The evaluation of safety data by the US Food and Drug Administration (FDA) is a critical step in the review of marketing applications for drugs and biologics. It can be difficult to identify a safety signal, and importan...The evaluation of safety data by the US Food and Drug Administration (FDA) is a critical step in the review of marketing applications for drugs and biologics. It can be difficult to identify a safety signal, and important signals can be missed if not evaluated comprehensively. Adverse events reported by study participants constitute a major source of safety data, and while previously established standard term groupings have been useful for analysis (e.g., Standardised MedDRA Queries), the Office of New Drugs (OND) at the FDA determined a need for more clinically meaningful groupings specifically designed for use in premarket drug safety evaluation. To improve safety signal detection and analyses of adverse reactions, OND developed standard groupings of adverse event terms known as OND Custom Medical Queries (OCMQs). OCMQs are intended to capture clinically meaningful groupings (i.e., safety signals) represented in premarketing data. OND has seen great utility in OCMQs during premarket drug safety evaluations, as they have improved OND's ability to detect safety signals and to distinguish and quantify adverse reactions in clinical trial data.
Jacobson MH, Sabidó M, Afonso AS
… +11 more, Ajao A, Alghamdi EA, Andrade SE, Bennett D, Kharat V, Kürzinger ML, Le Noan-Lainé M, Mølgaard-Nielsen D, Murray G, Rivero-Ferrer E, Lopez-Leon S
INTRODUCTION: Major congenital malformations (MCMs) are a primary outcome of interest in pregnancy safety studies. OBJECTIVE: This study aimed to identify and summarize algorithms used to identify MCMs in routinely colle...INTRODUCTION: Major congenital malformations (MCMs) are a primary outcome of interest in pregnancy safety studies. OBJECTIVE: This study aimed to identify and summarize algorithms used to identify MCMs in routinely collected healthcare data sources in the USA, Canada, and Europe by conducting a systematic literature review. METHODS: We developed a search strategy to identify studies containing algorithms for MCMs from January 1, 2010, to April 11, 2025. Search terms included those related to MCMs as an outcome, routinely collected healthcare data, epidemiologic designs likely to incorporate algorithms, and pregnant individuals and/or infants. Study review and data extraction was conducted in duplicate using a standardized data collection form. RESULTS: Among the initially identified 2242 studies, 974 were selected for full-text review. Of these, 70.3% were excluded, leaving 289 studies. Over half (58.1%) of the included studies were from Europe, predominantly from Nordic countries using national register data (N = 135; 80.4%). Studies using claims (18.0%) or hospital discharge data (16.3%) were also common. Although there was heterogeneity in the timing of MCM assessment, 55.7% of studies collected MCMs through the infant's first year of life. Overall, algorithms varied across data source type and geography in the codes specified, rules, utilization of maternal versus infant records, and coding system. There were 27 (9.3%) validation studies, 70.4% of which were based on claims and/or electronic health record data only. Most had positive predictive values >70%, though this varied according to MCM type or anatomical site. CONCLUSION: We provide the first comprehensive systematic literature review of algorithms used to identify MCMs in routinely collected healthcare data, aiding researchers in their ability to generate reliable evidence in pregnancy safety pharmacoepidemiology.
BACKGROUND: Piperacillin-tazobactam combined with vancomycin is widely employed for broad-spectrum empiric coverage but has been increasingly associated with acute kidney injury (AKI). The comparative renal safety of sub...BACKGROUND: Piperacillin-tazobactam combined with vancomycin is widely employed for broad-spectrum empiric coverage but has been increasingly associated with acute kidney injury (AKI). The comparative renal safety of substituting vancomycin with teicoplanin remains uncertain. OBJECTIVE: This meta-analysis aimed to evaluate renal outcomes between piperacillin-tazobactam plus teicoplanin (TZP-TEI) versus piperacillin-tazobactam plus vancomycin (TZP-VAN). METHODS: PubMed, Scopus, and Cochrane Central were searched for studies comparing TZP-TEI versus TZP-VAN in hospitalized patients. The primary outcome was AKI incidence, defined by Kidney disease: Improving global outcomes (KDIGO) or RIFLE (Risk of renal dysfunction, Injury to kidney, Failure or Loss of kidney function, and End-stage kidney disease) criteria. Data were analyzed using Review Manager, with heterogeneity assessed via the I statistic. RESULTS: A total of 908 patients were included from five cohort studies, four of which applied propensity-score matching (PSM), with reported ages ranging from 56.8 to 79 years. The TZP-TEI regimen was associated with a significantly reduced rate of AKI compared with TZP-VAN (odds ratio [OR] 0.52; 95% confidence interval [CI] 0.30-0.89; p = 0.02; I = 51%). No statistically significant differences were observed between groups for AKI recovery (OR 0.68; 95% CI 0.41-1.12; p = 0.13; I = 0%) or for 30-day all-cause mortality (OR 1.34; 95% CI 0.77-2.32; p = 0.30; I = 0%). Subgroup analyses stratified by AKI severity (KDIGO stages 1-3 or RIFLE criteria) demonstrated consistent directionality across stages, with no significant differences observed within PSM or non-PSM cohorts. CONCLUSION: The TZP-TEI combination was associated with a significantly lower incidence of AKI than was TZP-VAN. Further studies are warranted to validate these findings, optimize teicoplanin dosing within the TZP-TEI combination, and inform therapeutic drug monitoring implementation in high-risk hospitalized patients.
INTRODUCTION: At times it is necessary to withdraw drugs after they have been approved because of lack of effectiveness or safety concerns. Health Canada does not keep a list of withdrawn drugs. OBJECTIVE: The aim of thi...INTRODUCTION: At times it is necessary to withdraw drugs after they have been approved because of lack of effectiveness or safety concerns. Health Canada does not keep a list of withdrawn drugs. OBJECTIVE: The aim of this study was to generate a list of all drugs approved since 1990 and subsequently withdrawn from the Canadian market for safety or effectiveness reasons until the end of 2024. This list was used to examine trends in the number of withdrawals and the percent of new drugs that are approved but eventually withdrawn. METHODS: A list of withdrawn drugs was developed based on previous published research and supplemented by examining lists of withdrawn drugs in other jurisdictions. The time, in years, was calculated between the date of approval and withdrawal. The reasons for withdrawal came from either Health Canada documents or, if unavailable, from international sources. Withdrawals for commercial reasons were not included in the analysis. RESULTS: Of the 1094 drugs approved from January 1, 1990, to December 31, 2024, a total of 37 were withdrawn: 32 were new active substances (molecules never marketed before in any form) and five were other types of new drugs. The median time to withdrawal was 3.60 years (interquartile range 2.45-9.50). Approximately 5% of all new active substances approved in a 5-year period were eventually withdrawn over the period 1990-2009. Between 2010 and 2019, the withdrawal rate was < 2%. The most common reasons for withdrawal were cardiac and liver complications. CONCLUSION: As a percent of all drugs approved, relatively few drugs are withdrawn, and the number of drug withdrawals as a percent of approvals declined between 2010 and 2019.
BACKGROUND: Problems with medication management are consistently identified as key concerns for the quality of residential aged care (RAC). Incident reports can provide valuable information on key issues related to medic...BACKGROUND: Problems with medication management are consistently identified as key concerns for the quality of residential aged care (RAC). Incident reports can provide valuable information on key issues related to medication management; however, few studies have explored medication incidents in RAC settings. OBJECTIVES: To investigate the characteristics of medication incidents at different stages of medication management and identify the risk factors associated with incidents. METHODS: A retrospective longitudinal cohort study was conducted using medication incidence data from 25 RAC facilities in New South Wales, Australia. All medication incidents between 1 July 2014 and 31 August 2021 relating to 5709 aged care residents aged ≥ 65 years were included. The outcome measure was the medication incidence rate (IR), quantified as the number of medication incidents per 1000 resident days. A multilevel Poisson regression model was performed to identify risk factors associated with exposure to medication incidents. RESULTS: A total of 5708 medication incidents were analysed. The overall medication IR was 1.81 per 1000 resident days (95% CI 1.76, 1.86). Of 5709 residents, 35% (n = 2016) had at least one recorded medication incident, of which 1095 (> 50%) had more than one. The majority of the incidents were associated with medication administration (3023 incidents, 53%), followed by supply (n = 1546, 27%) and monitoring the response to the medication (n = 548, 9.6%). The outcome of the incident on residents was reported in 5165 (90%) incidents, with 724 (14%) requiring the resident to be monitored by the hospital, general practitioner (GP), or staff. Respite admissions were associated with a higher risk of medication incidents including potentially harmful incidents, compared with permanent admissions (rate ratio (RR) = 1.908, 95% CI 1.646, 2.211, p < 0.01). Residents with Parkinson's disease had a 1.5-fold greater risk of a medication incident (RR = 1.586, 95% CI 1.318, 1.908) compared with residents without Parkinson's. The administration of more than five medications (polypharmacy) was associated with an increased risk of medication incidents (RR = 2.019, 95% CI 1.930, 2.111). CONCLUSIONS: Medication incidents affected more than one-third of older adults in RAC facilities. Improvement strategies should focus on medication administration, supply and monitoring, with particular attention given to respite residents and those with multimorbidity and polypharmacy.
INTRODUCTION: Yellow Card Vaccine Monitor (YCVM) was established by the UK Medicines and Healthcare products Regulatory Agency (MHRA) to facilitate active monitoring of adverse drug reactions following COVID-19 vaccinati...INTRODUCTION: Yellow Card Vaccine Monitor (YCVM) was established by the UK Medicines and Healthcare products Regulatory Agency (MHRA) to facilitate active monitoring of adverse drug reactions following COVID-19 vaccination and further characterise safety in populations under-represented in clinical trials. OBJECTIVE: This study explored the profile of individuals registered to the YCVM platform and the suspected adverse drug reactions reported following a COVID-19 vaccination on this data platform. METHODS: Using a stratified random selection approach, individuals were invited to register and actively contacted to seek further information on the vaccines received and adverse reactions they experienced. Exploratory analyses were conducted to characterise the demographics of individuals registered in the YCVM, and to summarise the adverse drug reaction data reported by recruited individuals between November 2020 and December 2022. Detailed analyses of the sub-cohort of pregnant and breastfeeding females were conducted to characterise these individuals. Data for two suspected adverse reactions, menstrual disorders and tinnitus, were extracted and analysed to demonstrate how YCVM supported regulatory assessment of these safety signals which originally arose from other data sources. RESULTS: 36,604 individuals registered, with 30,281 reporting vaccination. Median (interquartile range) follow-up was 184 days (14-367). Demographics of the recruited cohort reflected the vaccinated population and timing of invitations. 15,764 (52.1%) of those reporting vaccination reported experiencing at least one adverse reaction. However, nearly all were expected acute reactions and 4134 (13.7%) reported an event considered medically serious. The data raised no safety concerns in pregnant and breastfeeding females. Reporting of menstrual disorders appeared stimulated by media interest, as seen in spontaneous reporting systems. Data on the incidence of tinnitus were used to support regulatory action on this signal. CONCLUSION: Active surveillance using the YCVM provided a complementary data source for monitoring the safety of COVID-19 vaccines. However, further efforts are needed to recruit ethnic minorities. The technology developed has enhanced regulatory vigilance options and could be valuable in the future for actively monitoring the safety of innovative products used in small populations.
BACKGROUND: Adverse event reporting systems are an important source of safety signals for drug use in pregnancy, but their usefulness in the identification of potential drug-drug interactions (DDIs) remains unclear. OBJE...BACKGROUND: Adverse event reporting systems are an important source of safety signals for drug use in pregnancy, but their usefulness in the identification of potential drug-drug interactions (DDIs) remains unclear. OBJECTIVE: Our objective was to explore the reliability of signal detection for pharmacokinetic DDIs during pregnancy in adverse event reporting systems, focusing on potential interactions between antipsychotics (APs) or antidepressants (ADs) and drugs modifying cytochrome P450 (CYP450) activity, increasing the occurrence of gestational diabetes mellitus (GDM). METHODS: Reports related to the use of drugs during pregnancy were identified in VigiBase, the World Health Organization (WHO) global database of adverse event reports. Potential interacting drugs were selected based on WHO Drug Standardised Drug Groupings for CYP450 isoenzymes involved in the metabolic pathway of the AP or AD of interest. We conducted statistical interaction analysis using the omega disproportionality measure and including concomitant medication to identify potential DDIs, followed by a case series review for supporting evidence. Evaluation was subjective by author consensus. RESULTS: Of the 30 drug-drug-event combinations considered, statistical signals emerged for escitalopram, citalopram, and sertraline and the simultaneous use of CYP2D6 inhibitors with a higher relative reporting rate of GDM. However, case series review of reports did not support the existence of these DDIs because of uncertainties regarding the actual timing of medication use reported as concomitant. CONCLUSION: Statistical signals of DDIs between ADs and potential interacting drugs during pregnancy were identified but not pursued further after case reviews. Uncertainty around medication use and event timing affected the reliability of the outcomes. These findings highlight the need to validate signals using detailed report data and stress the importance of accurate medication reporting.
INTRODUCTION: Adverse drug reactions (ADRs), including those resulting from drug interactions, remain a leading cause of morbidity and mortality. Structured product labels (SPLs) serve as a primary source for drug safety...INTRODUCTION: Adverse drug reactions (ADRs), including those resulting from drug interactions, remain a leading cause of morbidity and mortality. Structured product labels (SPLs) serve as a primary source for drug safety information. Having machine-readable product labels, including adverse reactions (ARs) and drug interactions, readily available would allow researchers to streamline medication safety studies. However, extracting this information is complex and requires the use of natural language processing (NLP) methods. OBJECTIVE: In this study, we explored the application of generative language models in the extraction of drug safety information from SPLs. METHODS: We compared multiple generative LLMs (GPT, Llama, and Mixtral) to two baseline methods in the task of extracting adverse reactions (ARs) from SPLs. We explored various factors, such as prompting strategies and term complexity, that impact the performance of these models in the extraction of ARs. Finally, we explored the generative models' capacity to extract drug interactions from a separate section of SPLs without additional fine-tuning or training, demonstrating their flexibility and adaptability for information retrieval. RESULTS: We found that generative language models, specifically GPT-4, are able to match or exceed the performance of previous state-of-the-art models without additional training or fine-tuning. Additionally, we found that the specific SPL section, surrounding context, and complexity of the AR term impacted the extraction performance. Finally, we demonstrated the generalizability of these models by applying them to a separate task of extracting drug names from the drug interaction section where curated training data are not available. CONCLUSION: Generative language models demonstrate significant potential for automating drug safety information extraction from SPLs, offering a promising avenue for improving post-market surveillance and reducing ADRs. Future work should focus on refining prompting strategies and expanding the models' capabilities to handle increasingly complex and nuanced drug safety information.
BACKGROUND: It is unknown whether nR value [(ALT or AST/ULN) ÷ (AP/ULN)] ≥ 5 is better than alkaline phosphatase less than twice the upper limit of normal (AP < 2x ULN) in identifying hepatocellular drug-induced liver in...BACKGROUND: It is unknown whether nR value [(ALT or AST/ULN) ÷ (AP/ULN)] ≥ 5 is better than alkaline phosphatase less than twice the upper limit of normal (AP < 2x ULN) in identifying hepatocellular drug-induced liver injury (HC DILI) consistent with Hy's law in clinical trials. OBJECTIVE: We aimed to compare nR value ≥ 5 and AP < 2x ULN in clinical trial DILI cases with ALT or AST ≥ 3x ULN and total bilirubin (TB) > 2x ULN. METHODS: We retrospectively categorized clinical trial, DILI cases from July 2020 to April 2024 with ALT or AST ≥ 3x ULN and jaundice as meeting nR value ≥ 5, AP < 2x ULN, both, or neither. We determined positive predictive values (PPVs) and sensitivities for HC DILI-related fatality (death or liver transplant) and acute liver failure (ALF). RESULTS: Of 1314 liver injuries across 73 drug applications, 294 (22%) were attributed to DILI; 55 had ALT or AST ≥ 3x ULN and TB > 2x ULN. We excluded three cases (Gilbert's, high baseline enzymes, hepatitis B reactivation). Of 52 remaining, 16 (31%) met nR ≥ 5, five (10%) AP < 2x ULN, 21 (40%) both, and 10 (19%) neither. There were four DILI fatalities. Excluding one cholestatic fatality, nR ≥ 5 and AP < 2x ULN had PPVs for HC DILI fatality of 8 and 4%, respectively; sensitivities were 100 and 33%, respectively. One patient survived HC DILI-related ALF. Including this ALF case with the fatalities, nR ≥ 5 and AP < 2x ULN had PPVs of 11 and 4%, respectively; sensitivities were 100 and 25%, respectively. All fatalities and ALF cases were due to different drugs. CONCLUSION: While the number of cases with the most severe DILI outcomes was small, particularly those that resulted in fatalities or ALF, nR ≥ 5 better approximated Hy's Law and was more sensitive than AP < 2x ULN in detecting fatalities and ALF.
BACKGROUND AND OBJECTIVE: Missing data and unmeasured confounding may bias results of external comparator (EC) studies. Previous research quantified these effects, but there were still knowledge gaps in terms of studying...BACKGROUND AND OBJECTIVE: Missing data and unmeasured confounding may bias results of external comparator (EC) studies. Previous research quantified these effects, but there were still knowledge gaps in terms of studying a broader set of missing data-handling approaches. This knowledge gap is addressed by investigating four different ways to handle missing data for a set of four distinct marginal estimators. METHODS: An extensive simulation study was conducted based on two real EC case studies. Four different variants of missing data-handling approaches were assessed in terms of bias and other performance characteristics. Specifically, multiple imputation (MI) for the trial and EC cohorts was conducted by applying within-cohort MI, across-cohort MI and a mixed within-across-cohort MI scheme. Dropping a covariate from the analysis model if missingness exceeded a certain threshold was also added as an analysis strategy. All simulation results were generated for a set of four marginal estimators: the average treatment effect of the untreated (ATU), the average treatment effect (ATE), the average treatment effect of the treated (ATT), and the average treatment effect in the overlap population (ATO). Missingness was simulated to occur only in the EC cohort, and propensity score weighting was applied as causal inference method. RESULTS: Overall, within-cohort MI and the ATU showed best performance in terms of mitigating bias, while the strategy of leaving out prognostic factors (covariates) due to a higher percentage of missingness performed worst. CONCLUSIONS: Performances of four missing data-handling strategies were assessed for a set of four different marginal estimators. Our results add clarity with regard to potential residual bias for researchers conducting EC studies when using propensity score weighting in the case of missing data or unmeasured confounding. This enables researchers to select most appropriate statistical approaches to minimise bias, potentially by including an additional bias estimation and correction step.
Medical devices are indispensable in modern healthcare. They enable the prevention, diagnosis, and treatment of diseases while enhancing patient outcomes. However, the increasing complexity of these devices, particularly...Medical devices are indispensable in modern healthcare. They enable the prevention, diagnosis, and treatment of diseases while enhancing patient outcomes. However, the increasing complexity of these devices, particularly those incorporating advanced technologies such as artificial intelligence (AI) introduces new challenges to their safe use. The vulnerabilities of medical devices can lead to adverse events ranging from minor complications to severe injuries or fatalities, and there is an increasing health risk to those devices that are interconnected to electronic health management systems and internet protocols. Despite efforts by regulatory authorities such as the US Food and Drug Administration (FDA), European Medicines Agency (EMA), and Health Canada, disparities in reporting systems and monitoring practices persist globally, hindering effective safety oversight. This paper explores the current landscape of medical device safety, focusing on regulatory frameworks, reporting systems, and the challenges posed by fragmented data collection and underreporting. It highlights the critical role of postmarket surveillance (PMS) in identifying risks and ensuring device performance in real-world settings. The integration of emerging technologies, such as AI for predictive safety and blockchain for traceability, offers promising solutions to enhance monitoring and mitigate risks early in the device lifecycle. In addition, the paper examines harmonization efforts led by organizations such as The International Medical Device Regulators Forum (IMDRF), the International Society of Pharmacovigilance (ISoP) and the World Health Organazition (WHO), which aim to standardize reporting practices and improve global collaboration. Key recommendations include leveraging real-world data, enhancing cybersecurity measures, and fostering international cooperation to streamline regulatory processes. By addressing these challenges and embracing innovation, stakeholders can ensure that medical devices continue to advance healthcare while maintaining the highest safety standards. Such collective efforts are essential for safeguarding patient trust and improving global health outcomes.
INTRODUCTION: A case-crossover study is a self-controlled design most appropriate for evaluating transient medication exposures. However, it has increasingly been used in studies of chronic medications and can cause bias...INTRODUCTION: A case-crossover study is a self-controlled design most appropriate for evaluating transient medication exposures. However, it has increasingly been used in studies of chronic medications and can cause bias in effect estimates that vary based on the pattern of medication use. The goal of this study was to evaluate the magnitude of this bias across different medication-use patterns. OBJECTIVE: To quantify the magnitude of the bias introduced by different medication patterns and evaluate different case-crossover approaches to mitigate the bias. METHODS: We conducted a simulation study evaluating the bias introduced by (1) seven common medication patterns separately, and (2) cohort with 15 different patterns combined. We evaluated each scenario under risk ratios of 0.50, 0.75, 1.00, 1.50, and 2.00. Each approach was analyzed using conditional logistic regression comparing the probability of exposure on the outcome day to 30 days prior. A case-time-control design was used in each of the scenarios. Sensitivity analysis was performed to evaluate the impact on the estimates when changing the length of the risk and control windows. We conducted a real-world example focusing on sodium-glucose co-transporter-2 inhibitor users as real-world examples. RESULTS: The case-crossover design resulted in unbiased estimates when patterns were consistent with transient exposures but were biased upward with prolonged exposure patterns. The magnitude of the bias varies by patterns or pattern combinations. When evaluating prolonged exposures individually or combined as a cohort with mixture patterns, case-time-control with extended risk and control window (30 days) produced unbiased results (mean bias ≤ 0.03). CONCLUSION: Researchers who use the case-crossover design to evaluate non-transient exposures should implement recommended methods to account for biases.
Cell and gene therapies, including CAR T-cells, CRISPR-based genome editing, and next-generation viral and non-viral delivery platforms, are transforming treatment paradigms across cancer, rare genetic disorders, immune...Cell and gene therapies, including CAR T-cells, CRISPR-based genome editing, and next-generation viral and non-viral delivery platforms, are transforming treatment paradigms across cancer, rare genetic disorders, immune dysregulation, and neurodegenerative disease. These therapies offer curative potential but also present safety challenges owing to prolonged biological activity, systemic immune engagement, and lasting genomic alterations. This review examines the range of related toxicities, including immune complications, genotoxicity, and organ-specific effects, with attention to atypical presentations, gaps in clinical trial safety capture, and disparities in global long-term follow-up infrastructure. Central to our analysis is a risk-adaptive, digitally enabled pharmacovigilance model that incorporates real-world data, artificial intelligence-based signal detection, and seamless pediatric-to-adult follow-up to proactively protect patients while supporting innovation. Integrated safety dashboards, pediatric transition roadmaps, and predictive monitoring tools are proposed as practical solutions to improve coordination among sponsors, regulators, and clinical sites. We also outline best practices for aligning risk evaluation and mitigation strategies with risk management plans and examine how wearable biosensors, electronic patient-reported outcomes, and multi-omics biomarkers contribute to near real-time safety surveillance. Ethical priorities such as informed consent, data privacy, and equitable access are addressed throughout. By positioning pharmacovigilance as a proactive and predictive foundation within the therapeutic landscape, this review offers a forward-looking blueprint to advance innovation while ensuring long-term patient safety.
BACKGROUND: The natural health products (NHPs) industry is a key stakeholder in pharmacovigilance for NHPs. However, the specific contributions that the NHPs industry makes to pharmacovigilance for NHPs are not well-unde...BACKGROUND: The natural health products (NHPs) industry is a key stakeholder in pharmacovigilance for NHPs. However, the specific contributions that the NHPs industry makes to pharmacovigilance for NHPs are not well-understood. OBJECTIVE: This scoping review aimed to identify and map published literature describing the contributions of the NHPs industry to pharmacovigilance activities for NHPs. Assessment of benefit-harm balance for individual NHPs/natural ingredients is outside the scope of this review. METHODS: Using predetermined keywords and Medical Subject Headings, seven international electronic biomedical journal databases were searched to identify articles describing the contributions of the NHPs industry to pharmacovigilance for NHPs in relation to product surveillance and stakeholders' views on the NHPs industry and its pharmacovigilance activities. RESULTS: Of the 2285 records identified, 40 articles (representing 40 studies) met the inclusion criteria for this review. Among these, 33 described post-marketing surveillance activities and seven explored stakeholders' views. Of the articles describing post-marketing surveillance studies, 22 were authored and/or sponsored by the industry; the remaining 11 involved contributions from the NHPs industry in the form of safety data submitted as spontaneous reports to a national or state pharmacovigilance database. Contributions of the NHPs industry were primarily through passive surveillance via spontaneous reporting. In total, 13 active surveillance studies were undertaken by the NHPs industry, mainly in clinical care settings such as medical centres, hospitals, and private practices, and were focused on single products. There were limited findings relating to stakeholders' views on pharmacovigilance and the NHPs industry's involvement. CONCLUSIONS: The NHPs industry contributes to pharmacovigilance for NHPs primarily through passive surveillance measures. Active surveillance involving the NHPs industry was typically undertaken in non-community settings. Additional research exploring stakeholders' views on and preparedness for participating in active surveillance involving the industry, focusing particularly on models based on the consumer-industry reporting pathway, could identify new strategies for strengthening post-marketing safety monitoring for NHPs.