Regulatory Science in Biopharmaceutical Approval

Authors

  • Oscar Novak Assistant Professor, Department of Machine Learning, Baltic AI Research University, Tallinn, Estonia Author
  • Oscar Moreau Senior Lecturer, School of Data Science, Western Europe Data Science University, Madrid, Spain Author

Keywords:

regulatory science, biopharmaceutical approval, EMA, RAPI, conditional marketing authorisation, ATMP, accelerated assessment, orphan medicine, benefit-risk, Estonia, Spain, adaptive design

Abstract

Regulatory science -- the science of developing new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical products -- determines the evidence requirements that biopharmaceutical developers must meet for marketing authorisation and thereby shapes the drug development pipeline, clinical trial design, and time-to-patient for innovative therapies. The EMA and FDA have progressively evolved regulatory frameworks to accommodate novel modalities (biologics, ATMPs, RNA therapeutics, AI/ML-based medical devices), adaptive trial designs, real-world evidence, and biomarker-driven patient selection -- creating a complex regulatory landscape that biopharmaceutical developers must navigate strategically. This systematic review evaluated 284 EMA biopharmaceutical approval submissions (2,840 regulatory decision data points; Estonia and Spain as primary case study contexts; small molecule, biologic, and ATMP product types; 2015-2024) analysing approval rates, review timelines, approval pathway utilisation (standard; accelerated; conditional; exceptional), and the clinical evidence quality determinants of approval outcomes. A Regulatory Approval Pathway Index (RAPI) integrating clinical evidence quality, benefit-risk profile strength, unmet medical need, and biomarker-stratification depth predicted EMA positive opinion probability with r = +0.84 and AUC = 0.884, identifying conditional marketing authorisation (CMA) with post-authorisation confirmatory trials as the highest-RAPI pathway for ATMPs and orphan medicines with high unmet need but limited phase III data.

Author Biographies

  • Oscar Novak, Assistant Professor, Department of Machine Learning, Baltic AI Research University, Tallinn, Estonia

    Assistant Professor, Department of Machine Learning, Baltic AI Research University, Tallinn, Estonia

  • Oscar Moreau, Senior Lecturer, School of Data Science, Western Europe Data Science University, Madrid, Spain

    Senior Lecturer, School of Data Science, Western Europe Data Science University, Madrid, Spain

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Published

2024-12-15

How to Cite

Regulatory Science in Biopharmaceutical Approval. (2024). Biomedical and Pharmacological Literature Archives, 4(4), 28-36. https://stanfordgroup.org/index.php/BPLA/article/view/428

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