About this course
This intensive 6-month research program provides a deep dive into the molecular mechanisms of inter-individual drug response through the lens of genomics. Students will explore the transition from traditional trial-and-error prescribing to AI-assisted precision therapy. The course integrates bioinformatics with clinical pharmacology, focusing on how genetic variations like SNPs (Single Nucleotide Polymorphisms) influence drug metabolism and pharmacokinetics. By employing Machine Learning models and Deep Learning algorithms, participants will learn to predict Adverse Drug Reactions (ADRs) and optimize therapeutic efficacy. The curriculum is heavily research-oriented, emphasizing genomic data analysis, biomarker discovery, and the ethical implementation of Next-Generation Sequencing (NGS) in clinical settings.
What you will learn
The fundamental architecture of the human genome and the role of genetic polymorphisms in medicine.
Advanced techniques for identifying pharmacogenetic biomarkers using AI-driven predictive modeling.
How to utilize CPIC (Clinical Pharmacogenetics Implementation Consortium) guidelines for dosage adjustment.
Integration of Big Data and Electronic Health Records (EHR) with genomic profiles.
The application of Deep Learning for drug repurposing and novel drug target discovery.
Skills you will gain
Genotyping
Bioinformatics
Data-Mining
Machine-Learning
Pharmacokinetics
Biostatistics Sequencing
Phenotyping
Coding (Python/R)
Clinical-Interpretation
Certification
Available
Issued by EIMT
Course curriculum
12 modules
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Module 1: PI – Introduction to Pharmacogenomics
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Module 2: PB – Fundamentals of Bioinformatics
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Module 3: PL – Databases & Basics of Linux Operating System
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Module 4: PP – Python for Pharmacogenomic Data Analysis
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Module 5: PR – R for Data Analysis in Pharmacogenomics
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Module 6: PGWAS – Introduction to Genome-Wide Association Studies (GWAS)
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Module 7: PDD – Pharmacogenomic Applications in Drug Discovery
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Module 8: PCT – Pharmacogenomics in Cancer Treatment
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Module 9: PVC – Variant Calling Analysis using GATK
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Module 10: PCI – Clinical Implementation of Pharmacogenomics & Regulatory Guidelines
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Final Project: PRP – Research Project in Computational Pharmacogenomics
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1) Overview of genomics and pharmacogenomics
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2) How the genome conveys information to the rest of the body
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3) Basic concepts about genetic diseases and mutations
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4) Basic principles of genetics
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5) Introduction to Genomes, Variation and Population Genetics,Introduction to 1000Genome project and ENCODE
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6) Introduction to NCBI database
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7) Introduction to DbSNP
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8) Clinvar Database
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9) Association studies in Pharmacogenomics, Linking NGS/Microarray/other technologies to bedside, Analyzing gene mutations
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10) Role of Pharmacogenomics in Drug development
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Introduction to Bioinformatics
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Introduction to Genomics data resources : Gene,protein database
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Ensembl genome database
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UCSC database
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Genome Browser
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Alignment tools- A)BLAST(online)
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BLAST(standalone) Introduction
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Multiple sequence Alignment(clustalW)
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Multiple sequence Alignment(MEGA)
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Primer Designing using PRIMER3 tool and validation using blast
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PubMed Database
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KEGG database
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UniProt Database
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Pharmacogenomic databases (e.g., PharmGKB, DrugBank)
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Bioinformatics Tools for Drug Response Prediction(PolyPhen-2, SIFT)
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Introduction to Python
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Data Types
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String Handling
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Data Structure
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Control Structure
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Function
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File Handling
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Data Manipulation
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Data Visualization
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Biopython
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Introduction and Installation of R
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Data Types in R
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Data Structure
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File Handling
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Control Structure
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Function
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Package Management
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Data Manipulation
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Data Visualization
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Statistical Analysis
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Sequencing Technologies its applications,Introduction to NGS and DNAseq,
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Basic Terminologies in NGS
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Understanding of SRA database
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Tools installation in Linux for Variation Calling
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Quality control (FastQC)
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Trimming of Reads (Trimmomatic)
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Indexing of Genome (BWA) and Alignment of Reads (BWA)
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Variation calling using GATK
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Variant Effect Prediction(VEP)
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Variation Visualization (IGV)
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Introduction to RNAseq and it’s basic terminologies
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Tools installation in Linux for Gene Expression analysis
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Quality control and Trimming of reads
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Indexing of Genome and Alignment of Reads
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Normalization of Data (Cufflinks)
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Merging of Data and Differential expression of genes
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Understanding of DEG results
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Annotation of DEG
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Functional and Pathway Enrichment Analysis
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Network Analysis
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Introduction to Drug Development
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Compound Databases in Drug Discovery
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Principles of Pharmacokinetics
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Pharmacodynamics and Drug Action
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Drug Metabolism and Pharmacokinetic Modeling
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Pharmacovigilance and Drug Safety
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Pharmacogenomics and Drug Target Selection
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Pharmacogenomics in Clinical Trials
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Pharmacogenomics for Predicting Drug Efficacy and Safety
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Application of NOMAD in Pharmacogenomics
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Genetic Basis of Cancer
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Targeted Therapies and Biomarkers
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Genomic Profiling Techniques
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The Cancer Genome Atlas (TCGA)
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Functional Genomics in Cancer Research (CRISPR/Cas9 Technology)
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Introduction to GWAS
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Impact of genetic diversity on individual drug responses.
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Methods and Technologies in GWAS
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Clinical Implications
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GWAS analysis using real data
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Installation of HAIL package
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Plots before quality check
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Quality control based on Sample level
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genotype quality and Variants level
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GWAS analysis and Annotations
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Foundations of Clinical Pharmacogenomics,Stategies,Interpretation of Data
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Different Genotyping Technologies
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Overview of regulatory bodies governing pharmacogenomics, FDA (U.S. Food and Drug Administration), EMA (European Medicines Agency)
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CPIC (Clinical Pharmacogenetics Implementation Consortium) for drug-gene pairs and dosing recommendations.
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Challenges and opportunities in Clinical Implementation Introduction to Regulatory Guidelines
What you need to start
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A foundational background in Biology, Genetics, or Pharmacy.
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Basic understanding of Molecular Biology concepts.
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Familiarity with statistics (Introduction to Bio-statistics is a plus).
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No prior coding experience is required, though interest in Python for Data Science is encouraged.
Who this course is for
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Medical Professionals (MDs, Pharmacists) looking to specialize in Personalized Medicine.
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Bioinformaticians and Data Scientists aiming to apply AI in Healthcare.
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Biotechnology Researchers focused on drug discovery and genomic research.
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Graduate Students in Life Sciences seeking a high-tech edge in the Genomics job market.
