QSAR in Toxicology. 3. Prediction of Chronic Toxicities

Cronin, Mark T.D.; Dearden, John C.

doi:10.1002/qsar.19950140402

Cited by 54 publications

(18 citation statements)

References 75 publications

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“…No matter how sophisticated statistical analysis of LD 50 data is performed, it cannot provide us with any useful information automatically [27,29,[41][42][43]. The only correct way to get this information is to check various mechanistic hypotheses in a multi-step SAR and QSAR analysis with various "causal" descriptors that support deductive chemical thinking [26,44,45].…”

Section: Analysis Of Animal Data (Ld 50 )mentioning

confidence: 97%

In Silico Technology for Identification of Potentially Toxic Compounds in Drug Discovery

Didžiapetris¹,

Reynolds²,

Japertas³

et al. 2006

CAD

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This review gives the background to analysis of toxicity data, development of predictive algorithms, and applications of these algorithms in lead selection and optimization. The considered algorithms predict acute toxicity (Mouse and Rat LD 50 ), genotoxicity (Ames Test), carcinogenicity, and organspecific health effects (based on diverse animal and human studies). These tools can aid drug design in several ways. Often lead selection is based on the use of simple molecular properties (logP, MW, H-bonding) to define either a 'druglike' or 'leadlike' chemical space. These definitions need to be supplemented with substructurespecific considerations that account for variable chemical reactivity, ionization, and fuzzy-specific interactions with various biological constituents. The available toxicity predictions can fill these gaps to a certain extent, by supplementing or replacing various pre-defined filters of "alert substructures" that ignore the dependence of chemical reactivity and toxicity on substituent effects and whole-molecule ADME effects. In drug discovery these tools can help to prioritize in vitro measurements and estimate animal toxicity, although multiple data gaps in their training sets restrict their usefulness. A partial solution to this problem is calculation of 95% confidence intervals (or continuous probabilities) that indicate toxicological similarity of a given compound to the training set. If a compound is not too dissimilar, "hazard substructures" can be automatically generated, thus suggesting possible mechanistic explanations and structural modifications of the lead compound. The best solution however is to develop new predictive algorithms based on companyspecific data, and there are available analytical and development software tools that can help to do this. It is also necessary to continuously improve the existing organ-specific health effect predictions by adding new data (for existing and new endpoints) and improving the overall methodology used in data analysis.

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Section: Analysis Of Animal Data (Ld 50 )mentioning

confidence: 97%

In Silico Technology for Identification of Potentially Toxic Compounds in Drug Discovery

Didžiapetris¹,

Reynolds²,

Japertas³

et al. 2006

CAD

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“…This is due to a combination of the previous three points. (5) Possibly, that the QSAR and modelling community has not viewed reproductive toxicity as an area of concern or interest. This may be because there are not readily available databases for modelling such as there are in some environmental endpoints (e.g.…”

Section: In Silico Models For Reproductive Toxicity -The Drawbacksmentioning

confidence: 98%

(Q)SARs for Predicting Effects Relating to Reproductive Toxicity

Cronin

Worth

2008

QSAR Comb. Sci.

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This paper reviews the current status of structure-based methods for predicting adverse reproductive effects in mammals. The methods described include (Quantitative) Structure -Activity Relationships ((Q)SARs), expert systems and the less formalised approaches of read-across within (chemical) categories. There are a number of problems with applying QSARs to reproductive toxicology notably the complexity, subtlety and sometimes ill-defined nature of the endpoints and lack of data available for modelling. A small number of ((Q)SARs have been developed for individual classes of compounds for well-defined effects. These are supplemented by expert systems approaches of all types [e.g. DEREK for Windows, TOPKAT, MC4PC, PASS, Organisation for Economic Cooperation and Development (OECD) QSAR Application Toolbox] for a variety of endpoints associated with reproductive toxicology. By far the largest, and best developed, group of models are those for receptor binding effects related to endocrine disruption, in particular to the Oestrogen Receptor (ER) and, to a lesser extent, the Androgen Receptor (AR). Strategies to improve predictive capabilities for reproductive toxicology are suggested.

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“…The model worked for a vast number of biological problems, and its success is demonstrated clearly by its widespread diffusion. At present, the QSAR science is one of the basic tools of modern drug and pesticide design and has an increasing role in environmental sciences [Cronin and Dearden, 1995;Hansch and Leo, 1995;Benigni et al, 2007b;Greene and Naven, 2009;Benigni and Bossa, 2011].…”

Section: Introductionmentioning

confidence: 99%

Flexible use of qsar models in predictive toxicology: A case study on aromatic amines

Benigni

Bossa

2011

Environ and Mol Mutagen

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Over the last years, predictive toxicology approaches based on Structure-Activity Relationships have emerged as fundamental tools in the regulatory assessments of chemicals, especially in those programs where regulatory constraints and assessment schemes limit the amount of data available from experimental test methods. Both the qualitative (e.g., Structural Alerts) and the quantitative (Quantitative Structure-Activity Relationships, QSAR) approach can play important roles. However, the two approaches are not familiar to the same extent to the regulators that most often use only the qualitative approach, so that the potentiality of the more sophisticated QSAR approach is neglected. In fact, QSAR is a very flexible tool that allows the user to modulate its response according to different goals and requirements. Here, we present a non-naïve approach to the use of a QSAR relative to a dichotomous biological activity (such as mutagen/nonmutagen), and we show how the user can maximize alternatively the reliability of the prediction of negative compounds (i.e., safe chemicals) or that of positive chemicals (i.e., chemicals that pose high hazard). Because of the environmental and industrial importance of the class of aromatic amines, we apply the approach to a previously published QSAR on the Salmonella typhimurium mutagenicity of these chemicals.

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QSAR in Toxicology. 3. Prediction of Chronic Toxicities

Cited by 54 publications

References 75 publications

In Silico Technology for Identification of Potentially Toxic Compounds in Drug Discovery

In Silico Technology for Identification of Potentially Toxic Compounds in Drug Discovery

(Q)SARs for Predicting Effects Relating to Reproductive Toxicity

Flexible use of qsar models in predictive toxicology: A case study on aromatic amines

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