Terranova, Nadia, Renard, Didier, Shahin, Mohamed, Menon, Sujetha, Cao, Youfang, Hop, Cornelis, Hayes, Sean, Stodtmann, Sven, Tensfeldt, Thomas, Vaddady, Pavan, Ellinwood, Nicholas and Lu, James (2023) Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices. Clinical Pharmacology & Therapeutics, 115 (4). pp. 658-672. ISSN 0009-92361532-6535

Abstract

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-
the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.

Item Type:	Article
Keywords:	Humans Artificial Intelligence Drug Discovery Machine Learning Algorithms Natural Language Processing
Date Deposited:	16 Apr 2024 00:46
Last Modified:	16 Apr 2024 00:46
URI:	https://oak.novartis.com/id/eprint/50545