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.

Pathogenesis of Alzheimer's disease (AD), which is characterised by accumulation of extracellular deposits of beta-amyloid peptide (Abeta) in the brain, has recently been linked to vascular disorders such as ischemia and stroke. Abeta is constantly produced in the brain from amyloid precursor protein (APP) through its cleavage by beta- and gamma-secretases and certain Abeta species are toxic for neurones. The brain has an endogenous mechanism of Abeta removal via proteolytic degradation and the zinc metalloproteinase neprilysin (NEP) is a critical regulator of Abeta concentration. Down-regulation of NEP could predispose to AD. By comparing the effects of hypoxia and oxidative stress on expression and activity of the Abeta-degrading enzyme NEP in human neuroblastoma NB7 cells and rat primary cortical neurones we have demonstrated that hypoxia reduced NEP expression at the protein and mRNA levels as well as its activity. On contrary in astrocytes hypoxia increased NEP mRNA expression.

The steady state concentration of the Alzheimer's amyloid-beta peptide in the brain represents a balance between its biosynthesis from the transmembrane amyloid precursor protein (APP), its oligomerisation into neurotoxic and stable species and its degradation by a variety of amyloid-degrading enzymes, principally metallopeptidases. These include, among others, neprilysin (NEP) and its homologue endothelin-converting enzyme (ECE), insulysin (IDE), angiotensin-converting enzyme (ACE) and matrix metalloproteinase-9 (MMP-9). In addition, the serine proteinase, plasmin, may participate in extracellular metabolism of the amyloid peptide under regulation of the plasminogen-activator inhibitor. These various amyloid-degrading enzymes have distinct subcellular localizations, and differential responses to aging, oxidative stress and pharmacological agents and their upregulation may provide a novel and viable therapeutic strategy for prevention and treatment of Alzheimer's disease. Potential approaches to manipulate expression levels of the key amyloid-degrading enzymes are highlighted.

Hepatotoxicity is a major cause of pharmaceutical drug attrition and is also a concern within other chemical industries. In silico approaches to the prediction of hepatotoxicity are an important tool in the early identification of adverse effects in the liver associated with exposure to a chemical. Here, we describe work in progress to develop an expert system approach to the prediction of hepatotoxicity, focussing particularly on the identification of structural alerts associated with its occurrence. The development of 74 such structural alerts based on public-domain literature and proprietary data sets is described. Evaluation results indicate that, whilst these structural alerts are effective in identifying the hepatotoxicity of many chemicals, further research is needed to develop additional structural alerts to account for the hepatotoxicity of a number of chemicals which is not currently predicted. Preliminary results also suggest that the specificity of the structural alerts may be improved by the combined use of applicability domains based on physicochemical properties such as log P and molecular weight. In the longer term, the performance of predictive models is likely to benefit from the further integration of diverse data and prediction model types.