Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans

Abstract

The objectives of this study were (i) to develop a screening-level Quantitative property-property relationship (QPPR) for intrinsic clearance (CLint) obtained from in vivo animal studies and (ii) to incorporate it with human physiology in a PBPK model for predicting the inhalation pharmacokinetics of VOCs. CLint, calculated as the ratio of the in vivo V max (μmol/h/kg bw rat) to the K m (μM), was obtained for 26 VOCs from the literature. The QPPR model resulting from stepwise linear regression analysis passed the validation step (R 2 = 0.8; leave-one-out cross-validation Q 2 = 0.75) for CLint normalized to the phospholipid (PL) affinity of the VOCs. The QPPR facilitated the calculation of CLint (L PL/h/kg bw rat) from the input data on log P ow, log blood: water PC and ionization potential. The predictions of the QPPR as lower and upper bounds of the 95% mean confidence intervals (LMCI and UMCI, resp.) were then integrated within a human PBPK model. The ratio of the maximum (using LMCI for CLint) to minimum (using UMCI for CLint) AUC predicted by the QPPR-PBPK model was 1.36 ± 0.4 and ranged from 1.06 (1,1-dichloroethylene) to 2.8 (isoprene). Overall, the integrated QPPR-PBPK modeling method developed in this study is a pragmatic way of characterizing the impact of the lack of knowledge of CLint in predicting human pharmacokinetics of VOCs, as well as the impact of prediction uncertainty of CLint on human pharmacokinetics of VOCs.

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

“…In silico models have shown potential for predicting these TK parameters with approaches ranging from simple physicochemical property-based equations to quantitative structure–activity relationships (QSARs) to isoenzyme-specific binding models. − However, most models have focused exclusively on predicting these parameters for pharmaceuticals, ,− with prediction outside this domain relatively limited. Additionally, many existing models for Cl int and f up are constructed with proprietary data, descriptors, or software, which limits access to the models and evaluation of prediction quality within a desired chemical space.…”

Evaluating the Impact of Uncertainties in Clearance and Exposure When Prioritizing Chemicals Screened in High-Throughput Assays

“…Presently, in vivo clearance values extrapolated from in vitro studies are used to prioritize chemicals in risk assessment . In addition to in vitro measurements, several approaches exist that attempt to predict clearance mechanisms or rates in an in silico environment using physicochemical descriptors. ,, The present study utilizes a two-step in silico approach that first determines primary clearance mechanism (either hepatic metabolism or renal clearance), and then predicts clearance rate.…”