Steady State Volume of Distribution (VDss) Distribution

import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
import numpy as np

# 1. VDss data (Log L/kg)
bin_centers = [-1.2, -1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 
               0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6]

frequencies = [0.20, 1.33, 6.88, 10.23, 11.17, 14.12, 14.85, 
               13.96, 10.07, 7.16, 4.05, 3.20, 1.78, 0.77, 0.24]

# 2. Gaussian Fit Parameters (Provided)
amplitude = 14.90
mean = -0.04540
sd = 0.5412

# Generate smooth curve
x_smooth = np.linspace(min(bin_centers), max(bin_centers), 300)
y_smooth = amplitude * np.exp(-0.5 * ((x_smooth - mean) / sd)**2)

# 3. Define colors (VDss Classification)
colors = []
for val in bin_centers:
    # High VDss (> 0.5 Log) -> Tissues
    if val > 0.5:
        colors.append('royalblue')
    # Low VDss (< -0.6 Log) -> Plasma
    elif val < -0.6:
        colors.append('firebrick')
    # Medium VDss -> Body Water
    else:
        colors.append('gold')

# 4. Create the chart
plt.figure(figsize=(7, 6))

# A. Bars
plt.bar(bin_centers, frequencies, width=0.15, color=colors, edgecolor='black', alpha=0.7, label='Data Frequency')

# B. Gaussian curve
plt.plot(x_smooth, y_smooth, color='darkorange', linewidth=2, label='Gaussian Fit')

# 5. Tags and Titles
plt.xlabel('VDss (Steady State Volume of Distribution) [Log L/kg]', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('VDss Distribution', fontsize=14)

# Axle settings
plt.xticks(np.arange(-1.2, 1.8, 0.2))
plt.xlim(-1.4, 1.8)
plt.ylim(0, 16)

# 6. Legend
legend_elements = [
    Line2D([0], [0], color='darkorange', lw=2.5, label=f'Fit (Mean={mean}, SD={sd})'),
    Patch(facecolor='firebrick', edgecolor='black', alpha=0.7, label='Low VDss (Plasma Restricted)'),
    Patch(facecolor='gold', edgecolor='black', alpha=0.7, label='Medium VDss (Body Water)'),
    Patch(facecolor='royalblue', edgecolor='black', alpha=0.7, label='High VDss (Tissue Accumulation)'),
]

plt.legend(handles=legend_elements, loc='upper right', framealpha=0.9)
plt.grid(axis='y', linestyle='--', alpha=0.5)
plt.tight_layout()

plt.show()