{"id":2971,"date":"2025-03-05T23:00:00","date_gmt":"2025-03-06T15:11:22","guid":{"rendered":"https:\/\/www.unilim.fr\/p-and-t\/?page_id=2971"},"modified":"2025-03-06T16:15:04","modified_gmt":"2025-03-06T15:15:04","slug":"pharmacokinetic-modeling-and-personalized-medicine","status":"publish","type":"page","link":"https:\/\/www.unilim.fr\/p-and-t\/en\/research\/pharmacokinetic-modeling-and-personalized-medicine\/","title":{"rendered":"Pharmacokinetic Modeling and Personalized Medicine"},"content":{"rendered":"
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Our research group develops innovative mechanistic and data-driven modeling tools to personalize treatments. We integrate multi-source and multi-scale data (genomics, pharmacokinetics, proteomics, etc.) to better understand and predict patients’ responses to drugs.<\/p>\n

We use two main types of models: pharmacokinetic models, which describe the absorption, distribution, metabolism, and excretion of drugs in the body, and machine learning models, which capture complex and nonlinear relationships within the data. These approaches enable the simulation of treatment effects, dose personalization, and therapeutic strategy adjustments based on individual patient responses, with applications in transplantation, infectious diseases, and other fields.<\/p>\n

Our goal is to optimize the benefit-risk balance for each patient through personalized medicine.<\/p>\n <\/div>\n <\/div>\n

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Jean-Baptiste Woillard<\/h4>\n

\n Project manager<\/span> <\/p>\n

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Our team develops tools to personalize treatments by combining pharmacokinetic modeling and machine learning. Pharmacokinetic modeling studies how a drug is absorbed, distributed, and eliminated by the body, helping us better understand its behavior.<\/p>\n

Machine learning comes into play to analyze complex and nonlinear relationships between different data sources, such as genomics, drug concentrations, and therapeutic responses. By leveraging these techniques, we can more accurately predict a patient’s response to treatment.<\/p>\n

This approach helps physicians:<\/p>\n