Laboratory of Drug Metabolism and Pharmacokinetics, Showa Pharmaceutical University, 3-3165 Higashi-tamagawa Gakuen, Machida, Tokyo 194-8543, Japan.
Fujitsu Kyusyu Systems, Higashi-hie, Hakata-ku, Fukuoka 812-0007, Japan.
Chem Res Toxicol. 2021 Feb 15;34(2):507-513. doi: 10.1021/acs.chemrestox.0c00336. Epub 2021 Jan 12.
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 reported blood substance concentration values in animals. The purpose of the present study was to estimate these three input pharmacokinetic parameters using a machine learning algorithm applied to a broad range of chemical properties obtained from several cheminformatics software tools. These estimated 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 generated by 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 and estimated input parameters were well correlated ( ≥ 0.83). In summary, by using PBPK models consisting of chemical receptor (gut), metabolizing (liver), excreting (kidney), and central (main) compartments with -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.
最近开发的计算模型可以估计大鼠体内的血浆、肝和肾中的工业化学物质浓度。通常，简化的生理相关药代动力学（PBPK）模型系统的输入参数值（即吸收速率常数、全身循环体积和肝内在清除率）是经过计算得出的，以与动物血液物质浓度的实测或报告值拟合度最佳。本研究的目的是使用机器学习算法估算这三个药代动力学参数，该算法应用于从几种化学信息学软件工具获得的广泛的化学性质。然后将这些估算的参数纳入 PBPK 模型中，以预测大鼠体内的内暴露。采用这种方法，我们为具有广泛化学结构的 246 种药物、食品成分和工业化学品建立了简化的 PBPK 模型。我们之前已经为其中 158 种物质生成了 PBPK 模型，而对于文献中提供了浓度系列数据的 88 种物质，则是新建模的。吸收速率常数、全身循环体积和肝内在清除率的值可以通过包含 14 到 26 个物理化学性质的方程生成。经过虚拟口服给药后，使用传统确定和估算的输入参数的大鼠 PBPK 模型中 246 种化合物在血浆、肝脏和肾脏中的输出浓度值相关性很好（≥0.83）。总之，使用由化学受体（肠道）、代谢（肝脏）、排泄（肾脏）和中央（主要）隔室组成的 PBPK 模型，并由衍生的输入参数构成，可以对新化学物质进行正向剂量测定，从而提供口服给药后药物和化学物质的血浆/组织浓度，从而有助于评估血液毒性、肝毒性或肾毒性的潜力作为风险评估的一部分。
