Safety Signal Detection in the Drug Development Process

Software Innovations in Clinical Drug Development and Safety - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-4666-8726-4.ch005 ◽

2015 ◽

pp. 64-89

Author(s):

Ramin B. Arani ◽

Antoni F.Z. Wisniewski

Keyword(s):

Adverse Event ◽

Drug Development ◽

Signal Detection ◽

Safety Information ◽

Safety Signal ◽

Drug Development Process ◽

Safety And Efficacy ◽

Strength Of Association ◽

Efficacy Profile ◽

Complicated Task

Drug development is a complex set of inter-linked processes in which the cumulative understanding of a drug's safety and efficacy profile is shaped during different learning phases. Often, drugs are approved based on limited safety information, for example in highly at risk or rare disease populations. Therefore, post approval, regulatory organizations have mandated proactive surveillance strategies that include the collection of reported adverse events experienced by exposed populations, some of whom may have been on treatment for extended periods of time. Analyzing these accumulating adverse event reports to understand their clinical significance, given the limitations imposed by the methods of data collection, is a complicated task. The aim of this chapter is to provide the readers with a general understanding of safety signal detection and assessment, followed by a description of statistical methods (both classical and Bayesian) typically utilized for quantifying the strength of association between a drug and an adverse event.

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Chronic Cannabis Users: A New Special Population to Consider for Drug Development

Current Reviews in Clinical and Experimental Pharmacology ◽

10.2174/2772432816666210515145638 ◽

2021 ◽

Vol 16 ◽

Author(s):

Gina Pastino ◽

Diana Shuster

Keyword(s):

Clinical Pharmacology ◽

Drug Development ◽

Cannabis User ◽

Special Population ◽

Safety And Efficacy ◽

New Drug ◽

Metabolic Interactions ◽

Widespread Availability ◽

Baseline Characteristics ◽

Efficacy Profile

The use and acceptance of cannabis, either medically or recreationally, has substantially outpaced the collection of data necessary to evaluate its use in any population. However, the mere widespread availability does not imply the absence of risk or confirmation of efficacy and should not be treated as such. There is enough data to suggest that not only does the potential for pharmacokinetic and metabolic interactions exist, but also that baseline characteristics for a given population could be different in chronic cannabis users. Either or both of these may impact the safety and efficacy profile for any new drug in development. As such, we encourage drug developers to consider that the cannabis user may very well be a special population that warrants its own clinical pharmacology evaluation.

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Treprostinil: Safety Signal Detection Based on Adverse Event Reporting System Database

Journal of Pharmacovigilance ◽

10.4172/2329-6887.1000140 ◽

2014 ◽

Vol 02 (04) ◽

Cited By ~ 1

Author(s):

Shraddha Shinde

Keyword(s):

Adverse Event ◽

Signal Detection ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Safety Signal ◽

Event Reporting ◽

System Database

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Harnessing scientific literature reports for pharmacovigilance

Applied Clinical Informatics ◽

10.4338/aci-2016-11-ra-0188 ◽

2017 ◽

Vol 26 (01) ◽

pp. 291-305

Author(s):

Alfred Sorbello ◽

Anna Ripple ◽

Joseph Tonning ◽

Monica Munoz ◽

Rashedul Hasan ◽

...

Keyword(s):

Data Mining ◽

Adverse Event ◽

Signal Detection ◽

User Satisfaction ◽

Scientific Literature ◽

Usability Testing ◽

Analytical Tool ◽

Drug Event ◽

Safety Signal ◽

Safety Signals

Summary Objectives: We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers’ capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. Methods: A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We leveraged Medical Subject Heading (MeSH) indexing terms assigned to published citations in PubMed/MEDLINE to generate candidate drug-adverse event pairs for quantitative data mining. Six FDA regulatory reviewers participated in usability testing by employing the tool as part of their ongoing real-life pharmacovigilance activities to provide subjective feedback on its practical impact, added value, and fitness for use. Results: All usability test participants cited the tool’s ease of learning, ease of use, and generation of quantitative ADE safety signals, some of which corresponded to known established adverse drug reactions. Potential concerns included the comparability of the tool’s automated literature search relative to a manual ‘all fields’ PubMed search, missing drugs and adverse event terms, interpretation of signal scores, and integration with existing computer-based analytical tools. Conclusions: Usability testing demonstrated that this novel tool can automate the detection of ADE safety signals from published literature reports. Various mitigation strategies are described to foster improvements in design, productivity, and end user satisfaction.

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The impact of database restriction on pharmacovigilance signal detection of selected cancer therapies

Therapeutic Advances in Drug Safety ◽

10.1177/2042098616685010 ◽

2017 ◽

Vol 8 (5) ◽

pp. 145-156 ◽

Cited By ~ 4

Author(s):

Manfred Hauben ◽

Eric Hung ◽

Jennifer Wood ◽

Amit Soitkar ◽

Daniel Reshef

Keyword(s):

Adverse Event ◽

Signal Detection ◽

Safety Information ◽

Adverse Event Reporting System ◽

Package Insert ◽

The United States ◽

Drug Event ◽

Reference Set ◽

Oncology Drug ◽

The Impact

Background: The aim of this study was to investigate whether database restriction can improve oncology drug pharmacovigilance signal detection performance. Methods: We used spontaneous adverse event (AE) reports in the United States (US) Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Positive control (PC) drug medical concept (DMC) pairs were selected from safety information not included in the product’s first label but subsequently added as label changes. These medical concepts (MCs) were mapped to the Medical Dictionary for Regulatory Activities (MedDRA) preferred terms (PTs) used in FAERS to code AEs. Negative controls (NC) were MCs with circumscribed PTs not included in the corresponding US package insert (USPI). We calculated shrinkage-adjusted observed-to-expected (O/E) reporting frequencies for the aforementioned drug–PT pairs. We also formulated an adjudication framework to calculate performance at the MC level. Performance metrics [sensitivity, specificity, positive and negative predictive value (PPV, NPV), signal/noise (S/N), F and Matthews correlation coefficient (MCC)] were calculated for each analysis and compared. Results: The PC reference set consisted of 11 drugs, 487 PTs, 27 MCs, 37 drug–MC combinations and 638 drug–event combinations (DECs). The NC reference set consisted of 11 drugs, 9 PTs, 5 MCs, 40 drug–MC combinations and 67 DECs. Most drug–event pairs were not highlighted by either analysis. A small percentage of signals of disproportionate reporting were lost, more noise than signal, with no gains. Specificity and PPV improved whereas sensitivity, NPV, F and MCC decreased, but all changes were small relative to the decrease in sensitivity. The overall S/N improved. Conclusion: This oncology drug restricted analysis improved the S/N ratio, removing proportionately more noise than signal, but with significant credible signal loss. Without broader experience and a calculus of costs and utilities of correct versus incorrect classifications in oncology pharmacovigilance such restricted analyses should be optional rather than a default analysis.

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Intelligent Drug Development

10.1093/oso/9780199974580.001.0001 ◽

2014 ◽

Author(s):

Michael Tansey

Keyword(s):

Clinical Trials ◽

Cost Effectiveness ◽

Drug Development ◽

Clinical Research ◽

Development Process ◽

Drug Development Process ◽

Individual Trial ◽

Specific Technology

Clinical research is heavily regulated and involves coordination of numerous pharmaceutical-related disciplines. Each individual trial involves contractual, regulatory, and ethics approval at each site and in each country. Clinical trials have become so complex and government requirements so stringent that researchers often approach trials too cautiously, convinced that the process is bound to be insurmountably complicated and riddled with roadblocks. A step back is needed, an objective examination of the drug development process as a whole, and recommendations made for streamlining the process at all stages. With Intelligent Drug Development, Michael Tansey systematically addresses the key elements that affect the quality, timeliness, and cost-effectiveness of the drug-development process, and identifies steps that can be adjusted and made more efficient. Tansey uses his own experiences conducting clinical trials to create a guide that provides flexible, adaptable ways of implementing the necessary processes of development. Moreover, the processes described in the book are not dependent either on a particular company structure or on any specific technology; thus, Tansey's approach can be implemented at any company, regardless of size. The book includes specific examples that illustrate some of the ways in which the principles can be applied, as well as suggestions for providing a better context in which the changes can be implemented. The protocols for drug development and clinical research have grown increasingly complex in recent years, making Intelligent Drug Development a needed examination of the pharmaceutical process.

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