AI for Pharmacogenomics and Personalized Dosing

Module: Healthcare AI | Difficulty: Advanced

Drug Metabolism (Michaelis-Menten)

Population PK Model

Dose Optimization

Pharmacogenomic Associations

| Gene | Drug | Effect | Clinical Action | |------|------|--------|-----------------| | CYP2D6 | Codeine | Poor metabolizer | Alternative analgesic | | CYP2C19 | Clopidogrel | Poor metabolizer | Alternative antiplatelet | | HLA-B*5701 | Abacavir | Hypersensitivity | Contraindicated | | VKORC1 | Warfarin | Dose requirement | Dose adjustment | | TPMT | Azathioprine | Toxicity risk | Dose reduction |

import torch
import torch.nn as nn

class PKPDModel(nn.Module):
    def __init__(self, input_dim=50, hidden_dim=64):
        super().__init__()
        self.patient_encoder = nn.Sequential(
            nn.Linear(input_dim, hidden_dim), nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim))
        self.pk_params = nn.Sequential(
            nn.Linear(hidden_dim, 32), nn.ReLU(),
            nn.Linear(32, 4), nn.Softplus())
        self.pd_params = nn.Sequential(
            nn.Linear(hidden_dim, 32), nn.ReLU(),
            nn.Linear(32, 3))

    def forward(self, patient_features):
        encoded = self.patient_encoder(patient_features)
        pk = self.pk_params(encoded)
        pd = self.pd_params(encoded)
        return pk, pd

class DoseOptimizer(nn.Module):
    def __init__(self, patient_dim=50, hidden_dim=64):
        super().__init__()
        self.patient_encoder = nn.Sequential(
            nn.Linear(patient_dim, hidden_dim), nn.ReLU())
        self.dose_head = nn.Sequential(
            nn.Linear(hidden_dim, 32), nn.ReLU(),
            nn.Linear(32, 1), nn.Softplus())

    def forward(self, patient_features):
        encoded = self.patient_encoder(patient_features)
        dose = self.dose_head(encoded) * 100
        return dose

pk_model = PKPDModel(input_dim=50)
patient = torch.randn(1, 50)
pk_params, pd_params = pk_model(patient)
print(f'PK params (Vmax, Km, ka, ke): {pk_params.shape}')
print(f'PD params (Emax, EC50, Hill): {pd_params.shape}')

dose_optimizer = DoseOptimizer(patient_dim=50)
optimal_dose = dose_optimizer(patient)
print(f'Optimal dose: {optimal_dose.item():.1f} mg')

Research Insight: Pharmacogenomic AI models can predict individual drug metabolism based on genetic variants, enabling personalized dosing. The challenge is that most pharmacogenomic studies have been conducted in populations of European ancestry, limiting generalizability to other populations. Multi-ethnic pharmacogenomic studies and transfer learning approaches are needed to ensure equitable personalized medicine.