1

TOXICOINFORMATICS: A PREDICTIVE TOXICOLOGY

Presented BySawani KhareM.S.(Pharm.)

Dept. of Pharmacoinformatics,NIPER, S. A. S. Nagar.

2

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

3

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

4

Continue…

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

5

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

6

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

7

Predictive Toxicology Techniques

Drug and chemical toxicity databases

QSAR

Human knowledgebased methods

In silico ADME/Toxapproaches

8

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

9

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

10

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

11

Sanderson D, Eamshaw C (1991) Computer prediction of possible toxic action from chemical structure:the DEREK system. Hum Exp Toxicol 10: 261-273

DEREK

12

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

13

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)

14

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

15

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

16

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

17

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

18

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

19

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

20

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

21

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

22

23

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

24

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

25

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

Back