commit

README.md

@@ -12,7 +12,7 @@ short_description: Model split-dose protocols for lisdexamfetamine/Vyvanse
 
 # 💊 Lisdexamfetamine Split-Dose Modeller
 
-An interactive pharmacokinetic modeling tool for visualizing and optimizing split-dosing protocols of **lisdexamfetamine** (Vyvanse®).
+An interactive pharmacokinetic modeling tool for visualizing split-dosing protocols of **lisdexamfetamine** (Vyvanse®).
 
 ## Overview
 
@@ -21,7 +21,6 @@ This tool helps ADHD patients and healthcare providers explore different split-d
 ## Features
 
 - **Manual Protocol Design**: Configure custom dosing schedules with 2-4 doses per day
-- **Automatic Optimization**: Find the smoothest concentration curve using differential evolution
 - **Visual Feedback**: Real-time visualization of predicted blood concentration curves
 - **Downloadable Protocols**: Generate markdown protocols with precise timing and dosing instructions
 - **Dose Range**: Support for 30mg to 70mg total daily doses
@@ -52,8 +51,7 @@ The model is based on published PK parameters:
 ## Technology
 
 - **Framework**: Gradio
-- **Modeling**: SciPy (one-compartment PK model)
-- **Optimization**: Differential evolution algorithm
+- **Modeling**: NumPy (one-compartment PK model)
 - **Visualization**: Matplotlib
 
 ## License

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 import numpy as np
 import matplotlib.pyplot as plt
 from datetime import datetime, timedelta
-from scipy.optimize import differential_evolution
 
 # Pharmacokinetic parameters for Lisdexamfetamine/d-amphetamine
 # Based on standard adult male parameters
@@ -93,42 +92,7 @@ def create_protocol_markdown(total_dose, doses, dose_times, start_time_str, tota
 
     return md
 
-def optimize_protocol(total_dose, num_doses, max_interval=6):
-    """Find the smoothest dosing curve (minimal peaks and troughs)."""
-
-    def objective(params):
-        """Minimize variation in concentration (standard deviation)."""
-        intervals = params[:num_doses-1]
-        percentages = params[num_doses-1:]
-
-        time, concentration, _, _, _ = simulate_protocol(
-            total_dose, num_doses, intervals, percentages
-        )
-
-        # Minimize standard deviation to get smoother curve
-        # Also penalize if concentration drops too low
-        std_dev = np.std(concentration)
-        min_conc = np.min(concentration)
-
-        # Penalty for very low concentrations
-        penalty = 0
-        if min_conc < 0.1 * np.max(concentration):
-            penalty = 1000
-
-        return std_dev + penalty
-
-    # Bounds: intervals (0.5 to max_interval hours), percentages (5% to 60%)
-    bounds = [(0.5, max_interval)] * (num_doses - 1)  # intervals
-    bounds += [(5, 60)] * num_doses  # percentages
-
-    result = differential_evolution(objective, bounds, seed=42, maxiter=100)
-
-    optimal_intervals = result.x[:num_doses-1].tolist()
-    optimal_percentages = result.x[num_doses-1:].tolist()
-
-    return optimal_intervals, optimal_percentages
-
-def plot_concentration_curve(time, concentration, therapeutic_threshold=0.2):
+def plot_concentration_curve(time, concentration, therapeutic_threshold=0.2, start_time_str="07:00"):
     """Create matplotlib plot of concentration curve with sub-therapeutic shading."""
     fig, ax = plt.subplots(figsize=(10, 6))
 
@@ -147,7 +111,27 @@ def plot_concentration_curve(time, concentration, therapeutic_threshold=0.2):
     # Add therapeutic threshold line
     ax.axhline(y=threshold, color='#FF6B6B', linestyle='--', linewidth=1, alpha=0.7)
 
-    ax.set_xlabel('Time (hours)', fontsize=12)
+    # Parse start time and create custom x-axis labels
+    start_time = datetime.strptime(start_time_str, "%H:%M")
+
+    # Create x-axis tick positions (every 3 hours)
+    tick_positions = np.arange(0, 25, 3)
+    tick_labels = []
+
+    for hours_offset in tick_positions:
+        clock_time = start_time + timedelta(hours=float(hours_offset))
+        if hours_offset == 0:
+            # First dose - show only clock time
+            label = clock_time.strftime('%H:%M')
+        else:
+            # Subsequent times - show clock time with relative hours
+            label = f"{clock_time.strftime('%H:%M')}\n(+{int(hours_offset)})"
+        tick_labels.append(label)
+
+    ax.set_xticks(tick_positions)
+    ax.set_xticklabels(tick_labels, fontsize=9)
+
+    ax.set_xlabel('Time', fontsize=12)
     ax.set_ylabel('Relative Blood Concentration', fontsize=12)
     ax.set_title('Predicted Blood Concentration Over Time', fontsize=14, fontweight='bold')
     ax.grid(True, alpha=0.3)
@@ -157,8 +141,9 @@ def plot_concentration_curve(time, concentration, therapeutic_threshold=0.2):
 
     # Add max marker
     max_idx = np.argmax(concentration)
+    peak_time = start_time + timedelta(hours=time[max_idx])
     ax.plot(time[max_idx], concentration[max_idx], 'ro', markersize=8)
-    ax.annotate(f'Peak: {time[max_idx]:.1f}h',
+    ax.annotate(f'Peak: {peak_time.strftime("%H:%M")} (+{time[max_idx]:.1f}h)',
                 xy=(time[max_idx], concentration[max_idx]),
                 xytext=(10, 10), textcoords='offset points',
                 bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.7))
@@ -185,7 +170,7 @@ def manual_protocol(total_dose, num_doses, interval1, interval2, interval3,
         total_dose, int(num_doses), intervals, percentages, start_time
     )
 
-    fig = plot_concentration_curve(time, concentration)
+    fig = plot_concentration_curve(time, concentration, start_time_str=start_time)
     md = create_protocol_markdown(total_dose, doses, dose_times, start_time, total_water)
 
     # Create dose summary
@@ -195,28 +180,6 @@ def manual_protocol(total_dose, num_doses, interval1, interval2, interval3,
 
     return fig, md, summary
 
-def optimize_and_display(total_dose, num_doses, max_interval, start_time, total_water):
-    """Optimize protocol and display results."""
-    intervals, percentages = optimize_protocol(total_dose, num_doses, max_interval)
-
-    time, concentration, doses, dose_times, norm_pct = simulate_protocol(
-        total_dose, num_doses, intervals, percentages, start_time
-    )
-
-    fig = plot_concentration_curve(time, concentration)
-    md = create_protocol_markdown(total_dose, doses, dose_times, start_time, total_water)
-
-    # Create optimization results summary
-    summary = "**Optimized Protocol:**\n\n"
-    summary += "**Intervals (hours between doses):**\n"
-    for i, interval in enumerate(intervals):
-        summary += f"- After dose {i+1}: {interval:.1f} hours\n"
-    summary += "\n**Dose Breakdown:**\n"
-    for i, (dose, pct) in enumerate(zip(doses, norm_pct)):
-        summary += f"- Dose {i+1}: {dose:.1f}mg ({pct:.1f}%)\n"
-
-    return fig, md, summary, *intervals, *percentages
-
 # Create Gradio interface
 with gr.Blocks(title="Lisdexamfetamine Split-Dose Modeller", theme=gr.themes.Soft()) as app:
     gr.Markdown("""
@@ -292,61 +255,6 @@ with gr.Blocks(title="Lisdexamfetamine Split-Dose Modeller", theme=gr.themes.Sof
                 outputs=[download_btn_manual]
             )
 
-        with gr.Tab("Optimize for Smoothest Curve"):
-            gr.Markdown("""
-            ### Automatic Optimization
-
-            Find the smoothest possible concentration curve by automatically optimizing dose timing and amounts.
-            """)
-
-            with gr.Row():
-                total_dose_opt = gr.Dropdown(label="Total Daily Dose (mg)",
-                                            choices=[30, 40, 50, 60, 70],
-                                            value=70)
-                num_doses_opt = gr.Slider(label="Number of Doses", minimum=2, maximum=4, step=1, value=2)
-                max_interval_opt = gr.Slider(label="Maximum Interval Between Doses (hours)",
-                                            minimum=2, maximum=8, step=0.5, value=6)
-
-            with gr.Row():
-                start_time_opt = gr.Textbox(label="First Dose Time (HH:MM)", value="07:00")
-                total_water_opt = gr.Number(label="Total Water Volume (ml)", value=700, minimum=100, maximum=1000)
-
-            optimize_btn = gr.Button("Find Optimal Protocol", variant="primary")
-
-            with gr.Row():
-                plot_output_opt = gr.Plot(label="Optimized Concentration Curve")
-
-            with gr.Row():
-                summary_opt = gr.Markdown()
-
-            # Hidden components to store optimized values
-            with gr.Row(visible=False):
-                opt_interval1 = gr.Number()
-                opt_interval2 = gr.Number()
-                opt_interval3 = gr.Number()
-                opt_pct1 = gr.Number()
-                opt_pct2 = gr.Number()
-                opt_pct3 = gr.Number()
-                opt_pct4 = gr.Number()
-
-            with gr.Row():
-                protocol_output_opt = gr.Textbox(label="Optimized Dosing Protocol", lines=15)
-                download_btn_opt = gr.DownloadButton(label="Download Protocol")
-
-            optimize_btn.click(
-                fn=optimize_and_display,
-                inputs=[total_dose_opt, num_doses_opt, max_interval_opt, start_time_opt, total_water_opt],
-                outputs=[plot_output_opt, protocol_output_opt, summary_opt,
-                        opt_interval1, opt_interval2, opt_interval3,
-                        opt_pct1, opt_pct2, opt_pct3, opt_pct4]
-            )
-
-            protocol_output_opt.change(
-                fn=lambda x: gr.DownloadButton(value=x, visible=True),
-                inputs=[protocol_output_opt],
-                outputs=[download_btn_opt]
-            )
-
         with gr.Tab("About"):
             gr.Markdown("""
             ## About This Tool
@@ -388,7 +296,6 @@ with gr.Blocks(title="Lisdexamfetamine Split-Dose Modeller", theme=gr.themes.Sof
             This tool is designed to help ADHD patients and their healthcare providers:
             - Visualize the effects of different split-dosing strategies
             - Understand how dose timing affects blood concentration patterns
-            - Explore optimization strategies for smoother therapeutic coverage
             - Plan and document split-dosing protocols
 
             ### Technical Details
@@ -398,8 +305,6 @@ with gr.Blocks(title="Lisdexamfetamine Split-Dose Modeller", theme=gr.themes.Sof
             - First-order elimination (Ke ≈ 0.069 h⁻¹, from t½ = 10h)
             - Linear dose-response relationship
 
-            For optimization, the tool uses differential evolution to minimize concentration variance while maintaining adequate therapeutic levels.
-
             ---
 
             **Version**: 1.0

requirements.txt

@@ -1,4 +1,3 @@
 gradio>=5.9.1
 numpy>=1.24.0
 matplotlib>=3.7.0
-scipy>=1.10.0

test_plot.py

@@ -0,0 +1,24 @@
+#!/usr/bin/env python3
+"""Quick test to verify the plot displays time labels correctly."""
+
+import numpy as np
+from datetime import datetime, timedelta
+from app import plot_concentration_curve, simulate_protocol
+
+# Test with a simple 2-dose protocol starting at 07:00
+time, concentration, doses, dose_times, norm_pct = simulate_protocol(
+    total_dose=70,
+    num_doses=2,
+    intervals=[6],
+    percentages=[50, 50],
+    start_time_str="07:00"
+)
+
+# Create the plot
+fig = plot_concentration_curve(time, concentration, start_time_str="07:00")
+
+# Save to file for inspection
+fig.savefig('/tmp/test_plot.png', dpi=100, bbox_inches='tight')
+print("✓ Plot created successfully!")
+print(f"✓ Saved to /tmp/test_plot.png")
+print(f"✓ Time labels should show: 07:00, 10:00 (+3), 13:00 (+6), etc.")