Toxicogenomics of precision-cut liver slices for prediction of human liver toxicity

Hepatotoxicity is one of the major causes of adverse drug reactions and withdrawal of drugs from the market. Prediction of hepatotoxicity based on animal models is often not successful due to species differences between animals and humans. Therefore there is a need to screen new drugs for hepatotoxicity in human at an earlier stage. The aim of this study was to validate human precision-cut liver slices (PCLS) as an ex vivo model to predict drug-induced hepatotoxicity and unravel the possible mechanisms involved in human. These PCLS represent a mini “liver-model” that closely resembles the human liver from which it is prepared, with all cell types present in their original tissue matrix configuration. Human PCLS were exposed to hepatotoxicants known to cause liver necrosis, liver cholestasis or idiosyncratic toxicity. Gene expression analysis was performed and identified the genes (among the total of 20,000 genes) that were regulated by the hepatotoxic drugs. Moreover, the role of those regulated genes in signalling pathways, which can play a role in the toxicity, was revealed. We were able to identify gene patterns that could discriminate the different groups of hepatotoxicants. Our results indicate that in the future this human PCLS model can be used to identify and characterize adverse reactions of drugs or chemicals in man. The use of human tissue in addition to animal experiments will contribute to a better prediction of toxicity in humans and also to reduction in the use of experimental animals for toxicology research and drug development

Validation of precision-cut liver slices to study drug-induced cholestasis:A transcriptomics approach

Hepatotoxicity is one of the major reasons for withdrawal of drugs from the market. Therefore, there is a need to screen new drugs for hepatotoxicity in humans at an earlier stage. The aim of this study was to validate human precision-cut liver slices (PCLS) as an ex vivo model to predict drug-induced cholestasis and identify the possible mechanisms of cholestasis-induced toxicity using gene expression profiles. Five hepatotoxicants, which are known to induce cholestasis (alpha-naphthyl isothiocyanate, chlorpromazine, cyclosporine, ethinyl estradiol and methyl testosterone) were used at concentrations inducing low (<30 %) and medium (30-50 %) toxicity, based on ATP content. Human PCLS were incubated with the drugs in the presence of a non-toxic concentration (60 µM) of a bile acid mixture (portal vein concentration and composition) as model for bile acid-induced cholestasis. Regulated genes include bile acid transporters and cholesterol transporters. Pathway analysis revealed that hepatic cholestasis was among the top ten regulated pathways, and signaling pathways such as farnesoid X receptor- and liver X receptor-mediated responses, which are known to play a role in cholestasis, were significantly affected by all cholestatic compounds. Other significantly affected pathways include unfolded protein response and protein ubiquitination implicating the role of endoplasmic reticulum stress. This study shows that human PCLS incubated in the presence of a physiological bile acid mixture correctly reflect the pathways affected in drug-induced cholestasis in the human liver. In the future, this human PCLS model can be used to identify cholestatic adverse drug reactions of new chemical entities

Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

International audienc

Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

International audienc

Pharmacometrics modeling coupled with machine learning for early prediction of survival following atezolizumab monotherapy in non-small cell lung cancer

International audienc

Supporting decision making and early prediction of survival for oncology drug development using a pharmacometrics-machine learning based model

International audienc