Grañana-Castillo Sandra, Williams Angharad, Pham Thao, Khoo Saye, Hodge Daryl, Akpan Asangaedem, Bearon Rachel, Siccardi Marco
Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK.
Institute of Life Course and Medical Sciences, University of Liverpool and Liverpool University Hospitals NHS FT, Liverpool, UK.
Clin Pharmacokinet. 2023 May;62(5):737-748. doi: 10.1007/s40262-023-01229-3. Epub 2023 Mar 29.
Metabolic inducers can expose people with polypharmacy to adverse health outcomes. A limited fraction of potential drug-drug interactions (DDIs) have been or can ethically be studied in clinical trials, leaving the vast majority unexplored. In the present study, an algorithm has been developed to predict the induction DDI magnitude, integrating data related to drug-metabolising enzymes.
The area under the curve ratio (AUC) resulting from the DDI with a victim drug in the presence and absence of an inducer (rifampicin, rifabutin, efavirenz, or carbamazepine) was predicted from various in vitro parameters and then correlated with the clinical AUC (N = 319). In vitro data including fraction unbound in plasma, substrate specificity and induction potential for cytochrome P450s, phase II enzymes and uptake, and efflux transporters were integrated. To represent the interaction potential, the in vitro metabolic metric (IVMM) was generated by combining the fraction of substrate metabolised by each hepatic enzyme of interest with the corresponding in vitro fold increase in enzyme activity (E) value for the inducer.
Two independent variables were deemed significant and included in the algorithm: IVMM and fraction unbound in plasma. The observed and predicted magnitudes of the DDIs were categorised accordingly: no induction, mild, moderate, and strong induction. DDIs were assumed to be well classified if the predictions were in the same category as the observations, or if the ratio between these two was < 1.5-fold. This algorithm correctly classified 70.5% of the DDIs.
This research presents a rapid screening tool to identify the magnitude of potential DDIs utilising in vitro data which can be highly advantageous in early drug development.
代谢诱导剂可能使服用多种药物的人面临不良健康后果。在临床试验中，仅对一小部分潜在的药物相互作用（DDIs）进行了研究，或者从伦理角度可以进行研究，而绝大多数情况尚未得到探索。在本研究中，开发了一种算法来预测诱导性药物相互作用的强度，该算法整合了与药物代谢酶相关的数据。
根据各种体外参数预测在存在和不存在诱导剂（利福平、利福布汀、依非韦伦或卡马西平）的情况下，与被影响药物发生药物相互作用时的曲线下面积比（AUC），然后将其与临床AUC（N = 319）进行关联。整合了体外数据，包括血浆中未结合分数、细胞色素P450、II相酶以及摄取和外排转运体的底物特异性和诱导潜力。为了表示相互作用潜力，通过将每种感兴趣的肝酶代谢的底物分数与诱导剂相应的体外酶活性（E）值的倍数增加相结合，生成了体外代谢指标（IVMM）。
两个自变量被认为具有显著性并纳入算法：IVMM和血浆中未结合分数。根据药物相互作用的观察和预测强度进行分类：无诱导、轻度、中度和强诱导。如果预测与观察结果属于同一类别，或者两者之间的比值<1.5倍，则认为药物相互作用得到了很好的分类。该算法正确分类了70.5%的药物相互作用。
本研究提出了一种快速筛选工具，利用体外数据识别潜在药物相互作用的强度，这在药物早期开发中可能具有很大优势。
