ArticleFrontiers in pharmacology2024
Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling.
Article in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 25 citations in OpenAlex.
- Machine Learning-Driven Prediction of Dose-Linear Pharmacokinetics: Utilizing Molecular Descriptors to Guide Formulation Strategy.ACS omega · 2026Article
- The innovation disconnect: Can artificial intelligence finally align academic discovery with industry needs?Indian journal of pharmacology · 2026Article
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- Interactions between cytochrome P450 enzymes and traditional Chinese herbs: A comprehensive review.Journal of the Chinese Medical Association : JCMA · 2026Review
- Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty.NPJ systems biology and applications · 2026Article
- Construction, evaluation, and applications of renal barrier-on-a-chip system.Bioactive materials · 2026Review
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
- From Polyphenols to Prodrugs: Bridging the Blood-Brain Barrier with Nanomedicine and Neurotherapeutics.International journal of molecular sciences · 2026Review
- Artificial intelligence-assisted photodynamic diagnosis and photodynamic therapy against cancer.Frontiers in oncology · 2026Review
- AI-powered programmable virtual humans toward human physiologically-based drug discovery.Drug discovery today · 2025Review
- Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Formulation Strategy of BCS-II Drugs by Coupling Mechanistic In-Vitro and Nonclinical In-Vivo Data with PBPK: Fundamentals of Absorption-Dissolution to Parameterization of Modelling and Simulation.AAPS PharmSciTech · 2025Review
- Computational identification of novel natural inhibitors against triple mutant DNA gyrase A in fluoroquinolone-resistantBiochemistry and biophysics reports · 2025Article
- Risks of anti-World journal of gastroenterology · 2025Article
- Prediction of pharmacokinetic/pharmacodynamic properties of aldosterone synthase inhibitors at drug discovery stage using an artificial intelligence-physiologically based pharmacokinetic model.Frontiers in pharmacology · 2025Article
- The Application of Artificial Intelligence in Drug ADME Research.Current drug metabolism · 2025Article
- Drug repurposing of natural depsipeptide fromFrontiers in pharmacology · 2025Article
- Prediction of drug concentrations in humans for long-acting injectable suspensions by a semi-mechanical muscle compartment model: a case study of paliperidone palmitate.Frontiers in pharmacology · 2025Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.