Key Laboratory of Drug Metabolism and Pharmacokinetics, School of Pharmacy, China Pharmaceutical University, 24 Tongjiaxiang Rd, Nanjing, 210009, People's Republic of China.
Jiangsu Key Laboratory of Carcinogenesis and Intervention, School of Basic Medical Sciences and Clinical Pharmacy, China Pharmaceutical University, Nanjing, People's Republic of China.
Clin Pharmacokinet. 2024 Aug;63(8):1147-1165. doi: 10.1007/s40262-024-01404-0. Epub 2024 Aug 5.
In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug-drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate.
This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes.
This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( or ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of and , the accuracy of DDI predictions, and the false-negative rate of risk scale.
First, the reliability of the in vivo and values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C-t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5 × margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives.
This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments.
在临床实践中，大量潜在的药物组合需要快速准确地评估药代动力学药物-药物相互作用（DDI），并提供相应的调整建议。目前，临床 DDI 评估的方法主要依赖于从临床试验数据进行基本推断。然而，由于这些方法没有全面考虑各种关键因素，如抑制剂的抑制强度、剂量和类型，以及底物的代谢分数和肠道可用性，因此其准确性受到限制。
本研究旨在提出一种高效准确的临床药代动力学介导的 DDI 评估工具，全面考虑抑制剂的抑制强度和剂量、底物的肠道可用性和代谢分数对 DDI 结果的影响。
本研究侧重于细胞色素 P450 3A4 酶抑制引起的 DDI，利用广泛的临床试验数据建立一种计算底物代谢分数和肠道可用性以及抑制剂浓度和抑制强度（ 或 ）的方法。然后，使用这些参数预测涉及 33 种底物和 20 种抑制剂的 DDI 结果。我们还定义了底物的风险指数和抑制剂的强度指数，以建立临床 DDI 风险量表。参数计算的训练集由 73 项临床试验组成。验证集包括 89 项涉及 53 种药物的临床 DDI 试验，用于评估体内 值和 的可靠性、DDI 预测的准确性以及风险量表的假阴性率。
首先，使用基本静态模型评估了本研究中计算的体内 值和 的可靠性。与其他方法获得的值相比，本研究的值显示出较低的几何平均倍差和均方根误差。此外，将这些值纳入基于生理学的药代动力学-DDI 模型，当底物的代谢酶被抑制时，有助于更好地拟合 C-t 曲线。其次，与验证集中的临床观察相比，研究药物的曲线下面积比预测值在 81%的情况下在 1.5 倍误差范围内。最后，本研究中开发的临床 DDI 风险量表仅以 5.6%的严重假阴性发生率预测验证集中的实际风险。
本研究提供了一种快速准确的方法来评估临床实践中药代动力学介导的 DDI 风险，为合理联合用药和剂量调整提供了基础。
