toxicoinformatics: a predictive toxicology
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TOXICOINFORMATICS: A PREDICTIVE TOXICOLOGY
Presented BySawani KhareM.S.(Pharm.)
Dept. of Pharmacoinformatics,NIPER, S. A. S. Nagar.
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Flow of Contents Introduction
Toxicity testing in drug development
Importance of in silico techniques
Predictive toxicology techniques
Tools for toxicity prediction
Strengths and limitations
Case study
Conclusion
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IntroductionToxicoinformatics: computational approach used to
elucidate the mechanism of chemical toxicity
Integration of modern computing and information technology with molecular biology to improve risk assessment of chemical
A mixture of strategies used to forecast the interaction
between chemical/molecule and biological system is called as toxicology
Simulation of anticipated effects of new or known chemicals is called as Predictive Toxicology
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Used for predicting preclinical toxicological end points, clinical adverse effects and metabolism of pharmaceutical substances.
Methods such as predictive QSAR, modeling of toxicity, e-tox, i-drug discovery, predictive ADMET
Showing utility for producing information for the pharmaceutical industry at the design stage to help identify lead compounds with low toxicological liability.
Luis G, Valerio J (2009) In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol 241:356-370
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Toxicity Testing in Drug Development
DiscoveryLead
OptimizationToxicology
Clinical Development
Target identificationDetermine relevance
of toxic effects
Identify toxicophoresand their effects
Predictive relevance foradverse effects in
human
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Importance of In Silico Techniques in vivo toxicology is the best standard for identification of
the side effects caused by drug, but this alone cannot help
Extremely useful due to lower drug-development costs, faster drug development
Help to resolve safety issues of the pharmaceuticals
This also helps in selection of candidate for lead optimization and potential drug development
Predict the toxic endpoints such as carcinogenicity, mutagenicity, genotoxicity, skin sensitization and irritation, teratogenicity, hepatotoxicity, neurotoxicity
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Predictive Toxicology Techniques
Drug and chemical toxicity databases
QSAR
Human knowledgebased methods
In silico ADME/Toxapproaches
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Tools for Toxicity PredictionRoughly classified into expert systems and data driven
systems
Expert systems try to formalize the knowledge of human experts who assessed the toxicity of compounds
Data driven systems require experimental data from which predictive models can be derived
Expert systems Data driven systems
DEREK LAZAR
METAPC TOPKAT
METEOR MC4PC
OncoLogicTM
Merlot C (2010) Computational toxicology- a tool for early safety evaluation. Drug Discov Today 15:16-22
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Expert System Rules about generalized relationships between structure
and biological activity that are derived from human expert opinion and interpretation of toxicological data to predict the potential toxicity of novel chemicals
Has a list of chemical substructures that have been identified as being toxic
The human rules lead to the identification of chemical features in a region of the whole molecule being in silico screened or a known chemical class that due to its presence provides a reasoned conclusion or concern level about the toxicity of a query chemical
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DEREK Deductive Estimation of Risk from Existing Knowledge
Marketed by LHASA UK Ltd. Predictions are made on the basis of a series of rules relating
chemical structure to toxicity Query structures may be entered at a graphical interface or
automatic processing conducted via a link to a structural database
The results of each comparison are presented on the screen, toxophores are highlighted sequentially and displayed along with their associated toxic effect
Endpoints: carcinogenicity, mutagenicity, genotoxicity, skin sensitization and irritation, teratogenicity, respiratory sensitization, reproductive toxicity
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Sanderson D, Eamshaw C (1991) Computer prediction of possible toxic action from chemical structure:the DEREK system. Hum Exp Toxicol 10: 261-273
DEREK
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OncoLogicTM
Rule based expert system for the prediction of carcinogenicity and works on QSAR analysis
Developed and marketed by LogiChem Inc.
It is a system in which the rules are structured in a hierarchical decision tree structure
Users start by selecting the subsystem according to the type of substance they are interested in from four main categories, fibers, metals or metal-containing compounds, polymers or organics
It is having 40,000 rule and 10,000 organic compounds data
Endpoint- Carcinogenecity
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Data Driven system Used to make predictions for compounds with similar
structures that most probably manifest the toxicological effect through the same mechanism
Techniques used to establish predictive models are partial-least squares (PLS), multiple linear regression (MLR), recursive partitioning, support vector machines(SVM), decision trees, k-nearest neighbors(KNN)
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Lazar
Open source inductive database for the prediction of chemical toxicity
Derives predictions for query structures from a database with experimentally determined toxicity data
Generates predictions by searching the database for compounds that are similar with respect to a given toxic activity
Uses data mining algorithms to derive predictions for untested compounds from experimental training data
Any dataset with chemical structures and biological activities can be used as training data
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It works in three steps: (i)identifies similar compounds in the training data (ii) creates a local prediction model (iii) uses the local model to predict properties of the query compound
Lazar uses local QSAR models, similar to the read across procedure.
Maunz A, Gütlein M, Rautenberg M, Vorgrimmler D (2013) Lazar: a modular predictive toxicology framework Front Pharmacol 4:1-13
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TOPKATTOxicity Prediction by Komputer Assisted Technology
Originally developed by Health Designs and now marketed by Accelrys
Structures are entered into the program using SMILES codes of chemical structures and the user then selects the appropriate module or endpoint
The query compound is then analyzed to ensure that it is covered by or lies within the optimum prediction space (OPS) for the module selected
Endpoints- Carcinogenicity, Mutagenicity, Skin sensitization, Eye irritancy
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Strengths Less expensive
Less time consuming
Gives higher throughput
Have higher reproducibility
Less requirement of compound synthesis
Can undergo constant optimization
Reduce the use of animals
predict ADME related properties on virtual structures
Enables exploration of chemical space without the need to create wet laboratory synthesis and experimental testing
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Limitations Low assurance and transparency of the high quality
experimental data used to build the training data set
Incorrect molecular structure or erroneous data from toxicology studies leads to inaccurate prediction
If molecule is not within the applicability of domain the prediction is invalid
It is difficult to explain multiple mechanisms of toxicity using a single model
Many in silico toxicology systems employing QSAR techniques for toxicity prediction rely on 2D representation instead of 3D representation
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Case study The performance of two computer programs, DEREK and
TOPKAT, was examined
The results of over 400 Ames tests conducted at GlaxoSmithKline on a wide variety of chemical classes were compared with the mutagenicity predictions
Criteria:DEREK- discordant if false positive or false negativeTOPKAT- if probability is
(i) >= 0.7- mutagenic (ii) <=0.3- non mutagenic (iii) 0.3-0.7 indeterminate
DEREK v.17.1 (Java client) and TOPKAT 5.01 for Windows were used
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Result and conclusion
Comparative analysis for the performance
Concordance Discordance
DEREK 65% 35%
TOPKAT 73% 27%
60% of the compounds were incorrectly predicted by TOPKAT as negative but were mutagenic in the Ames test
For DEREK, 54% of the Ames-positive molecules had no structural alerts and were predicted to be non-mutagenic
Cariello N, Wilson J (2002) Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity. Mutagenesis 17:321-329
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Conclusion Present in silico tools are mature enough to play an
important role in the preclinical assessment of toxicity
The accuracy is mainly depend on databases, hence still have not achieved the major breakthrough due to lack of sufficiently large datasets covering more complex toxicological endpoints
In silico techniques help to significantly reduce drug development costs by succeeding in predicting adverse drug reactions in preclinical studies
The accuracy of in silico predictions mainly depends on the database used
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Toxicity databases Computational toxicology approaches are mainly aimed
towards the building of databases Valued set of electronic information that can be related to
the toxicity of substances of which the information is accessible by computer
Serve as prediction models or data mining tool They contain authenticated structures obtained from
experiments on substance-induced toxicity or other scientific evidences
e.g. DevTox, GAC, NTP, TOXNET These databases are still very small as compared to the
compound libraries in the industry Two types, private and public/open source Back
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QSARMathematical models that connect experimental
measures with a set of descriptors determined from a set of compounds
Widely used primarily for lead discovery and optimization also used in toxicology and regulations
Proved to be cost effective for prioritizing untested chemicals over more extensive and costly experimental evaluation
Quantifies features of the new chemical structure so that overall toxic properties of the compound can be predicted
The most commonly modeled QSAR endpoint in toxicology is carcinogenicity Back
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Human knowledge-based methods Built upon experimental data representing one or more toxic
manifestations of chemicals
System based on induced rules is called an automated rule-induction system while a system based on expert rules is referred to as a knowledge based system
Human expert opinion is tested during the experiments and rules identifying structural alerts are derived
The computer stores the information derived from experimental measures and then use on demand a piece of knowledge that has been formalized and input by experts
power of the system is linked to the amount of expert time invested in feeding it and to the availability of reliable and high-quality datasets
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