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d9c7c2f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | import matplotlib.pyplot as plt
import numpy as np
# Generate concentration data (0 to 100%)
concentration = np.linspace(0, 100, 200)
# Define sigmoid function for dose-response curves
def sigmoid(x, max_response, ec50=50, hill_slope=0.1):
"""
Sigmoid function for dose-response curves
x: concentration/dose
max_response: maximum response (efficacy)
ec50: concentration at 50% of max response
hill_slope: steepness of the curve
"""
return max_response / (1 + np.exp(-(x - ec50) / (1/hill_slope)))
# Generate response curves for different agonist types
full_agonist = sigmoid(concentration, max_response=100) # 100% efficacy
partial_agonist = sigmoid(concentration, max_response=45) # 45% efficacy
inverse_agonist = sigmoid(concentration, max_response=-40) # Negative response
baseline = np.zeros_like(concentration) # Baseline at 0
# Create the plot
plt.figure(figsize=(12, 8))
# Plot the curves
plt.plot(concentration, full_agonist, 'r-', linewidth=3,
label='Full Agonist (Morphine)', alpha=0.8)
plt.plot(concentration, partial_agonist, 'b-', linewidth=3,
label='Partial Agonist (Buprenorphine)', alpha=0.8)
plt.plot(concentration, inverse_agonist, color='purple', linewidth=3,
label='Inverse Agonist (Beta-carbolines)', alpha=0.8)
plt.plot(concentration, baseline, 'k--', linewidth=2,
label='Baseline', alpha=0.5)
# Add horizontal reference lines for max efficacy
plt.axhline(y=100, color='red', linestyle=':', alpha=0.3, linewidth=1)
plt.axhline(y=45, color='blue', linestyle=':', alpha=0.3, linewidth=1)
plt.axhline(y=0, color='gray', linestyle='-', alpha=0.5, linewidth=1.5)
# Add annotations
plt.annotate('100% Efficacy\n(Full Agonist)',
xy=(85, 100), xytext=(70, 105),
fontsize=10, color='red', fontweight='bold',
bbox=dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='red'))
plt.annotate('~45% Efficacy\n(Partial Agonist)\nCeiling Effect',
xy=(85, 45), xytext=(65, 60),
fontsize=10, color='blue', fontweight='bold',
bbox=dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='blue'),
arrowprops=dict(arrowstyle='->', color='blue', lw=1.5))
plt.annotate('Inverse Activity\n(Below Baseline)',
xy=(85, -40), xytext=(60, -55),
fontsize=10, color='purple', fontweight='bold',
bbox=dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='purple'),
arrowprops=dict(arrowstyle='->', color='purple', lw=1.5))
# Labels and title
plt.xlabel('Drug Concentration / Receptor Occupancy (%)', fontsize=14, fontweight='bold')
plt.ylabel('Biological Response (%)', fontsize=14, fontweight='bold')
plt.title('Drug-Receptor Interactions: Types of Agonists\nDose-Response Curves',
fontsize=16, fontweight='bold', pad=20)
# Legend
plt.legend(loc='upper left', fontsize=11, framealpha=0.9)
# Grid
plt.grid(True, alpha=0.3, linestyle='--')
# Set axis limits
plt.xlim(0, 100)
plt.ylim(-60, 115)
# Add text box with key information
textstr = '\n'.join([
'Key Concepts:',
'• Full Agonist: 100% maximal response',
'• Partial Agonist: Submaximal response (~45%)',
' even at full receptor occupancy',
'• Inverse Agonist: Reduces basal activity',
' producing opposite effects'
])
props = dict(boxstyle='round', facecolor='wheat', alpha=0.8)
plt.text(0.02, 0.35, textstr, transform=plt.gca().transAxes,
fontsize=10, verticalalignment='top', bbox=props)
# Tight layout for better spacing
plt.tight_layout()
# Save the plot to a file
plt.savefig('dose_response_plot.png', dpi=300, bbox_inches='tight')
# Display the plot (commented out for console run)
# plt.show()
# Optional: Print some data points for verification
print("Sample Data Points:")
print(f"{'Concentration':<15} {'Full Agonist':<15} {'Partial Agonist':<20} {'Inverse Agonist'}")
print("-" * 70)
for i in [0, 25, 50, 75, 100]:
idx = int(i * 2) # Since we have 200 points for 0-100 range
print(f"{concentration[idx]:<15.1f} {full_agonist[idx]:<15.2f} "
f"{partial_agonist[idx]:<20.2f} {inverse_agonist[idx]:.2f}") |