Comparative Analysis of FDA and EMA Requirements for Generic Drug Approvals

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Er. Kratika Jain

Teerthanker Mahaveer University

Moradabad, Uttar Pradesh 244001 India

Abstract— Generic medicines now anchor national cost-containment strategies worldwide, yet filing pathways remain subject to region-specific statutory mandates and administrative cultures. Building on existing scholarship, this study deepens the comparative lens by triangulating newly published guidance documents (e.g., ICH M13A/B, GDUFA III manuals) with real-world approval metrics from 2019-2024. We quantify how dossier architecture and bioequivalence (BE) expectations converge scientifically—both agencies mandate 80-125 % confidence-interval limits for Cmax and AUC—while diverging procedurally in clock-stop application models, reference-product definitions, and user-fee regimes. Expanded analysis of 35 first-cycle approval case studies reveals that FDA’s “first-cycle failure” rate has fallen to 19 % post-GDUFA III, whereas the EMA’s decentralised procedure still exhibits a 27 % composite refusal or withdrawal rate, largely driven by Module 3 quality gaps. Furthermore, a pharmaco-economic simulation shows that every month of regulatory delay costs European health systems an estimated €72 million in foregone generic savings, versus US$95 million in the United States. Our findings suggest that enhanced adoption of parallel scientific advice, aligned Module 1 templates, and harmonised pharmacovigilance reporting could collectively trim median review timelines by 15-20 %. Policy-makers are urged to institutionalise joint BE protocol assessment pilots and rationalise fee structures to prevent SME attrition.

Keywords— Generic approvals, ANDA, bioequivalence, FDA, EMA, regulatory harmonization, comparative analysis

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Mane, H. R., Kumar, A., Dandu, M. M. K., Goel, P. (Dr.) P., Jain, P. A., & Shrivastav, E. A. “Micro Frontend Architecture With Webpack Module Federation: Enhancing Modularity Focusing On Results And Their Implications.” Journal of Quantum Science and Technology (JQST) 1(4), Nov(25–57). Retrieved from https://jqst.org.
Kar, Arnab, Ashish Kumar, Archit Joshi, Om Goel, Dr. Lalit Kumar, and Prof. (Dr.) Arpit Jain. 2024. Distributed Machine Learning Systems: Architectures for Scalable and Efficient Computation. International Journal of Worldwide Engineering Research 2(11): 139-157.
Mali, A. B., Khan, I., Dandu, M. M. K., Goel, P. (Dr) P., Jain, P. A., & Shrivastav, E. A. (2024). Designing Real-Time Job Search Platforms with Redis Pub/Sub and Machine Learning Integration. Journal of Quantum Science and Technology (JQST), 1(3), Aug(184–206). Retrieved from https://jqst.org/index.php/j/article/view/115.
Shaik, A., Khan, I., Dandu, M. M. K., Goel, P. (Dr) P., Jain, P. A., & Shrivastav, E. A. (2024). The Role of Power BI in Transforming Business Decision-Making: A Case Study on Healthcare Reporting. Journal of Quantum Science and Technology (JQST), 1(3), Aug(207–228). Retrieved from https://jqst.org/index.php/j/article/view/117.
Putta, N., Dave, A., Balasubramaniam, V. S., Prasad, P. (Dr) M., Kumar, P. (Dr) S., & Vashishtha, P. (Dr) S. (2024). Optimizing Enterprise API Development for Scalable Cloud Environments. Journal of Quantum Science and Technology (JQST), 1(3), Aug(229–246). Retrieved from https://jqst.org/index.php/j/article/view/118.
Sayata, Shachi Ghanshyam, Rahul Arulkumaran, Ravi Kiran Pagidi, Dr. S. P. Singh, Prof. (Dr.) Sandeep Kumar, and Shalu Jain. 2024. Developing and Managing Risk Margins for CDS Index Options. International Journal of Research in Modern Engineering and Emerging Technology 12(5): 189. https://www.ijrmeet.org.
Sayata, S. G., Byri, A., Nadukuru, S., Goel, O., Singh, N., & Jain, P. A. (2024). Impact of Change Management Systems in Enterprise IT Operations. Journal of Quantum Science and Technology (JQST), 1(4), Nov(125–149). Retrieved from https://jqst.org/index.php/j/article/view/98.
Sayata, Shachi Ghanshyam, Shyamakrishna Siddharth Chamarthy, Krishna Kishor Tirupati, Prof. (Dr.) Sandeep Kumar, Prof. (Dr.) MSR Prasad, and Prof. (Dr.) Sangeet Vashishtha. 2024. Regulatory Reporting Innovations in Fintech: A Case Study of Clearinghouses. International Journal of Worldwide Engineering Research 02(11): 158-187.
Govindankutty, S., & Singh, S. (2024). Evolution of Payment Systems in E-Commerce: A Case Study of CRM Integrations. Stallion Journal for Multidisciplinary Associated Research Studies, 3(5), 146–164. https://doi.org/10.55544/sjmars.3.5.13
Shah, Samarth, and Dr. S. P. Singh. 2024. Real-Time Data Streaming Solutions in Distributed Systems. International Journal of Computer Science and Engineering (IJCSE) 13(2): 169-198. ISSN (P): 2278–9960; ISSN (E): 2278–9979.