Leveraging AI in QSP model development

The field of QSP is rapidly evolving, especially with its increasing integration with Artificial Intelligence (AI), the QSP modeling, at its core, aims to describe biological relationships mathematically, providing a mechanistic understanding of drug actions across multiple scales. Here are some key themes emerging:•Enhanced Drug Development: QSP plays a crucial role in various stages of drug development, from understanding disease mechanisms to predicting clinical outcomes and optimizing dosing regimens. Its application is being seen from target identification to clinical trials and regulatory submissions.•The Power of AI/ML: The synergy between QSP and AI/ML is unlocking new possibilities. AI/ML can assist in knowledge discovery from vast amounts of literature, aid in model building and parameterization, enhance the generation of virtual patient populations, and even contribute to hypothesis generation. This integration can accelerate the modeling life cycle.•Regulatory Acceptance: Regulatory bodies like the FDA are increasingly recognizing the value of QSP in drug development and review processes. There's a growing emphasis on "fit-for-purpose" models and the establishment of best practices for their development and qualification•Best Practices and Collaboration: The community is actively working on defining best practices to maximize the use and reuse of QSP models, emphasizing transparency, documentation, and interdisciplinary collaboration. Effective communication between modelers and stakeholders is crucial.•Applications Across Diseases: QSP modeling is being applied to a wide range of therapeutic areas, including neuropsychiatric disorders, immune-oncology, and rare diseases. The convergence of QSP with AI holds immense potential to improve efficiency, reduce attrition rates in drug development, and enhance our understanding of drug mechanisms and patient variability. It's an exciting time to be in this interdisciplinary field!

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