The replacement of expensive medications marketed by companies that bore the cost of development and testing, by low-cost "generics" once patent protection ends, is a common step in the life cycle of most drugs. Although generics are required to meet strict bioequivalence standards, the introduction of first-time and subsequent generics may lead to unease and increased reports of adverse effects or therapeutic failure. An established causal relationship between these effects and the switch from innovator to generic products can be challenging to assess. With the ongoing debate, this paper primarily explores how the regulatory framework governing generic drug approval may have contributed to this medical conundrum. In addition, we also offer a perspective on the potential role of pharmacogenetics within this setting.

This article presents a pharmacovigilance (PV) curriculum which was jointly developed by the World Health Organization (WHO) and the International Society of Pharmacovigilance (ISoP). It is an expanded update of a PV curriculum which WHO and ISoP developed in 2014 and responds to major scientific, technological, regulatory, social and public health developments that have reshaped PV over the last 12 years. Topics such as advanced therapies, global health emergencies, antimicrobial resistance, digital health, artificial intelligence, real-world evidence, medical device safety, the environmental impact of medicines, risk communication and patient engagement are addressed. The curriculum was developed through an international collaboration involving 82 contributors from different regions and professional backgrounds. The original three-level hierarchical structure was retained, with each chapter divided into four sections and each section into five or six subsections. The number of chapters increased from 15 to 26, the number of practical tasks from 40 to 125 and the number of references from 260 to 469. Practical tasks and references are provided as Electronic Supplementary Materials. The curriculum provides a comprehensive and structured inventory of contemporary PV and thus represents a reference that lecturers and institutions can use to develop context-specific training programmes. Its structure provides conceptual clarity while allowing adaptation to different settings, learners' roles and prior knowledge, local priorities, available time and resources. It is not intended to prescribe a single standardised course. Instead, it supports the development of introductory, advanced, comprehensive or topic-specific PV education programmes. The update reflects a shared commitment of the WHO and the ISoP to strengthening PV education and capacity across regulatory, academic, healthcare, public health and industrial settings in accordance with the recommendation of the 2026 World Health Assembly to strengthen PV.

Disproportionality analysis of spontaneous reporting systems is usually used to identify positive signals, meaning drug-event pairs reported more often than expected. The same statistical structure also has a lower-reporting side. A reporting odds ratio (ROR) above unity indicates higher-than-expected reporting, whereas an ROR below unity indicates lower-than-expected reporting. Neither result directly estimates incidence, absolute risk, or causality. Existing critiques of inverse disproportionality signals rightly warn against interpreting ROR < 1 as protection, risk reduction, a beneficial reaction, or therapeutic effect. The same caution applies to ROR > 1. Higher-than-expected reporting is not, by itself, causal evidence of harm. This narrative methodological review argues that positive and inverse disproportionality signals should be interpreted according to the same scientific principles. This does not imply symmetric regulatory action thresholds. In this review, "symmetric scientific standards" means that claims of the same strength require commensurate evidence in both directions; it does not mean identical case-level information, bias mechanisms, detectability, predictive value, regulatory priority, or follow-up procedures. Instead, it calls for interpretation that is consistent with the strength of the claim. To make this approach practical, the review outlines a symmetry check that asks whether critiques of inverse signals also apply to positive signals and whether they address descriptive reporting or causal interpretation. It also proposes a claim-strength framework in which the required evidence depends on the claim being made, not on the direction of the signal. Under this approach, inverse signals may be reported as lower-than-expected reporting. They may also support hypothesis generation or candidate prioritization for follow-up evaluation when robust to comparator and sensitivity analyses. Claims of protection or risk reduction require external validation.