Computer Software for Risk Assessment

Application of genomics in preclinical drug safety evaluation

Understanding the response of biological systems to xenobiotics is fundamental to the evaluation of drug safety. Toxicologists have traditionally gathered pathological, morphological, chemical and biochemical information from in vivo studies of preclinical species in order to assess drug safety and to determine how new drugs can be safely administered to the human patient population. In recent years the emerging "-omics" technologies have been developed and integrated into preclinical studies in order to better assess drug safety by gaining information on the cellular and molecular events underlying adverse drug reactions. Genomics approaches in particular have become readily available and are being applied in several stages of drug development. The burgeoning literature on what has become known as "toxicogenomics" has for the most part highlighted successful applications of gene expression profiling in predictive toxicology, enabling decisions to be made on the developability of a compound early in the drug development process. It is also becoming apparent that toxicogenomic approaches are good starting points to develop experiments designed to gain a mechanistic insight into drug toxicities within and across species. Gene expression arrays permit the measurement of responses of essentially all the genes in the entire genome to be monitored, and knowledge of the function of the genes affected can identify the potential mechanisms to then be confirmed using conventional biochemical, toxicological and pathological approaches. As toxicologists put these technologies into practice they build up a knowledge base to better characterize toxicities at the molecular level and to make the search for much needed, novel biomarkers of toxicity more achievable.

Chemoinformatics and Drug Discovery

This article reviews current achievements in the field of chemoinformatics and their impact on modern drug discovery processes. The main data mining approaches used in cheminformatics, such as descriptor computations, structural similarity matrices, and classification algorithms, are outlined. The applications of cheminformatics in drug discovery, such as compound selection, virtual library generation, virtual high throughput screening, HTS data mining, and in silico ADMET are discussed. At the conclusion, future directions of chemoinformatics are suggested.

Predicting toxicity: a mechanism of action model of chemical mutagenicity

The increasing importance of theoretical studies for predicting toxicology has aroused the interest of many computational chemists. A new approach has been developed, based on studying at the molecular level two potential mechanisms of action that are related to compound mutagenicity. This approach is the first example that considers both the toxicant and the biological target molecules involved in the interaction. Using some calculated descriptors and a simulation of the interaction chemical, compounds can be classified. More important, the approach helps in understanding and explaining both the correct and the incorrect results, and gives a deeper understanding of the toxic mechanisms. The model has been applied to many compounds and the results are compared with experimental results reported for the corresponding Salmonella tests.

Predicting human safety: screening and computational approaches

Current preclinical safety evaluation programs use a combination of computational methods, mechanistic in vitro screening and - primarily - in vivo experimentation to predict human toxicity. The rapid transition of pharmaceutical R&D into electronic R&D (e-R&D) makes it imperative that predictive safety testing also develops into an information-rich, knowledge-based process in the near future. Accordingly, enhanced databases and computational tools are expected to change the way the pharmaceutical industry assesses drug toxicity during discovery and early development. Expert use of prediction tools should lead to lower failure rates in drug development and decrease the cost and time involved in successful drug approval.

Structure-based methods for predicting mutagenicity and carcinogenicity: are we there yet?

There is a great deal of current interest in the use of commercial, automated programs for the prediction of mutagenicity and carcinogenicity based on chemical structure. However, the goal of accurate and reliable toxicity prediction for any chemical, based solely on structural information remains elusive. The toxicity prediction challenge is global in its objective, but limited in its solution, to within local domains of chemicals acting according to similar mechanisms of action in the biological system; to predict, we must be able to generalize based on chemical structure, but the biology fundamentally limits our ability to do so. Available commercial systems for mutagenicity and/or carcinogenicity prediction differ in their specifics, yet most fall in two major categories: (1) automated approaches that rely on the use of statistics for extracting correlations between structure and activity; and (2) knowledge-based expert systems that rely on a set of programmed rules distilled from available knowledge and human expert judgement. These two categories of approaches differ in the ways that they represent, process, and generalize chemical-biological activity information. An application of four commercial systems (TOPKAT, CASE/MULTI-CASE, DEREK, and OncoLogic) to mutagenicity and carcinogenicity prediction for a particular class of chemicals-the haloacetic acids (HAs)-is presented to highlight these differences. Some discussion is devoted to the issue of gauging the relative performance of commercial prediction systems, as well as to the role of prospective prediction exercises in this effort. And finally, an alternative approach that stops short of delivering a prediction to a user, involving structure-searching and data base exploration, is briefly considered.