In Silico Prediction of Input Parameters for Simplified Physiologically Based Pharmacokinetic Models for Estimating Plasma, Liver, and Kidney Exposures in Rats after Oral Doses of 246 Disparate Chemicals
In Silico Prediction of Input Parameters for Simplified Physiologically Based Pharmacokinetic Models for Estimating Plasma, Liver, and Kidney Exposures in Rats after Oral Doses of 246 Disparate Chemicals
复制标题
计算机模拟预测简化的基于生理学的药代动力学模型的输入参数，用于估计大鼠口服 246 种不同化学品后的血浆、肝脏和肾脏暴露
DOI：
10.1021/acs.chemrestox.0c00336
复制
发表时间：
2021
影响因子：
4.1
通讯作者：
Yamazaki Hiroshi
中科院分区：
文献类型：
--
作者：
Kamiya Yusuke;Handa Kentaro;Miura Tomonori;Yanagi Mayu;Shigeta Kazuki;Hina Shiori;Shimizu Makiko;Kitajima Masato;Shono Fumiaki;Funatsu Kimito;Yamazaki Hiroshi
最近开发的计算模型可以估计大鼠血浆，肝脏和肾脏中工业化学品的浓度。通常，输入参数值（即，吸收速率常数、体循环体积和肝固有清除率），以给出与动物体内测定或测定的体内代谢物质浓度值的最佳拟合。本研究的目的是使用机器学习算法来估计这三个输入药代动力学参数，该算法适用于从几种化学信息学软件工具获得的广泛的化学性质。然后将这些计算机估计的参数纳入PBPK模型，用于预测大鼠的内暴露。根据这种方法，为246种药物、食品成分和具有广泛化学结构的工业化学品建立了简化的PBPK模型。我们以前生成的PBPK模型为158这些物质，而88个浓度系列的数据在文献中是新的建模。吸收速率常数、体循环容量和肝脏固有清除率的值可以通过包含14至26个理化性质的硅方程生成。在虚拟口服给药后，使用传统测定和计算机估计的输入参数的大鼠PBPK模型中的246种化合物在血浆、肝脏和肾脏中的输出浓度值具有良好的相关性（r≥ 0.83）。总之，通过使用由硅衍生输入参数内的化学受体（肠道）、代谢（肝脏）、排泄（肾脏）和中央（主要）隔室组成的PBPK模型，新化学品的前向剂量测定可提供口服给药后药物和化学品的血浆/组织浓度，从而有助于估计血液毒性、肝毒性或肾毒性潜力，作为风险评估的一部分。
Recently developed computational models can estimate plasma, hepatic, and renal concentrations of industrial chemicals in rats. Typically, the input parameter values (i.e., the absorption rate constant, volume of systemic circulation, and hepatic intrinsic clearance) for simplified physiologically based pharmacokinetic (PBPK) model systems are calculated to give the best fit to measured or reportedin vivoblood substance concentration values in animals. The purpose of the present study was to estimatein silicothese three input pharmacokinetic parameters using a machine learning algorithm applied to a broad range of chemical properties obtained from several cheminformatics software tools. Thesein silicoestimated parameters were then incorporated into PBPK models for predicting internal exposures in rats. Following this approach, simplified PBPK models were set up for 246 drugs, food components, and industrial chemicals with a broad range of chemical structures. We had previously generated PBPK models for 158 of these substances, whereas 88 for which concentration series data were available in the literature were newly modeled. The values for the absorption rate constant, volume of systemic circulation, and hepatic intrinsic clearance could be generatedin silicoby equations containing between 14 and 26 physicochemical properties. After virtual oral dosing, the output concentration values of the 246 compounds in plasma, liver, and kidney from rat PBPK models using traditionally determined andin silicoestimated input parameters were well correlated (r≥ 0.83). In summary, by using PBPK models consisting of chemical receptor (gut), metabolizing (liver), excreting (kidney), and central (main) compartments within silico-derived input parameters, the forward dosimetry of new chemicals could provide the plasma/tissue concentrations of drugs and chemicals after oral dosing, thereby facilitating estimates of hematotoxic, hepatotoxic, or nephrotoxic potential as a part of risk assessment.