ArticleBritish journal of clinical pharmacology2025
Artificial intelligence modelling of tyrosine kinase inhibitors at risk of malabsorption and bioavailability-enhancing strategies.
Article in British journal of clinical pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence modelling of tyrosine kinase inhibitors at risk of malabsorption and bioavailability-enhancing strategies.British journal of clinical pharmacology · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThe study aims to predict and improve the absorption of tyrosine kinase inhibitors (TKIs) in patients with malabsorption issues, particularly those who have undergone bariatric surgery or are using proton-pump inhibitors. The research involves 2 main components: the development of an artificial intelligence (AI) model to identify TKIs that are susceptible to reduced absorption in these patients, and the application of case-specific absorption enhancements based on 2 clinical scenarios.
methodsA fully connected neural network was applied using pH-dependent solubility data from 137 785 compounds to identify TKIs at risk of reduced bioavailability when bypassing the stomach. The clinical impact of gastric acid suppressants on the bioavailability of 71 approved TKIs was evaluated by correlating the AI solubility estimates with the effect of proton-pump inhibitors on clinical exposure. Furthermore, absorption enhancement was applied in 2 clinical scenarios, with therapeutic drug monitoring assessing the effectiveness of the enhancement.
resultsTwo AI models were developed to predict the difference in molecular aqueous solubility between acid and neutral pH and to capture the concentration available for intestinal absorption in healthy and in patients with malabsorption issues. The AI model capturing the solubility difference between acidic and neutral pH demonstrated predictive capabilities, with a Pearson correlation coefficient of .95 and a coefficient of determination of .90. It predicted that the solubility difference, between acidic and neutral pH, was associated with clinical bioavailability (P < .001). Nilotinib, lapatinib and dacomitinib were ranked as TKIs with the highest risk of malabsorption in case of increased stomach pH.
conclusionWe developed an AI-based model that predicts TKIs that are at risk of malabsorption when bypassing the stomach or used with gastric acid suppressants. Drugs with a high malabsorption risk can be enhanced by various methods to improve bioavailability.
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