drug toxicity

/Tag:drug toxicity

Predicting Human Drug Toxicity and Safety via Animal Tests: Can Any One Species Predict Drug Toxicity in Any Other, and Do Monkeys Help?

Jarrod Bailey, Michelle Thew and Michael Balls

Animals are still widely used in drug development and safety tests, despite evidence for their lack of predictive value. In this regard, we recently showed, by producing Likelihood Ratios (LRs) for an extensive data set of over 3,000 drugs with both animal and human data, that the absence of toxicity in animals provides little or virtually no evidential weight that adverse drug reactions will also be absent in humans. While our analyses suggest that the presence of toxicity in one species may sometimes add evidential weight for risk of toxicity in another, the LRs are extremely inconsistent, varying substantially for different classes of drugs. Here, we present further data from analyses of other species pairs, including nonhuman primates (NHPs), which support our previous conclusions, and also show in particular that test results inferring an absence of toxicity in one species provide no evidential weight with regard to toxicity in any other species, even when data from NHPs and humans are compared. Our results for species including humans, NHPs, dogs, mice, rabbits, and rats, have major implications for the value of animal tests in predicting human toxicity, and demand that human-focused alternative methods are adopted in their place as a matter of urgency.
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The Use of Computer Models in Pharmaceutical Safety Evaluation

Scott Boyer

With the ever increasing volume of data available to scientists in drug discovery and development, the opportunity to leverage an increasing amount of these data in the assessment of drug safety is clear. The challenge in an environment of increasing data volume is in the structuring and the analysis of these data, such that decisions can be made without excluding information or overstating their meaning. Informatics and modelling play a crucial role in addressing this challenge in two basic ways: a) the data are structured and analysed in a transparent and objective way; and b) new experiments are designed with the model as part of the design process, much like modern experimental physics. Enhancing the use and impact of informatics and modelling on drug discovery is not simply a matter of increasing processor speed and memory capacity. The transformation of raw data to usable, and useful, information is a scientific, technical and, perhaps most importantly, cultural challenge within drug discovery. This review will highlight some of the history, current approaches and promising future directions in this rapidly expanding area.
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In Silico Toxicology in Drug Discovery — Concepts Based on Three-dimensional Models

Angelo Vedani and Martin Smiesko

Animal testing is still compulsory worldwide, for the approval of drugs and chemicals produced in large quantities. Computer-assisted (in silico) technologies are considered to be efficient alternatives to in vivo experiments, and are therefore endorsed by many regulatory agencies, e.g. for use in the European REACH initiative. Advantages of in silico methods include: the possible study of hypothetical compounds; their low cost; and the fact that such virtual experiments are typically based on human data, thus making the question of interspecies transferability obsolete. Since the mid-1990s, computer-based technologies have become an indispensable tool in drug discovery — used primarily to identify small molecules displaying a stereospecific and selective binding to a regulatory macromolecule. Since toxic effects are still responsible for some 20% of the late-stage failures, there is a continuing need for in silico concepts which can be used to estimate a compound’s ADMET (adsorption, distribution, metabolism, elimination, toxicity) properties — in particular, toxicity. The aim of this paper is to provide an insight into computational technologies that allow for the prediction of toxic effects triggered by pharmaceuticals. As most adverse and toxic effects are mediated by unwanted interactions with macromolecules involved in biological regulatory systems, we have focused on methodologies that are based on three-dimensional models of small molecules binding to such entities, and discuss the results at the molecular level.
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