Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope

Abstract

Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate in preclinical models, and it can originate from pharmacologically unrelated drug effects, such as pathway interference, metabolism, and drug accumulation. Because liver toxicity still ranks among the top reasons for drug attrition, the reliable prediction of adverse hepatic effects is a substantial challenge in drug discovery and development. To this end, more effort needs to be focused on the development of improved predictive in-vitro and in-silico approaches. Current computational models often lack applicability to novel pharmaceutical candidates, typically due to insufficient coverage of the chemical space of interest, which is either imposed by size or diversity of the training data. Hence, there is an urgent need for better computational models to allow for the identification of safe drug candidates and to support experimental design. In this context, a large data set comprising 3712 compounds with liver related toxicity findings in humans and animals was collected from various sources. The complex pathology was clustered into 21 preclinical and human hepatotoxicity endpoints, which were organized into three levels of detail. Support vector machine models were trained for each endpoint, using optimized descriptor sets from chemometrics software. The optimized global human hepatotoxicity model has high sensitivity (68%) and excellent specificity (95%) in an internal validation set of 221 compounds. Models for preclinical endpoints performed similarly. To allow for reliable prediction of "truly external" novel compounds, all predictions are tagged with confidence parameters. These parameters are derived from a statistical analysis of the predictive probability densities. The whole approach was validated for an external validation set of 269 proprietary compounds. The models are fully integrated into our early safety in-silico workflow.

Predicting Drug-Induced Cholestasis with the Help of Hepatic Transporters—An in Silico Modeling Approach

“…33 The data are hierarchically clustered by the authors into three levels of hepatotoxicity: level 0 corresponds to general hepatotoxicity, level 1 corresponds to clinical chemistry findings and morphological finding as distinguished parts of general hepatotoxicity, and level 2 discriminates both clinical chemistry and morphological findings into hepatocellular and hepatobiliary injury. We use only clinical data, i.e.…”

“…Most recently, Muller et al., 32 in order to model DILI, also modeled some more hepatotoxicity end points, including cholestasis. Moreover, Mulliner et al 33 presented a multilevel modeling approach for DILI, where cholestasis was also included as a morphological hepatobiliary finding. However, examining the liver transporters contribution was not within the scope of their work.…”

In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

“…At the other end of the spectrum, QSAR analyses have been performed on large datasets using multivariate techniques (34), with particularly large, information-rich, datasets becoming available e.g. Mulliner et al (35) utilised information for over 3700 drugs. Other approaches have also used biological information to support predictions from chemistry alone (36–39).…”

Microengineered Bioartificial Liver Chip for Drug Toxicity Screening

“…Inefficiency of in vivo animal models to accurately predict the toxic side effects of drugs in humans is the major challenge . For instance, roughly 50% of the drugs found to be responsible for liver injury during clinical trials did not cause any liver damage in animal experiments …”