Zhivkova Zvetanka Dobreva
Faculty of Pharmacy, Medical University of Sofia, Sofia 1000, Bulgaria.
J Pharm Pharm Sci. 2017;20(0):135-147. doi: 10.18433/J3MG71.
The success of a new drug candidate is determined not only by its efficacy and safety, but also by proper pharmacokinetic behavior. The early prediction of pharmacokinetic parameters could save time and resources and accelerate drug development process. Plasma clearance (CL) is one of the key determinants of drug dosing regimen. The aim of the study is development of quantitative structure - pharmacokinetics relationships (QSPkRs) for the CL.
A dataset consisted of 263 basic drugs, which chemical structures were described by 154 descriptors. Genetic algorithm, stepwise regression and multiple linear regression were used for variable selection and model development. Predictive ability of the models was assessed by internal and external validation. Results. A number of significant QSPkR models for the CL were derived with respect to the primary elimination pathway (renal excretion, metabolism, or CYP3A4 mediated biotransformation), as well for the unbound clearance (CLu). The models were able to predict 52 - 80% of the drugs from external validation sets within the 2-fold error of the experimental values with geometric mean fold error 1.57 - 2.00.
Plasma protein binding was the major restrictive factor for the CL of drugs, primarily cleared by metabolism. The clearance was favored by lipophilicity and several structural features like OH-groups, aromatic rings, large hydrophobic centers, aliphatic groups, connected with electro-negative atoms, and non-substituted aromatic C-atoms. The presence of Cl-atoms and abundance of 6-member aromatic rings or fused rings had negative effect. The presence of ether O-atoms contributed negatively to the CL of both metabolism and renally excreted drugs, and urine excretion was favored by the presence of 3-valence N-atoms. These findings give insight on the main structural features governing plasma CL of basic drugs and could serve as a guide for lead optimization in the drug development process. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
一种新药候选物的成功不仅取决于其疗效和安全性，还取决于适当的药代动力学行为。药代动力学参数的早期预测可以节省时间和资源，并加速药物开发过程。血浆清除率（CL）是药物给药方案的关键决定因素之一。本研究的目的是开发CL的定量构效关系（QSPkRs）。
一个数据集由263种碱性药物组成，其化学结构由154个描述符描述。采用遗传算法、逐步回归和多元线性回归进行变量选择和模型开发。通过内部和外部验证评估模型的预测能力。结果。针对主要消除途径（肾排泄、代谢或CYP3A4介导的生物转化）以及非结合清除率（CLu），推导了许多关于CL的重要QSPkR模型。这些模型能够在实验值的2倍误差范围内预测外部验证集中52%-80%的药物，几何平均倍数误差为1.57-2.00。
血浆蛋白结合是主要通过代谢清除的药物CL的主要限制因素。亲脂性以及一些结构特征，如羟基、芳香环、大的疏水中心、与电负性原子相连的脂肪族基团和未取代的芳香族碳原子，有利于清除。氯原子的存在以及六元芳香环或稠环的丰富对清除有负面影响。醚氧原子的存在对代谢和经肾排泄药物的CL均有负面影响，三价氮原子的存在有利于尿液排泄。这些发现揭示了控制碱性药物血浆CL的主要结构特征，并可为药物开发过程中的先导优化提供指导。本文接受发表后评论。注册读者（见“读者须知”）可通过点击本期内容页面上的摘要进行评论。
