Abstract

Traditional toxicity risk assessment approaches have until recently focussed mainly on histochemical readouts for cell death. Modern toxicology methods attempt to deduce a mechanistic understanding of pathways involved in the development of toxicity, by using transcriptomics and other big data-driven methods such as high-content screening. Here, we used a recently described optimised method to differentiate human induced pluripotent stem cells (hiPSCs) to hepatocyte-like cells (HLCs), to assess their potential to classify hepatotoxic and non-hepatotoxic chemicals and their use in mechanistic toxicity studies. The iPSC-HLCs could accurately classify chemicals causing acute hepatocellular injury, and the transcriptomics data on treated HLCs obtained by TempO-Seq technology linked the cytotoxicity to cellular stress pathways, including oxidative stress and unfolded protein response (UPR). Induction of these stress pathways in response to amiodarone, diclofenac, and ibuprofen, was demonstrated to be concentration and time dependent. The transcriptomics data on diclofenac-treated HLCs were found to be more sensitive in detecting differentially expressed genes in response to treatment, as compared to existing datasets of other diclofenac-treated in vitro hepatocyte models. Hence iPSC-HLCs generated by transcription factor overexpression and in metabolically optimised medium appear suitable for chemical toxicity detection as well as mechanistic toxicity studies.

Highlights

  • Chemical safety assessments are crucial to the research and development pipeline of pharmaceutical, nanomaterial, agrochemical, and cosmetic industries

  • SBAD2-3x-AAGLY-hepatocyte-like cells (HLCs) showed lower BFC metabolization than HLC progeny from the 3x-human embryonic stem cell (hESC) line we described previously [15], possibly due to donor-specific differences

  • primary human hepatocytes (PHHs) culture models suffer from inter-individual variation, rapid dedifferentiation and loss of mature hepatocyte characteristics

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Summary

Introduction

Chemical safety assessments are crucial to the research and development pipeline of pharmaceutical, nanomaterial, agrochemical, and cosmetic industries. Most late-stage pre-clinical studies and industrial risk assessment approaches depend upon the use of whole-animal models, such as rodents. Differences in phase I and II metabolising enzymes and transporters [1,2] negatively impact risk assessment accuracy and extrapolation to chemical responses in humans. This is a major reason for the high cost and poor efficiency of drug development [3]. Animal-free chemical safety evaluation models need to be developed, that sufficiently predict human toxicological responses and can be used by industry and regulatory bodies

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