High-content analysis/screening for predictive toxicology: application to hepatotoxicity and genotoxicity.

High-content imaging/analysis has emerged as a powerful tool for predictive toxicology as it can be used for identifying and mitigating potential safety risks during drug discovery. By careful selection of end-points, some cellular assays can show better predictivity than routine animal toxicity testing for certain adverse events. Here, we present the perhaps most utilized high-content screening assays for predictive toxicology in the pharmaceutical industry. Multi-parametric imaging of cell health in simple and cost-effective model systems can be used to predict human hepatotoxicity and elucidate mechanisms of toxicity, and imaging of bile salt transport inhibition in sandwich-cultured hepatocytes can be used to predict cholestasis-inducing compounds. Imaging of micronuclei formation in simple cell models can be used to detect genotoxic potential and elucidate anuegenic or clastogenic mode of actions. The hope is that application of these relatively predictive assays during drug discovery will reduce toxicity and safety-related attrition of drug development programmes at later stages.

A high content screening assay to predict human drug-induced liver injury during drug discovery.

INTRODUCTION: Adverse drug reactions are a major cause for failures of drug development programs, drug withdrawals and use restrictions. Early hazard identification and diligent risk avoidance strategies are therefore essential. For drug-induced liver injury (DILI), this is difficult using conventional safety testing. To reduce the risk for DILI, drug candidates with a high risk need to be identified and deselected. And, to produce drug candidates without that risk associated, risk factors need to be assessed early during drug discovery, such that lead series can be optimized on safety parameters. This requires methods that allow for medium-to-high throughput compound profiling and that generate quantitative results suitable to establish structure-activity-relationships during lead optimization programs. METHODS: We present the validation of such a method, a novel high content screening assay based on six parameters (nuclei counts, nuclear area, plasma membrane integrity, lysosomal activity, mitochondrial membrane potential (MMP), and mitochondrial area) using ~100 drugs of which the clinical hepatotoxicity profile is known. RESULTS DISCUSSION: We find that a 100-fold TI between the lowest toxic concentration and the therapeutic Cmax is optimal to classify compounds as hepatotoxic or non-hepatotoxic, based on the individual parameters. Most parameters have ~50% sensitivity and ~90% specificity. Drugs hitting ≥2 parameters at a concentration below 100-fold their Cmax are typically hepatotoxic, whereas non-hepatotoxic drugs typically hit <2 parameters within that 100-fold TI. In a zone classification model, based on nuclei count, MMP and human Cmax, we identified an area without a single false positive, while maintaining 45% sensitivity. Hierarchical clustering using the multi-parametric dataset roughly separates toxic from non-toxic compounds. We employ the assay in discovery projects to prioritize novel compound series during hit-to-lead, to steer away from a DILI risk during lead optimization, for risk assessment towards candidate selection and to provide guidance of safe human exposure levels.