Date of Award
Spring 3-6-2019
Degree Type
Dissertation
Degree Name
PhD Health Sciences
Department
Health and Medical Sciences
Advisor
Deborah A. DeLuca, JD
Committee Member
Terrence F. Cahill, Ed.D.
Committee Member
Glenn Beamer, Ph.D.
Keywords
meta-analysis, meta-regression, orphan drugs, clinical trials, regulatory success, regulatory approval
Abstract
Background and Purpose of the Study: Developed an algorithm (AODI) for predicting probability of regulatory success (PRS) for new orphan drugs after phase II testing has been conductedwith the objective of providing a tool to improve drug portfolio decision-making.Methods: Examined 132 studies from recent publications (2005 onwards). Data on safety, efficacy, operational, market, and company characteristics were obtained from public sources. Meta-analysis and meta-regressions were used to provide an unbiased approach to assess overall predictability and to identify the most important individual predictors.Results: Found that a simple three-factor model (disease prevalence, clinical trial duration and clinical trial participation) had high specificity for predicting regulatory approval (success).Conclusion:smaller clinical trial participation, shorter clinical trials duration and lower rare disease prevalence were found to be highly associated with the Probability of Regulatory Success (PRS) of orphan drugs.
Recommended Citation
Florent, Milky C., "Meta-analysis to Identify and Evaluate Factors Associated with Regulatory Approval of Orphan Drugs (OD) to Develop an Algorithm for Predicting Regulatory Approval (Success) and to Develop a Standardized Tool to Improve Orphan Drug Portfolio Decision-making" (2019). Seton Hall University Dissertations and Theses (ETDs). 2629.
https://scholarship.shu.edu/dissertations/2629
Included in
Medicinal and Pharmaceutical Chemistry Commons, Pharmacy Administration, Policy and Regulation Commons
