Thank you, Yi-Hua, for this interesting question.
The AI field that began with predicting bacterial mutagenicity and hERG inhibition has now become more complex and has expanded into entire discovery and development processes. What I have been interested in is the application of AI/ML methodologies in the direct prediction of concentration profiles based on input data as the resulting output may exhibit characteristics (such as shape) that can be easily observed in vivo experiments. However, when solely relying on iterative testing, this approach may be able to predict the curve but fails to provide insights into the fundamental drivers that shape the curve. Hence, understanding the differences between predictions from AI and physiological model (PBPK) may inform the unknown aspects of biology and provide opportunities to elucidate the underlying mechanisms. However, it remains to be seen how AI methods will be the ability to adapt to data sets that change in quality over time.
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Pankajini Mallick PhD
Principal Scientist, Preclinical Pharmacokinetics | DMPK
San Diego CA
Disclaimer: Opinions expressed are solely my own and do not express the views or opinions of my employer.
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Original Message:
Sent: 06-26-2023 20:43
From: Yi-Hua Chiang
Subject: Applications of Artificial intelligence in PPDM
Applications of Artificial intelligence in PPDM, are we ready?
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Yi-Hua Chiang
Ph.D. Student
University of Florida
Gainesville FL
[email protected]
Disclaimer: Opinions expressed are solely my own and do not express the views or opinions of my employer.
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