%0 Journal Article %1 noauthororeditor %A Vellanki, Jahnavi %D 2025 %E Vellanki, Jahnavi %J International Journal of Computer Science, Engineering and Information Technology (IJCSEIT) %K Artificial Compliance, Discovery, Drug Intelligence, Learning, Machine Medicine Personalized Pharmacovigilance, Regulatory %N 3 %P 11 %R 10.5121/ijcseit.2025.15301 %T Discuss How AI and ML Can Optimize Lifecycle Management Practices within Pharmaceutical Companies fromDevelopment to Post-Market Surveillance %U https://aircconline.com/ijcseit/V15N3/15325ijcseit01.pdf %V 15 %X The pharma industry is in the midst of a digital revolution, with Artificial Intelligence (AI) and Machine Learning (ML) becoming potent tools to streamline lifecycle management at every stage—right from early drug discovery to post-market surveillance. This paper seeks to critically evaluate how AI and ML technologies are transforming pharma processes by enhancing efficiency, accuracy, decision-making, and patient outcomes. We discuss the use of predictive algorithms in target identification, artificial intelligence-based simulations in clinical trial design, machine-based compliance monitoring in manufacturing, and real time analytics in pharmacovigilance. The focus is on the convergence of emerging technologies like blockchain, which complements data transparency and security when integrated with AI platforms. Although the promise of these technologies is enormous, the paper also discusses ongoing challenges such as data silos, algorithmic bias, and regulatory barriers. This research integrates current literature to present a unified perspective of AI and ML applications in pharma, detailing future directions and industry implications. Finally, the findings emphasize that although AI is no silver bullet, its strategic implementation can significantly enhance lifecycle efficiency and innovation in drug development.

@article{noauthororeditor, abstract = {The pharma industry is in the midst of a digital revolution, with Artificial Intelligence (AI) and Machine Learning (ML) becoming potent tools to streamline lifecycle management at every stage—right from early drug discovery to post-market surveillance. This paper seeks to critically evaluate how AI and ML technologies are transforming pharma processes by enhancing efficiency, accuracy, decision-making, and patient outcomes. We discuss the use of predictive algorithms in target identification, artificial intelligence-based simulations in clinical trial design, machine-based compliance monitoring in manufacturing, and real time analytics in pharmacovigilance. The focus is on the convergence of emerging technologies like blockchain, which complements data transparency and security when integrated with AI platforms. Although the promise of these technologies is enormous, the paper also discusses ongoing challenges such as data silos, algorithmic bias, and regulatory barriers. This research integrates current literature to present a unified perspective of AI and ML applications in pharma, detailing future directions and industry implications. Finally, the findings emphasize that although AI is no silver bullet, its strategic implementation can significantly enhance lifecycle efficiency and innovation in drug development. }, added-at = {2025-07-16T16:16:25.000+0200}, author = {Vellanki, Jahnavi}, biburl = {https://www.bibsonomy.org/bibtex/240c42c63c88c1c80b5f2dc770f215d39/ijcseit}, doi = {10.5121/ijcseit.2025.15301}, editor = {Vellanki, Jahnavi}, interhash = {ca47df0f3f53fdfdad3af8fdb72fd2c7}, intrahash = {40c42c63c88c1c80b5f2dc770f215d39}, issn = {2231 - 3117 [Online] ; 2231 - 3605 [Print]}, journal = {International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)}, keywords = {Artificial Compliance, Discovery, Drug Intelligence, Learning, Machine Medicine Personalized Pharmacovigilance, Regulatory}, language = {English}, month = {june}, number = 3, pages = 11, timestamp = {2025-07-16T16:16:25.000+0200}, title = {Discuss How AI and ML Can Optimize Lifecycle Management Practices within Pharmaceutical Companies fromDevelopment to Post-Market Surveillance}, url = {https://aircconline.com/ijcseit/V15N3/15325ijcseit01.pdf}, volume = 15, year = 2025 }