Toxicity predictions

Risk Uncoupler, cerebral oedema, eye irritant File DEREK 1 

If any of the alkyl groups are large, then cumulative effects on the lymphatic system are possible 

An example of the output from the DEREK program (from Judson 1992, with permission of the Society of Chemical Industry). 

This equation uses a somewhat unusual double logarithm of the mouse toxicity data, to make this variable conform better to a normal distribution. A better predictive equation for rat toxicity was obtained by combining structural descriptors with the mouse toxicity data as shown in Table 9.4. 

Finally, how well do such prediction systems work In 1990, a challenge was issued to interested parties to make predictions for 44 compounds that were then being tested for rodent carcinogenicity in bioassays by the 

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As computing capabiUty has improved, the need for automated methods of determining connectivity indexes, as well as group compositions and other stmctural parameters, for existing databases of chemical species has increased in importance. New naming techniques, such as SMILES, have been proposed which can be easily translated to these indexes and parameters by computer algorithms. Discussions of the more recent work in this area are available (281,282). SMILES has been used to input Contaminant stmctures into an expert system for aquatic toxicity prediction by generating LSER parameter values (243,258). 

Issues with Toxicity Prediction Good Practice and Recommendations Acknowledgments References... 

The modern science of in silico toxicity prediction has made great strides since its inception in 1962 [13]. Nevertheless, there are still many problems to be overcome [107], and it is to be hoped that future work in this essential field will take into account the following recommendations ... 

Hansch and Leo [13] described the impact of Hpophihdty on pharmacodynamic events in detailed chapters on QSAR studies of proteins and enzymes, of antitumor drugs, of central nervous system agents as well as microbial and pesticide QSAR studies. Furthermore, many reviews document the prime importance of log P as descriptors of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties [5-18]. Increased lipophilicity was shown to correlate with poorer aqueous solubility, increased plasma protein binding, increased storage in tissues, and more rapid metabolism and elimination. Lipophilicity is also a highly important descriptor of blood-brain barrier (BBB) permeability [19, 20]. Last, but not least, lipophilicity plays a dominant role in toxicity prediction [21]. 

Another recent tool has been developed within the ORCHESTRA project. The tool keeps into account both the chemometric information and the toxicity predictions done by the model, and in particular what kind of errors have been done by the model. It applies to the CAESAR QSAR models. Furthermore, this tool is based not only on the a priori data and information, as the other approaches, but also on the a posteriori result of the model. The user knows if the model can or cannot be used for a certain compound. In some cases a warning is given, recommending expert opinion. In all cases the reasons for the reliability is given, and it can be evaluated in a transparent way. 

Simeonova, P.P. and Erdely, A. (2009) Engineered nanoparticle respiratory exposure and potential risks for cardiovascular toxicity predictive tests and biomarkers. Inhalation Toxicology,... 

Lo Piparo E., Fratev, F., Mazzatorta, P., Smiesko, M., Fritz, J.I., Benfenati, E. (2006). QSAR Models for Daphnia magna toxicity prediction of Benzoxazinone allelochemicals and their transformation products. Journal of Agricultural and Food Chemistry 54 1111-1115. 

Biological activity May mimic naturally occurring molecules Primary mechanism of toxicity Predictive based on mechanism Less predictive... 

Molecular Connectivity-QSAR Model for PAH Chronic Toxicity Prediction... 

Elizabeth Barrett Browning after hearing a discussion of toxicity prediction, How can I kill thee, let me count the ways . A recent article by Stouch et al. [74] presents a thoughtful analysis of the validation effort for four such ADME/Tox models. Oprea et al. [75, 76[ have compared drugs leads with compounds in development and in the marketplace and shown that compounds increase in molecular weight and logP as they progress to the bedside. In silico approaches certainly have their place in the pharmaceutical industry as one more tool to increase the probability of success [77]. 

A number of approaches are available or under development to predict metabolism, including expert systems such as MetabolExpert (Compudrug), Meteor (Lhasa), MetaFore [42] and the databases Metabolite (MDL) and Metabolism (Synopsys) [43]. Ultimately such programs may be linked to computer-aided toxicity prediction based on quantitative structure-toxicity relationships and expert systems for toxicity evaluation such as DEREK (Lhasa) (see also Chapter 8) [44]. 

An example of commercial software for using such models for toxicity prediction is MULTICASE [49]. 

Mohan, C.G., Gandhi, T, Garg, D. and Shinde, R. (2007) Computer-assisted methods in chemical toxicity prediction. Mini-Reviews in Medicinal Chemistry, 7, 499-507. 

Key words FETAX, Frog, Embryo, Teratogenesis, Xenopus, Developmental and reproductive toxicity, Predictivity, Embryotoxicity... 

TOPKAT Toxicity prediction http www.accelrys.com/products/... 

An example of another approach is DEREK, a pnblicly available expert system designed to assist chemists and toxicologists in predicting toxicological hazards based on analysis of chemical strnctnre (see table 9.1). DEREK differs from other compnter methods for toxicity prediction in that it makes qnalitative rather than qnantitative predictions and does not rely on algebraic or statistical relationships. 

As the use of metabonomics advances, there are several challenges facing scientists using this tool that must be addressed in order to make it more mainstream and more relevant to predicting toxicity, and useful for hazard identification, human risk assessment and clinical medicine. First, advancing the use of metabonomics to identify mechanisms of toxicity is essential, and such efforts should help to increase the overall usefulness, validity, and relevance of toxicity prediction and biomarker development. Second, the use of metabonomic evaluations in the course of chronic toxicity rather than the heretofore emphasis on acute studies will help to establish its place in following the... 

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