AI-Driven Pharmacomicrobiomics: Microbial Drug Metabolism, Multi-Omics & Precision Therapeutics
From Gut Microbial Drug Metabolism to Multi-Omics Biomarkers and Explainable AI for Predicting Therapeutic Response
Skills you will gain:
About Program:
The gut microbiome is emerging as an important determinant of drug metabolism, therapeutic efficacy, toxicity, and inter-individual variability in treatment response.
This 3-day hands-on workshop introduces participants to the rapidly evolving field of pharmacomicrobiomics, combining microbiome analysis, pharmacometabolomics, biomarker discovery, and computational drug-response prediction.
Participants will explore how microbial communities and their metabolites influence drug biotransformation and PK/PD, and learn how microbiome, metabolomics, and clinical features can be integrated to identify therapeutic-response biomarkers and develop interpretable predictive models.
Each day includes a practical Google Colab-based hands-on session, enabling participants to translate research concepts into reproducible computational workflows.
Aim: 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.
Program Objectives:
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
What you will learn?
📅 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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
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
Career Supporting Skills
Program Outcomes
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