Walker Joseph R, Brown Karen, Rohatagi Shashank, Bathala Mohinder S, Xu Chao, Wickremasingha Prachi K, Salazar Daniel E, Mager Donald E
Daiichi Sankyo Pharma Development, 399 Thornall Street, Edison, NJ 08837, USA.
J Clin Pharmacol. 2009 Oct;49(10):1185-95. doi: 10.1177/0091270009340783.
Quantitative structure-property relationship (QSPR) models were developed to correlate physicochemical properties of structurally unrelated drugs with extent of in vitro binding to colesevelam, and predicted values were compared with drug exposure changes in vivo following coadministration. The binding of 17 drugs to colesevelam was determined by an in vitro dissolution drug-binding assay. Data from several clinical studies in healthy volunteers to support administration of colesevelam in diabetic patients were also collected along with existing in vivo literature data and compared with in vitro results. Steric, electronic, and hydrophobic descriptors were calculated for test compounds, and univariate and partial least squares regression approaches were used to derive QSPR models to evaluate which of the molecular descriptors correlated best with in vitro binding. A quadrant analysis evaluated the correlation between predicted/actual in vitro binding results and the in vivo data. The in vitro binding assay exhibited high sensitivity, identifying those compounds with a low probability of producing relevant in vivo drug interactions. Drug lipophilicity was identified as the primary determinant of in vitro binding to colesevelam by the final univariate and partial least squares models (R(2) = 0.69 and 0.98; Q(2) = 0.48 and 0.59). The in vitro assay and in silico models represent predictive tools that may allow investigators to conduct only informative clinical drug interaction studies with colesevelam.
建立了定量构效关系(QSPR)模型,以关联结构不相关药物的理化性质与体外与考来维仑的结合程度,并将预测值与联合给药后体内的药物暴露变化进行比较。通过体外溶出药物结合试验测定了17种药物与考来维仑的结合。还收集了来自健康志愿者的几项临床研究数据,以支持考来维仑在糖尿病患者中的给药,并与现有的体内文献数据进行比较,并与体外结果进行比较。计算了测试化合物的空间、电子和疏水描述符,并使用单变量和偏最小二乘回归方法推导QSPR模型,以评估哪些分子描述符与体外结合相关性最佳。象限分析评估了预测/实际体外结合结果与体内数据之间的相关性。体外结合试验显示出高灵敏度,可识别那些产生相关体内药物相互作用可能性低的化合物。最终的单变量和偏最小二乘模型确定药物亲脂性是体外与考来维仑结合的主要决定因素(R(2) = 0.69和0.98;Q(2) = 0.48和0.59)。体外试验和计算机模拟模型代表了预测工具,可使研究人员仅进行与考来维仑相关的信息丰富的临床药物相互作用研究。