{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T17:33:01Z","timestamp":1791567181912,"version":"4.3.4"},"reference-count":174,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceutics"],"abstract":"<jats:p>Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug\u2019s physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine\/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.<\/jats:p>","DOI":"10.3390\/pharmaceutics16080978","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T08:40:04Z","timestamp":1721896804000},"page":"978","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6016-634X","authenticated-orcid":false,"given":"Yehuda","family":"Arav","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, Israeli Institute for Biological Research, P.O. Box 19, Ness-Ziona 7410001, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alqahtani, M.S., Kazi, M., Alsenaidy, M.A., and Ahmad, M.Z. (2021). Advances in Oral Drug Delivery. Front. Pharmacol., 12.","DOI":"10.3389\/fphar.2021.618411"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4893","DOI":"10.1021\/acs.jcim.9b00460","article-title":"Prediction of Oral Bioavailability in Rats: Transferring Insights from in Vitro Correlations to (Deep) Machine Learning Models Using in Silico Model Outputs and Chemical Structure Parameters","volume":"59","author":"Schneckener","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Homayun, B., Lin, X., and Choi, H.J. (2019). Challenges and recent progress in oral drug delivery systems for biopharmaceuticals. 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