Jinghai J. Xu

Cellular Imaging Predictions of Clinical Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is the most common adverse event causing drug nonapprovals and drug withdrawals. Using drugs as test agents and measuring a panel of cellular phenotypes that are directly linked to key mechanisms of hepatotoxicity, we have developed an in vitro testing strategy that is predictive of many clinical outcomes of DILI. Mitochondrial damage, oxidative stress, and intracellular glutathione, all measured by high content cellular imaging in primary human hepatocyte cultures, are the three most important features contributing to the hepatotoxicity prediction. When applied to over 300 drugs and chemicals including many that caused rare and idiosyncratic liver toxicity in humans, our testing strategy has a true-positive rate of 50-60% and an exceptionally low false-positive rate of 0-5%. These in vitro predictions can augment the performance of the combined traditional preclinical animal tests by identifying idiosyncratic human hepatotoxicants such as nimesulide, telithromycin, nefazodone, troglitazone, tetracycline, sulindac, zileuton, labetalol, diclofenac, chlorzoxazone, dantrolene, and many others. Our findings provide insight to key DILI mechanisms, and suggest a new approach in hepatotoxicity testing of pharmaceuticals.

Synergistic Drug-Cytokine Induction of Hepatocellular Death as an in vitro Approach for the Study of Inflammation-Associated Idiosyncratic Drug Hepatotoxicity

Idiosyncratic drug hepatotoxicity represents a major problem in drug development due to inadequacy of current preclinical screening assays, but recently established rodent models utilizing bacterial LPS co-administration to induce an inflammatory background have successfully reproduced idiosyncratic hepatotoxicity signatures for certain drugs. However, the low-throughput nature of these models renders them problematic for employment as preclinical screening assays. Here, we present an analogous, but high-throughput, in vitro approach in which drugs are administered to a variety of cell types (primary human and rat hepatocytes and the human HepG2 cell line) across a landscape of inflammatory contexts containing LPS and cytokines TNF, IFN gamma, IL-1 alpha, and IL-6. Using this assay, we observed drug-cytokine hepatotoxicity synergies for multiple idiosyncratic hepatotoxicants (ranitidine, trovafloxacin, nefazodone, nimesulide, clarithromycin, and telithromycin) but not for their corresponding non-toxic control compounds (famotidine, levofloxacin, buspirone, and aspirin). A larger compendium of drug-cytokine mix hepatotoxicity data demonstrated that hepatotoxicity synergies were largely potentiated by TNF, IL-1 alpha, and LPS within the context of multi-cytokine mixes. Then, we screened 90 drugs for cytokine synergy in human hepatocytes and found that a significantly larger fraction of the idiosyncratic hepatotoxicants (19%) synergized with a single cytokine mix than did the non-hepatotoxic drugs (3%). Finally, we used an information theoretic approach to ascertain especially informative subsets of cytokine treatments for most highly effective construction of regression models for drug- and cytokine mix-induced hepatotoxicities across these cell systems. Our results suggest that this drug-cytokine co-treatment approach could provide a useful preclinical tool for investigating inflammation-associated idiosyncratic drug hepatotoxicity.

A Predictive Ligand-Based Bayesian Model for Human Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both preapproval and postapproval stages. There has been increased interest in developing predictive in vivo, in vitro, and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study, we applied machine learning, a Bayesian modeling method with extended connectivity fingerprints and other interpretable descriptors. The model that was developed and internally validated (using a training set of 295 compounds) was then applied to a large test set relative to the training set (237 compounds) for external validation. The resulting concordance of 60%, sensitivity of 56%, and specificity of 67% were comparable to results for internal validation. The Bayesian model with extended connectivity functional class fingerprints of maximum diameter 6 (ECFC_6) and interpretable descriptors suggested several substructures that are chemically reactive and may also be important for DILI-causing compounds, e.g., ketones, diols, and α-methyl styrene type structures. Using Smiles Arbitrary Target Specification (SMARTS) filters published by several pharmaceutical companies, we evaluated whether such reactive substructures could be readily detected by any of the published filters. It was apparent that the most stringent filters used in this study, such as the Abbott alerts, which captures thiol traps and other compounds, may be of use in identifying DILI-causing compounds (sensitivity 67%). A significant outcome of the present study is that we provide predictions for many compounds that cause DILI by using the knowledge we have available from previous studies. These computational models may represent cost-effective selection criteria before in vitro or in vivo experimental studies.

Role of Hepatic Transporters in the Disposition and Hepatotoxicity of a HER2 Tyrosine Kinase Inhibitor CP-724,714

CP-724,714, a potent and selective orally active HER2 tyrosine kinase inhibitor, was discontinued from clinical development due to unexpected hepatotoxicity in cancer patients. Based on the clinical manifestation of the toxicity, CP-724,714 likely exerted its hepatotoxicity via both hepatocellular injury and hepatobiliary cholestatic mechanisms. The direct cytotoxic effect, hepatobiliary disposition of CP-724,714, and its inhibition of active canalicular transport of bile constituents were evaluated in established human hepatocyte models and in vitro transporter systems. CP-724,714 exhibited direct cytotoxicity using human hepatocyte imaging assay technology with mitochondria identified as a candidate organelle for its off-target toxicity. Additionally, CP-724,714 was rapidly taken up into human hepatocytes, partially via an active transport process, with an uptake clearance approximately fourfold higher than efflux clearance. The major human hepatic uptake transporter, OATP1B1, and efflux transporters, multidrug resistance protein 1 (MDR1) and breast cancer resistance protein, were involved in hepatobiliary clearance of CP-724,714. Furthermore, CP-724,714 displayed a concentration-dependent inhibition of cholyl-lysyl fluorescein and taurocholate (TC) efflux into canaliculi in cryopreserved and fresh cultured human hepatocytes, respectively. Likewise, CP-724,714 inhibited TC transport in membrane vesicles expressing human bile salt export pump with an IC(50) of 16 microM. Finally, CP-724,714 inhibited the major efflux transporter in bile canaliculi, MDR1, with an IC(50) of approximately 28 microM. These results suggest that inhibition of hepatic efflux transporters contributed to hepatic accumulation of drug and bile constituents leading to hepatocellular injury and hepatobiliary cholestasis. This study provides likely explanations for clinically observed adverse liver effects of CP-724,714.

Multiple effects of acetaminophen and p38 inhibitors: Towards pathway toxicology

The majority of drug‐related toxicities are idiosyncratic, with little pathophysiological insight and mechanistic understanding. Pathway toxicology is an emerging field of toxicology in the post‐genomic era that studies the molecular interactions between toxicants and biological pathways as a way to bridge this knowledge gap. Using two case studies – acetaminophen and p38 MAPK inhibitors – this review illustrates how a pathway‐based perspective has advanced our understanding of compound and target‐based toxicities. The advancement of pathway toxicology will be dependent on integrated applications of techniques from basic sciences and a fundamental understanding of the interdependence of multiple biological pathways in living organisms.

Biochemical and pharmacological characterization of human c-Met neutralizing monoclonal antibody CE-355621.

The c-Met proto-oncogene is a multifunctional receptor tyrosine kinase that is stimulated by its ligand, hepatocyte growth factor (HGF), to induce cell growth, motility and morphogenesis. Dysregulation of c-Met function, through mutational activation or overexpression, has been observed in many types of cancer and is thought to contribute to tumor growth and metastasis by affecting mitogenesis, invasion, and angiogenesis. We identified human monoclonal antibodies that bind to the extracellular domain of c-Met and inhibit tumor growth by interfering with ligand-dependent c-Met activation. We identified antibodies representing four independent epitope classes that inhibited both ligand binding and ligand-dependent activation of c-Met in A549 cells. In cells, the antibodies antagonized c-Met function by blocking receptor activation and by subsequently inducing downregulation of the receptor, translating to phenotypic effects in soft agar growth and tubular morphogenesis assays. Further characterization of the antibodies in vivo revealed significant inhibition of c-Met activity (≥ 80% lasting for 72–96 h) in excised tumors corresponded to tumor growth inhibition in multiple xenograft tumor models. Several of the antibodies identified inhibited the growth of tumors engineered to overexpress human HGF and human c-Met (S114 NIH 3T3) when grown subcutaneously in athymic mice. Furthermore, lead candidate antibody CE-355621 inhibited the growth of U87MG human glioblastoma and GTL-16 gastric xenografts by up to 98%. The findings support published pre-clinical and clinical data indicating that targeting c-Met with human monoclonal antibodies is a promising therapeutic approach for the treatment of cancer.