PMCID string | Title string | Sentences string |
|---|---|---|
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Three replicate 10 × 10 combination matrices are generated for each drug pair, along with three replicates of each single-agent dose-response. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Twelve replicates each of cell death (orange) and vehicle (yellow) controls are included for normalization. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | d Overview of the Combocat analytical pipeline. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Raw data are mapped to corresponding combination matrices and subsequently normalized to percentage cell death using controls. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Synergy is then quantified using the Bliss independence model. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Results are summarized across the screen to identify top synergistic interactions. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | a Practical representation of drug synergy, where the combined effect of two drugs exceeds the expected effect (“E”) based on models like additivity or Bliss independence. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | b Examples of traditional high-throughput screens employing small (e.g., 3 × 3), sparse, or asymmetric (e.g., 2 × 7) matrix formats, which limit dose density. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | c Combocat’s streamlined approach for dense and reproducible drug combination screening. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The 384-well plate format contains Drugs 1 and 2 (represented by upper and lower triangles, respectively), with concentrations shown ranging from low (blue) to high (red). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Three replicate 10 × 10 combination matrices are generated for each drug pair, along with three replicates of each single-agent dose-response. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Twelve replicates each of cell death (orange) and vehicle (yellow) controls are included for normalization. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | d Overview of the Combocat analytical pipeline. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Raw data are mapped to corresponding combination matrices and subsequently normalized to percentage cell death using controls. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Synergy is then quantified using the Bliss independence model. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Results are summarized across the screen to identify top synergistic interactions. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Combination therapy is a promising frontier—but also a vast one whose comprehensive exploration has been limited by the sheer number of possible drug combinations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | With each additional agent tested, the number of total possible 2-drug combinations increases quadratically, making exhaustive experimental testing impractical. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | While large-scale single-agent screens have evaluated tens of thousands of compounds, drug combination screens have remained constrained by technical and resource limitations, preventing them from achieving comparable scale. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | These constraints often force researchers to choose between the number of combinations tested and the number of concentrations measured per drug, or dose density. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This tradeoff can prove problematic, as dense measurements across a broad range of concentrations are crucial for capturing the nuanced dose-response landscapes needed to reliably detect synergy patterns. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Consequently, some of the largest reported combination studies have adopted smaller, sparser, or asymmetric matrix formats (Fig. 1b). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Advancements in robotic liquid handling technologies, such as acoustic dispensing, have revolutionized compound transfer, enabling more flexible and precise experimental designs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Acoustic liquid handlers can dispense nanoliter volumes from any well of a source plate to any well of a destination plate without physical contact, enhancing throughput and minimizing resource consumption. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Despite the promise of these technological advances, their impact on drug combination screening has so far been limited by a lack of integrated experimental and analytical platforms that fully leverage their capabilities. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Here, we introduce Combocat, an open-source, end-to-end platform that integrates experimental and analytical workflows, underpinned by detailed protocols and documentation to enable reproducible, interpretable, and user-friendly drug combination screens. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Combocat operates in two complementary modes, each facilitating fast and efficient combination screening. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The “dense mode” measures all pairwise dose combinations in a dense 10 × 10 matrix format, tested in triplicate to ensure high fidelity and data quality. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Using dense mode, we generated a comprehensive reference dataset of over 290,000 unique combination measurements (spanning 806 unique drug combinations) across diverse drugs and cell types. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | While this approach is ideal for high-quality data-rich screens, scaling dense mode to thousands of combinations is resource-intensive. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To address this, we developed a machine learning-assisted “sparse mode”. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Sparse mode reduces the number of direct experimental measurements by testing only the diagonal (10 dose pairs) of the 10 × 10 matrix and single-agent responses, then uses predictive modeling to obtain the remaining 90 values. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Trained on the dense mode reference dataset, the predictive models in sparse mode enable ultrahigh-throughput screening with minimal resources. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Notably, several other machine learning frameworks have been developed to predict synergy in different ways: TranSynergy leverages drug-target and gene-dependency features, SynToxProfiler computes integrated efficacy-toxicity-synergy surfaces, and comboFM uses tensor factorization to impute missing dose-response values. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | In contrast, Combocat’s sparse mode measures only the 10 diagonal dose pairs and recovers the complete 10 × 10 response landscape via an ensemble of 90 per-pair regression models. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | As proof of concept, we used sparse mode to screen 9045 drug combinations in the neuroblastoma cell line CHP-134, representing the largest number of unique combinations tested in a single cell line to date. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | By integrating advanced liquid handling technologies with machine learning into an open-source and user-friendly platform, Combocat offers a scalable, efficient solution for accelerating the discovery of drug combinations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To address longstanding challenges in drug combination screens—such as limited dose density, poor interpretability, and operational complexity–we developed Combocat to integrate experimental and analytical workflows into a user-friendly framework. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The “dense mode” workflow facilitates comprehensive, dense measurements by systematically evaluating all pairwise dose combinations in a 10 × 10 matrix format (Fig. 1c). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | A customized acoustic liquid handling protocol prepares each 384-well plate with two drugs at ten concentrations each, including three replicate 10 × 10 combination matrices, single-agent dose-response curves, and internal controls (for example, twelve replicates each of cell death and vehicle controls). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This design ensures robust normalization and reproducibility while reducing the time and effort typically associated with high-throughput combination screens. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Building on this experimental foundation, the analytical pipeline (Fig. 1d) maps raw data to their corresponding matrix positions, normalizes values to percentage cell death, and quantifies synergy using the Bliss independence model. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Results are then filtered and ranked, enabling researchers to rapidly identify top-performing combinations and visualize complete synergy landscapes without extensive post-processing. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | By uniting dense measurements with a streamlined analytical workflow, Combocat’s dense mode provides a high-fidelity framework for characterizing drug interactions in a reproducible and operationally straightforward manner. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Applying dense mode, we generated a reference dataset of 806 drug combinations comprising over 290,000 individual measurements in the 10 × 10 matrix format (Supplementary Data 1). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This dataset was designed to evaluate a diverse range of drugs and cell types, ensuring generalizability across multiple experimental contexts. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Assay quality was high, as indicated by Z′ values (mean = 0.747; Supplementary Fig. 1) and low standard deviations in both single-agent and combination measurements across the screen (Fig. 2a, b).Fig. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | 2Dense combination screening.a Distribution of single-agent standard deviations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The standard deviation is measured across each dose of all single-agent drugs across their respective three replicates. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | b Distribution of combination standard deviations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For each drug combination, the standard deviation is measured across all 100 dose pairs and their respective three replicates. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | c Mean adjusted Bliss synergy (Blissadj.) |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | for all tested drug combinations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Each point represents the mean Blissadj. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | value across the 100 dose pairs per combination. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | SMX_TMP is highlighted as the top-ranking combination. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | d Dose-response curves for SMX (top) and TMP (bottom). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Four-parameter log-logistic curves were fit to the mean cell death values from the 10 doses. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Individual replicate values are represented by smaller gray dots. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Horizontal dashed lines correspond to 50% cell death. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Vertical dashed lines correspond to IC50 concentrations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | e Cell death combination matrix, represented as the mean of the three replicates. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | f Bliss synergy matrix calculated from (e). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | g Cell death for each single agent and their combination at doses corresponding to the maximum synergy. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The red dashed line indicates the expected response value under the Bliss model. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | h–k Like (d–g), for the combination of AZD1390 and CX-5461. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | a Distribution of single-agent standard deviations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The standard deviation is measured across each dose of all single-agent drugs across their respective three replicates. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | b Distribution of combination standard deviations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For each drug combination, the standard deviation is measured across all 100 dose pairs and their respective three replicates. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | c Mean adjusted Bliss synergy (Blissadj.) |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | for all tested drug combinations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Each point represents the mean Blissadj. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | value across the 100 dose pairs per combination. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | SMX_TMP is highlighted as the top-ranking combination. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | d Dose-response curves for SMX (top) and TMP (bottom). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Four-parameter log-logistic curves were fit to the mean cell death values from the 10 doses. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Individual replicate values are represented by smaller gray dots. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Horizontal dashed lines correspond to 50% cell death. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Vertical dashed lines correspond to IC50 concentrations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | e Cell death combination matrix, represented as the mean of the three replicates. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | f Bliss synergy matrix calculated from (e). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | g Cell death for each single agent and their combination at doses corresponding to the maximum synergy. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The red dashed line indicates the expected response value under the Bliss model. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | h–k Like (d–g), for the combination of AZD1390 and CX-5461. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | During analysis, we identified occasional spurious single-agent measurements, a common challenge in high-throughput screens. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | These anomalies, often resulting from incomplete drug transfer or other technical issues, can distort synergy calculations by introducing outliers into the dataset (Supplementary Fig. 2a–d). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To address this, we implemented a rigorous quality control (QC) pipeline that flags and adjusts anomalous data points using predefined thresholds for variability, dose-response residuals, and monotonicity (Supplementary Fig. 2e–g; see “Methods” section for details). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Flagged values can be excluded from subsequent synergy calculations, yielding an “adjusted Bliss synergy” (Blissadj.) |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | that omits these measurements. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This QC approach ensures accurate synergy quantification by minimizing the impact of outliers. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To evaluate the platform’s capacity to identify and replicate known synergistic interactions, we first examined the top-ranked combination in our dataset: sulfamethoxazole (SMX) and trimethoprim (TMP) in E. coli cells (Fig. 2c). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This pair is a well-established example of synergistic drug behavior, widely used to treat various bacterial infections. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Both SMX and TMP displayed sigmoidal dose-dependent relationships (Fig. 2d), and when combined, exhibited strong synergy (Fig. 2e–g), aligning with established expectations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | We further evaluated an additional known example of synergy included in our screen—the combination of AZD1390 and CX-5461—which has been reported as synergistic in Neuroblastoma cells. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The single-agent responses and strong synergy patterns (Fig. 2h–k) closely aligned to reported findings. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Collectively, these examples highlight Combocat’s ability to faithfully reproduce known synergistic interactions and provide a framework for understanding complex combination landscapes. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | While dense mode offers high-quality, data-rich combination measurements, scaling it to thousands of combinations can become resource-intensive. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To address this, we developed a “sparse mode” workflow, which minimizes direct experimental measurements while still capturing synergy information between drug pairs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The sparse mode assay is miniaturized into a 1536-well format and separates the single agents and combinations into their own plates (Supplementary Fig. 3a, b). |
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