AI Innovations in Drug Delivery and Pharmaceutical Sciences; Advancing Therapy through Technology

Role of Artificial Intelligence in Drug Product Design and Optimization of Process Parameters

Author(s): Pankaj Kumar Pandey*, Manoj Likhariya, Juhi Bhadoria, Kuldeep Vinchurkar and Priya Jain

Pp: 163-198 (36)

DOI: 10.2174/9789815305753124010011

* (Excluding Mailing and Handling)

Abstract

The integration of artificial intelligence (AI) in pharmaceutical research has revolutionized drug product design and the optimization of process parameters, marking a paradigm shift in the traditional drug development paradigm. This abstract explores the multifaceted role of AI in these critical aspects of pharmaceutical manufacturing. The chapter elaborates the significance of AI in revolutionizing processes like drug discovery, formulation optimization, personalized medicine development, predictive analytics, drug design, improved patient outcomes, and many more. In drug product design, AI-driven methodologies have demonstrated unparalleled capabilities in expediting the identification of novel drug candidates and predicting their pharmacokinetic properties. Machine learning algorithms analyze vast datasets, including molecular structures, biological interactions, and clinical trial outcomes, to unravel complex relationships and generate insights that guide rational drug design. This accelerates the discovery process and enhances the efficiency of lead optimization, ultimately reducing the time and costs associated with drug development. Furthermore, AI plays a pivotal role in optimizing process parameters during drug manufacturing. The pharmaceutical industry faces challenges in ensuring the reproducibility, scalability, and cost-effectiveness of production processes. AI algorithms, particularly in combination with process analytical technologies (PAT), enable real-time monitoring and control, ensuring the quality and consistency of drug products. Through iterative learning and adaptive control, AI-driven systems can dynamically optimize manufacturing parameters, minimizing variations and ensuring the robustness of the production process. In conclusion, the incorporation of AI in drug product design and process optimization is transformative, fostering innovation and efficiency in the pharmaceutical industry. As the field continues to evolve, collaborative efforts between computational scientists, chemists, and engineers are essential to harness the full potential of AI, ultimately advancing drug development and improving patient outcomes.


Keywords: Artificial intelligence, Algorithms, Decision making, Drug product design, Human intelligence, Machine learning, Product development, Regulatory process, Speech recognition, Visual perception.

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