Pharmacogenomics_Informed_Pharmacovigilance_Presentation-1.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.