The Role of Pharmacoepidemiology in Industry

Gatto, Nicolle M.; Sobel, Rachel E.; Geier, Jamie; Mo, Jingping; Bate, Andrew; Reynolds, Robert F.

doi:10.1002/9781119413431.ch7

Cited by 4 publications

(4 citation statements)

References 62 publications

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“…RWD has long been used by industry for pharmacoepidemiologic studies and across the drug and vaccine development lifecycle (Bate et al, 2016;Gatto et al, 2019;Garcia-Gancedo and Bate, 2022). The use of ML for the wider use of RWD continues to attract interest for routine PV activities, and we anticipate this trend to continue.…”

Section: Major Trends and Opportunities In Industry Application Of Ml...mentioning

confidence: 99%

An industry perspective on the use of machine learning in drug and vaccine safety

Painter

Kassekert²,

Bate³

2023

Front. Drug Saf. Regul.

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In recent years there has been growing interest in the use of machine learning across the pharmacovigilance lifecycle to enhance safety monitoring of drugs and vaccines. Here we describe the scope of industry-based research into the use of machine learning for safety purposes. We conducted an examination of the findings from a previously published systematic review; 393 papers sourced from a literature search from 2000–2021 were analyzed and attributed to either industry, academia, or regulatory authorities. Overall, 33 papers verified to be industry contributions were then assigned to one of six categories representing the most frequent PV functions (data ingestion, disease-specific studies, literature review, real world data, signal detection, and social media). RWD and social media comprised 63% (21/33) of the papers, signal detection and data ingestion comprised 18% (6/33) of the papers, while disease-specific studies and literature reviews represented 12% (4/33) and 6% (2/33) of the papers, respectively. Herein we describe the trends and opportunities observed in industry application of machine learning in pharmacovigilance, along with discussing the potential barriers. We conclude that although progress to date has been uneven, industry is very interested in applying machine learning to the pharmacovigilance lifecycle, which it is hoped may ultimately enhance patient safety.

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Section: Major Trends and Opportunities In Industry Application Of Ml...mentioning

confidence: 99%

An industry perspective on the use of machine learning in drug and vaccine safety

Painter

Kassekert²,

Bate³

2023

Front. Drug Saf. Regul.

Self Cite

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“…There is a large body of knowledge that explores the impacts of selection bias, censoring, health system context, clinical coding practices, and other issues related to the use of RWD to support medicine development, safety evaluation and pharmacovigilance, and commercialization. 47 However, many of these topics have not been explored with respect to their impact on genotype-phenotype associations, or they have been limited to very specific phenotypes. Given the large amount of work in the research space across the disease phenome, there is a clear need to investigate the value of RWD study designs in the context of genetic studies.…”

Section: Emerging Needs For Researchmentioning

confidence: 99%

Integration of Real‐World Data and Genetics to Support Target Identification and Validation

Davitte

Stott-Miller

Ehm

et al. 2021

Clin Pharma and Therapeutics

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Even modest improvements in the probability of success of selecting drug targets which are ultimately approved can substantially reduce the costs of research and development. Drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. A key enabler of identifying and validating these genetically validated targets is access to association results from genome-wide genotyping, whole-exome sequencing, and whole-genome sequencing studies with observable traits (often diseases) across large numbers of individuals. Today, linkage between genotype and real-world data (RWD) provides significant opportunities to not only increase the statistical power of genome-wide association studies by ascertaining additional cases for diseases of interest, but also to improve diversity and coverage of association studies across the disease phenome. As RWD-genetics linked resources continue to grow in diversity of participants, breadth of data captured, length of observation, and number of participants, there is a greater need to leverage the experience of RWD experts, clinicians, and highly experienced geneticists together to understand which lessons and frameworks from general research using RWD sources are relevant to improve genetics-driven drug discovery and development. This paper describes new challenges and opportunities for phenotypes enabled by diverse RWD sources, considerations in the use of RWD phenotypes for disease gene identification across the disease phenome, and challenges and opportunities in leveraging RWD phenotypes in target validation. The paper concludes with views on the future directions for phenotype development using RWD, and key questions requiring further research and development to advance this nascent field.

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“…RWE has been used to monitor drug safety and expand indications for FDA-approved medications. 2,3 However, RWD has been used on a limited basis by regulatory authorities to evaluate the effectiveness of medications. 4 With the substantial increase in the availability of RWD, pharmacoepidemiology study designs for causal inference and new statistical methods, there is increased potential for generating RWE on drug safety and effectiveness.…”

Section: Introductionmentioning

confidence: 99%

“…The use of evidence from analyses of RWD, hereafter referred to as real world evidence (RWE), for decision‐making is not new. RWE has been used to monitor drug safety and expand indications for FDA‐approved medications 2,3 . However, RWD has been used on a limited basis by regulatory authorities to evaluate the effectiveness of medications 4 .…”

Section: Introductionmentioning

confidence: 99%

Staging and clean room: Constructs designed to facilitate transparency and reduce bias in comparative analyses of real‐world data

Muntner,

Hernandez,

Kent

et al. 2024

Pharmacoepidemiology and Drug

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PurposeWe describe constructs designed to protect the integrity of the results from comparative analyses using real‐world data (RWD): staging and clean room.MethodsStaging involves performing sequential preliminary analyses and evaluating the population size available and potential bias before conducting comparative analyses. A clean room involves restricted access to data and preliminary results, policies governing exploratory analyses and protocol deviations, and audit trail. These constructs are intended to allow decisions about protocol deviations, such as changes to design or model specification, to be made without knowledge of how they might affect subsequent analyses. We describe an example for implementing staging with a clean room.ResultsStage 1 may involve selecting a data source, developing and registering a protocol, establishing a clean room, and applying inclusion/exclusion criteria. Stage 2 may involve attempting to achieve covariate balance, often through propensity score models. Stage 3 may involve evaluating the presence of residual confounding using negative control outcomes. After each stage, check points may be implemented when a team of statisticians, epidemiologists and clinicians masked to how their decisions may affect study outcomes, reviews the results. This review team may be tasked with making recommendations for protocol deviations to address study precision or bias. They may recommend proceeding to the next stage, conducting additional analyses to address bias, or terminating the study. Stage 4 may involve conducting the comparative analyses.ConclusionsThe staging and clean room constructs are intended to protect the integrity and enhance confidence in the results of analyses of RWD.

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The Role of Pharmacoepidemiology in Industry

Cited by 4 publications

References 62 publications

An industry perspective on the use of machine learning in drug and vaccine safety

An industry perspective on the use of machine learning in drug and vaccine safety

Integration of Real‐World Data and Genetics to Support Target Identification and Validation

Staging and clean room: Constructs designed to facilitate transparency and reduce bias in comparative analyses of real‐world data

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