AI-Driven Pharmacomicrobiomics: Microbial Drug Metabolism, Multi-Omics & Precision Therapeutics

To provide participants with a practical understanding of gut microbiome–drug interactions and equip them with computational skills to investigate microbial drug metabolism, pharmacometabolomics, therapeutic-response biomarkers, and microbiome-informed precision pharmacotherapy.

By the end of the workshop, participants will be able to:

• Understand the principles of pharmacomicrobiomics and microbiome–drug interactions
• Explore microbial mechanisms influencing drug metabolism, efficacy and toxicity
• Understand microbiome-mediated changes in pharmacokinetics and pharmacodynamics
• Analyze microbiome and metabolomics datasets associated with therapeutic response
• Identify microbial and metabolic biomarkers linked to drug efficacy and adverse response
• Integrate microbiome, metabolite and clinical features for patient stratification
• Build machine-learning models for drug-response prediction
• Apply explainable AI approaches such as SHAP to interpret predictive biomarkers
• Understand the principles and emerging applications of precision pharmacotherapy and model-informed precision dosing

📅 Day 1: Pharmacomicrobiomics & Microbial Drug Metabolism

• Gut microbiome as a determinant of drug efficacy, toxicity and inter-individual variability
• Bidirectional drug–microbiome interactions
• Microbial drug biotransformation: activation, inactivation and toxic metabolite formation
• Microbial enzymes and pathways affecting drug absorption, metabolism and bioavailability
• Microbiome effects on pharmacokinetics (PK) and pharmacodynamics (PD)
• Research case studies: metformin, irinotecan, levodopa, tacrolimus and cancer immunotherapy
• Identifying microbiome signatures associated with responders vs non-responders

🧪 Hands-On: Microbiome–Drug Response Profiling

• Using a curated microbiome/drug-response dataset in Google Colab.
• Microbiome features → Drug-response groups → Differential taxa → Response-associated microbial signatures

🧰 Tools: Python, Pandas, SciPy, Scikit-learn, Matplotlib

📅 Day 2: Pharmacometabolomics & Multi-Omics Biomarker Discovery

• Introduction to pharmacometabolomics for precision medicine
• Linking microbial taxa → metabolic pathways → metabolites → drug response
• Key microbiome-derived metabolites:
  • Short-chain fatty acids
  • Secondary bile acids
  • Indole derivatives
  • Aromatic microbial metabolites
• Metabolomics data preprocessing: missing values, normalization and transformation
• PCA and differential metabolite analysis
• Microbiome–metabolome correlation analysis
• Identifying metabolic biomarkers of drug efficacy and toxicity
• Integrating microbiome + metabolomics for patient stratification

🧪 Hands-On: Microbiome + Metabolomics Integration

• Analyze a simplified multi-omics dataset to:
• Normalize metabolites → PCA → Identify differential metabolites → Correlate microbes & metabolites → Prioritize drug-response biomarkers

🧰 Tools: Python, Pandas, Scikit-learn, SciPy, Matplotlib

📅 Day 3: AI-Driven Drug Response Prediction & Precision Dosing

• From microbiome biomarkers to precision pharmacotherapy
• Integrating: Microbiome + Metabolites + Clinical Variables
• Feature selection for drug-response modeling
• Machine learning for predicting:
  • Responders vs non-responders
  • Drug efficacy
  • Adverse-response/toxicity risk
• Random Forest / XGBoost-based prediction
• Model validation: ROC-AUC, sensitivity, specificity and cross-validation
• SHAP explainability for identifying microbiome/metabolite drivers
• Introduction to Model-Informed Precision Dosing (MIPD)
• Connecting microbiome signatures with PK/exposure and dosing scenarios
• Current limitations and clinical translation of microbiome-guided dosing

🧪 Hands-On: Explainable AI for Precision Drug Response

• Build an interpretable ML workflow:
• Microbial + metabolite features → ML model → Drug-response prediction → SHAP interpretation → Patient stratification

🧰 Tools: Python, Scikit-learn/XGBoost, SHAP, Pandas, Matplotlib

This workshop is suitable for:

• PhD Scholars & Research Scholars
• Academicians & Faculty Members
• Microbiome Researchers
• Bioinformatics & Computational Biology Researchers
• Pharmacology & Pharmaceutical Science Researchers
• Clinical Pharmacology Researchers
• Metabolomics & Multi-Omics Researchers
• Drug Discovery & Development Scientists
• Precision Medicine Researchers
• Biotechnology & Life Science Professionals
• Clinical Research Professionals
• Pharmaceutical and Biotechnology Industry Professionals

Participants will gain practical experience in:

• Pharmacomicrobiomics and microbial drug biotransformation
• Microbiome–drug interaction analysis
• Pharmacometabolomics data analysis
• Microbiome–metabolome integration
• Drug-response biomarker discovery
• Patient stratification
• Machine-learning-based therapeutic-response prediction
• Model validation and performance interpretation
• Explainable AI for identifying microbial and metabolic drivers
• Translating multi-omics biomarkers toward precision medicine and individualized therapy