Pharmacogenomics_Informed_Pharmacovigilance_Expanded.pptx

Pharmacogenomics-Informed Pharmacovigilance: A New Paradigm for
Personalized Drug Safety

1. Pharmacogenomics-Informed Pharmacovigilance: A New Paradigm for Personalized Drug Safety Name: __________________ Institution: __________________ Date: 18 October 2025

2. Introduction • Pharmacovigilance monitors, detects, and prevents adverse drug reactions (ADRs). • Pharmacogenomics studies genetic variation influencing drug response. • Combining both enables personalized and safer medicine.

3. Why It Matters • ADRs cause ~10% of hospital admissions globally. • Genetic differences alter metabolism and toxicity risk. • Integrating genomics allows proactive, not reactive, pharmacovigilance.

4. Overview of Pharmacogenomics • Studies how genetic variants affect drug metabolism and efficacy. • Key enzymes: CYP2C9, CYP2D6, HLA alleles, TPMT. • Enables dose optimization and ADR prevention.

5. Pharmacogenomics-Informed Pharmacovigilance Concept • Integrates genetic data into ADR detection and prediction systems. • Enables proactive safety monitoring based on genetic profiles. • Supports personalized drug risk assessment.

6. Traditional vs Genomic Pharmacovigilance Traditional: • Relies on spontaneous ADR reporting. • Limited predictive capacity. Genomic: • Combines genomics + real-world data. • Predictive, preventive, and precise.

7. Methodological Framework • Data integration: EHRs + Biobanks + Genomic databases. • AI and ML for signal detection and prediction. • Use of omics data for precision pharmacovigilance.

8. Case Study 1 – Abacavir • HLA-B*57:01 linked to hypersensitivity reaction. • Genetic screening prevents ADRs effectively. • Example of successful pharmacogenomic integration.

9. Case Study 2 – Carbamazepine • HLA-B*15:02 associated with Stevens-Johnson syndrome. • Pre-prescription genotyping prevents severe ADRs in Asian populations.

10. Case Study 3 – Warfarin • CYP2C9 and VKORC1 influence dose response. • Pharmacogenomic algorithms reduce bleeding risks.

11. Ethical and Regulatory Aspects • Protecting patient genomic privacy. • Ensuring informed consent and data security. • Regulatory agencies (FDA, EMA) encourage genomic labeling.

12. Challenges in Implementation • Lack of standardized genomic data sharing. • Cost and limited access in low-resource settings. • Integration issues with existing pharmacovigilance systems.

13. Future Directions • AI-driven personalized risk prediction. • Multi-omics integration for broader insight. • Global data-sharing frameworks for precision safety.

14. Clinical Implications • Enables personalized prescribing. • Improves patient safety and outcomes. • Reduces hospitalizations from ADRs.

15. References (AMA Style) 1. Roden DM, McLeod HL, Relling MV, et al. Pharmacogenomics. Lancet. 2019;394(10197):521–532. 2. Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions. JAMA. 1998;279(15):1200–1205. 3. Caudle KE, Dunnenberger HM, Freimuth RR, et al. Standardization of pharmacogenetic information. Clin Pharmacol Ther. 2017;102(4):623–626. 4. Swen JJ, Nijenhuis M, de Boer A, et al. Pharmacogenetics: from bench to byte. Br J Clin Pharmacol. 2011;72(6):1153–1164. 5. Phillips KA, Veenstra DL, Oren E, Lee JK, Sadee W. Potential role of pharmacogenomics. JAMA. 2001;286(18):2270–2279. 6. Relling MV, Evans WE. Pharmacogenomics in the clinic. Nature. 2015;526(7573):343–350. 7. Mallal S, Phillips E, Carosi G, et al. HLA-B*5701 screening for hypersensitivity to abacavir. N Engl J Med. 2008;358(6):568–579. 8. Ferrell PB, McLeod HL. Carbamazepine, HLA-B*1502, and SJS. Pharmacogenomics. 2008;9(10):1543–1546. 9. Johnson JA, Cavallari LH. Warfarin pharmacogenetics. Trends Cardiovasc Med. 2015;25(1):33–41. 10. EMA and FDA Guidelines on Pharmacogenomic Data Submissions. 2020.

16. Abstract Pharmacogenomics-informed pharmacovigilance integrates genetic data with drug safety systems to identify, prevent, and manage adverse drug reactions (ADRs). This approach enhances precision in prescribing by accounting for genetic variability in drug metabolism, efficacy, and toxicity. The integration of genomic data, electronic health records (EHRs), and AI technologies establishes a proactive, predictive, and personalized pharmacovigilance framework.

17. Expanded Introduction Pharmacovigilance ensures the safe use of medicines by monitoring, detecting, and assessing adverse drug reactions (ADRs). Globally, ADRs account for nearly 10% of hospital admissions and are among the top 10 causes of death in developed countries. Pharmacogenomics— the study of genetic influences on drug response— offers a pathway to understand interindividual variations in drug safety and efficacy. By merging these fields, healthcare can transition from reactive to predictive pharmacovigilance.

18. Pharmacogenomics Overview Pharmacogenomics investigates how genetic variants in drug-metabolizing enzymes, transporters, and targets influence therapeutic outcomes. Major genes include CYP450 family (CYP2C9, CYP2D6), TPMT, HLA alleles, and VKORC1. Genotype-guided prescribing reduces ADRs and improves drug efficacy by tailoring doses to patient-specific genetic profiles.

19. Conceptual Framework: Pharmacogenomics-Informed Pharmacovigilance This model integrates pharmacogenomic data into ADR monitoring to predict drug safety risks before exposure. It uses genomic biomarkers, EHR data, and AI algorithms to identify genetic predispositions to ADRs. Such integration supports preemptive risk assessment and improves regulatory decision-making for safer therapeutics.

20. Methodological Framework 1. Data Collection: Integration of EHRs, biobanks, and genomic databases. 2. Analysis: Use of AI and machine learning to predict ADRs based on genotypes. 3. Validation: Cross-validation with real-world data and clinical outcomes. 4. Implementation: Clinical decision support systems (CDSS) assist clinicians with genotype-based prescribing. 5. Feedback Loop: Continuous learning through post-market genomic surveillance.

21. Case Study 1 – Abacavir HLA-B*57:01 is linked to hypersensitivity reactions in abacavir-treated patients. Pharmacogenetic screening prior to therapy prevents nearly all severe ADRs. The FDA recommends genetic testing before prescribing abacavir, illustrating successful genomic integration into pharmacovigilance.

22. Case Study 2 – Carbamazepine HLA-B*15:02 is associated with Stevens-Johnson syndrome and toxic epidermal necrolysis in Asian populations. Pre-prescription genetic testing has drastically reduced the incidence of these life-threatening reactions.

23. Case Study 3 – Warfarin Variants in CYP2C9 and VKORC1 affect warfarin metabolism and sensitivity. Pharmacogenomic dosing algorithms minimize bleeding risk and optimize therapeutic outcomes. The CPIC guidelines support genotype-guided warfarin dosing.

24. Additional Case Studies • Clopidogrel: CYP2C19 poor metabolizers experience reduced antiplatelet activity, increasing cardiovascular risk. • Irinotecan: UGT1A1*28 variant leads to severe neutropenia and diarrhea; dose reduction prevents toxicity. • Codeine: CYP2D6 ultrarapid metabolizers risk morphine toxicity.

25. Ethical and Regulatory Considerations Genomic pharmacovigilance must address privacy, consent, and equitable data sharing. Guidelines from ICH E15, FDA, and EMA promote ethical genomic data use. Data anonymization, secure storage, and transparent consent frameworks are essential to maintain trust and compliance.

26. Challenges in Implementation • Lack of standardized data-sharing platforms. • Limited accessibility to genetic testing in low-resource regions. • Integration difficulties with current pharmacovigilance databases. • Ethical and legal barriers regarding genetic data use. • Cost implications of widespread genotyping.

27. Future Directions Advances in AI, multi-omics integration, and federated learning will enable predictive ADR modeling. Global collaborations and standardized genomic databases can support real-time, precision pharmacovigilance. The future paradigm emphasizes prevention, personalization, and proactive safety assessment.

28. Clinical Implications Pharmacogenomics-informed pharmacovigilance enhances patient safety by preventing ADRs, optimizing therapy, and improving healthcare efficiency. It reduces hospitalization rates and supports evidence-based personalized medicine practices.

29. Conclusion Integrating pharmacogenomics into pharmacovigilance represents a transformative shift from population-based to individualized drug safety. Despite challenges in data management and accessibility, this approach promises substantial improvement in patient outcomes and healthcare sustainability.

30. References 1. Roden DM, McLeod HL, Relling MV, et al. Lancet. 2019;394:521–532. 2. Caudle KE, Dunnenberger HM, Freimuth RR, et al. Clin Pharmacol Ther. 2017;102:623–626. 3. Swen JJ, de Boer A, et al. Br J Clin Pharmacol. 2011;72:1153–1164. 4. Relling MV, Evans WE. Nature. 2015;526:343–350. 5. Johnson JA, Cavallari LH. Trends Cardiovasc Med. 2015;25:33–41. 6. Phillips KA, et al. JAMA. 2001;286:2270–2279. 7. Mallal S, et al. N Engl J Med. 2008;358:568–579. 8. Ferrell PB, McLeod HL. Pharmacogenomics. 2008;9:1543–1546. 9. FDA & EMA Guidelines on Pharmacogenomic Data Submissions. 2020. 10. CPIC Guidelines for Gene-Drug Pairs. 2023.