In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data.
In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data.
复制标题
缺乏临床药代动力学实验数据的人类口服候选药物的药代动力学特征的计算机预测。
DOI：
10.1007/s13318-022-00758-9
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发表时间：
2022
影响因子：
1.9
通讯作者：
Wang,Junmei
中科院分区：
文献类型：
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作者：
Zhai,Jingchen;Ji,Beihong;Liu,Shuhan;Zhang,Yuzhao;Cai,Lianjin;Wang,Junmei
背景和目的计算机模拟方法，可以产生高质量的生理为基础的药代动力学（PBPK）模型的任意候选药物是非常需要的，以选择可开发的候选药物，逃避药物磨损，因为穷人的药代动力学曲线。本研究的目的是开发一种新的协议，初步预测的PBPK模型的基础上的一个结构相似的模板药物的靶药物的浓度分布，通过结合两个软件平台PBPK建模，Simplified模拟器和ADMET Predictor.MethodsThe方法进行了评估，利用13个药物对18种药物在Simplified软件的内置数据库。所有药物对的Tanimoto评分（TS）均不低于0.5。由于药物对中的每种药物都可以作为靶标和模板，因此在这项工作中研究了26组药物。通过逐步将模板药物的相应参数替换为目标药物的ADMET Predictor预测值，构建了目标药物的三个版本（V1，V2和V3）模型。V1代表分子量（MW）的替代，V2包括参数MW、血浆中未结合分数（fu）、血液-血浆分配比（B/P）、辛醇-缓冲液分配系数的对数（logPo：w）和酸解离常数（pKa）的替代。在V3中，修改了所有上述参数以及人空肠有效渗透性（Peff）、Vd和细胞色素P450（CYP）代谢参数（Km、Vmax或克林特）。归一化均方根误差（NRMSE）被用于评价的模型performance.ResultsWe发现，三个版本的模型的性能取决于药物对的结构相似性。对于第I组药物对（TS ≤ 0.7），V2和V3在NRMSE方面的表现优于V1;对于第II组药物对（0.7 < TS ≤ 0.9），10个V3模型中有8个模型的NRMSE < 0.2，我们采用该临界值来判断模拟的浓度-时间（C-T）曲线是否令人满意。V3优于V1和V2版本。对于属于组III的两个药物对（TS > 0.9），V2优于V1和V3，表明更多不必要的替换会降低PBPK模型的性能。我们还研究了如何预测准确性的ADMET预测器，以及它与Simplified的合作影响使用Simplified.ConclusionIn结论构建的PBPK模型的质量，我们产生了实用的指导应用两个主流软件包，ADMET预测器和Simplified，构建PBPK模型的药物或药物候选人，缺乏ADME参数的模型构建。
Backgrounds and ObjectivesIn silico methods which can generate high-quality physiologically based pharmacokinetic (PBPK) models for arbitrary drug candidates are greatly needed to select developable drug candidates that escape drug attrition because of the poor pharmacokinetic profile. The purpose of this study is to develop a novel protocol to preliminarily predict the concentration profile of a target drug based on the PBPK model of a structurally similar template drug by combining two software platforms for PBPK modeling, the SimCYP simulator and ADMET Predictor.MethodsThe method was evaluated by utilizing 13 drug pairs from 18 drugs in the built-in database of the SimCYP software. All drug pairs have Tanimoto scores (TS) no less than 0.5. As each drug in a drug pair can serve as both target and template, 26 sets were studied in this work. Three versions (V1, V2 and V3) of models for the target drug were constructed by replacing the corresponding parameters of the template drug step by step with those predicted by ADMET Predictor for the target drug. V1 represents the replacement of molecular weight (MW), V2 includes the replacement of parameter MW, fraction unbound in plasma (fu), blood-to-plasma partition ratio (B/P), logarithm of the octanol-buffer partition coefficient (logPo:w) and acid dissociation constant (pKa). In V3, all above-mentioned parameters as well as human jejunum effective permeability (Peff),Vdand cytochrome P450 (CYP) metabolism parameters (Km,Vmaxor CLint) are modified. Normalized root mean square error (NRMSE) was used for the evaluation of the model performance.ResultsWe found that the performance of the three versions of the models depends on structural similarity of the drug pairs. For Group I drug pairs (TS ≤ 0.7), V2 and V3 performed better than V1 in terms of NRMSE; for Group II drug pairs (0.7 < TS ≤ 0.9), 8 out of 10 V3 models had NRMSE < 0.2, the cutoff we applied to judge whether the simulated concentration-time (C–T) curve was satisfactory or not. V3 outperformed the V1 and V2 versions. For the two drug pairs belonging to Group III (TS > 0.9), V2 outperformed V1 and V3, suggesting more unnecessary replacement can lower the performance of PBPK models. We also investigated how the prediction accuracy of ADMET Predictor as well as its collaboration with SimCYP influences the quality of PBPK models constructed using SimCYP.ConclusionIn conclusion, we generated practical guidance on applying two mainstream software packages, ADMET Predictor and SimCYP, to construct PBPK models for drugs or drug candidates that lack ADME parameters in model construction.