Developing Structure−Activity Relationships for the Prediction of Hepatotoxicity

Abstract

Drug-induced liver injury is a major issue of concern and has led to the withdrawal of a significant number of marketed drugs. An understanding of structure-activity relationships (SARs) of chemicals can make a significant contribution to the identification of potential toxic effects early in the drug development process and aid in avoiding such problems. This process can be supported by the use of existing toxicity data and mechanistic understanding of the biological processes for related compounds. In the published literature, this information is often spread across diverse sources and can be varied and unstructured in quality and content. The current work has explored whether it is feasible to collect and use such data for the development of new SARs for the hepatotoxicity endpoint and expand upon the limited information currently available in this area. Reviews of hepatotoxicity data were used to build a structure-searchable database, which was analyzed to identify chemical classes associated with an adverse effect on the liver. Searches of the published literature were then undertaken to identify additional supporting evidence, and the resulting information was incorporated into the database. This collated information was evaluated and used to determine the scope of the SARs for each class identified. Data for over 1266 chemicals were collected, and SARs for 38 classes were developed. The SARs have been implemented as structural alerts using Derek for Windows (DfW), a knowledge-based expert system, to allow clearly supported and transparent predictions. An evaluation exercise performed using a customized DfW version 10 knowledge base demonstrated an overall concordance of 56% and specificity and sensitivity values of 73% and 46%, respectively. The approach taken demonstrates that SARs for complex endpoints can be derived from the published data for use in the in silico toxicity assessment of new compounds.

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

“…There have been efforts recently to use computational models to predict DILI or idiosyncratic hepatotoxicity. We are aware of at least three studies that tackled predicting DILI dmd.aspetjournals.org using either linear discriminant analysis, artificial neural networks, and machine learning algorithms (OneR) (Cruz-Monteagudo et al, 2008), support vector machine (Fourches et al, 2010a), or structural alerts (Greene et al, 2010). A major limitation of these previous global models for DILI (and for many computational toxicology models) is their use of very small test sets in all cases.…”

“…A third study created a knowledge base with structural alerts from 1266 chemicals. Although not strictly a machine learning method, the alerts created were used to predict results for 626 Pfizer compounds (sensitivity of 46%, specificity of 73%, and concordance of 56% for the latest version) (Greene et al, 2010). …”

A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model

“…In one approach, drugs were classified by most-, less-, and no-DILI concern according to the DILI concern disclosed in the FDAapproved drug labels (Chen et al 2011). In another independent approach, drugs were categorized into four hepatotoxic groups (Greene et al 2010): NE (no evidence for hepatotoxicity in any species), WE (weak evidence for human hepatotoxicity with <10 case reports, but generally considered not to present a risk for liver injury in humans), HH (evidence for hepatotoxicity in humans), and AH (animal hepatotoxicity observed, not tested in humans). Using the two approaches in combination, a drug is defined as human hepatotoxicity positive, if it is classified as most-DILI-concern or HH, or as negative, if it is classified as no-DILI-concern or NE.…”

“…Indeed, in silico models can assist in the prediction of safety of new drug candidates and allow the rapid screening of literately unlimited number of chemicals. Thus, several studies reported the utility of in silico models for the prediction of human hepatotoxicity (Ekins et al 2010;Greene et al 2010;Chen et al 2013b), and recently, Chen et al reported for oral medications at high daily doses and lipophilicity a significant increased risk for clinical DILI as determined by the 'rule-of-two' (RO2, i.e., daily dose ≥100 mg/day & logP ≥ 3) (Chen et al 2013a). While the RO2 model alone added value to the prediction of clinically relevant human hepatotoxicity (Kaplowitz 2013), its performance was still insufficient due to the limited sensitivity caused by a high rate of false negatives.…”

Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data

“…Approaches for predicting hepatotoxicity in silico have been proposed based on chemical structure similarity through the development of quantitative structure-activity relationship (QSAR) models (Chen et al, 2014;Greene et al, 2010). Integration of QSAR models with data from other sources can provide context information, bringing opportunities to improve a model's predictive power, for instance, integrating ontology annotations of toxic pathologies (Myshkin et al, 2012) or data from in vitro based assays in human hepatocytes (O'Brien et al, 2006).…”