In Vitro and In Vivo Biotransformation Profiling of 6PPD-Quinone toward Their Detection in Human Urine.

The antioxidant N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) and its oxidized quinone product 6PPD-quinone (6PPD-Q) in rubber have attracted attention due to the ecological risk that they pose. Both 6PPD and 6PPD-Q have been detected in various environments that humans cohabit. However, to date, a clear understanding of the biotransformation of 6PPD-Q and a potential biomarker for exposure in humans are lacking. To address this issue, this study presents a comprehensive analysis of the extensive biotransformation of 6PPD-Q across species, encompassing both in vitro and in vivo models. We have tentatively identified 17 biotransformation metabolites in vitro, 15 in mice in vivo, and confirmed the presence of two metabolites in human urine samples. Interestingly, different biotransformation patterns were observed across species. Through semiquantitative analysis based on peak areas, we found that almost all 6PPD-Q underwent biotransformation within 24 h of exposure in mice, primarily via hydroxylation and subsequent glucuronidation. This suggests a rapid metabolic processing of 6PPD-Q in mammals, underscoring the importance of identifying effective biomarkers for exposure. Notably, monohydroxy 6PPD-Q and 6PPD-Q-O-glucuronide were consistently the most predominant metabolites across our studies, highlighting monohydroxy 6PPD-Q as a potential key biomarker for epidemiological research. These findings represent the first comprehensive data set on 6PPD-Q biotransformation in mammalian systems, offering insights into the metabolic pathways involved and possible exposure biomarkers.

MetNC: Predicting Metabolites in vivo for Natural Compounds.

Natural compounds (NCs) undergo complicated biotransformation in vivo to produce diverse forms of metabolites dynamically, many of which are of high medicinal value. Predicting the profiles of chemical products may help to narrow down possible candidates, yet current computational methods for predicting biotransformation largely focus on synthetic compounds. Here, we proposed a method of MetNC, a tailor-made method for NC biotransformation prediction, after exploring the overall patterns of NC in vivo metabolism. Based on 850 pairs of the biotransformation dataset validated by comprehensive in vivo experiments with sourcing compounds from medicinal plants, MetNC was designed to produce a list of potential metabolites through simulating in vivo biotransformation and then prioritize true metabolites into the top list according to the functional groups in compound structures and steric hindrance around the reaction sites. Among the well-known peers of GLORYx and BioTransformer, MetNC gave the highest performance in both the metabolite coverage and the ability to short-list true products. More importantly, MetNC seemed to display an extra advantage in recommending the microbiota-transformed metabolites, suggesting its potential usefulness in the overall metabolism estimation. In summary, complemented to those techniques focusing on synthetic compounds, MetNC may help to fill the gap of natural compound metabolism and narrow down those products likely to be identified in vivo.

Development of human biotransformation QSARs and application for PBT assessment refinement.

Toxicokinetics heavily influence chemical toxicity as the result of Absorption, Distribution, Metabolism (Biotransformation) and Elimination (ADME) processes. Biotransformation (metabolism) reactions can lead to detoxification or, in some cases, bioactivation of parent compounds to more toxic chemicals. Moreover, biotransformation has been recognized as a key process determining chemical half-life in an organism and is thus a key determinant for bioaccumulation assessment for many chemicals. This study addresses the development of QSAR models for the prediction of in vivo whole body human biotransformation (metabolism) half-lives measured or empirically-derived for over 1000 chemicals, mainly represented by pharmaceuticals. Models presented in this study meet regulatory standards for fitting, validation and applicability domain. These QSARs were used, in combination with literature models for the prediction of biotransformation half-lives in fish, to refine the screening of the potential PBT behaviour of over 1300 Pharmaceuticals and Personal Care Products (PPCPs). The refinement of the PBT screening allowed, among others, for the identification of PPCPs, which were predicted as PBTs on the basis of their chemical structure, but may be easily biotransformed. These compounds are of lower concern in comparison to potential PBTs characterized by large predicted biotransformation half-lives.

In vitro and in vivo biotransformation of WMS-1410, a potent GluN2B selective NMDA receptor antagonist.

Structural modification of the GluN2B selective NMDA receptor antagonist ifenprodil led to the 3-benzazepine WMS-1410 with similar GluN2B affinity but higher receptor selectivity. Herein the in vitro and in vivo biotransformation of WMS-1410 is reported. Incubation of WMS-1410 with rat liver microsomes and different cofactors resulted in four hydroxylated phase I metabolites, two phase II metabolites and five combined phase I/II metabolites. With exception of catechol 4, these metabolites were also identified in the urine of a rat treated with WMS-1410. However the metabolites 7, 8 and 12 clearly show that the catechol metabolite 4 was also formed in vivo. As shown for ifenprodil the phenol of WMS-1410 represents the metabolically most reactive structural element. The biotransformation of WMS-1410 is considerably slower than the biotransformation of ifenprodil indicating a higher metabolic stability. From the viewpoint of metabolic stability the bioisosteric replacement of the phenol of WMS-1410 by a metabolically more stable moiety should be favourable.

Biotransformation model of neutral and weakly polar organic compounds in fish incorporating internal partitioning.

A model for whole-body in vivo biotransformation of neutral and weakly polar organic chemicals in fish is presented. It considers internal chemical partitioning and uses Abraham solvation parameters as reactivity descriptors. It assumes that only chemicals freely dissolved in the body fluid may bind with enzymes and subsequently undergo biotransformation reactions. Consequently, the whole-body biotransformation rate of a chemical is retarded by the extent of its distribution in different biological compartments. Using a randomly generated training set (n = 64), the biotransformation model is found to be: log (HLφfish ) = 2.2 (±0.3)B - 2.1 (±0.2)V - 0.6 (±0.3) (root mean square error of prediction [RMSE] = 0.71), where HL is the whole-body biotransformation half-life in days, φfish is the freely dissolved fraction in body fluid, and B and V are the chemical's H-bond acceptance capacity and molecular volume. Abraham-type linear free energy equations were also developed for lipid-water (Klipidw ) and protein-water (Kprotw ) partition coefficients needed for the computation of φfish from independent determinations. These were found to be 1) log Klipidw = 0.77E - 1.10S - 0.47A - 3.52B + 3.37V + 0.84 (in Lwat /kglipid ; n = 248, RMSE = 0.57) and 2) log Kprotw = 0.74E - 0.37S - 0.13A - 1.37B + 1.06V - 0.88 (in Lwat /kgprot ; n = 69, RMSE = 0.38), where E, S, and A quantify dispersive/polarization, dipolar, and H-bond-donating interactions, respectively. The biotransformation model performs well in the validation of HL (n = 424, RMSE = 0.71). The predicted rate constants do not exceed the transport limit due to circulatory flow. Furthermore, the model adequately captures variation in biotransformation rate between chemicals with varying log octanol-water partitioning coefficient, B, and V and exhibits high degree of independence from the choice of training chemicals. The present study suggests a new framework for modeling chemical reactivity in biological systems.