Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors

The
intrinsic metabolic clearance rate (Clint) and the
fraction of the chemical unbound in plasma (fup) serve as important parameters for high-throughput toxicokinetic
(TK) models, but experimental data are limited for many chemicals.
Open-source quantitative structure–activity relationship (QSAR)
models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated
under the U.S. law, including pharmaceuticals, pesticides, and industrial
chemicals. As a case study to demonstrate their utility, model predictions
served as inputs to the TK component of a risk-based prioritization
approach based on bioactivity/exposure ratios (BERs), in which a BER
< 1 indicates that exposures are predicted to exceed a biological
activity threshold. When applied to a subset of the Tox21 screening
library (6484 chemicals), we found that the proportion of chemicals
with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%)
parameters. Further, when considering only the chemicals in the Tox21
set with in vitro data, there was a high concordance
of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848,
90.4%). Thus, the presented QSARs may be suitable for prioritizing
the risk posed by many chemicals for which measured in vitro TK data are lacking.

内在代谢清除率（intrinsic metabolic clearance rate, Clint）与血浆游离分数（fraction of the chemical unbound in plasma, fup）是高通量毒代动力学（high-throughput toxicokinetic, TK）模型的核心参数，但多数化学品的相关实验数据较为匮乏。针对这两类参数，本研究开发了开源定量构效关系（quantitative structure–activity relationship, QSAR）模型，可为受美国法律法规监管的各类化学品（涵盖药品、农药与工业化学品）提供可靠的计算机模拟预测（in silico）结果。为验证模型的应用价值，研究以基于生物活性/暴露比（bioactivity/exposure ratios, BERs）的风险优先化方法的毒代动力学模块为应用场景，将模型预测结果作为其输入参数；其中当BER＜1时，表明预测暴露量超出了生物活性阈值。将该方法应用于Tox21筛选库的一个子集（共6484种化学品）时，结果显示，采用计算机模拟预测参数或体外实验（in vitro）参数时，BER＜1的化学品占比均为17.5%（分别为1133/6484与148/848）。进一步针对Tox21库中具备体外实验数据的848种化学品开展分析，结果表明，采用计算机模拟预测参数与体外实验参数时，被归类为BER＜1或BER＞1的化学品一致性高达90.4%（767/848）。综上，本研究开发的定量构效关系模型可用于优先评估诸多缺乏实测毒代动力学实验数据的化学品所带来的风险。