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.