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The PDODD panel contained 20 biomarkers, and 13 renal, hepatic, diabetes, and cardiac disease status variables. Demographic characteristics, anthropometric measurements (body weight, body surface area, waist circumference), blood (plasma volume, albumin), renal (creatinine, glomerular filtration rate, urine flow, and urine albumin to creatinine ratio), and hepatic (R\u2010value, hepatic steatosis index, drug\u2010induced liver injury index), blood cell (systemic inflammation index, red cell, lymphocyte, neutrophils, and platelet counts) biomarkers, and medical questionnaire responses from the National Health and Nutrition Examination Survey (NHANES) were included. The tabular VAE (TVAE) generative model was implemented with the Synthetic Data Vault Python library. The joint distributions of the generated data vs. test data were compared using graphical univariate, bivariate, and multidimensional projection methods and distribution proximity measures. The PDODD biomarkers related to disease progression were altered as expected in renal, hepatic, diabetes, and cardiac diseases. The continuous PDODD panel variables generated by the TVAE satisfactorily approximated the distribution in the test data. The TVAE\u2010generated distributions of some discrete variables deviated from the test data distribution. The age distribution of TVAE\u2010generated continuous variables was similar to the test data. The TVAE algorithm demonstrated potential as an AI model for continuous PDODD and could be useful for generating virtual populations for clinical trial simulations.<\/jats:p>","DOI":"10.1111\/cts.13872","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T13:27:23Z","timestamp":1719840443000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Variational autoencoders for generative modeling of drug dosing determinants in renal, hepatic, metabolic, and cardiac disease states"],"prefix":"10.1111","volume":"17","author":[{"given":"Raginee R.","family":"Titar","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Sciences University at Buffalo, The State University of New York  Buffalo New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murali","family":"Ramanathan","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences University at Buffalo, The State University of New York  Buffalo New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,7]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"National Institutes of Health.The promise of precision medicine. 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