatment and 42% completed 2 cycles. Median number of cycles completed was 2 (range: 1 to 66). The proportion of patients treated with 2 cycles was similar between capsule and tablet formulations ( Supplementary Table S1 ). The median relative dose intensity was generally high (approximately 70% to 100%) and consistent across treatment cycles 1 to 3.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "All 64 patients had at least 1 treatment-emergent adverse event (TEAE) during the study; 44 (68.8%) patients had at least 1 TEAE of Grade 3 or higher intensity. Treatment-emergent AEs that led to treatment discontinuation were reported for 9 (14.1%) patients: 3 from progressive disease; 2 from elevated liver enzymes; 1 from hyponatremia, confusion, and hypertension and 1 each from diarrhea, fatigue, and thrombocytopenia. Only disease progression led to treatment discontinuation in more than 2 patients (3 [4.7%] patients). In total, 9 (14.1%) patients died while on study, for all of whom the cause of death was reported as disease progression; none of these deaths were considered related to the investigational product.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The most frequently reported TEAEs (≥15%) regardless of relationship to study drug are presented in Table 2 . Hematologic TEAEs occurring in more than 2 patients in any dose group were anemia, thrombocytopenia events, and lymphopenia. Hematologic TEAEs with an intensity of Grade 3 or higher included thrombocytopenia events in 13 (20.3%) and anemia in 7 (10.9%) patients. Thrombocytopenia led to discontinuation of pelabresib for 1 patient in the 125 mg tablet dose group.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "[TABLE] TABLE 2 Summary of most frequently reported treatment-emergent adverse events regardless of relationship to study drug Capsule formulation Tablet formulation System organ class preferred term 6 mg N = 5 n (%) 12 mg N = 3 n (%) 24 mg N = 3 n (%) 48 mg N = 7 n (%) 80 mg N = 6 n (%) 120 mg N = 4 n (%) 170 mg N = 7 n (%) 230 mg N = 9 n (%) 300 mg N = 3 n (%) 125 mg N = 7 n (%) 225 mg N = 10 n (%) Overall N = 64 n (%) Hematologic events Thrombocytopenia events a 0 0 1 (33.3) 1 (14.3) 3 (50.0) 2 (05.0) 4 (57.1) 6 (66.7) 2 (66.7) 4 (57.1) 6 (60.0) 29 (45.3) Anemia 1 (20.0) 1 (33.3) 0 2 (28.6) 1 (16.7) 2 (50.0) 0 4 (44.4) 1 (33.3) 2 (28.6) 2 (20.0) 16 (25.0) Lymphopenia 0 0 0 1 (14.3) 2 (33.3) 1 (25.0) 1 (14.3) 4 (44.4) 0 1 (14.3) 3 (30.0) 13 (20.3) Nonhematologic events Gastrointestinal disorders Nausea 1 (20.0) 2 (66.7) 1 (33.3) 0 1 (16.7) 0 0 3 (33.3) 2 (66.7) 4 (57.1) 4 (40.0) 18 (28.1) Vomiting 1 (20.0) 1 (33.3) 1 (33.3) 1 (14.3) 3 (50.0) 1 (25.0) 0 2 (22.2) 0 0 3 (30.0) 13 (20.3) Diarrhea 1 (20.0) 0 0 0 0 1 (25.0) 3 (42.9) 2 (22.2) 2 (66.7) 1 (14.3) 2 (20.0) 12 (18.8) Other nonhematologic events Fatigue 2 (40.0) 1 (33.3) 1 (33.3) 3 (42.9) 2 (33.3) 2 (50.0) 1 (14.3) 3 (33.3) 0 3 (42.9) 5 (50.0) 23 (35.9) Decreased appetite 0 0 0 2 (28.6) 3 (50.0) 1 (25.0) 1 (14.3) 2 (22.2) 0 3 (42.9) 5 (50.0) 17 (26.6) Hyperglycemia 1 (20.0) 1 (33.3) 0 0 0 1 (25.0) 1 (14.3) 2 (22.2) 1 (33.3) 1 (14.3) 4 (40.0) 12 (18.8) Dysgeusia 0 0 0 0 1 (16.7) 0 0 3 (33.3) 1 (33.3) 1 (14.3) 4 (40.0) 10 (15.6) a Include MedDRA PTs platelet count decreased and thrombocytopenia.", "non_text_elements": { "figures": [], "tables": [ "tables/PMC10010313/table_2.md" ] } }, { "aggregated_text": "Gastrointestinal TEAEs occurred in 43 (67.2%) patients, with at least 1 TEAE occurring in each dose group. Gastrointestinal TEAEs occurring in more than 2 patients in any dose group were nausea, vomiting, and diarrhea. Of these, diarrhea was the only TEAE with an intensity of Grade 3 or higher, which occurred in 4 (6.3%) patients (all were treated with the capsule formulation of pelabresib). Diarrhea also led to discontinuation of pelabresib for 1 patient in the 230 mg capsule dose group. Other non-hematologic TEAEs occurring in more than 2 patients in any dose group were fatigue, decreased appetite, hyperglycemia, and dysgeusia.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Treatment-emergent SAEs were reported for 27 (42.2%) patients. Serious AEs reported for more than 1 patient (in descending order) were disease progression (7 patients grade 5 and 1 patient grade 3), diarrhea (4 patients grade 3), acute renal failure (1 patient grade 4 and 2 patients grade 2), pain (2 patients grade 3), and pneumonia (2 patients grade 3).", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "There was no apparent dose relationship for most TEAEs; however, a pattern is difficult to detect owing to the small number of patients in each dose group.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The relationship between platelet count values on Cycle 1 Day 1 and Cycle 1 Day 14 and pelabresib treatment group is displayed in Fig. 1A and 1B . To accommodate for different baseline platelet count values in different patients, changes in platelet count values were expressed as a fraction or ratio of the baseline value. There was a strong correlation between the Day 14:Day 1 platelet count ratio and pelabresib dose ( R 2 = 0.81).", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "[FIGURE] FIGURE 1 Relationship between pelabresib and platelet count values. A, Day 14 platelet count as a function of steady state AUC of pelabresib. B, Day 14 mean platelet count as a percent of baseline compared with pelabresib mean AUC. The coefficient of determination ( R 2 ) = 0.8122, obtained from a simple linear regression model with mean platelet count ratio as the dependent variable and pelabresib capsule dose as the independent variable (tablet doses were multiplied by 1.34 to convert to capsule doses). C, Mean platelet count over time. AUC, area under the curve; CPI-0610, pelabresib; CXDX, Cycle number Day number; X mg C, Capsule pelabresib dose; X mg T, Tablet pelabresib dose. [IMAGE]", "non_text_elements": { "figures": [ "figures/PMC10010313/figure_1.md" ], "tables": [] } }, { "aggregated_text": "When examining platelet count values from individual patients over the course of several cycles, it appeared that after the decline, platelet count values rebounded within the next cycle of treatment, typically peaking by the end of the first week of the next cycle (i.e., Cycle 2, Day 8; Fig. 1C ). This trend continued throughout multiple cycles of pelabresib treatment.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "3.3", "subsections": [], "section_title": "Dose Limiting Toxicities", "paragraphs": [ { "aggregated_text": "Fifty-six out of the 64 treated patients were evaluable for DLT determination ( Supplementary Table S2 ). No DLTs were observed in the 15 evaluable patients treated at 6 mg, 12 mg, 24 mg and 120 mg capsule cohorts. One DLT each was observed in the first 3 evaluable patients treated at 48 mg, 80 mg, 170 mg, 230 mg and 300 mg capsule cohorts, and at 125 mg and 225 mg tablet cohorts. These cohorts were then expanded to additional patients except the 300 mg capsule cohort as a new micronized tablet formulation was introduced at that time.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "One out of 7 evaluable patients treated at 225 mg QD tablet cohort developed a DLT (Grade 4 platelet count decrease; Supplementary Table S2 ), which ordinarily would have led to escalation to the next, higher dose cohort; however, based on the PK/PD, safety, and clinical activity results combined with the results of the other concurrently conducted phase 1 study 0610–02 in patients with myelodysplastic syndromes/myeloproliferative neoplasms (MDS/MPN), the 225 mg QD tablet dose was determined to be the MTD.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "A total of 8 patients had 8 DLTs during the DLT assessment period (Cycle 1; Supplementary Table S2 ). These 8 DLTs included hematologic events ( n = 5), gastrointestinal (GI) events ( n = 2), and rash ( n = 1).", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Three of five hematologic DLT events were Grade 4 thrombocytopenia events (include MedDRA PTs of platelet count decreased and thrombocytopenia). These DLTs of thrombocytopenia events were dose dependent in both frequency and intensity, reversible, noncumulative and not associated with bleeding events. Of these 3 thrombocytopenia events, one event did not require a dose reduction; one required a dose reduction (from 300 mg QD capsule to 170 mg QD capsule); and the other required a dose interruption for approximately 2 weeks, followed by a dose reduction in the following cycle (from 225 mg to 150 mg QD tablet). The remaining two hematologic DLT events were neutropenia, reported for 2 patients: Grade 3 febrile neutropenia lasting for 4 days in 1 patient (dose unchanged from 80 mg QD capsule) and 1 Grade 4 neutropenia lasting for 3 days in the second patient (dose interrupted for approximately 1 week, followed by the same dose of 125 mg QD tablet in the following cycle); Grade 4 neutropenia was not associated with fever, infections or any serious consequences and resolved in 2 days with dose interruption; both neutropenia DLT events were resolved.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The 2 gastrointestinal DLT events were grade 3 diarrhea reported for 2 patients. One patient had diarrhea for 2 days associated with dehydration, required withdrawal of pelabresib (230 mg QD capsule), and the second patient had diarrhea for 7 days associated with dehydration requiring i.v. fluids, did not require a change in dosing (170 mg QD capsule). Both gastrointestinal DLT events were resolved.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The remaining DLT event was a grade 3 erythematous maculopapular rash covering approximately 40% of total body surface area for 11 days that required systemic steroids and a dose interruption for approximately 1.5 weeks, followed by a dose reduction in the following cycle (from 48 mg QD to 24 mg QD capsule).", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "3.4", "subsections": [], "section_title": "Antitumor Activity", "paragraphs": [ { "aggregated_text": "All 64 patients included in the Safety Population were assessed for tumor response.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The overall response rate [complete response (CR) + partial response (PR)] was 6.2% (4 of 64 patients; Table 3 ) in this heavily pretreated population (95.3% of overall patients had at least 1 prior chemotherapy). The best overall response (BOR) included CR observed in 2 (3.1%) patients (1 with T-cell/histocyte-rich DLBCL treated with 48 mg capsule ( Supplementary Fig. S2 ), 1 with ABC-DLBCL treated with 230 mg capsule) and PR observed in 2 (3.1%) patients (1 with follicular lymphoma treated with 230 mg capsule, 1 with ABC-DLBCL treated with 300 mg capsule). It is worth noting that all patients that achieved CR or PR had previously been treated with at least 2 prior chemotherapy regimens. A total of 21 (32.8%) patients achieved stable disease as their BOR; 5 patients (3 patients with follicular lymphoma and 1 patient each with nodal marginal zone B-cell lymphoma and diffuse large B-cell lymphoma) had prolonged (>6 months) stable disease.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "[TABLE] TABLE 3 Demographic characteristics and disease characteristics Response Overall N = 64 Overall response rate (CR + PR), n (%) 4 (6.2) Best overall response, n (%) CR 2 (3.1) PR 2 (3.1) SD 21 (32.8) Time to response (CR or PR), cycles 2–6 Median duration of response (CR or PR), weeks (range) 8.4 (6.1–46.1) Time to treatment (CR or PR), cycles 4–18 NOTE: Tumor response was determined according to the 2007 Revised Response Criteria for Malignant Lymphoma. Abbreviations: CR, complete response; PR, partial response; SD, stable disease.", "non_text_elements": { "figures": [], "tables": [ "tables/PMC10010313/table_3.md" ] } }, { "aggregated_text": "Patients who achieved CR or PR had a time to response between 2 and 6 cycles [CR: 4 (77 days) and 6 (129 days) cycles; PR: 2 (57 days) and 4 (80 days) cycles], median duration of response was 8.4 weeks (range: 6.1 weeks to 46.1 weeks), and remained on treatment for a range of 4 to 18 cycles.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "3.5", "subsections": [], "section_title": "Pharmacokinetics", "paragraphs": [ { "aggregated_text": "Mean plasma concentration–time profiles of pelabresib for the groups of patients evaluated at each dose level are shown in Fig. 2A for the initial dose and in Fig. 2B for the Day 14 dose. Pelabresib was rapidly absorbed with peak plasma concentrations occurring at a median time of 1.9 hours for both the initial dose (range: 0.5– 7.4 hours) and the Day 14 dose (range: 0.9–7.9 hours). Plasma concentrations declined from the peak in a manner that appeared to be biphasic for the majority of patients. Across the capsule dose range studied, pelabresib exposure increased with dose in an approximately proportional manner up to 170 mg ( Fig. 2C ) and exhibited a trend toward saturation of absorption at capsule doses greater than 170 mg. The micronized tablet formulation, developed to address potential solubility limitations of the capsule formulation and minimize the number of capsules, was tested after the 300 mg capsule cohort. Pelabresib exposure was approximately 60% greater for the tablet as compared with the capsule ( Fig. 2C ) and was selected for use in subsequent clinical trials. The geometric mean (geometric CV) terminal half-life of pelabresib was 11.6 (48.2%) hours for the capsule and 15.3 (45.0%) hours for the tablet. Following 14 days of continuous QD dosing, the mean (±SD) accumulation of pelabresib was minimal, 20.5 ± 7.2% for the capsule formulation and 4.3 ± 1.0% for the tablet, consistent with the estimated half-life of each formulation and confirming the suitability of a once-daily dosing schedule. The geometric mean apparent oral clearance of pelabresib in patients receiving the tablet dosage form was 12.3 (27.2%) L/h. In the 10 patients with lymphoma who were treated with the MTD dose of 225 mg QD, the mean maximum plasma concentration (C max ) and AUC 0–24 after the first dose was 2,021 ng/mL and 20,700 ng/h/mL, respectively, which were higher than the mean respective values achieved with capsule doses of 170, 230, and 300 mg QD.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "[FIGURE] FIGURE 2 Mean pelabresib plasma profiles and dose proportionality. A, Mean pelabresib plasma profile over time at Cycle 1 Day 1. B, Mean pelabresib plasma profile over time at Cycle 1 Day 14. C, Dose proportionality of mean steady-state AUC of patients treated with capsule and tablet doses of pelabresib. Subjects receiving a dose other than the planned dose were excluded from the calculation of concentration summary statistics; Predose/negative nominal time were set to 0. [IMAGE]", "non_text_elements": { "figures": [ "figures/PMC10010313/figure_2.md" ], "tables": [] } } ] }, { "order": "3.6", "subsections": [], "section_title": "Pharmacodynamics", "paragraphs": [ { "aggregated_text": "Based on a whole blood assay developed in preclinical studies, IL8 and CCR1 were the most strongly BET-regulated genes exhibiting the strongest exposure dependent downregulation. Thus, examination of these genes in patient samples allowed for the identification of the minimum threshold of exposure required for BET target engagement.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The expression of IL8 ( Fig. 3A ) and CCR1 ( Fig. 3B ) in patient whole blood at 2 hours, 6 hours and 8 hours after administration of pelabresib was tested to understand the PD effect of pelabresib. Patients from dose cohorts above the 120 mg capsule dose showed appreciable changes in the expression of IL8 , while patients from dose cohorts above the 170 mg capsule dose showed appreciable changes in expression of CCR1 after administration of pelabresib. Patients treated with both tablet doses showed appreciable changes in the expression of IL8 and CCR1 after administration of pelabresib. Although there is some variation in expression at lower doses, suppression of IL8 and CCR1 was much more apparent at exposures conferred by capsule doses greater than or equal to 120 and 170 mg doses, respectively, including both tablet dose levels of pelabresib. The maximum reduction of IL8 expression was observed in patients treated at the 120 mg capsule dose level which approximately corresponds to a 75 mg tablet dose. Decreased expression of both IL8 and CCR1 are seen as early as 2 hours postdose, suggesting rapid transcriptional regulation following treatment. The extent of the decrease of IL8 and CCR1 gene expression observed in patients achieving a CR or PR was consistent with the effect observed in other nonresponding patients treated at the same dose levels.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "[FIGURE] FIGURE 3 Gene expression analysis of IL8 and CCR1 transcript levels after treatment with pelabresib. A, Relative expression of IL8 blood mRNA levels prior to and 2, 6, and 8 hours after pelabresib treatment. B, Relative expression of CCR1 blood mRNA levels prior to and 2, 6, and 8 hours after pelabresib treatment. Relative mRNA expression of IL8 and CCR1 after dosing is shown relative to the pretreatment values (y axis). C, Relative IL8 and CCR1 mRNA expression compared to the Cycle 1 Day 14 steady-state AUC. IL8 and CCR1 are represented as the average at the 2-hour time point following pelabresib administration over that of baseline in each dose group. The steady state (C1D14) AUC0–24 (hour/ng/mL, geometric mean of each dose level) is shown on the right y axis. T = tablet 125 mg and 225 mg were tablet formulations; all other doses were capsule formulation. [IMAGE]", "non_text_elements": { "figures": [ "figures/PMC10010313/figure_3.md" ], "tables": [] } }, { "aggregated_text": "Given that pelabresib exposure increased as dose increased, the relationships between gene expression of IL8 and CCR1 and both pelabresib dose and exposure (AUC 0–24 ) were evaluated. IL8 and CCR1 expression change at the 2-hour timepoint postdose was selected as the inter-patient variation of both genes was less than the 6- or 8-hour timepoints. As shown in Fig. 3C , there is a correlation between suppression of IL8 or CCR1 and exposure to pelabresib, as represented by the geometric mean of pelabresib AUC 0–24 at each dose level. Suppression of BET target gene expression increased with dose escalation.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "To further explore the relationship between pelabresib exposure and suppression of gene expression, individual maximum gene suppression (the nadir of individual gene expression versus time profiles) was plotted as a continuous function of individual C max and AUC for both CCR1 and IL8 . An inhibitory maximum effect (E max ) model of drug effect was fit to the data and demonstrates that both IL8 and CCR1 expression are clearly reduced as pelabresib exposure increases ( Supplementary Fig. S3 ). Examination of the curves and the half-maximal inhibitory concentration (IC 50 ) values shows that IL8 gene expression is more sensitive (steeper relationship and left-shifted) compared with CCR1 gene expression in the pelabresib concentration range studied. Of note, while the model fit for CCR1 exhibited high coefficient of variation on E max and IC 50 , indicating the data may be insufficient to fully represent the dynamic range for this gene, the trend of suppression with increasing exposure is clear even if not precise. In total, based on this analysis, pharmacodynamic responses of at least 50% suppression in gene expression are evident at AUC and C max as low as 4,000 ng/hr/mL and 500 ng/mL, respectively. These exposures were achieved on average at capsule doses of 80 mg and above and were exceeded at both tablet doses.", "non_text_elements": { "figures": [], "tables": [] } } ] } ], "section_title": "Results", "paragraphs": [] }, { "order": "4", "subsections": [], "section_title": "Discussion", "paragraphs": [ { "aggregated_text": "On the basis of these results, pelabresib (previously known as CPI-0610) has potential antitumor activity, particularly in heavily pretreated patients with NF-kB -driven relapse/refractory lymphoma, at doses below the MTD with clear BET-driven target suppression and appeared to have an acceptable safety profile. All of the doses of pelabresib demonstrated rapid absorption and a half-life that supported daily dosing. Suppression of BET target genes was much more apparent at exposures conferred by capsule doses greater than or equal to 170 mg including both tablet dose levels of pelabresib. Decreases in platelet count values were dose dependent, reversible, and non-cumulative. Based on the PK/PD, safety, and clinical activity results together with the other concurrently conducted phase 1 study 0610–02 in patients with MDS/MPN, the 225 mg QD tablet dose was determined to be the MTD. Taking into account the evidence for pelabresib to suppress gene expression and platelet count values, the starting dose selected for phase 2 studies (RP2D) was the 125 mg QD tablet dose for 14 days with a 7-day break. This dose regimen provided PK parameters leading to sustained BET target gene suppression with a manageable safety profile; including changes in platelet count values.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The starting dose for this study, 6 mg orally QD, took into account the results of toxicology studies in dogs and rats as well as predictions of the PK of pelabresib in humans. Although standard starting dose calculations suggested a dose of 23 mg/day (1/6 of the highest non-severely toxic dose in the dog), predictions of human PK suggested that a dose as low as 15 mg/day could achieve therapeutic exposures to pelabresib. Since therapeutic and toxic exposures were expected to be similar, the starting dose in this study was therefore lowered to 6 mg/day. For the capsule formulation of pelabresib, 230 mg was considered the MTD (2 patients experienced a DLT out of 9). This data suggested that the MTD was much higher than the dose estimated to achieve therapeutic exposures to pelabresib.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Initially, patients were enrolled to the study and treated with the capsule formulation of pelabresib, but the new micronized tablet formulation, designed to address potential solubility limitations of the capsule formulation and minimize the number of capsules, was introduced toward the end of enrollment to the 300 mg capsule cohort. The PK profile of the tablet formulation was similar to that of the capsule. Maximum plasma concentrations were reached at a median time of 2 hours and the mean apparent terminal phase half-life was 15 hours, supporting QD dosing. Dose proportional increases in systemic exposure were observed within the dose range of 6 to 170 mg QD administered in capsule form; however, AUC values were similar across the 170 to 300 mg dose range, suggesting a limitation on drug absorption, potentially related to the low aqueous solubility of the crystalline form of pelabresib. The tablet formulation with micronized drug substance had improved oral exposure relative to the higher capsule doses of pelabresib.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Dose-related thrombocytopenia, a class effect for all BET inhibitors ( 14 ), was the principal DLT. In this study, the greatest decrease of platelet counts generally occurred during the first treatment cycle. Decreases in platelet count values were reversible as platelet count values tended to decrease during dosing days and to stabilize or recover during periods of dosing interruption. Changes in platelet count values were also dose dependent as there was a strong correlation between change in platelet count from baseline to the end of the first treatment cycle and pelabresib treatment group. Taken together, thrombocytopenia was dose dependent, in both frequency and intensity, and was reversible and non-cumulative, and these data supported the dosing schedule of pelabresib QD for 14 days, followed by a 7-day break, with cycles of treatment repeated every 21 days. The 7-day break from treatment built into each cycle allowed for recovery from on-target normal tissue toxicity yet maintain pharmacologically active doses of pelabresib.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The dosing regimen of pelabresib QD for 14 days, followed by a 7-day break was supported by the data in this study. This dosing regimen was initially chosen based on the aim of achieving continuous inhibition of the expression of MYC and other genes (like B-cell lymphoma 2) for approximately 2 weeks, since preclinical studies suggested longer exposure times are associated with greater antitumor activity. The 7-day break permitted recovery from on-target normal tissue toxicity, including thrombocytopenia observed in preclinical toxicology studies. In the current study, accumulation of pelabresib was minimal (approximately 40% at most) and with its estimated half-life confirmed the suitability of a once-daily for 14 days dosing schedule. The reversibility of decreased platelet count values with the 7-day break further supported the dosing regimen of pelabresib QD for 14 days, followed by a 7-day break.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "In progressive lymphoma patients, the QD and efficacy data suggest doses well below the MTD could provide therapeutic benefit as a single agent. Suppression of IL8 and CCR1 was apparent at exposures conferred by capsule doses greater than or equal to 120 and 170 mg doses of pelabresib (including both tablet doses), respectively. It was observed that as exposure increased with dose escalation, suppression of BET target gene expression was increased as well. Patients treated with as low as 48 mg QD pelabresib achieved complete remission during the study. This preliminary efficacy data suggests modest clinical activity at doses lower than the MTD with acceptable PK and PD parameters and less hematological toxicity; thus, it is conceivable lower dose pelabresib may confer antitumor activity with a reasonable adverse event profile when paired with other agents.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "This was a phase 1 study designed to evaluate safety and MTD, which also shed light on pelabresib exposure and pharmacodynamic effects in humans. Further exploration of the efficacy of this treatment is warranted. Because thrombocytopenia is a class effect known for BET inhibitors and decreases in platelet count values were dose dependent and BET target gene suppression was observed with both tablet doses, the lower 125 mg tablet dose was chosen for future studies.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "There are currently no ongoing studies in lymphomas, but the 125 mg tablet dose of pelabresib is currently being studied as monotherapy and in combination with ruxolitinib in phase 2 (MANIFEST NCT02158858) and phase 3 (MANIFEST-2 NCT04603495) studies of individuals with myelofibrosis. Pelabresib regulates megakaryocyte differentiation and proliferation and reduces proinflammatory cytokine levels resulting in a potential change of the natural course of clonal myelofibrosis ( 15, 16 ). Kleppe and colleagues have shown that these benefits increased when BET inhibition was combined with ruxolitinib, suggesting synergism by cooperated downregulation of JAK-driven oncogenic activity and BET-driven proinflammatory signaling ( 17 ).", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "In this first-in-human dose escalation study in patients with lymphomas, including non-Hodgkin or Hodgkin lymphoma, all doses of pelabresib demonstrated rapid absorption and a half-life that supported daily dosing. Pelabresib demonstrated antitumor activity with an overall response rate of 6.2% in these heavily pretreated patients with refractory/relapsed lymphomas. The 225 mg pelabresib tablet QD was the MTD and doses above the 170 mg capsule (including both the 125 and 225 mg tablet doses QD) were capable of suppressing BET target genes and appeared to have an acceptable safety profile. Thrombocytopenia was dose dependent, non-cumulative, reversible, and manageable with QD treatment for 14 days with a 7-day break. Both doses of the tablet formulation are capable of BET target gene suppression but due to the dose dependent nature of the observed platelet count decrease the 125 mg tablet dose has a better safety profile. Thus, the RP2D of pelabresib in myelofibrosis development is 125 mg tablet QD for 14 days with a 7-day break and ongoing phase 3 registrational studies in myelofibrosis in combination with ruxolitinib is being tested.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "5", "subsections": [], "section_title": "Supplementary Material", "paragraphs": [ { "aggregated_text": "Supplementary Data Supplemental tables with the number of patients treated with number of cycles and the incidence of DLTs. Click here for additional data file.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Figure S1 CONSORT diagram of patient disposition throughout study Click here for additional data file.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Figure S2 Representative image of complete response after treatment with pelabresib. Click here for additional data file.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Figure S3 PK/PD Analysis of the Relationship Between Maximum Relative Change in Gene Expression and Pelabresib Exposure Metrics. Click here for additional data file.", "non_text_elements": { "figures": [], "tables": [] } } ] } ]

Cancer Research Communications

PMC10010313

10.1158/2767-9764.CRC-22-0060

CRC-22-0060

[ [ "Blum", "Kristie A.", "aff1" ], [ "Supko", "Jeffrey G.", "aff2" ], [ "Maris", "Michael B.", "aff3" ], [ "Flinn", "Ian W.", "aff4" ], [ "Goy", "Andre", "aff5" ], [ "Younes", "Anas", "aff6" ], [ "Bobba", "Suresh", "aff7" ], [ "Senderowicz", "Adrian M.", "aff7" ], [ "Efuni", "Sergey", "aff7" ], [ "Rippley", "Ronda", "aff7" ], [ "Colak", "Gozde", "aff8" ], [ "Trojer", "Patrick", "aff7" ], [ "Abramson", "Jeremy S.", "aff2" ] ]

[ [ "aff1", "Emory University School of Medicine, Atlanta, Georgia." ], [ "aff2", "Massachusetts General Hospital, Boston, Massachusetts." ], [ "aff3", "Colorado Blood Cancer Institute, Denver, Colorado." ], [ "aff4", "Sarah Cannon Research Institute and Tennessee Oncology PLLC, Nashville, Tennessee." ], [ "aff5", "John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, New Jersey." ], [ "aff6", "Memorial Sloan Kettering Cancer Center, New York City, New York." ], [ "aff7", "Constellation Pharmaceuticals (a Morphosys Company), Boston, Massachusetts." ], [ "aff8", "Constellation Pharmaceuticals (a Morphosys Company), Boston, Massachusetts." ] ]

2,022

01-8-2022

11-8-2022

Research Article; Epigenetics; Hematological Cancers; Lymphomas; Clinical Trial Results; Phase I clinical trials; Small Molecule Agents; Epigenetic-Targeted Agents

null

Authors’ Disclosures K.A. Blum reports grants from Constellation Pharmaceuticals during the conduct of the study. J.G. Supko reports other from Constellation Pharmaceuticals during the conduct of the study. M.B. Maris reports other from Constellation Pharmaceuticals during the conduct of the study. I.W. Flinn reports grants from Constellation Pharmaceuticals during the conduct of the study; other from Abbvie, other from Astrazeneca, other from Beigene, other from Century Therapeutics, other from Genentech, other from Gilead Sciences, other from Great Point Partners, other from Hutchison Medipharma, other from Iksuda Therapeutics, other from Innocare Pharma, other from Janssen, other from Juno Therapeutics, other from Kite Pharma, other from Morphosys, other from Nurix Therapeutics, other from Pharmacyclics, other from Roche, other from Seattle Genetics, other from Servier Pharmaceuticals, other from Takeda, other from TG Therapeutics, other from Unum Therapeutics, other from Verastem, other from Vincerx Pharma, other from Yingli Pharmaceuticals, grants from Abbvie, grants from Acerta Pharma, grants from Agios, grants from Arqule, grants from Astrazeneca, grants from Beigene, grants from Biopath, grants from Bristol Myers Squibb, grants from Calibr, grants from Calgb, grants from Celgene, grants from City of Hope National Medical Center, grants from Constellation Pharmaceuticals, grants from Curis, grants from CTI Biopharma, grants from Fate Therapeutics, grants from Forma Therapeutics, grants from Forty Seven, grants from Genentech, grants from Gilead Sciences, grants from Innocare Pharma, grants from IGM Biosciences, grants from Incyte, grants from Infinity Pharmaceuticals, grants from Janssen, grants from Kite Pharma, grants from Loxo, grants from Merck, grants from Millennium Pharmaceuticals, grants from Morphosys, grants from Myeloid Therapeutics, grants from Novartis, grants from Nurix, grants from Pfizer, grants from Pharmacyclics, grants from Portola Pharmaceuticals, grants from Rhizen Pharmaceuticals, grants from Roche, grants from Seattle Genetics, grants from Tessa Therapeutics, grants from TCR2, grants from TG Therapeutics, grants from Trillium Therapeutics, grants from Triphase Research & Development Corp, grants from Unum Therapeutics, and grants from Verastem outside the submitted work. A. Goy reports other from Acerta, personal fees and other from AstraZeneca, personal fees and other from Bristol Myers, personal fees and other from Celgene, other from Genentech, personal fees and other from Hoffman La Roche, other from Infinity, personal fees and other from Janssen, other from Karyopharm, personal fees and other from Kite Pharma, personal fees and other from MorphoSys, other from Pharmacyclics, other from Seattle Genetics, other from Verastem, personal fees and other from Alloplex, personal fees and other from Clinical Advances in Hematology & Oncology, personal fees and other from COTA, personal fees and other from Elsevier's PracticeUpdate Oncology, personal fees and other from GENOMIC TESTING COOPERATIVE, LCA, personal fees and other from Gilead, personal fees and other from Medscape, personal fees and other from Michael J Hennessey Associates, INC, personal fees and other from Novartis, personal fees and other from Onclive Peer Exchange, personal fees and other from Physcians Education Resource, LLC, personal fees and other from Rosewell Park, personal fees and other from Resilience, personal fees and other from Vincerx, and personal fees and other from Xcenda-Amerisource outside the submitted work. A. Younes reports other from AstraZeneca during the conduct of the study; other from AstraZeneca outside the submitted work. A.M. Senderowicz reports a patent to USE PELABRESIB IN MYELOFIBROSIS pending. G. Colak reports being an employee of Constellation Pharmaceuticals. J.S. Abramson reports grants from Constellation during the conduct of the study. No other disclosures were reported.

[ { "ref_id": "bib1", "pmid_cited": "24751816", "doi_cited": "", "article_title": "Targeting bromodomains: epigenetic readers of lysine acetylation", "name": "P Filippakopoulos; S Knapp", "year": "2014", "journal": "Nat Rev Drug Discov", "journal_type": "journal" }, { "ref_id": "bib2", "pmid_cited": "24905006", "doi_cited": "", "article_title": "The mechanisms behind the therapeutic activity of BET bromodomain inhibition", "name": "J Shi; CR Vakoc", "year": "2014", "journal": "Mol Cell", "journal_type": "journal" }, { "ref_id": "bib3", "pmid_cited": "24119843", "doi_cited": "", "article_title": "Super-enhancers in the control of cell identity and disease", "name": "D Hnisz; BJ Abraham; TI Lee; A Lau; V Saint-Andre; AA Sigova; ", "year": "2013", "journal": "Cell", "journal_type": "journal" }, { "ref_id": "bib4", "pmid_cited": "23582323", "doi_cited": "", "article_title": "Selective inhibition of tumor oncogenes by disruption of super-enhancers", "name": "J Lovén; HA Hoke; CY Lin; A Lau; DA Orlando; CR Vakoc; ", "year": "2013", "journal": "Cell", "journal_type": "journal" }, { "ref_id": "bib5", "pmid_cited": "25049379", "doi_cited": "", "article_title": "Blockade of oncogenic IκB kinase activity in diffuse large B-cell lymphoma by bromodomain and extraterminal domain protein inhibitors", "name": "M Ceribelli; PN Kelly; AL Shaffer; GW Wright; W Xiao; Y Yang; ", "year": "2014", "journal": "Proc Natl Acad Sci U S A", "journal_type": "journal" }, { "ref_id": "bib6", "pmid_cited": "", "doi_cited": "", "article_title": "Preclinical development and clinical validation of a whole blood pharmacodynamic marker assay for the BET bromodomain inhibitor ZEN-3694 in metastatic castration-resistant prostate cancer (mCRPC) patients", "name": "L Tsujikawa; K Norek; C Calosing; S Attwell; D Gilham; N Sharma; ", "year": "2017", "journal": "", "journal_type": "other" }, { "ref_id": "bib7", "pmid_cited": "29263030", "doi_cited": "", "article_title": "BET Proteins as Targets for Anticancer Treatment", "name": "A Stathis; 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CC BY

no

[ { "type": "text", "content": "Discounting money and health effects from communicable and noncommunicable diseases in Thailand" } ]

The purpose of this study was to determine the discount rates for money and health outcomes in the Thai context, including the discount rates for communicable and non-communicable diseases. Moreover, this study aimed to explore the socio-demographic characteristics that influence discounting. The computer-based experimental design was used to obtain time preferences for money and health in a total of 1202 Chiang Mai province population, aged 25–50, individually interviewed by trained interviewers. Money-related questions were carried out in all subjects. For health-related questions, all subjects were randomly assigned in a 1:1 ratio for response to questions about Coronavirus Disease 2019 (COVID-19) (N = 602) and air pollution (N = 600). A choice-based elicitation procedure was performed in the experiment to obtain the indifference values from subjects’ time preferences. The cumulative weighting functions were generated using the indifference values to indicate the degree of discounting. The discount factors were computed from the cumulative weighting functions. The discount rates were estimated using a continuous approximation based on the relationship between the discount factors and the parameters governing the discounting model. The Tobit model was applied to investigate the relationships between discounting and socio-demographic characteristics. Discounting for money was greater than discounting for health. Money and health had annual discount rates of 6.2% and 1.3%, respectively. Furthermore, in the COVID -19 situation, the annual discount rate for health was higher than that in the air pollution situation (2.4% vs. 0.7%). Generation X subjects (aged 42 years and above), children under the age of 15 in the household, and underlying diseases were positively related to discounting, while household income was negatively related to discounting. Health should be discounted at a lower rate than money. Moreover, different discount rates should be considered for different types of diseases.

[]

[ { "order": "1", "subsections": [], "section_title": "Introduction", "paragraphs": [ { "aggregated_text": "Discounting is a method of adjusting the future costs and benefits of healthcare interventions such as devices, medicines, vaccines, procedures, and healthcare systems to their present value. The impact of discounting, according to the context of economic evaluation, is dependent on the timing of costs and benefits, implying that society values future costs and benefits less than current costs and benefits. As a result, discounting costs and health outcomes should be taken into account 1 .", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Several controversies in terms of theoretical rationale exist regarding whether costs and health benefits should be discounted at the same rate 2 , 3 . This is because health is unlike other resources. Hence, there is no direct opportunity to invest in it to produce future gains. Several arguments support using the same discount rate for costs and health outcomes. To begin, the consistency argument 4 stated that future health outcomes are valued relative to costs, and thus future health outcomes should be discounted relative to current costs. Next, the paralyzing paradox 5 revealed that new interventions with lower cost-effectiveness ratios are preferred to those with higher cost-effectiveness ratios when there is no or a lower discount rate for health outcomes than for costs. As a result, if the decision is postponed, the new interventions appear to be more cost-effective. However, some arguments exist to support using the different discount rates for costs and health outcomes. For instance, criticisms of the consistency argument 6 argued that health benefits may change over time as technology improves, resulting in a lower cost to save lives, or it may become more expensive to save lives due to environmental or other factors. As a result, different discount rates for costs and health benefits should be considered. Furthermore, criticisms of the paralyzing paradox 7 argued that the option of infinitely deferring health intervention is irrelevant to policymaking because budgets must be spent. Furthermore, the political nature of public decisions regarding resource allocation cannot be neglected. Therefore, the paradox has limited application in the real world.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Delay discounting is another issue that plays an important role in different outcomes. Based on the review study conducted by Odum et al. 8 , which finally included 53 articles for data analysis, it was found that: (1) in comparison to monetary outcomes, non-monetary outcomes were discounted more steeply. This result demonstrates that there is widespread and consistent evidence of steeper discounting of non-monetary outcomes across various outcomes, populations, and methodologies. (2) A positive correlation existed between the degree of delay discounting for monetary and non-monetary outcomes. This finding indicates that individuals who have a tendency to discount one outcome steeply also tend to discount other outcomes steeply.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Most of the recommendations for discounting costs and health outcomes are taken into action by government agencies or regulatory bodies. For example, the National Institute of Clinical Excellence (NICE), an organization in the United Kingdom (UK) that provides national guidance and advice to improve public health policy and social care, recommends discounting the costs and benefits of healthcare interventions at the same rate (3.5% per year) 9 . In addition, according to the Thai Guidelines for Health Technology Assessment (HTA), the recommended annual discount rate for the cost and outcome of the base-case analysis is 3%, and sensitivity analysis should be performed by varying the discount rate from 0 to 6% 10 . However, discounting at different rates is recommended in several countries. For instance, the National Health Care Institute of the Netherlands (Zorginstituut Nederland) recommends that the costs be discounted at a higher rate than the health outcomes, probably with the annual discount rate for costs and outcomes at 4% and 1.5%, respectively 11 . Moreover, the Belgian Health Care Knowledge Centre, an organization under the Ministry of Public Health and Social Affairs, Kingdom of Belgium, recommends that costs should be discounted at a rate of 3% and health effects at a rate of 1.5% annually 12 .", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Previous studies on discounting in economic evaluation revealed that the choice experiment was commonly conducted to estimate the discount rate of monetary and/or health outcomes. The discount rate for monetary and health outcomes was found in the range of 2–28.5% and 3–29.4%, respectively 13 – 18 . Moore and Viscusi 13 estimated the discount rates for health benefits by asking workers in their choices of job risk and wage to consider their future utility. The discount rate equaled approximately 2% 13 . Cairns 14 conducted an empirical study to evaluate the discount rate in the UK. The mean financial discount rate was significantly higher than the health discount rate derived from the choices in terms of postponing or expediting a period of illness (28.5% vs. 3%) 14 . Cropper et al. 15 found the median discount rates range from 17% for a five-year horizon to 3.7% for a horizon of 100 years. In addition, van der Pol and Cairns 16 revealed the median rates of discount to be in the range of 5.5–9.1% for own health by measuring the time-preference rate between two scenarios occurred in terms of the days of current and future ill-health. A study by Attema et al. 17 applied the direct method, which requires no assumptions about the utility function, to estimate the discount rate and found the median discount rates to be 6.5% for money and 2.2% for health outcomes. Fredslund et al. 18 reported the estimated annual discount rates for money and health outcomes at 27.5% and 29.4%, respectively. These previous studies revealed controversies over discount rates based on the experimental design whether costs and health benefits should be discounted at the same rate.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "In Thailand, the annual discount rates for cost and outcome at the same rate of 3% are still recommended for the economic evaluation of healthcare interventions in accordance with the Thai HTA guidelines 10 . However, these recommended discount rates may not be suitable or reflect Thailand’s current economic conditions. Furthermore, different risk perceptions can lead to risk-taking behaviors 19 , implying that the health consequences of communicable diseases may differ from those of non-communicable diseases. As a result, there are research gaps in investigating the appropriate discount rate for costs and health outcomes in Thailand, as well as whether the discount rates for communicable and non-communicable diseases should be the same. However, the limited transportation and travel measures imposed to control the spread of COVID-19 in Thailand during the time of this study entitled Chiang Mai Province in northern Thailand to be selected as the only representative area for the investigation.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Therefore, the purpose of this study was to determine the discount rates for costs and health outcomes in the Thai context, including discount rates for health derived from communicable and non-communicable diseases. Moreover, this study aimed to explore the socio-demographic characteristics that influence discounting.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2", "subsections": [ { "order": "2.1", "subsections": [], "section_title": "Experimental design", "paragraphs": [ { "aggregated_text": "An experimental design method from a previous study by Attema et al. 17 was applied in this study. In brief, all subjects in the experiment were asked to choose between their money and health over the next 20 years based on their current age. There were two sets of questions (health- and money-related questions). The order of the question sets was randomly selected. Furthermore, the COVID-19 situation or respiratory diseases caused by air pollution respondents were also chosen at random for the health-related questions. Each health- or money-related question was completed by the subjects. The experiment was then run until finished or until the maximum number of iterations was reached, which was 4.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "For each iteration, a choice-based elicitation procedure was performed to obtain the subjects’ time preferences. After subjects had chosen their preferred option, the next iteration was altered to make the chosen option less attractive and the non-chosen option more attractive. The indifference values were determined after the experiment was completed. Detailed information on eliciting indifference values is provided in the data analysis section.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "In terms of health, the subjects were required to imagine that they had suffered from either COVID-19 or an air pollution situation. The interviewer informed the subjects that new health technology such as a vaccine or screening program was available and effective for disease treatment, resulting in the subjects’ full health state. The subjects were then asked to choose the most preferable period based on changes in their health condition (Fig. 1 A and B). For the money-related questions, the subjects were asked to imagine that their income or purchasing power had increased by 20%. The interviewer asked the subjects to select their preferred period based on changes in their income (Fig. 1 C). [FIGURE] Figure 1 Example of questions about health and money. COVID-19, Coronavirus disease 2019. [IMAGE]", "non_text_elements": { "figures": [ "figures/PMC9969024/figure_1.md" ], "tables": [] } } ] }, { "order": "2.2", "subsections": [], "section_title": "Population", "paragraphs": [ { "aggregated_text": "The sample size was calculated using a formula by Vaughan et al. 20 , 21 and adjusted by a design effect, which was a correction factor used to adjust the required sample size for cluster or complex sampling methods 22 . The modified formula was shown as follow. \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$N={\\left[\\frac{{Z}_{\\alpha /2}V}{\\Delta }\\right]}^{2}D$$\\end{document} N = Z α / 2 V Δ 2 D [IMAGE] N is the required sample size. Z α/2 is the percentage of (1−α) confidence interval statistic and refers to the critical value from the Z-score that corresponds to α/2. In this study, α is set at 0.05. Therefore, Z α/2 or Z 0.025 corresponds to 1.96 for a 95% confidence interval. V denotes the coefficient of variation, Δ is an acceptable difference between the true population mean and the sample estimate, and D is the design effect. The values for V, D, and Δ in this study were set at 2, 1.5, and 0.15, respectively, in accordance with the study on evaluating the willingness to pay in Thailand’s context by Thavorncharoensap et al. 23 As a result, the required sample size is 1025. To account for the incomplete interviews, the sample size was adjusted to 1200 subjects.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The following subjects were eligible for this study: (1) Chiang Mai Province residents aged 25–50 years (as of September 30th, 2020) with Thai nationality according to the civil registration; (2) the participants must have lived in their own residence for at least 1 year before the data collection date; (3) be able to read and write Thai; and (4) be able to provide informed consent. The subjects who presented with a disability, acute illness, or cognitive impairment, or who had been unemployed for more than 1 year before taking the questionnaire were excluded.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "A stratified multistage random sampling technique was performed to recruit the subjects in Chiang Mai Province, Thailand. To begin, all districts in Chiang Mai Province were classified into 5 groups based on their geographic locations and the Chiang Mai Province’s patient referral system. Next, a quota sampling technique was used to select the districts based on the population ratios among groups. All districts in groups 1 and 2 were selected. For groups 3–5, two districts per group were randomly selected. As a result, 16 out of 25 districts in Chiang Mai Province were selected. For each selected district, 2–4 subdistricts were purposely selected. The accidental sampling method was used to select subjects from the general Thai population who met the eligibility criteria. Required and actual samples for each selected district are presented in Table S1 .", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.3", "subsections": [], "section_title": "Procedure", "paragraphs": [ { "aggregated_text": "A total of 1202 subjects were asked to complete the money-related questionnaire. For health-related questions, all subjects were randomly assigned in a 1:1 ratio. A total of 602 subjects were assigned to the first group and were asked to complete a questionnaire about the COVID-19 situation. For the second group, 600 people completed a questionnaire about scenarios of air pollution with fine particulate matter.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.4", "subsections": [], "section_title": "Study instrument", "paragraphs": [ { "aggregated_text": "The questionnaires were developed based on the literature review and were revised in response to recommendations from the expert meeting. There were two versions of the questionnaire. The first version was associated with COVID-19 scenarios, whereas the second version was involved with air pollution caused by fine particulate matter, or PM2.5. Each version included the following 3 major parts: (1) socio-demographic data; (2) health status; and (3) time preferences for money and health. The questionnaires were tested for content validity based on the expert meeting’s recommendations. Following the development of all questionnaires, a face validity test was conducted.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The experiment was run on a web-based computer and was developed based on a questionnaire involving time preferences for money and health (part 3 in each of the questionnaires). The web-based experiment was also tested for validity and reliability.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.5", "subsections": [], "section_title": "Outcomes", "paragraphs": [ { "aggregated_text": "The primary outcome of this study was the discount rates for money and health determined from the subjects' indifference values and the secondary outcome was the socio-demographic characteristics that influence discounting.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.6", "subsections": [], "section_title": "Data collection", "paragraphs": [ { "aggregated_text": "The officers in local organizations such as the District Office, District Public Health Office, Sub-district Municipality, and Health Promoting Hospital were contacted to coordinate and recruit the volunteers or subjects in the selected districts. The general Thai population that met the eligibility criteria of this study was included in the study. The questionnaire survey sessions were conducted in local government meeting rooms near the study subjects’ homes. Depending on the number of subjects, there were 12–15 respondents per session and 4–6 sessions per day. After respondents registered, they were assigned a well-trained interviewer to help them with the interview. All interviewers were trained to standardize the data collection process, including the study protocol, informed consent, and experimental design, and they also underwent a practice with a pilot sample.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Face-to-face, computer-assisted, one-on-one interviews were performed to collect data. The questionnaires consist of 3 parts: (1) demographic data, (2) health data, and (3) time preference experiment. The lead instructor explained each step of this survey, but the standardized details were presented to the respondents via the instructional video clips. Subjects privately completed the demographic data and answered the health questions with the help of the interviewer. In the web-based experiment, subjects decided and indicated their preferred option in Fig. 1 (option A or B). The interviewers then assisted in entering the selected option to avoid errors. The time required for the entire process was estimated to be 40–60 min per session. Data collection lasted approximately 4 months, from April to July 2021.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.7", "subsections": [ { "order": "2.7.1", "subsections": [], "section_title": "Step 1: measurement of individual indifference values", "paragraphs": [ { "aggregated_text": "A choice-based elicitation procedure was performed to obtain the subjects’ time preferences for money and health. The health and money profiles covered the next 20-year time interval. The time interval was divided into 7 time points: t 0 , t .125 , t .25 , t .5 , t .75 , t .875 , and t 1 , respectively. The subjects’ current age was represented by time point t 0 , and their age in the next 20 years was represented by time point t 1 . The elicitation was performed in 4 iterations at each time point.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The elicitation procedure began at time point t .5 . In the first iteration, each subject was asked to choose between two options (A vs. B) for their preferred period. For the 2nd and 3rd iterations, the period in the selected option was shortened by half to make the selected option less attractive and the non-chosen option more attractive. After three iterations, the cut-off value for the 4th iteration was calculated as the indifference value by averaging the smallest value of the preferred option A and the largest value of the preferred option B. After 4 iterations, the indifference value of time point t .5 was analyzed to determine the earlier benefit for which a subject was indifferent concerning a given future benefit. Table S2 reveals an example of choice-based elicitation for time point t .5 .", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Next, the elicitation of the remaining 4 time points was examined. The order of elicitation at time points t .25 and t .75 was then assigned at random. After that, the order of elicitation at time points t .125 and t .875 was randomized. Finally, the indifference values for 5 time points per subject were collected.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Because the remaining intervals were narrowed to allow eliciting new values, some subjects did not complete all questions. The subjects who always selected all first-period options or all later-period options were categorized as extremely impatient or extremely patient, respectively. Those subjects who preferred the first-period option (t .125 = t .25 = 0) were labeled as impatient with those preferring the later-period option (t .0.75 = t .875 = 20) as patient. These four types of subjects were grouped as ‘extreme subjects.’ The remainders, on the other hand, were classified as ‘non-extreme subjects.’", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The subjects with extreme preferences were excluded from the measurements of cumulative weighting functions and discount rates. However, all subjects were included in the analysis of the relationships between discounting and socio-demographic characteristics using the Tobit model.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.7.2", "subsections": [], "section_title": "Step 2: summary of the indifference values", "paragraphs": [ { "aggregated_text": "The descriptive statistics were used to summarize the indifference values from the choice-based elicitation and were displayed as mean, standard deviation (SD), median, and interquartile range (IQR).", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.7.3", "subsections": [], "section_title": "Step 3: measurement of cumulative weights", "paragraphs": [ { "aggregated_text": "The cumulative weighting functions were generated using the mean and median of the indifference values from each time point. The degree of discounting was indicated by the area under the cumulative weighting function. The greater the size of this area, the more the subjects discount the future.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.7.4", "subsections": [], "section_title": "Step 4: measurement of discount factors and discount rates", "paragraphs": [ { "aggregated_text": "The discount factors were computed from the cumulative weighting functions in the discounting model 17 . Due to only 5 data points per subject being obtained, the following one-parameter discounting models were used: (1) constant discounting; (2) proportional discounting; (3) power discounting; (4) dual exponential discounting; and (5) periodic discounting model. The cumulative weighting functions and discount factor equations for each discounting model are listed in Table 1 . [TABLE] Table 1 Cumulative weighting functions and discount factor equations. Discounting model Cumulative weighting function \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${C}_{({t}_{j})}$$\\end{document} C ( t j ) [IMAGE] Discount factor ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${d}_{t})$$\\end{document} d t ) [IMAGE] (1) Constant discounting \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${C}_{({t}_{j})}= \\frac{1-{e}^{-\\delta {t}_{j}}}{1-{e}^{-{\\delta }_{T}}}$$\\end{document} C ( t j ) = 1 - e - δ t j 1 - e - δ T [IMAGE] \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${d}_{t}=\\frac{1}{1+\\delta }$$\\end{document} d t = 1 1 + δ [IMAGE] (2) Proportional discounting \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${C}_{({t}_{j})}= \\frac{\\mathrm{ln}(1+\\kappa {t}_{j})}{\\mathrm{ln}(1+\\kappa T)}$$\\end{document} C ( t j ) = ln ( 1 + κ t j ) ln ( 1 + κ T ) [IMAGE] \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${d}_{t}=\\frac{1}{1+\\kappa t}$$\\end{document} d t = 1 1 + κ t [IMAGE] (3) Power discounting \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${C}_{({t}_{j})}= \\frac{{(1+{t}_{j})}^{1-\\propto }-1}{{(1+T)}^{1-\\propto }-1}$$\\end{document} C ( t j ) = ( 1 + t j ) 1 - ∝ - 1 ( 1 + T ) 1 - ∝ - 1 [IMAGE] \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${d}_{t}={\\left(\\frac{1}{1+t}\\right)}^{\\propto }$$\\end{document} d t = 1 1 + t ∝ [IMAGE] (4) Dual exponential discounting \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${C}_{({t}_{j})}= \\frac{{{t}_{j}-e}^{r{t}_{j}}-{e}^{-r{t}_{j}}}{{T-e}^{rT}-{e}^{-rT}}$$\\end{document} C ( t j ) = t j - e r t j - e - r t j T - e rT - e - r T [IMAGE] \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${d}_{t}=0.5{e}^{-rt}-0.5{e}^{rt}+1$$\\end{document} d t = 0.5 e - r t - 0.5 e rt + 1 [IMAGE] (5) Periodic discounting \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${C}_{({t}_{j})}= \\frac{{\\rho t}_{j}-\\mathrm{sin}\\left({\\rho t}_{j}\\right)+(\\mathrm{cos}\\left({\\rho t}_{j}\\right)-1)}{\\rho T-\\mathrm{sin}\\left(\\rho T\\right)+(\\mathrm{cos}\\left(\\rho T\\right)-1)}$$\\end{document} C ( t j ) = ρ t j - sin ρ t j + ( cos ρ t j - 1 ) ρ T - sin ρ T + ( cos ρ T - 1 ) [IMAGE] \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${d}_{t}=0.5+0.5\\mathrm{cos}\\left(\\rho t\\right)-0.5\\mathrm{sin}(\\rho t)$$\\end{document} d t = 0.5 + 0.5 cos ρ t - 0.5 sin ( ρ t ) [IMAGE] Remark: δ, κ, ∝ , r, and p represent parameters governing discounting model.", "non_text_elements": { "figures": [], "tables": [ "tables/PMC9969024/table_1.md" ] } }, { "aggregated_text": "To determine which discount model best described subjects' preferences, this study used these 5 discounting models to fit the cumulative weighting functions. The subject’s indifference values for 5 time points were used to compute the individual parameters for 5 discounting models. The squared error for each model is calculated using the following equation. \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathop \\sum \\limits_{j = 1}^{5} \\left( {C\\left( {t_{j} } \\right) - C\\left( {\\hat{t}_{j} } \\right)} \\right)^{2} \\;{\\text{where}}\\;C\\left( {\\hat{t}_{j} } \\right) = \\left[ {0.125,\\;0.25,\\;0.5,\\;0.75,\\;0.875} \\right].$$\\end{document} ∑ j = 1 5 C t j - C t ^ j 2 where C t ^ j = 0.125 , 0.25 , 0.5 , 0.75 , 0.875 . [IMAGE]", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Then, the individual best-fitting model was selected based on the minimum squared error. Finally, the best-fitting discounting model was the one with the highest proportion of subjects for whom each of the discount models fit best.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Discount rates, or the rates at which future consequences are devalued, were estimated using a continuous approximation based on the relationship between the discount factors and the parameter governing the discounting model. Each discounting model yielded the discount rate results. The discount rate results were chosen using the best-fit discounting model.", "non_text_elements": { "figures": [], "tables": [] } } ] } ], "section_title": "Data analyses", "paragraphs": [ { "aggregated_text": "The statistical analyses were divided into 4 steps. First, a choice-based elicitation procedure was performed to measure the individual’s indifference values of time preference for money and health outcomes. Second, the indifference values from each subject were summarized using descriptive statistics. Third, the cumulative weighting functions were generated using the indifference values from each time point and the discount rates were measured using a continuous approximation. Finally, the socio-demographic variables influencing the discount rate were explored. The detailed information was provided as follows.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.8", "subsections": [], "section_title": "The effect of socio-demographic variables on discounting", "paragraphs": [ { "aggregated_text": "Because the areas under the normalized weighting functions were censored between 0 and 1, the Tobit model was applied to investigate the relationships between discounting and socio-demographic characteristics 24 . There were 5 different models: model I combined discounting for money and health to test whether discounting was domain-specific; models II and III separately regressed discounting for money and health; models IV and V regressed health discounting in the COVID-19 and air pollution situations, respectively.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.9", "subsections": [], "section_title": "Data analysis tools", "paragraphs": [ { "aggregated_text": "Descriptive statistics and the Tobit model were performed using the STATA software version 14.0 (StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP). The continuous approximation for discount rate was analyzed using R software (R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria).", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.10", "subsections": [], "section_title": "Ethical approval and consent to participate", "paragraphs": [ { "aggregated_text": "The ethics approval was obtained from the Institutional Review Board of the Faculty of Pharmacy, Chiang Mai University (Certificate of Approval No.006/2564/E). All procedures were carried out in accordance with the applicable guidelines and regulations. The study protocol was explained to all subjects. All subjects signed informed consent forms after agreeing to participate in the study.", "non_text_elements": { "figures": [], "tables": [] } } ] } ], "section_title": "Methods", "paragraphs": [ { "aggregated_text": "A computer-based experimental design was used, along with a choice-based elicitation procedure, to obtain time preferences for money and health. The experiment based on money-related questions was carried out in all subjects. For health-related questions, the subjects were divided into two subgroups, one to answer questions about COVID-19 and the other about air pollution caused by fine particulate matter. The data were collected from a large representative sample of the Chiang Mai Province population aged 25–50 years.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "3", "subsections": [ { "order": "3.1", "subsections": [], "section_title": "Demographic characteristics", "paragraphs": [ { "aggregated_text": "A total of 1202 subjects participated in the experiment, ranging from 25 to 50 years old, with a mean age of 37.3 ± 7.9 years. Of those, 66.6% were female, 40.9% were generation X (aged 42 years and above), and 42.4% were married. The average number of years of education was 12.9 (standard deviation; SD 0.1). The average monthly household income was 30,497 THB (SD 41,611). In the money and health scenarios, the extreme subjects were 47.9% and 62.5%, respectively. Except for underlying diseases that are risk factors for COVID-19 and/or respiratory diseases, all characteristics were similar between COVID-19 and air pollution situations. The detailed descriptions of the subjects’ characteristics are reported in Table 2 . [TABLE] Table 2 Socio-demographic characteristics. Characteristics Number of subjects (Percent) COVID-19 (N = 602) Air pollution (N = 600) Total (N = 1202) p -value* (1) Male 200 (33.2) 201 (33.5) 401 (33.4) 0.918 (2) Mean age (standard deviation) 37.3 (7.9) 37.3 (7.9) 37.3 (7.9) 0.925 (3) Generation X subjects 255 (42.4) 237 (39.5) 492 (40.9) 0.313 (4) Married 260 (43.2) 249 (41.5) 509 (42.4) 0.553 5) Children below 15 years of age in the household 292 (48.5) 309 (51.5) 601 (50.0) 0.299 (6) Elderly in the household (aged ≥ 60 years) 301 (50.0) 276 (46.0) 577 (48.0) 0.165 (7) Mean years of education (standard deviation) 13.1 (0.1) 12.8 (0.2) 12.9 (0.1) 0.126 (8) Government sector career 224 (37.2) 225 (37.5) 449 (37.4) 0.917 (9) Underlying diseases that are risk factors for COVID-19 and/or respiratory diseases 149 (24.8) 58 (9.7) 207 (17.2) < 0.001 (10) Current smoker 75 (12.5) 88 (14.7) 163 (13.6) 0.263 (11) Mean household income per month (standard deviation) 29,242 (25,843) 31,757 (52,990) 30,497 (41,611) 0.294 (12) Extreme subjects Money 287 (47.6) 289 (48.2) 576 (47.9) 0.865 Health 389 (64.6) 362 (60.3) 751 (62.5) 0.125 (13) Money-related scenario first presented to subjects 305 (50.7) 306 (51.0) 611 (50.8) 0.907 Remark: * Statistical analysis using independent t-test.", "non_text_elements": { "figures": [], "tables": [ "tables/PMC9969024/table_2.md" ] } } ] }, { "order": "3.2", "subsections": [], "section_title": "Cumulative weighting functions", "paragraphs": [ { "aggregated_text": "A table with descriptive statistics was presented in the Supplement (Table S3 ). Statistical analyses confirmed that the mean indifference values for health were significantly higher than the mean indifference values for money at all time points (Wilcoxon test, p < 0.001). The median and mean cumulative weighting functions for money and health are demonstrated in Fig. 2 . The cumulative weighting function for money was greater than that for health, as shown in Fig. 2 A and B. Therefore, this indicates that there was more discounting for money than for health. The degree of discounting is indicated by the cumulative weighting function. The mean areas for money and health were 0.592 and 0.513, respectively, while the median areas for money and health were 0.603 and 0.506, indicating a positive discounting. Statistical tests revealed that the elicited values differed significantly between money and health (ANOVA with repeated measures, p < 0.001). [FIGURE] Figure 2 Cumulative weighting functions. COVID-19, Coronavirus disease 2019; PM2.5, Air pollution with fine particulate matter. [IMAGE]", "non_text_elements": { "figures": [ "figures/PMC9969024/figure_2.md" ], "tables": [] } }, { "aggregated_text": "The mean areas for the COVID-19 situation and the air pollution situation in the health scenario (Fig. 2 C) were 0.523 and 0.502, respectively, while the median areas were 0.531 and 0.494. These results indicated that the COVID-19 situation was discounted more than the air pollution situation. However, the values elicited were similar in both situations (ANOVA with repeated measures, p 0.899).", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Figure 3 depicts the relation between the health and the money area measures. The number of data points was reflected in the dots. The correlation between discounting for health and discounting for money was moderate (Kendall’s correlation coefficient 0.202). The positive correlation between non-monetary outcome and monetary outcome founded in this study supported the delay discounting 8 . [FIGURE] Figure 3 Relation between the area measures for money and health. [IMAGE]", "non_text_elements": { "figures": [ "figures/PMC9969024/figure_3.md" ], "tables": [] } } ] }, { "order": "3.3", "subsections": [], "section_title": "Discounting models", "paragraphs": [ { "aggregated_text": "The analyses used 5 parametric forms to fit the cumulative weighting functions to determine which discount function best described subjects’ preferences. The medians of the individual estimates of the parameters in each of the models are shown in Table 3 . In addition, constant discounting provided the best fit for 46.49% of the health subjects and 59.75% of the money subjects (Table S4 ). [TABLE] Table 3 Estimated discount functions. Scenario Estimated discount functions (median, [quantile]) Constant discounting model Proportional discounting model Power discounting model Dual exponential discounting model Periodic discounting model Health 0.013 [− 0.053, 0.096] 0.149 [− 0.031, 0.271] 0.115 [− 0.561, 0.659] 0.090 [0.000, 0.120] 0.530 [0.230, 1.250] Money 0.062 [0.011, 0.137] 0.122 [0.135, 0.577] 0.463 [0.104, 0.862] 0.010 [0.070, 0.120] 0.445 [0.190, 1.050] COVID-Health 0.024 [− 0.043, 0.117] 0.032 [− 0.028, 0.359] 0.203 [− 0.442, 0.722] 0.090 [0.000, 0.120] 0.570 [0.230, 1.100] COVID-Money 0.068 [0.015, 0.141 0.143 [0.017, 0.610] 0.511 [0.131, 0.871] 0.100 [0.080, 0.120] 0.470 [0.200, 1.115] PM-Health 0.007 [0.001, 0.085] 0.007 [0.006, 0.212] 0.061 [− 0.593, 0.593] 0.090 [0.000, 0.110] 0.210 [− 0.480, 0.517] PM-Money 0.058 [0.006, 0.130] 0.109 [0.007, 0.514] 0.438 [0.057, 0.831] 0.114 [0.080, 0.129] 0.467 [0.220, 1.270] Remarks: (1) Estimated discount functions based on non-extreme subjects. (2) [ ] depicts 1st and 3rd quantile of discount factor.", "non_text_elements": { "figures": [], "tables": [ "tables/PMC9969024/table_3.md" ] } }, { "aggregated_text": "According to the estimated parameters in the constant discounting model (Table 3 ), the discount rate for money was higher than the discount rate for health, at 6.2% and 1.3%, respectively. Meanwhile, the discount rate for health in the COVID-19 situation was higher than that in the air pollution situation, at 2.4% and 0.7%, respectively.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Figure 4 demonstrates the cumulative distribution functions of the discount rates for money and health. The distribution for health was mostly above the distribution for money, indicating that money was discounted more than health (Fig. 4 A). Furthermore, the distribution for the air pollution situation was mostly higher than that for the COVID-19 situation, indicating that the COVID-19 situation was discounted more than the air pollution situation (Fig. 4 B). [FIGURE] Figure 4 Cumulative distribution functions of the discount rates. Representations: COVID-19 = Coronavirus disease 2019, PM2.5 = Air pollution with fine particulate matter. [IMAGE]", "non_text_elements": { "figures": [ "figures/PMC9969024/figure_4.md" ], "tables": [] } } ] }, { "order": "3.4", "subsections": [], "section_title": "Effect of socio-demographic characteristics on discounting", "paragraphs": [ { "aggregated_text": "Table 4 shows the estimation results of the Tobit models that described the area under the normalized cumulative weighting functions for money and health by socio-demographic variables. The pseudo-R 2 in each model indicates that the goodness of fit is low. [TABLE] Table 4 The effect of socio-demographic characteristics on discounting. Characteristics The area under the normalized utility function All samples Money Health Health (COVID-19) Health (air pollution) Male − 0.033 − 0.021 − 0.044 0.004 − 0.089 (0.028) (0.034) (0.046) (0.073) (0.058) Generation X 0.117*** 0.023 0.223*** 0.223*** 0.200*** (0.027) (0.032) (0.043) (0.070) (0.053) Married 0.012 0.020 0.005 − 0.017 0.018 (0.027) (0.032) (0.044) (0.070) (0.054) Children below 15 years of age in the household 0.056** 0.044 0.069* 0.160** − 0.006 (0.025) (0.030) (0.041) (0.066) (0.051) Elderly in the household (aged ≥ 60 years) − 0.020 − 0.014 − 0.026 − 0.031 − 0.019 (0.024) (0.028) (0.038) (0.062) (0.047) Years of education − 0.005 0.0003 − 0.010 − 0.011 − 0.006 (0.004) (0.005) (0.007) (0.012) (0.008) Government sector career − 0.006 0.008 − 0.023 − 0.065 0.005 (0.029) (0.035) (0.047) (0.077) (0.058) Underlying diseases that are risk factors for COVID-19 and/or respiratory diseases 0.067** 0.036 0.099* 0.168** − 0.030 (0.032) (0.038) (0.052) (0.074) (0.079) Current smoker − 0.007 − 0.060 0.053 0.052 0.061 (0.040) (0.047) (0.064) (0.105) (0.078) Household income − 0.061*** − 0.047** − 0.078*** − 0.073 − 0.088*** (0.018) (0.021) (0.028) (0.048) (0.034) Money-related scenario first − 0.027 − 0.087*** 0.046 0.009 0.078* (0.024) (0.028) (0.038) (0.061) (0.047) COVID-19 situation 0.101*** 0.029 0.184*** (0.024) (0.029) (0.039) Money scenario 0.379*** (0.024) Constant 1.002*** 1.259*** 1.102*** 1.223** 1.202*** (0.166) (0.198) (0.268) (0.448) (0.319) Sigma 0.540*** 0.456*** 0.615*** 0.695*** 0.539*** (0.012) (0.014) (0.020) (0.034) (0.020) pseudo-R 2 0.076 0.014 0.046 0.042 0.038 Number of datasets 2404 1202 1202 602 600 COVID-19, Coronavirus Disease 2019. Remarks: (1) ( ) depicts the standard error. (2) Tobit model, with the left-censored value at 0 and the right-censored value at 1. (3) *,**,*** denote statistically significant at the 0.01, 0.05, and 0.1 levels, respectively.", "non_text_elements": { "figures": [], "tables": [ "tables/PMC9969024/table_4.md" ] } }, { "aggregated_text": "Overall (Model I), several socio-demographic characteristics including generation X subjects (aged 42 years and above), children under the age of 15 in the household, and underlying diseases that are risk factors for COVID-19 and/or respiratory diseases, are positively related to discounting, while household income is negatively related. However, other variables such as marital status, education, occupation, and smoking had no effect on the discounting rate. The pooled regression reveals that discounting for money differed from discounting for health (p 0.024), confirming that discounting was domain-specific in our study.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Household income and money-related scenario first presented to subjects were negatively related to discounting in Model II (money scenario). Being generation X subjects was positively related to discounting in Models III (health scenario), IV (COVID-19 situation), and V (air pollution situation). Moreover, in Model III (health scenario) and Model IV, the COVID-19 situation, children under the age of 15 in the household, and underlying diseases that are risk factors for COVID-19 and/or respiratory diseases were positively related to discounting, while household income was negatively related to discounting in Models III and V.", "non_text_elements": { "figures": [], "tables": [] } } ] } ], "section_title": "Results", "paragraphs": [] }, { "order": "4", "subsections": [], "section_title": "Discussion", "paragraphs": [ { "aggregated_text": "To the best of our knowledge, this study is the experimental attempt to use a direct method to obtain time preferences for money and health in a large representative sample in Thailand. As the subjects discounted money more than health, discounting in this study was domain-specific. The correlation between discounting for money and health was moderate (Kendall’s correlation coefficient 0.202). These findings are in line with previous studies 17 , 25 . In particular, the study conducted by Attema et al. 17 reported Kendall's correlation coefficient of 0.22. As a result, we found the annual discount rate for money to be higher than that for health, at 6.2% and 1.3%, respectively. Furthermore, the annual discount rate for health in the COVID-19 situation was higher than that in the case of air pollution, at 2.4% and 0.7%, respectively.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Our findings regarding the discount rates are consistent with those of a previous study conducted by Attema et al. 17 . Their experiment was carried out on subjects aged 30–50 years, which is comparable to our study. The annual discount rates for money and health estimated from their study were 6.5% and 2.2%, respectively. However, these results differ from the findings from a study conducted by Fredslund et al. 18 which reveals the annual discount rate for money to be lower than that for health, at 27.8% and 29.4%, respectively. In contrast to our study, their subjects ranged in age from working to retirement age.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Furthermore, from the present study, the discount rate for health in the COVID -19 situation was higher than that in the air pollution situation (2.4% vs. 0.7%). This could be because the health consequences are different between communicable diseases and noncommunicable diseases; thus, leading to differences in risk perception and discounting degree. COVID-19 is the current pandemic that would have a significant impact on the population and economy at both the individual and the societal levels. As a result, there is an urgent need to protect against COVID-19 infection rather than the long-term effects caused by air pollution including respiratory diseases.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Furthermore, the pooled regression reveals that discounting for money differed from discounting for health (p 0.024), confirming that the discounting was domain-specific. A statistically significant discount was found to be positively correlated with the sample data in the money situation. This means that discounting for money is higher than discounting for health. According to Attema et al. 17 , the subjects who answered questions about money were positively correlated with a statistically significant discount. This is a piece of empirical evidence to confirm that discounting money and health are not the same thing. Health should be discounted at a lower rate than money.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Several limitations in this study should be taken into consideration. First, the study gathered data on time preferences for money and health in a large representative sample of the Chiang Mai Province population. This may limit the study’s generalizability to other provinces or Thailand's representatives. However, the findings of this study might be useful for future studies into determining appropriate discount rates for money and health in the context of health economic evaluation in Thailand. Second, COVID-19 and air pollution situations were used in this study to reflect the current health issues in Chiang Mai Province, particularly, the air pollution with fine particulate matter that occurs every year from January to April has been a common concern. As a result, these situations may have limited generalizability once applied to other contexts. Third, most subjects examined had extreme preferences. This might be due to the data were gathered between April and July 2021, which was the period of the COVID-19 pandemic in Thailand. There were limited vaccinations available to Thais at the time. COVID-19 had an impact on the economy at both the individual and the societal levels. Furthermore, air pollution from PM2.5 was a significant health concern in northern Thailand, particularly in Chiang Mai province. Therefore, there was an urgent need of the given benefits from both money and health scenarios. However, we established the study protocol and trained all interviewers to standardize the data collection process and avoid errors. Each step of the experimental design was explained by the interviewer. The respondents were shown the standardized information via instructional video clips. Before the experiment began, the subjects had the opportunity to request additional information or clarification.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "5", "subsections": [], "section_title": "Conclusion", "paragraphs": [ { "aggregated_text": "This study’s findings revealed that discounting for money was greater than discounting for health. Money and health had annual discount rates of 6.2% and 1.3%, respectively. Furthermore, in the COVID -19 situation, the annual discount rate for health was higher than that in the air pollution situation (2.4% vs. 0.7%). Health should be discounted at a lower rate than money. In addition, different discount rates should be considered for different types of diseases.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "6", "subsections": [ { "order": "6.1", "subsections": [], "section_title": "", "paragraphs": [ { "aggregated_text": "Supplementary Tables.", "non_text_elements": { "figures": [], "tables": [] } } ] } ], "section_title": "Supplementary Information", "paragraphs": [] } ]

Scientific Reports

36849620

PMC9969024

10.1038/s41598-023-30559-2

30559

[ [ "Yadee", "Jirawit", "Aff1" ], [ "Yadee", "Jirawit", "Aff2" ], [ "Yadee", "Jirawit", "Aff3" ], [ "Permsuwan", "Unchalee", "Aff2" ], [ "Permsuwan", "Unchalee", "Aff3" ], [ "Guntawongwan", "Kansinee", "Aff4" ], [ "Himakalasa", "Woraluck", "Aff4" ], [ "Buddhawongsa", "Piyaluk", "Aff4" ] ]

[ [ "Aff1", "grid.7132.7 0000 0000 9039 7662 Degree Program in Pharmacy, Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand" ], [ "Aff2", "grid.7132.7 0000 0000 9039 7662 Department of Pharmaceutical Care, Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand" ], [ "Aff3", "grid.7132.7 0000 0000 9039 7662 Center for Medical and Health Technology Assessment (CM-HTA), Department of Pharmaceutical Care, Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand" ], [ "Aff4", "grid.7132.7 0000 0000 9039 7662 Center of Human Resource and Public Health Economics (CHPE), Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand" ] ]

2,023

01-01-2023

27-2-2023

Article

null

Competing interests The authors declare no competing interests.

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CC BY

no

[ { "type": "text", "content": "Endoscopic surgery suturing techniques: a randomized study on learning" } ]

Background Surgeons have widely adopted endoscopic suturing techniques using conventional laparoscopic instruments and the more advanced robotic systems. The FlexDex is a novel articulating laparoscopic needle driver providing enhanced dexterity in laparoscopic surgery. This study evaluates and compares the learning curve of endoscopic suturing with conventional laparoscopy, the FlexDex and robotic suturing in novices. Methods Participants performed a minimal invasive suturing task in three different ways in a randomized order: with a conventional laparoscopic needle driver, using the FlexDex needle driver and third, using the Da Vinci Si surgical system. Primary outcome was suturing task time. Secondary outcome parameters were assessment of suturing quality and workload perception. Results A total of 10 novice participants were included and completed a total of 300 sessions. Median (IQR) suturing time of the first 5 sessions was 231 s (188–291) in the laparoscopic group versus 378 s (282–471) in the FlexDex group versus 189 s (160–247) in the DaVinci Si group. The last 5 sessions showed significant reduction of median suturing time of 143 s (120–190), 232 s (180–265) and 172 s (134–199) respectively. Analysis identified that the learning curve for the laparoscopic needle driver and Da Vinci Si was reached in 5 sessions, compared to 8 sessions for the Flexdex. The laparoscopic needle driver and Da Vinci Si showed a significant shorter median suturing time compared to the FlexDex (p = 0.00). The FlexDex quality assessment scores were significantly lower compared to the laparoscopic (p = 0.00) and robotic (p = 0.00) scores and perceived workload remains high for the FlexDex users. Conclusions Ex vivo endoscopic suturing with the FlexDex demonstrated a prolonged learning curve compared to laparoscopic and robotic suturing. The learning curve of the FlexDex is fundamentally different from conventional laparoscopic and robotic instruments. This study provides further insights in the implementation and training of endoscopic suturing techniques.

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[ { "order": "1", "subsections": [], "section_title": "Background", "paragraphs": [ { "aggregated_text": "Over the years constant innovation and technological advancement have facilitated surgeons to expand their practice of laparoscopic surgery [ 1 – 8 ]. Nonetheless, laparoscopic intracorporeal suturing remains an important technical barrier in laparoscopic surgery [ 9 , 10 ]. It is an advanced skill to master with an extensive learning curve, mainly due to the limited degrees of freedom of the surgical instruments and the fulcrum effect [ 11 , 12 ]. The implementation of robotic systems, including the da Vinci Surgical System, offers a solution for the challenges of intracorporeal suturing [ 13 , 14 ]. The 3D imaging, tremor elimination, enhanced ergonomics and articulating instruments improve surgical performance and make complex tasks such as suturing easier to perform [ 15 , 16 ]. Robotic assisted suturing has demonstrated to have a shorter learning curve compared to laparoscopy and is especially beneficial when suturing in deep and confined operating space [ 17 ].", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Meanwhile the majority of surgeons worldwide still do not have daily access to a robotic system [ 18 ]. The significant costs of robotic surgery remain an important restrictive factor. This financial gap between laparoscopic and robotic instruments has motivated the development of hand held articulating laparoscopic suturing instruments, such as the FlexDex Surgical laparoscopic needle driver (FlexDex, Inc., Brighton, MI), Kymerax (Terumo, Japan) [ 19 ] and the Radius Surgical System (Tuebingen Scientific. Medical GmbH, Germany)[ 20 , 21 ]. These instruments provide the surgeon with some of the dexterity benefits of robotic surgery. To date the acceptance and extensive use of articulating laparoscopic instruments remains limited. One of the main reasons is the significant learning curve [ 22 ]. In order to succeed an instrument should feel as a natural extension of the arm, wrist and hand and should be easy to use. The FlexDex is a novel mechanically articulated suturing device (Fig. 1 ). This arm mounted device is fundamentally different from previous articulating needle holders, since it converts wrist movements into articulation of the tip of the instrument. This design allows parallel mapping, translating the movement of the surgeon into respective movement of the end effector [ 23 ]. Use of the FlexDex could improve the ergonomics of the surgeon in comparison with conventional laparoscopic instrumentation. [FIGURE] Fig. 1 The FlexDex surgical laparoscopic needle driver [IMAGE]", "non_text_elements": { "figures": [ "figures/PMC8851769/figure_1.md" ], "tables": [] } }, { "aggregated_text": "The extent of the learning curve of the FlexDex is unknown. It is necessary to take learning curves of innovative instruments into account since it puts the effectiveness of innovations into perspective. The aim of this study was to evaluate and compare the learning curve of minimal invasive suturing by novices using the FlexDex, conventional laparoscopic needle driver and the Da Vinci Si surgical system. Our hypothesis was that the learning curve of the FlexDex is longer compared to laparoscopic and robotic suturing, since the device seems more complex compared to laparoscopic and robotic suturing. We expect novices to have the shortest learning curve of minimal invasive suturing with the DaVinci Si, due to the benefits in interface, dexterity and ergonomics [ 24 ].", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2", "subsections": [ { "order": "2.1", "subsections": [], "section_title": "Study setting", "paragraphs": [ { "aggregated_text": "This randomized crossover study was performed at the Meander Medical Center Amersfoort, The Netherlands. Ten junior residents without experience in laparoscopic surgery were recruited. Residents with prior endoscopic suturing experience were excluded from participation. Baseline demographic data of the participants were collected at the time of consent and participation was voluntary. Participants were consecutively randomized into one of the 3 groups by means of sealed envelope technique to determine the order of device of each session (e.g., ABC, BCA, CAB for 3 devices A, B and C). The first group started using the conventional laparoscopic needle driver (A), followed by the Da Vinci Si (B) and the FlexDex (C). The second group started using the Da Vinci Si and the third group started with the FlexDex. After completion of the first session the sequence per group alternated.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.2", "subsections": [], "section_title": "Suturing task", "paragraphs": [ { "aggregated_text": "The suturing tasks were performed within a laparoscopic box trainer environment (d-box laparoscopic simulator). The participants started the study with a brief training of 10 min on each modality and every session started with a ‘rope passing’ exercise designed for familiarization. This exercise was not part of the evaluation. Subsequently the participant performed the suturing task. A suturing pad with three premarked targets as exit points at 1-cm intervals was placed with an angle of 45° to the scope. The first step consisted of passing the needle through the most cranial target. This was followed by tying one surgeon’s knot and two square ties. The task was finished by passing the needle through the two remaining targets. Trocar placement, camera position, angle of the laparoscope and suture material (Vicryl 3–0 SH plus 26 mm, Ethicon, Johnson & Johnson) were standardized. Laparoscopic imaging was in 3D, in order to limit difference to the da Vinci Si surgical system imaging. In this study we used the 10 mm 3D EndoEye Star videoscope of Olympus for 3D laparoscopy. The tasks were completed bimanually. A reusable laparoscopic grasper was used as the left instrument during suturing with the conventional laparoscopic needle driver and the FlexDex. Two arms, next to the camera arm, were docked on the da Vinci Si system: the Wristed Needle Driver on the right side and the Cadiére Forceps on the left side. A maximum of 600 s was given to complete the suturing task per device. If suture breakage occurred, the participant started over and the time of the previous broken suture was added. Previous studies have shown that the learning curve for both laparoscopic and robotic instrumentation improved within five sessions and is flattened by the 10th session [ 17 , 25 ]. Since the learning curve in the current study was expected to be comparable, each participant performed 10 session with each suturing device.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.3", "subsections": [], "section_title": "Outcome variables", "paragraphs": [ { "aggregated_text": "The primary outcome of this study was task efficiency, defined as suturing task time (s). Secondary outcomes were the quality assessment of the laparoscopic suturing skills and the workload assessments. The suturing task was recorded by a digital video recorder and coded to blind the study session for the observers. Analysis of the videos was performed by 2 blinded observers with the Global Operative Assessment of Laparoscopic Skills (GOALS) checklist. This is a 29-point validated checklist for the assessment of endoscopic suturing skills [ 26 ]. Following each suturing task the participants were asked to complete the National Aeronautics and Space Administration Task Load Index (NASA-TLX) [ 27 ]. The NASA-TLX is a validated workload assessment tool, measuring self-ratings of satisfaction, performance and fatigue of the participants.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "2.4", "subsections": [], "section_title": "Statistical analyses", "paragraphs": [ { "aggregated_text": "Data analysis was performed in consultation with a statistician. Continuous variables were assessed for normality and were presented as medians and interquartile ranges if not normally distributed. Nonparametric tests were used for statistical analyses. The Kruskal Wallis test was used to analyze the differences in outcomes between the laparoscopic, robotic and FlexDex group. If the groups differed significantly, pairwise comparisons were performed to determine the significance between each group using the Mann–Whitney U test. The Wilcoxon signed rank test was used to test improvement within each group, comparing the first five sessions and last five sessions. Data was analyzed using SPSS version 26, with the significance level set at p = 0.05.", "non_text_elements": { "figures": [], "tables": [] } } ] } ], "section_title": "Methods", "paragraphs": [] }, { "order": "3", "subsections": [], "section_title": "Results", "paragraphs": [ { "aggregated_text": "Mean age of the ten participants was 30 years and the male–female ratio was 8:2. Together, the participants completed a total of 300 sessions. Suture breakage occurred in eight sessions, all with the DaVinci Si. The median completion time of the suturing task is displayed in Fig. 2 . Visual analysis of the curve of the conventional laparoscopic and robotic group shows the same pattern of improvement with a plateau reached after 5 sessions. The most rapid decline is seen in the FlexDex group. Median completion time at the start in the FlexDex group is high and a plateau in time is reached after 8 sessions. 5 participants could not complete the suturing task with the FlexDex at their first sessions within the time allotted (600 s). The resulting median times per suturing device are shown in Table 1 , set out in the first and last five sessions. Median (IQR) suturing time of the first 5 sessions was 231 s (188–291) in the laparoscopic group versus 378 s (282–471) in the FlexDex group versus 189 s (160–247) in the DaVinci Si group. Analysis of the last 5 sessions showed significant reduction of median suturing time of 143 s (120–190), 232 s (180–265) and 172 s (134–199) respectively. Minimal invasive suturing with the FlexDex remained significantly slower throughout the study compared with laparoscopic and robotic assistance (p = 0.00). [FIGURE] Fig. 2 Median time with IQR suturing task [IMAGE] [TABLE] Table 1 Total median time (s) of the suturing task per device Laparoscopic needle driver (n = 100) FlexDex (n = 100) DaVinci Si (n = 100) p value (a) Post hoc analysis (b) Lap vs FlexDex FlexDex vs DaVinci Si Lap vs Da Vinci Si Session 1–5 231 s (188, 291) 378 s (282, 471) 189 s (160, 247) 0.00 0.00 0.00 0.05 Session 6–10 143 s (120, 190) 232 s (180, 265) 172 s (134, 199) 0.00 0.00 0.00 0.07 p value (c) 0.00 0.00 0.00 Values are expressed in median and interquartile range n = total number of sessions (a) Kruskal Wallis Test (b) Mann–Whitney U test (c) Wilcoxon signed rank test", "non_text_elements": { "figures": [ "figures/PMC8851769/figure_2.md" ], "tables": [ "tables/PMC8851769/table_1.md" ] } }, { "aggregated_text": "Table 2 and Fig. 3 present the performance scores of the GOALS checklist. Overall the FlexDex scored significantly lower when compared to the laparoscopic needle driver and the Da Vinci Si. There were no significant differences between the GOALS scores of the laparoscopic needle driver and Da Vinci Si. Direct comparison of the first 5 sessions with the last 5 sessions showed statistically significant improvement for the FlexDex (p < 0.00). The DaVinci Si and laparoscopic needle driver did not show significant improvement in GOALS scores between the first and last five sessions. The mean workload scores for each of the six subscales of the NASA-TLX tool are presented in Fig. 4 . Analysis of the six subscale scores demonstrated that the perceived workload with the FlexDex were highest on each of the subscales. [TABLE] Table 2 Total median checklist scores (GOALS) of the suturing videos Laparoscopic needle driver FlexDex DaVinci Si p value (a) Post hoc analysis (b) Lap vs FlexDex FlexDex vs DaVinci Si Lap vs Da Vinci Si Session 1–5 17.5 (15.3, 19) 13 (11, 16) 19 (16, 21) 0.00 0.00 0.00 0.17 Session 6–10 18 (16.8, 19.3) 17 (14.5, 19) 18 (16, 20) 0.01 0.01 0.00 0.77 p value (c) 0.29 0.00 0.81 Values are expressed in median and interquartile range. A higher score indicates a better result, maximum score of 29 (a) Kruskal Wallis Test (b) Mann–Whitney U test (c) Wilcoxon signed rank test [FIGURE] Fig. 3 Median checklist scores (GOALS) of the suturing videos [IMAGE] [FIGURE] Fig. 4 Mean workload items of NASA-TLX scores [IMAGE]", "non_text_elements": { "figures": [ "figures/PMC8851769/figure_3.md", "figures/PMC8851769/figure_4.md" ], "tables": [ "tables/PMC8851769/table_2.md" ] } } ] }, { "order": "4", "subsections": [], "section_title": "Discussion", "paragraphs": [ { "aggregated_text": "This randomized, cross-sectional study confirmed a prolonged learning phase of endoscopic suturing with the FlexDex for novices compared to laparoscopic and robotic suturing. The FlexDex learning curve has a significantly high median suturing time at the start and is followed by a steep learning curve. Endoscopic suturing with the laparoscopic needle driver and the DaVinci Si showed similar characteristics in learning curve peaks, however the robotic group showed shorter median suturing time in the first five sessions. This study indicates that manipulation of the FlexDex is different from conventional laparoscopic and robotic systems and requires more training.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Limited research has been done on mechanically articulated suturing devices [ 21 , 28 , 29 ]. The Radius Surgical System is the only commercially available mechanically articulated suturing device of which learning curve studies have been published. The Radius Surgical System has a different design compared to the FlexDex, flexion is controlled by deflecting the instrument handle and rotation is controlled by a thumb knob on the handle. One study has evaluated the learning curve of one single surgeon with the Radius in performing intracorporeal urethra-vesical anastomosis on a pelvitrainer and stated a learning curve of less than 10 anastomoses [ 28 ]. Results from another study of one surgeon using the Radius on a colorectal anastomosis showed different results. They suggested that proficiency was achieved after 21 procedures, following a steep learning curve [ 28 ]. Despite the limited number of participants and the varying study results, these studies provide insight in the unique learning curve of articulating laparoscopic instruments: fast improvement after a challenging slow start. In the present study the completion time with the FlexDex was high in the first five sessions, mostly accounted by the fact that 5 participants could not complete the suturing task at their first session within the time allotted (600 s). This is however followed by a rapid decline in completion time. We could conclude that it needs more time to allow novice users to convert the rotational movements of the wrist and arm with the Flexdex into precise movements at the instrument tip.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Different parameters exist to demonstrate a learning process [ 17 , 30 ]. In addition to task completion time analysis, the performance curve of laparoscopic and robotic suturing showed a consistently high performance with limited increase during the study. In contrast, the median performance scores of the FlexDex were significantly lower (p < 0.05) than laparoscopic and robotic suturing and showed steady improvement throughout the study. Moreover, it did not reach a plateau phase at the end of the study. This suggests persistent learning of the participants and indicates the inability for FlexDex users to deliver the same suturing quality as seen in the robotic and laparoscopic group at this stage of their learning curve. The importance of incorporating multiple assessment parameters in a learning curve study was previously demonstrated [ 24 ]. This study compared the learning curve of laparoscopic versus robot assisted suturing on two different simulators. Outcome parameters were median time, off screen percentage and adequate knot percentage which resulted in three different learning curves. Median completion time and off screen percentage favoured the robot assisted group and adequate knot percentage favoured the laparoscopic group.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "This study was performed on novice users without minimal invasive suturing experience. In future studies it will be interesting to study the learning curve for experienced surgeons [ 24 ]. Surgeons already mastering minimal invasive suturing skills could acquire proficiency of a new device more easily, since they do not have to concentrate on the technical aspects of suturing and knot tying. Furthermore, translation to the clinical operative setting would be of value to identify the additive value of the FlexDex in complex surgery. The ergonomics and effectiveness of the FlexDex has been evaluated in small clinical studies [ 23 , 29 ]. Improved ergonomics and effectiveness of the FlexDex at difficult anatomic locations was demonstrated. The current study showed high workload scores for the FlexDex users. In this study these ergonomic benefits were not reflected in the reported workload scores. This is probably caused by the prolonged learning process of the participants.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "We can support the assumption that surgeons, after adequate training with the FlexDex, could benefit from the increased dexterity and the preservation of haptic feedback. Currently the Da Vinci surgical system does not have haptic feedback capabilities. Haptic feedback offers useful information about the tension of a knot during suturing. Our study supports the importance of haptic feedback for novices, since suturing with the DaVinci Si resulted in a relatively high amount of suture breakage (n = 8), a problem that has been described in previous studies [ 31 , 32 ]. In this study all suture breakages occurred during knot tying. Novice participants have difficulties to assess the applied forces of the robotic instruments on the suture. In practice, surgeons need to compensate the lack of haptic feedback by visual clues and experience [ 33 ].", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The main strengths of this study are the study design and the inclusion of three different suturing modalities. Another strength is the use of a box trainer with real instruments. The majority of previous learning curve studies on endoscopic suturing used virtual reality simulators with different simulator modalities and outcome parameters, limiting direct comparison and resulting in bias [ 17 , 24 ].", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "This study has several limitations to be mentioned. First is the limited number of endoscopic suturing sessions. Although we clearly denoted a prolonged learning curve in FlexDex users, there was still persistent learning visible for suturing quality in the FlexDex group.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "The second limitation of our study involves the intracorporeal suturing task. A more challenging suturing task within a limited working space might have demonstrated the articulating benefits of the FlexDex and the Da Vinci Si more substantial compared to conventional laparoscopy. Another limitation is the unbalanced group size in the block randomization, resulting in a total of 34 sequences starting with laparoscopy and 33 sessions starting with the FlexDex or the Da Vinci Si. There is a possibility of bias, however this design was accepted due to logistics. The minor imbalance in device sequence is not expected to have a significant effect on the results.", "non_text_elements": { "figures": [], "tables": [] } }, { "aggregated_text": "Notwithstanding these limitations, this study offers insight into the learning curve of the FlexDex in relation to other minimal invasive suturing modalities. For now, we consider surgeons most proficient at acquiring profit of the FlexDex are those with high exposure in intracorporeal suturing, such as in bariatric surgery. Furthermore, it could be very interesting to use the FlexDex in procedures with limited working space, such as in transanal minimally invasive surgery and paediatric surgery. Nonetheless this study indicates that surgeons need proper training and should not use the FlexDex for that one stitch a year.", "non_text_elements": { "figures": [], "tables": [] } } ] }, { "order": "5", "subsections": [], "section_title": "Conclusions", "paragraphs": [ { "aggregated_text": "This is the first study to report on the learning curve of the FlexDex, a novel mechanically articulating needle driver. The use of the FlexDex demonstrated a longer but steeper learning curve during minimal invasive suturing in novices compared to robotic suturing. The laparoscopic needle driver and Da Vinci Si showed a significant shorter median suturing time compared to the FlexDex. This study provides new insight in the further implementation and training of mechanically articulating needle holders to avoid learning curve effects for patients. Future studies should further clarify the potential benefits and limitations of each suturing modality in endoscopic surgery.", "non_text_elements": { "figures": [], "tables": [] } } ] } ]

BMC Surgery

35172810

PMC8851769

10.1186/s12893-022-01513-2

1513

[ [ "Voskens", "F. J.", "Aff1" ], [ "Voskens", "F. J.", "Aff3" ], [ "van der Schans", "E. M.", "Aff1" ], [ "van der Schans", "E. M.", "Aff3" ], [ "Ruurda", "J. P.", "Aff2" ], [ "Broeders", "I. A. M. J.", "Aff1" ], [ "Broeders", "I. A. M. J.", "Aff3" ] ]

[ [ "Aff1", "grid.414725.1 0000 0004 0368 8146 Department of Surgery, Meander Medical Center, Maatweg 3, Amersfoort, The Netherlands" ], [ "Aff2", "grid.7692.a 0000000090126352 Department of Gastro-Intestinal and Oncologic Surgery, University Medical Center, Utrecht, The Netherlands" ], [ "Aff3", "grid.6214.1 0000 0004 0399 8953 University of Twente, Robotics and Mechatronics, Enschede, The Netherlands" ] ]

2,022

01-01-2022

17-2-2022

Research Article

null

Competing interests Ivo A.M.J. Broeders is a consultant for Johnson & Johnson and Intuitive Surgical. Jelle P. Ruurda is a consultant for Intuitive Surgical. Frank J. Voskens and Emma M. van der Schans have no conflicts of interest or financial ties to disclose.

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", "year": "2017", "journal": "Jsls", "journal_type": "journal" }, { "ref_id": "CR10", "pmid_cited": "26143517", "doi_cited": "", "article_title": "Survey on barriers to adoption of laparoscopic surgery", "name": "N Fuchs Weizman; ", "year": "2015", "journal": "J Surg Educ", "journal_type": "journal" }, { "ref_id": "CR11", "pmid_cited": "27752811", "doi_cited": "", "article_title": "Ergonomics in the operating room", "name": "S Janki; ", "year": "2017", "journal": "Surg Endosc", "journal_type": "journal" }, { "ref_id": "CR12", "pmid_cited": "29302848", "doi_cited": "", "article_title": "Robot-assisted surgery and incisional hernia: a comparative study of ergonomics in a training model", "name": "A Sánchez; ", "year": "2018", "journal": "J Robot Surg", "journal_type": "journal" }, { "ref_id": "CR13", "pmid_cited": "12215022", "doi_cited": "", "article_title": "Robot-assisted surgical systems: a new era in laparoscopic surgery", "name": "JP Ruurda; TJMV van Vroonhoven; IAMJ Broeders", "year": "2002", "journal": "Ann R Coll Surg Engl", "journal_type": "journal" }, { "ref_id": "CR14", "pmid_cited": "11016320", "doi_cited": "", "article_title": "A comparison of robot-assisted versus manually constructed endoscopic coronary anastomosis", "name": "WD Boyd; ", "year": "2000", "journal": "Ann Thorac Surg", "journal_type": "journal" }, { "ref_id": "CR15", "pmid_cited": "18855053", "doi_cited": "", "article_title": "Ergonomics, user comfort, and performance in standard and robot-assisted laparoscopic surgery", "name": "RH van der Schatte Olivier; ", "year": "2009", "journal": "Surg Endosc", "journal_type": "journal" }, { "ref_id": "CR16", "pmid_cited": "19536599", "doi_cited": "", "article_title": "Robotic assistance improves intracorporeal suturing performance and safety in the operating room while decreasing operator workload", "name": "D Stefanidis; ", "year": "2010", "journal": "Surg Endosc", "journal_type": "journal" }, { "ref_id": "CR17", "pmid_cited": "20045162", "doi_cited": "", "article_title": "A comparison of laparoscopic and robotic assisted suturing performance by experts and novices", "name": "V Chandra; ", "year": "2010", "journal": "Surgery", "journal_type": "journal" }, { "ref_id": "CR18", "pmid_cited": "33389652", "doi_cited": "", "article_title": "Worldwide diffusion of robotic approach in general surgery", "name": "R Liu; Q Liu; Z Wang", "year": "2021", "journal": "Updates Surg", "journal_type": "journal" }, { "ref_id": "CR19", "pmid_cited": "28281112", "doi_cited": "", "article_title": "Performance of Kymerax© precision-drive articulating surgical system compared to conventional laparoscopic instruments in a pelvitrainer model", "name": "MA Sieber; B Fellmann-Fischer; M Mueller", "year": "2017", "journal": "Surg Endosc", "journal_type": "journal" }, { "ref_id": "CR20", "pmid_cited": "18035539", "doi_cited": "", "article_title": "Radius surgical system and conventional laparoscopic instruments in abdominal surgery: application, learning curve and ergonomy", "name": "N Di Lorenzo; I Camperchioli; AL Gaspari", "year": "2007", "journal": "Surg Oncol", "journal_type": "journal" }, { "ref_id": "CR21", "pmid_cited": "17516121", "doi_cited": "", "article_title": "Precision in stitches: Radius Surgical System", "name": "M Waseda; ", "year": "2007", "journal": "Surg Endosc", "journal_type": "journal" }, { "ref_id": "CR22", "pmid_cited": "", "doi_cited": "", "article_title": "", "name": "", "year": "", "journal": "", "journal_type": "other" }, { "ref_id": "CR23", "pmid_cited": "30785844", "doi_cited": "", "article_title": "Evaluating a solely mechanical articulating laparoscopic device: a prospective randomized crossover study", "name": "CN Criss; ", "year": "2019", "journal": "J Laparoendosc Adv Surg Tech A", "journal_type": "journal" }, { "ref_id": "CR24", "pmid_cited": "31754849", "doi_cited": "", "article_title": "Robot assisted versus laparoscopic suturing learning curve in a simulated setting", "name": "E Leijte; ", "year": "2020", "journal": "Surg Endosc", "journal_type": "journal" }, { "ref_id": "CR25", "pmid_cited": "15836846", "doi_cited": "", "article_title": "Comparison of skill training with robotic systems and traditional endoscopy: implications on training and adoption", "name": "HS Maniar; ", "year": "2005", "journal": "J Surg Res", "journal_type": "journal" }, { "ref_id": "CR26", "pmid_cited": "15931486", "doi_cited": "", "article_title": "Bimodal assessment of laparoscopic suturing skills: construct and concurrent validity", "name": "K Moorthy; ", "year": "2004", "journal": "Surg Endosc", "journal_type": "journal" }, { "ref_id": "CR27", "pmid_cited": "", "doi_cited": "", "article_title": "Development of NASA-TLX: results of empirical and theoretical research", "name": "S Hart", "year": "1988", "journal": "Human mental workload", "journal_type": "book" }, { "ref_id": "CR28", "pmid_cited": "17150300", "doi_cited": "", "article_title": "The radius surgical system—a new device for complex minimally invasive procedures in urology?", "name": "T Frede; ", "year": "2007", "journal": "Eur Urol", "journal_type": "journal" }, { "ref_id": "CR29", "pmid_cited": "30090854", "doi_cited": "", "article_title": "Comparing a mechanical analogue with the Da Vinci user interface: suturing at challenging angles", "name": "PL Anderson; ", "year": "2016", "journal": "IEEE Robot Autom Lett", "journal_type": "journal" }, { "ref_id": "CR30", "pmid_cited": "11513783", "doi_cited": "", "article_title": "Assessing laparoscopic manipulative skills", "name": "CD Smith; ", "year": "2001", "journal": "Am J Surg", "journal_type": "journal" }, { "ref_id": "CR31", "pmid_cited": "27928670", "doi_cited": "", "article_title": "Tensile strength and failure load of sutures for robotic surgery", "name": "A Abiri; ", "year": "2017", "journal": "Surg Endosc", "journal_type": "journal" }, { "ref_id": "CR32", "pmid_cited": "24397464", "doi_cited": "", "article_title": "Experience related factors compensate for haptic loss in robot-assisted laparoscopic surgery", "name": "TP Cundy; ", "year": "2014", "journal": "J Endourol", "journal_type": "journal" }, { "ref_id": "CR33", "pmid_cited": "26559538", "doi_cited": "", "article_title": "An experimental study about haptic feedback in robotic surgery: may visual feedback substitute tactile feedback?", "name": "G Meccariello; ", "year": "2016", "journal": "J Robot Surg", "journal_type": "journal" } ]

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[{"type":"text","content":"Scaling up of Eco-Bio-Social Strategy to Control"},{"type":"text","conten(...TRUNCATED)

"Aedes aegypti is a cosmopolitan vector for arboviruses dengue, Zika and chikungunya, disseminated i(...TRUNCATED)

[]

[{"order":"1","subsections":[],"section_title":"1. Introduction","paragraphs":[{"aggregated_text":"M(...TRUNCATED)

International Journal of Environmental Research and Public Health

33572650

PMC7908398

10.3390/ijerph18031278

ijerph-18-01278

[["de Macêdo","Suyanne Freire","af1-ijerph-18-01278"],["de Macêdo","Suyanne Freire","af2-ijerph-18(...TRUNCATED)

[["af1-ijerph-18-01278","Collective Health Postgraduate Program, State University of Ceará, Fortale(...TRUNCATED)

2,021

01-2-2021

31-1-2021

Study Protocol

null

"Conflicts of Interest\nThe authors declare no conflict of interest. The funders had no role in the (...TRUNCATED)

[{"ref_id":"B1-ijerph-18-01278","pmid_cited":"23563266","doi_cited":"","article_title":"The global d(...TRUNCATED)

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[{"type":"text","content":"Nurse-led, telephone-based secondary preventive follow-up benefits stroke(...TRUNCATED)

"Background The objective of this study was to analyze the impact of two forms of secondary prevent(...TRUNCATED)

[]

[{"order":"1","subsections":[],"section_title":"Background","paragraphs":[{"aggregated_text":"Patien(...TRUNCATED)

Trials

30646948

PMC6334622

10.1186/s13063-018-3131-4

3131

[["Irewall","Anna-Lotta","Aff1"],["Ögren","Joachim","Aff1"],["Bergström","Lisa","Aff1"],["Laurell"(...TRUNCATED)

[["Aff1","0000 0001 1034 3451 grid.12650.30 Department of Public Health and Clinical Medicine, (...TRUNCATED)

2,019

01-01-2019

15-1-2019

Research

null

"Ethics approval and consent to participate\nThe Regional Ethics Committee in Umeå approved the NAI(...TRUNCATED)

[{"ref_id":"CR1","pmid_cited":"21454819","doi_cited":"","article_title":"Risk and cumulative risk of(...TRUNCATED)

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[{"type":"text","content":"Safety, tolerability, pharmacokinetics, and pharmacodynamics of low dose (...TRUNCATED)

"Abstract Research has shown that psychedelics, such as lysergic acid diethylamide (LSD), have profo(...TRUNCATED)

[]

[{"order":"1","subsections":[],"section_title":"Introduction","paragraphs":[{"aggregated_text":"Lyse(...TRUNCATED)

Psychopharmacology

31853557

PMC7036065

10.1007/s00213-019-05417-7

5417

[["Family","Neiloufar","Aff1"],["Maillet","Emeline L.","Aff1"],["Williams","Luke T. J.","Aff1"],["Kr(...TRUNCATED)

[["Aff1","Eleusis Benefit Corporation, New York, NY USA"],["Aff2","grid.7445.2 0000 0001 2113 8111 (...TRUNCATED)

2,020

01-01-2020

18-12-2019

Original Investigation

null

Conflict of interest All authors were paid consultants of Eleusis Benefit Corporation.

[{"ref_id":"CR1","pmid_cited":"","doi_cited":"10.1016/j.biopsych.2019.03.876","article_title":"S125.(...TRUNCATED)

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[{"type":"text","content":"Impact of Vitamin E supplementation on vascular function in haptoglobin g(...TRUNCATED)

"Aims Vitamin E (Vit-E) may preferentially improve cardiovascular risk in haptoglobin 2-2 (Hp2-2) ge(...TRUNCATED)

[]

[{"order":"1","subsections":[],"section_title":"Introduction","paragraphs":[{"aggregated_text":"Type(...TRUNCATED)

Nutrition & Diabetes

32341356

PMC7186220

10.1038/s41387-020-0116-7

116

[["Dalan","Rinkoo","Aff1"],["Dalan","Rinkoo","Aff2"],["Dalan","Rinkoo","Aff3"],["Goh","Liuh Ling","A(...TRUNCATED)

[["Aff1","grid.240988.f Tan Tock Seng Hospital, Singapore, Singapore"],["Aff2","grid.59025.3b 00(...TRUNCATED)

2,020

01-01-2020

27-4-2020

Article

null

Conflict of interest The authors declare that they have no conflict of interest.

[{"ref_id":"CR1","pmid_cited":"20609967","doi_cited":"","article_title":"Diabetes mellitus, fasting (...TRUNCATED)

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[{"type":"text","content":"Effect of dolutegravir on folate, vitamin B12 and mean corpuscular volume(...TRUNCATED)

"Abstract Introduction Dolutegravir‐based antiretroviral therapy (ART) is the preferred antiretrov(...TRUNCATED)

[]

[{"order":"1","subsections":[],"section_title":"INTRODUCTION","paragraphs":[{"aggregated_text":"Dolu(...TRUNCATED)

Journal of the International AIDS Society

37766505

PMC10534059

10.1002/jia2.26174

JIA226174

[["Barlow‐Mosha","Linda Namutebi","jia226174-aff-0001"],["Ahimbisibwe","Grace Miriam","jia226174-a(...TRUNCATED)

[["jia226174-aff-0001","Makerere University‐Johns Hopkins University (MU‐JHU) Research Collabora(...TRUNCATED)

2,023

01-9-2023

27-9-2023

Research Article; Research Articles

null

"COMPETING INTERESTS\nAT has received funding for serving on a ViiV Healthcare advisory board with p(...TRUNCATED)

[{"ref_id":"jia226174-bib-0001","pmid_cited":"34965338","doi_cited":"","article_title":"Dolutegravir(...TRUNCATED)

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[{"type":"text","content":"Laparoscopic sacrohysteropexy versus vaginal hysterectomy and apical susp(...TRUNCATED)

"Introduction and hypothesis Laparoscopic mesh sacrohysteropexy offers a uterine-sparing alternative(...TRUNCATED)

[]

[{"order":"1","subsections":[],"section_title":"Introduction","paragraphs":[{"aggregated_text":"A wo(...TRUNCATED)

International Urogynecology Journal

34424347

PMC9270299

10.1007/s00192-021-04932-6

4932

[["Izett-Kay","Matthew L.","Aff1"],["Izett-Kay","Matthew L.","Aff2"],["Rahmanou","Philip","Aff3"],["(...TRUNCATED)

[["Aff1","grid.410556.3 0000 0001 0440 1440 Department of Urogynaecology, Women’s Centre, The Jo(...TRUNCATED)

2,022

01-01-2022

23-8-2021

Original Article

null

Conflicts of interest None.

[{"ref_id":"CR1","pmid_cited":"20966694","doi_cited":"","article_title":"Lifetime risk of undergoing(...TRUNCATED)

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[{"type":"text","content":"Randomized clinical trial on the use of a colon-occlusion device to assis(...TRUNCATED)

"Background Transrectal Natural Orifice Transluminal Endoscopic Surgery is currently limited by the (...TRUNCATED)

[]

[{"order":"1","subsections":[{"order":"1.1","subsections":[],"section_title":"Coloshield device","pa(...TRUNCATED)

Surgical Endoscopy

32968914

PMC8346441

10.1007/s00464-020-07992-9

7992

[["Cordewener","Carolin","Aff1"],["Zürcher","Manuel","Aff2"],["Müller","Philip C.","Aff3"],["Müll(...TRUNCATED)

[["Aff1","Pelvic Floor Unit, Clarunis, University Center for Gastrointestinal and Liver Diseases, 40(...TRUNCATED)

2,021

01-01-2021

23-9-2020

Article

null

"Disclosures\nThe Coloshield devices used in this trial were provided at no costs by A.M.I, Feldkirc(...TRUNCATED)

[{"ref_id":"CR1","pmid_cited":"21471754","doi_cited":"","article_title":"Transanal endoscopic micros(...TRUNCATED)

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