Pharmacogenomics of Adverse Drug Reactions: Integrating Genetic Biomarkers into Precision Pharmacotherapy

Pharmacogenetics and Pharmacogenomics as Enabling Technologies for Precision Drug Development and Personalized Therapy.

Pharmacogenomics-driven precision pharmacy: Mechanisms, challenges, and translational advances

The integration of artificial intelligence and machine learning approaches for pharmacogenomic prediction, the emergence of polygenic risk scores, and the role of multi-omics integration in refining therapeutic decision-making are examined.

Drug-induced adverse events in modern pharmacotherapy: mechanisms, clinical manifestations, and implications for risk assessment and management

Drug-induced adverse events remain a major challenge in modern pharmacotherapy, particularly with the increasing use of targeted therapies, biologics, and combination regimens. In clinical practice, toxicity is often difficult to predict, interpret, and manage because adverse events arise from complex interactions between drug mechanisms, patient susceptibility, and treatment context. This review provides a clinically oriented framework for understanding drug-induced adverse events across multiple levels. We first summarize key mechanistic drivers, including pathway perturbation, off-target effects, metabolic and mitochondrial dysfunction, and immune dysregulation. We then discuss how these mechanisms translate into organ-specific toxicity patterns involving the liver, heart, kidney, nervous system, and immune system, which represent the most common clinical presentations. In addition, we examine how safety profiles are defined and refined through different layers of evidence, including randomized clinical trials, meta-analyses, and real-world pharmacovigilance data. These complementary evidence sources are essential for identifying both common and rare adverse events, particularly those that emerge after broader clinical use. Importantly, this review highlights practical considerations for clinical risk assessment and management. We discuss key factors influencing toxicity risk, including patient comorbidity, polypharmacy, and baseline organ function, as well as the role of dynamic monitoring, biomarkers, and early signal detection. Emphasis is placed on translating mechanistic insight into actionable strategies for prevention, early recognition, and individualized management of adverse events. Overall, drug safety should be viewed as a dynamic and context-dependent process. Integrating mechanistic understanding with clinical evidence and real-world data can improve risk prediction and support more effective and personalized pharmacotherapy.

Pharmacogenomic and Drug Interactions Risk in Cardio-Oncology: A precision medicine perspective for India.

Cardio-oncology patients may face complex treatment regimens due to the concurrent existence of cancer and cardiovascular disease, leading to a considerable polypharmacy burden. This significantly increases the prospect of drug-drug interactions (DDIs) and gene-drug interactions. The majority of these interactions arise from comparable pharmacokinetic and pharmacological pathways associated with drug transporters and cytochrome P450 enzymes. The significance of pharmacogenomics in tailored treatment strategies are emphasised by the fact that genetic variability enhances individual differences in drug response, safety, and efficacy. This narrative review focus on the effects of key genetic polymorphisms (e.g., DPYD, CYP2C19, and CYP2C9) on the metabolism and efficacy of commonly prescribed anticancer and cardiovascular medications such as fluoropyrimidines, clopidogrel, and warfarin. In addition it explore the role of pharmacogenomic variants on drug-drug interactions within the field of cardio-oncology. The study ultimately emphasizes the necessity of precision medicine in India to address the genetic diversity and underrepresentation in global genomic databases. The absence of pharmacogenomic testing, infrastructural deficiencies, financial constraints, and insufficient clinical integration hinder the widespread use of this technology in India. The Genome India Project and other national initiatives establish the foundation for pharmacogenomic-guided therapy. Utilizing genetic data, together with artificial intelligence-based predictive tools, for clinical decision-making may enhance medication safety and yield optimal outcomes in Indian cardio-oncology patients.

Pharmacogenomic biomarkers in oncology: evidence, clinical utility, and barriers to implementation.

Interpatient variability in chemotherapy response and toxicity remains a major challenge in oncology. Pharmacogenomics (PGx) is an approach to address this challenge by combining somatic alterations that affect tumour sensitivity with germline variants that affect drug metabolism, transport and toxicity. This review provides a critical evaluation of clinically validated PGx biomarkers for chemotherapeutics and targeted therapy with a focus on translational relevance and strength of evidence. High-impact germline markers such as DPYD, TPMT, NUDT15 and UGT1A1 are highlighted as key determinants of genotype-guided dosing for improving safety without compromising efficacy. Somatic biomarkers such as EGFR, RAS, BRAF, and HER2 remain central to treatment selection, while resistance underscores the need for ongoing molecular assessment. A tiered implementation framework, barriers to progress, and future directions involving polygenic models, multi-omics, and artificial intelligence (AI) are discussed to advance safe, effective, and personalized chemotherapy.

Genomics-first association of pharmacogenomic risk phenotypes with adverse drug reactions in a healthcare-based population.

A portion of population ADR burden could possibly have been prevented through PGx-guided therapy and a portion of population ADR burden could possibly have been prevented through PGx-guided therapy.