Biology subjects

Williams, D. P.

Publications and source records attributed to Williams, D. P..

3 recordsLinked to original sources

bioRxiv2025

Hepatic cellular stress response pathways exhibit species differences in basal and inducible activity

Cellular stress response pathways such as the NRF2 oxidative stress response, endoplasmic reticulum (ER) stress response and macroautophagy afford protection against many forms of drug toxicity, including the liver toxicity associated with the formation of reactive drug metabolites. To maximise the translatability of preclinical toxicology studies, an understanding of the relative hepatic stress response capacities of humans and widely-used preclinical animal species is vital. In control liver tissue, the basal gene and protein expression of stress response pathway components was found to be greater in rodents than non-rodent preclinical species and humans. In addition, following in vitro exposure to pharmacological modulators of the NRF2 and ER stress responses, rodent hepatocytes generally displayed a greater capacity, relative to those of non-rodent preclinical species and humans, for adaptation to cellular stress. Consistent with the reported lower concordance of drug toxicity between humans and rats, the latter displayed a greater level of Torin1-induced autophagic flux than all other species, while the robust transcriptional responses to thapsigargin-induced endoplasmic reticulum stress and Bardoxolone- or Ki696-mediated NRF2 activation were comparable between mouse and rat hepatocytes. In all, our results indicate that rodent preclinical species possess a greater basal and adaptive hepatic capacity for mitigation of chemical insult than non- rodent preclinical species and humans. This study represents the first to provide a comprehensive comparison of stress response pathway capacity of humans and the animal species most commonly used for preclinical drug safety assessment. Our findings can be used to inform the selection of species for safety testing of drugs with a liability for reactive metabolite-mediated liver toxicity.

pharmacology and toxicology↗

bioRxiv2024

Improved Early Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data

Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. The existing suite of in vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing in silico prediction of DILI because it allows for the evaluation of large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predicts nine proxy-DILI labels and then uses them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILIst dataset and tested on a held-out external test set of 223 compounds from DILIst dataset. The best model, DILIPredictor, attained an AUC-ROC of 0.79. This model enabled the detection of top 25 toxic compounds compared to models using only structural features (2.68 LR+ score). Using feature interpretation from DILIPredictor, we were able to identify the chemical substructures causing DILI as well as differentiate cases DILI is caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as non-toxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity as well as the potential for mechanism evaluation. DILIPredictor is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download and local implementation via https://pypi.org/project/dilipred/. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/575128v2_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@1b3346org.highwire.dtl.DTLVardef@1c3460dorg.highwire.dtl.DTLVardef@12c38cdorg.highwire.dtl.DTLVardef@455aee_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

A Bayesian neural network for toxicity prediction

Predicting the toxicity of a compound preclinically enables better decision making, thereby reducing development costs and increasing patient safety. It is a complex issue, but in vitro assays and physico-chemical properties of compounds can be used to predict clinical toxicity. Neural networks (NNs) are a popular predictive tool due to their flexibility and ability to model non-linearities, but they are prone to overfitting and therefore are not recommended for small data sets. Furthermore, they dont quantify uncertainty in the predictions. Bayesian neural networks (BNNs) are able to avoid these pitfalls by using prior distributions on the parameters of a NN model and representing uncertainty about the predictions in the form of a distribution. We model the severity of drug-induced liver injury (DILI) to provide an example of a BNN performing better than a traditional but less flexible proportional odds logistic regression (POLR) model. We use appropriate metrics to evaluate predictions of the ordinal data type. To demonstrate the effect of a hierarchical prior for BNNs as an alternative to hyperparameter optimisation for NNs, we compare the performance of a BNN against NNs with dropout or penalty regularisation. We reduce the task to multiclass classification in order to be able to perform this comparison. A BNN trained for the multiclass classification produces poorer results than a BNN that captures the order. The current work lays a foundation for more complex models built on larger datasets, but can already be adopted by safety pharmacologists for risk quantification.

pharmacology and toxicology↗