ArticleFrontiers in pharmacology2025
Prediction of pharmacokinetic/pharmacodynamic properties of aldosterone synthase inhibitors at drug discovery stage using an artificial intelligence-physiologically based pharmacokinetic model.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative efficacy and safety of aldosterone synthase inhibitors in hypertension: a Bayesian network meta-analysis of randomised controlled rials.Open heart · 2025Pooled it
- Residual risk in cardiovascular and renal diseases and the potential role of aldosterone synthase inhibitors.Frontiers in cardiovascular medicine · 2026Review
- Applications of PBPK Modeling to Estimate Drug Metabolism and Related ADME Processes in Specific Populations.Pharmaceutics · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this study is to develop an artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) model to predict the pharmacokinetic (PK) and pharmacodynamic (PD) properties of aldosterone synthase inhibitors (ASIs), enabling selection of the right candidate with high potency and good selectivity at the drug discovery stage. On a web-based platform, an AI-PBPK model, integrating machine learning and a classical PBPK model for the PK simulation of ASIs, was developed. Baxdrostat, with the most clinical data available, was selected as the model compound. Following calibration and validation using published data, the model was applied to estimate the PK parameters of Baxdrostat, Dexfadrostat, Lorundrostat, BI689648, and the 11β-hydroxylase inhibitor LCI699. The PD of all five compounds was predicted based on plasma free drug concentrations. The results demonstrated that the PK/PD properties of an ASI could be inferred from its structural formula within a certain error range, providing a reference for early ASI lead compounds screening and optimization. Further validation and refinement of this model will enhance its predictive accuracy and expand its application in drug discovery.
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Registered trials
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 Socratic graph.