Artificial intelligence for compound pharmacokinetics prediction |
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| Affiliation: | 1. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA;2. Department of Chemical Physiology and Biochemistry, School of Medicine, Oregon Health and Science University, Portland, OR, 97239, USA;1. Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, 3800, Victoria, Australia;2. Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, 16802, Pennsylvania, USA;1. Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK;2. Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK;1. Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Cambridge, MA 02139, USA;2. Early Clinical Development, Pfizer Worldwide Research and Development, Groton, CT 06340, USA;3. Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA;4. Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, San Diego, CA 92121, USA;1. Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden;2. Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK |
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| Abstract: | Optimisation of compound pharmacokinetics (PK) is an integral part of drug discovery and development. Animal in vivo PK data as well as human and animal in vitro systems are routinely utilised to evaluate PK in humans. In recent years machine learning and artificial intelligence (AI) emerged as a major tool for modelling of in vivo animal and human PK, enabling prediction from chemical structure early in drug discovery, and therefore offering opportunities to guide the design and prioritisation of molecules based on relevant in vivo properties and, ultimately, predicting human PK at the point of design. This review presents recent advances in machine learning and AI models for in vivo animal and human PK for small-molecule compounds as well as some examples for antibody therapeutics. |
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| Keywords: | In vivo animal pharmacokinetics Human pharmacokinetics Machine learning Artificial intelligence |
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