Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices

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 & Quality (IQ) Consortium initiated the AI/ML working group (WG) 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 WG 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: (1) AI/ML‐enabled Analytics for Pharmacometrics & QSP Workflows; (2) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (3) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; (4) 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.

Machine Learning in Modeling Disease Trajectory and Treatment Outcomes: An Emerging Enabler for Model‐Informed Precision Medicine

“…Best practices should be followed by having human-in-the-loop and ensuring that the context of use and risk-informed credibility assessments are considered in the model development and validation. 2 With the rapid permeation of AI/ML into biomedical sciences and pharmaceutical research and development and the enthusiasm these advanced analytical techniques have gained in recent years, it is important to ensure purpose-oriented application to maximize impact. 28 Although we have highlighted the value of incorporating AI/ML algorithms in drug-disease modeling in this mini-review, not all questions and contexts of use will require these approaches.…”

“…Of note, in the realm of AI/ML, it is essential to exercise caution to prevent biased and non‐generalizable outcomes from data. Best practices should be followed by having human‐in‐the‐loop and ensuring that the context of use and risk‐informed credibility assessments are considered in the model development and validation 2 . With the rapid permeation of AI/ML into biomedical sciences and pharmaceutical research and development and the enthusiasm these advanced analytical techniques have gained in recent years, it is important to ensure purpose‐oriented application to maximize impact 28 .…”

“…These techniques enable integration of multimodal and multidimensional sources of variability in disease trajectory and the pharmacological response to therapeutics. 2 Such integration is vital across the pharmaceutical research and development lifecycle. It helps inform confidence in the biological target beginning early in drug discovery, selection of the right patient population for clinical development, and maximization of benefit versus risk through optimized dosage selection for all patients.…”

“…To address these challenges, artificial intelligence (AI)/machine learning (ML) and related advanced analytical approaches have rapidly permeated the disciplines of Pharmacometrics and Quantitative Systems Pharmacology. These techniques enable integration of multimodal and multidimensional sources of variability in disease trajectory and the pharmacological response to therapeutics 2 . Such integration is vital across the pharmaceutical research and development lifecycle.…”

Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine

“…AI solutions are brought to bear to accelerate every step of the process during R&D and post‐approval. In drug R&D, AI is used to interpret complex disease phenotypes, discover new targets, identify/optimize new compounds, and mine experimental and clinical data sources 20,21 . Post‐approval, AI is being used to optimize drug manufacture and distribution, as well as to ensure market access.…”