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The most intuitive question for market access for medicinal products is the benefit/risk (B/R) balance. The B/R assessment can conceptually be divided into subquestions related to establishing efficacy and safety. There are additional layers to the B/R ratio for medical products, including questions related to dose selection, clinical and nonclinical pharmacology, and drug quality. Explicitly stating the actual questions and how they contribute to the overall B/R provides a structure that fosters better informed cross-domain discussions. There is currently no systematic approach in the regulatory setting to assess and establish the acceptability of alternative methods and data sources. In most cases, the medicinal product sponsors tend to prioritize traditional data types and methods, which are well accepted by regulators for inclusion in regulatory submissions. This, in addition to the absence of rigor in the use and validation of new data types and methods, and the limited training of assessors in related fields can lead to increased regulatory skepticism toward new data types and methods. A data-knowledge backbone is needed to mitigate the uncertainty in efficacy and safety characterization. This white paper discusses the value of explicitly redefining and restructuring the regulatory scientific decision making around the scientific question to be addressed. The ecosystem proposed is based on three pillars: (i) a repository connecting questions, data, and methods; (ii) the development and validation of high-quality standards for data and methods; and (iii) credibility assessment. The ecosystem is applied to four use cases for illustration. The need for training and regulatory guidance is also discussed., (© 2023 The Authors. Clinical Pharmacology & Therapeutics © 2023 American Society for Clinical Pharmacology and Therapeutics.)

Background: Masking is a statistical issue by which true signals of disproportionate reporting are hidden by the presence of other products in the database. Masking is currently not perfectly understood. There is no algorithm to identify the potential masking drugs to remove them for subsequent analyses of disproportionality., Objective: The primary objective of our study is to develop a mathematical framework for assessing the extent and impact of the masking effect of measures of disproportionality., Method: We have developed a masking ratio that quantifies the masking effect of a given product. We have conducted a simulation study to validate our algorithm., Results: The masking ratio is a measure of the strength of the masking effect whether the analysis is performed at the report or event level, and the manner in which reports are allocated to cells in the contingency table significantly impact the masking mechanisms. The reports containing both the product of interest and the masking product need to be handled appropriately. The proposed algorithm can use simplified masking provided that underlying assumptions (in particular the size of the database) are verified. For any event, the strongest masking effect is associated with the drug with the highest number of records (reports excluding the product of interest)., Conclusion: Our study provides significant insights with practical implications for real-world pharmacovigilance that are supported by both real and simulated data. The public health impact of masking is still unknown., (Copyright © 2013 John Wiley & Sons, Ltd.)

Background: Masking is a statistical issue by which signals are hidden by the presence of other medicines in the database. In the absence algorithm, the impact of the masking effect has not been fully investigated., Objective: Our study is aimed at assessing the extent and the impact of the masking effect on two large spontaneous reporting databases., Study Design: Cross sectional study using a set of terms of importance for public health in two spontaneous reporting databases., Setting: The analyses were performed on EudraVigilance (EV) and the Pfizer spontaneous reporting database (PfDB)., Main Outcome Measure: Using the masking ratio, we have identified and removed the products inducing the highest masking effect., Results: Studying a total of almost 50 000 drug-event combinations masking had an impact on approximately 60% of drug-event combinations were masked by another product with a masking ratio >1 in EV and 84% in PfDB. The prevalence of important masking was quite rare (0.003% of the DECs) and mainly affected events rarely reported in EV. The products involved in the highest masking effects are products known to induce the reaction. The removal of the masking effect of the highest masking product has revealed 974 signals of disproportionate reporting in EV including true signals. The study shows that the original ranking provided by the quantitative methods included in our study is marginally affected by the removal of the masking product., Conclusion: Our study suggests that significant masking is rare in large spontaneous databases and mostly affects events rarely reported in EV., (Copyright © 2013 John Wiley & Sons, Ltd.)