# Lecture 25: Pharmacogenomics
## Genetics
---
## Learning Objectives
By the end of this lecture, students will be able to:
1. Define pharmacogenomics and explain how genetic variation influences drug response
2. Describe the role of pharmacokinetic genes (drug-metabolizing enzymes, transporters) in variable drug responses
3. Explain the clinical significance of CYP450 polymorphisms, particularly CYP2D6 and CYP2C19
4. Describe pharmacodynamic genetic variation (drug targets, HLA associations) and its impact on drug efficacy and adverse reactions
5. Interpret pharmacogenomic test results and metabolizer phenotype categories
6. Discuss the current clinical implementation of pharmacogenomics and its limitations
---
## Lecture Content
### I. Foundations of Pharmacogenomics
**Pharmacogenomics**: the study of how an individual's genetic makeup influences their response to drugs. Encompasses efficacy, dosing requirements, and risk of adverse drug reactions (ADRs) **Pharmacogenetics** (narrower term): originally focused on single-gene effects on drug response. Pharmacogenomics is the genome-wide extension. **Why pharmacogenomics matters**: Adverse drug reactions are a leading cause of hospitalization and death (estimated 4th-6th leading cause of death in the US) Standard dosing ("one size fits all") is inadequate — drug response varies widely among individuals; ~95% of individuals carry at least one actionable pharmacogenomic variant. **Historical milestones**: Succinylcholine sensitivity and pseudocholinesterase (butyrylcholinesterase) deficiency (1950s) — prolonged paralysis. Isoniazid acetylation polymorphism — slow vs. fast acetylators (NAT2 gene) Glucose-6-phosphate dehydrogenase (G6PD) deficiency and drug-induced hemolytic anemia (primaquine, sulfonamides, dapsone) These early examples established the principle that inherited variation affects drug response.
### II. Pharmacokinetic Variation: Drug-Metabolizing Enzymes
**Pharmacokinetics**: what the body does to the drug (absorption, distribution, metabolism, excretion — ADME) **Phase I metabolism** — functionalization reactions (primarily oxidation): **Cytochrome P450 (CYP) enzymes**: superfamily of heme-containing monooxygenases, primarily in the liver. Responsible for metabolism of ~70-80% of clinically used drugs. **Key polymorphic CYP enzymes**: **CYP2D6**: metabolizes ~25% of drugs (codeine, tamoxifen, many antidepressants, antipsychotics, beta-blockers) **CYP2C19**: metabolizes clopidogrel, PPIs, some antidepressants, voriconazole. **CYP2C9**: metabolizes warfarin, phenytoin, NSAIDs. **CYP3A4/5**: metabolizes ~50% of drugs; less polymorphic but still clinically relevant. **CYP2D6 polymorphism** — the most extensively studied: Over 100 known allelic variants (star alleles: *1, *2, *3, *4, *5, *10, *17, *41, etc.) **Metabolizer phenotypes**: **Poor metabolizer (PM)**: two non-functional alleles (e.g., *4/*4); no CYP2D6 activity. **Intermediate metabolizer (IM)**: one reduced-function and one non-functional allele; decreased activity. **Normal (extensive) metabolizer (NM/EM)**: two functional alleles; normal activity. **Ultra-rapid metabolizer (UM)**: gene duplications/multiplications (e.g., *1/*1xN); increased activity. Frequencies vary by ancestry: PM: ~5-10% of European populations; ~1-2% of East Asian populations. UM: ~1-2% of Northern Europeans; ~10-30% of East African and Middle Eastern populations. **Clinical example — Codeine and CYP2D6**: Codeine is a prodrug; CYP2D6 converts it to morphine (the active metabolite) PM: codeine is ineffective (no conversion to morphine) → therapeutic failure. UM: excessive morphine production → risk of respiratory depression, death (especially dangerous in children — FDA black box warning for post-tonsillectomy use).
Panel A: Overview diagram of drug metabolism — a drug enters the body, undergoes Phase I metabolism (CYP450 enzymes: oxidation, reduction, hydrolysis) and Phase II metabolism (conjugation: glucuronidation, acetylation, sulfation), producing metabolites that are excreted; the key CYP enzymes and their relative contributions to drug metabolism are shown as a pie chart (CYP3A4 ~50%, CYP2D6 ~25%, CYP2C9 ~10%, CYP2C19 ~5%, others). Panel B: CYP2D6 metabolizer phenotypes — four columns showing Poor, Intermediate, Normal, and Ultra-rapid metabolizers, each with a schematic of the CYP2D6 gene alleles (non-functional, reduced, functional, duplicated), the resulting enzyme activity level (bar graph), and the clinical consequence for a prodrug like codeine (no effect, reduced effect, normal effect, toxicity). Panel C: Population frequency distribution of CYP2D6 metabolizer phenotypes across different ancestry groups (European, East Asian, African, Middle Eastern) shown as stacked bar charts, highlighting the variation in PM and UM frequencies.
### III. Additional Pharmacokinetic Genes
**CYP2C19 and clopidogrel**: Clopidogrel is a prodrug activated by CYP2C19 (two-step conversion to active thiol metabolite) CYP2C19 PM (*2/*2, *2/*3): reduced active metabolite → diminished platelet inhibition → increased cardiovascular events. FDA black box warning on clopidogrel: recommends CYP2C19 testing. CYP2C19 UM (*17/*17): enhanced activation → increased bleeding risk. CPIC guideline: PMs should use prasugrel or ticagrelor instead of clopidogrel. **CYP2C9, VKORC1, and warfarin**: Warfarin has a narrow therapeutic index (risk of bleeding vs. clotting) CYP2C9 metabolizes S-warfarin (more potent enantiomer); *2 and *3 alleles reduce metabolism → lower dose required. VKORC1 (vitamin K epoxide reductase complex subunit 1): the drug target; -1639G>A variant → increased sensitivity → lower dose required. FDA-approved dosing algorithms incorporate CYP2C9, VKORC1 genotype, age, weight, and other factors. Clinical utility debated — genotype-guided dosing improves time in therapeutic range but impact on major outcomes still being studied. **Phase II enzymes**: **UGT1A1 (UDP-glucuronosyltransferase)**: metabolizes irinotecan (cancer chemotherapy); UGT1A1*28 (TA repeat variant) → reduced activity → increased risk of severe neutropenia and diarrhea. **TPMT (thiopurine S-methyltransferase)**: metabolizes azathioprine, 6-mercaptopurine; PM genotype → life-threatening myelosuppression at standard doses. **NAT2 (N-acetyltransferase 2)**: slow acetylators at risk for isoniazid-induced hepatotoxicity and peripheral neuropathy. **Drug transporters**: SLCO1B1: hepatic uptake transporter for statins; *5 variant → increased myopathy risk with simvastatin. ABCB1 (P-glycoprotein/MDR1): efflux transporter; polymorphisms affect bioavailability of many drugs.
### IV. Pharmacodynamic Variation: Drug Targets and Immune-Mediated Reactions
**Pharmacodynamics**: what the drug does to the body (drug-target interactions) **HLA-associated adverse drug reactions**: HLA genes encode cell-surface proteins that present peptides to T cells. Certain HLA alleles are strongly associated with severe immune-mediated drug reactions. **HLA-B*57:01 and abacavir**: abacavir hypersensitivity syndrome (fever, rash, GI symptoms, potentially fatal on rechallenge) Pre-prescription HLA-B*57:01 testing is standard of care for HIV patients starting abacavir. Testing has virtually eliminated abacavir hypersensitivity — a major pharmacogenomic success story. **HLA-B*15:02 and carbamazepine**: Stevens-Johnson syndrome / toxic epidermal necrolysis (SJS/TEN) High frequency in Southeast Asian populations; FDA recommends testing before prescribing carbamazepine to patients of Southeast Asian descent. **HLA-B*58:01 and allopurinol**: SJS/TEN risk; testing recommended before starting allopurinol in high-risk populations. **HLA-A*31:01 and carbamazepine**: associated with drug reaction with eosinophilia and systemic symptoms (DRESS) **Other pharmacodynamic variants**: VKORC1 and warfarin sensitivity (discussed above) IFNL3 (IL28B) and interferon-based hepatitis C treatment response (less relevant now with direct-acting antivirals) DPYD (dihydropyrimidine dehydrogenase): deficiency → life-threatening toxicity from fluoropyrimidines (5-FU, capecitabine); DPYD*2A and other variants.
Panel A: HLA-mediated adverse drug reaction mechanism — diagram showing the drug (e.g., abacavir) binding to HLA-B*57:01 on an antigen-presenting cell, altered peptide presentation to CD8+ T cells, immune activation, and the resulting hypersensitivity reaction; contrasted with a non-risk HLA allele where the drug does not bind and no immune reaction occurs. Panel B: Clinical pharmacogenomic testing workflow for abacavir — a decision tree starting from HIV patient requiring antiretroviral therapy → HLA-B*57:01 test → if positive, avoid abacavir (use alternative NRTI) → if negative, prescribe abacavir; the clinical impact (virtual elimination of hypersensitivity reactions) is noted. Panel C: Summary table of major HLA-drug associations — listing the drug, the HLA allele, the adverse reaction, the frequency of the risk allele in different populations, and the clinical recommendation (pre-prescription testing or avoidance).
### V. Clinical Implementation of Pharmacogenomics
**CPIC (Clinical Pharmacogenetics Implementation Consortium)**: Develops evidence-based guidelines for pharmacogenomic gene-drug pairs. Guidelines provide specific dosing recommendations based on genotype. Freely available at cpicpgx.org. Assigns levels of evidence: strong, moderate, optional. **PharmGKB (Pharmacogenomics Knowledge Base)**: curates pharmacogenomic information including variant annotations, drug labels, and clinical guidelines. **Preemptive vs. reactive testing**: **Reactive testing**: ordered when a specific drug is being prescribed (e.g., HLA-B*57:01 before abacavir) **Preemptive (panel-based) testing**: genotype multiple pharmacogenes in advance, store results in the medical record, and apply when relevant drugs are prescribed. Preemptive testing is being implemented at several academic medical centers (e.g., St. Jude, Vanderbilt, Mayo Clinic) **Challenges to widespread implementation**: Lack of clinician education and awareness. Integration into electronic health records and clinical decision support. Turnaround time for reactive testing. Cost and reimbursement issues. Underrepresentation of diverse populations in pharmacogenomic research → guidelines may not apply equally across ancestries. Need for randomized controlled trials demonstrating improved clinical outcomes.
### VI. The Future of Pharmacogenomics
Moving toward **routine preemptive pharmacogenomic testing** as part of standard medical care. Integration with electronic health records and clinical decision support systems for real-time alerts. Expansion of pharmacogenomic evidence to include more diverse populations. Pharmacogenomics as a foundation for **precision medicine** — tailoring treatment to the individual's genetic profile. Combination of pharmacogenomics with other -omics data (metabolomics, proteomics) for more comprehensive drug response prediction. Direct-to-consumer pharmacogenomic testing is available but raises concerns about interpretation accuracy, clinical context, and regulation.
---