Artificial intelligence modelling of tyrosine kinase inhibitors at risk of malabsorption and bioavailability-enhancing strategies.

IF 3.1 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Daan W Huntjens, Olivier J M Béquignon, Stefanie D Krens, Mark Löwenberg, Martine E D Chamuleau, Remco J Molenaar, Anita M G Kramers, Marianne A Kuijvenhoven, Imke H Bartelink
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引用次数: 0

Abstract

Aims: The 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.

Methods: A 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.

Results: Two 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.

Conclusion: We 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.

酪氨酸激酶抑制剂在吸收不良风险和生物利用度增强策略中的人工智能建模。
目的:该研究旨在预测和改善有吸收不良问题的患者对酪氨酸激酶抑制剂(TKIs)的吸收,特别是那些接受过减肥手术或正在使用质子泵抑制剂的患者。该研究包括两个主要组成部分:开发人工智能(AI)模型,以识别易导致这些患者吸收减少的tki,以及基于两种临床情况应用病例特异性吸收增强。方法:利用来自13785种化合物的ph依赖性溶解度数据,应用全连接神经网络来识别TKIs在绕过胃时生物利用度降低的风险。通过将AI溶解度估计与质子泵抑制剂对临床暴露的影响相关联,评估胃酸抑制剂对71种经批准的TKIs生物利用度的临床影响。此外,吸收增强在2种临床情况下应用,治疗药物监测评估增强的有效性。结果:开发了两个AI模型来预测酸性和中性pH值之间的分子水溶性差异,并捕获健康和有吸收不良问题的患者可用于肠道吸收的浓度。捕获酸性和中性pH之间溶解度差异的人工智能模型显示出预测能力,Pearson相关系数为0.95,决定系数为0.90。它预测酸性和中性pH值之间的溶解度差异与临床生物利用度有关(P结论:我们开发了一个基于人工智能的模型,可以预测TKIs在绕过胃或与胃酸抑制剂一起使用时存在吸收不良风险。具有高吸收不良风险的药物可以通过各种方法来提高生物利用度。
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来源期刊
CiteScore
6.30
自引率
8.80%
发文量
419
审稿时长
1 months
期刊介绍: Published on behalf of the British Pharmacological Society, the British Journal of Clinical Pharmacology features papers and reports on all aspects of drug action in humans: review articles, mini review articles, original papers, commentaries, editorials and letters. The Journal enjoys a wide readership, bridging the gap between the medical profession, clinical research and the pharmaceutical industry. It also publishes research on new methods, new drugs and new approaches to treatment. The Journal is recognised as one of the leading publications in its field. It is online only, publishes open access research through its OnlineOpen programme and is published monthly.
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