predictive tox proposal_ver1

Predictive Toxicology&

Toxicogenomics Proposal

forXXXX

Accommodator Consultancy Services, Lucknow

Dr Vibhor Mahendru

Ankur Khanna

Accommodator Consultancy Services Lucknow

Why Predictive Toxicology? 3R initiative trend of Reduce, Refine and Replace. In June last, India became

the first country in South Asia to ban the testing of cosmetics and its ingredients on animals, while it became the second country after Israel to ban animal testing for household products in January this year.

Computational docking and molecular dynamics simulation facility has been established.

Accelrys Discovery Studio software has been recently procured. Capability to identify sequence and 3D structure characterization,

visualization, analysis, PERL scripting, charting, and modeling of molecular systems that act as inputs for predictive toxicology.

Our toxicological prediction models would supplement current work being done in this field by virtue of validation.

As we highlighted, necessary hardware and software infrastructure has just been put in place to collaborate and fully use the potential of this exciting activity.


Predictive Toxicology Defined

It’s a mix of strategies used to forecast the interaction between chemical structures and biological systems.

It usually involves assessing human health risks based on data from non-clinical animal models and physicochemical properties.

They are designed to leverage revolutionary advances in molecular, cellular and computational science.

It leads to creating new non animal and human tests, in an objective and reproducible manner where possible to provide new scientific basis for safety testing.


Available Methods of Implementation1. Expert System –An expert system is a program that mimics human reasoning. An

expert system for toxicity endpoint(s) can be developed by constructing a knowledge base, e.g. from interviews with experts.

2. Data Mining – This Involves analyzing experimental toxicity data results and applying the insights gained from it on similar chemicals/compounds by virtue of common or similar descriptors for toxicity prediction where live testing is either not possible or cost prohibitory by applying mining algorithms based on mathematical principles. Computer aids in gaining insights while the implementation can be automated..

The goal of predictive toxicology is to accurately predict adverse effects of chemicals that lack experimental data based on structure activity relationship. The end result is a QSAR model which needs to be validated for accuracy and usefulness.

Validating the resultant model is very important to establish the quality of prediction. A number of techniques exist that help in validating the quality of prediction the entire exercise gives out.


Goal

Validation of Models

Benefits Potential to reduce animal usage and supports

ongoing 3R initiative (Replacement, Refinement and Reduction).

Offers the potential for reliable, reproducible, faster and more cost effective safety assessment in new product development, where the cost of failures late in development is prohibitive.

Leverage huge advancement in molecular biology and chemistry.

Ultimately saves on time, effort cost and more importantly animal and human lives.


Common Challenges


1.Representing the chemical compounds–SAR or Compound structure?

2.Determining which characteristics of chemical compounds could be useful for classifying them as toxic or not toxic

3.Data intensive experiments such as High Throughput Screening and High Content Screening, that act as training set for predictive toxicology are expensive.

4.Resulting toxicology assay data sets have to be integrated and curated before they can be used. They are cumbersome due to sheer volume, velocity and variety.

5.Its not very reliable to relate chemical structures to experimental activities through the representation of compounds in form of features.

6.Validation of test results are based on old science which take years to complete albeit newer methods are evolving.

7.It is difficult to predict if regulators would approve of new validation methods which should be quicker.

8.Non Linear substructures are very difficult to work with for mining.

9.Experimental data might exist for chemicals in 3D but technology is still evolving to leverage extra information for predictions.

10.Difficult to identify related and non relevant attributes that are know to cause skew in analysis and mining

Information in public domain


1. Consolidated exhaustive library exists that covers all toxicity assays conducted through out the world with results that can be leveraged for predictive toxicology. <http://www.epa.gov/nheerl/dsstox/>.

2. This library has been preprocessed, curated, integrated and homogenized with painstaking effort by the teams of ACTOR and DSSTox projects.

3. Reliability of the tests is increased with better validation methods.

4. A vast number of validation methods are available in all data mining tools, including Knime and SSAS. Big Data technology facilitates coordination and relating of cross disciplines for revolutionary info.

5. Using open source software helps build consensus.

6. There are tools albeit very few and in evolutionary phase that help predict relationship for nonlinear SAR’s.

7. As mentioned above, there are tools available that help predict 3d SAR’s.

8. All mining tools suggest related and non relevant attributes, which an expert can use for reliable predictions. The tools, have latest research incorporated into them and hence accelerate research.

High Quality Exhaustive Toxicology Data available

1. Users to search and query data from other EPA chemical toxicity databases including:

a. ToxRefDB (30 years and $2 billion worth of animal toxicity studies).

b. ToxCastDB (data from screening 1,000 chemicals in over 500 high-throughput assays).

c. ExpoCastDB (consolidate and link human exposure and exposure factor data for chemical prioritization).

d. DSSTox (provides high quality chemical structures and annotations).

2. Includes chemical structure, physico-chemical values, in vitro assay data and in vivo toxicology data.

3. Includes, but not limited to, high and medium production volume industrial chemicals, pesticides (active and inert ingredients), and potential ground and drinking water contaminants.


Compilation of all toxicity assays for public consumption


Predictive Toxicology Global Trends


1. The predictive accuracy of models using MOLFEA (Molecular Feature Miner algorithm) derived descriptors is 10-15% points higher than those using molecular properties alone(atomic descriptors included).

2. Data query and analysis tools have been developed that are useful for identifying patterns in experimental data. They provide context for biological data, including metabolic, gene expression and proteomic data, by applying the data to a network representation of biological processes. In doing so, they move beyond the traditional linear pathway view of biology to a network view, and use the network as a data integration tool to seamlessly merge disparate data streams.

3. Predictive toxicology is one of the focus areas of EPA (US Environment Protection Agency) in FY 2015.

4. It is becoming a standard practice in drug development for pharmaceutical companies and FDA to estimate clinical trial doses using computer models to evaluate why adverse events occur, and to determine the potential basis for variable patient response.

Global trends (continued.)


5. Another focus area is automatic classification of compounds based on 28 key biological and toxicity mechanisms classes identified. A series of two-class SVM models for each mechanism class were built. The results suggest that compounds with potentially undesirable mechanisms are surprisingly common in most compound collections.

6. A pathway-based framework is being developed that translates HTS/HCS data from in vitro studies into a plausible prediction of human toxicity which links molecular targets to adverse outcomes. Efforts are on to quantify such pathway level data and ways to determine if the effect is adverse(toxic),adaptive(compensatory) or therapeutic.

7. Guidelines are expected to be approved in final form this summer that would permit the use of genotoxicity QSAR models to replace actual testing. The guidelines state that a QSAR statistical-based methodology and expert alerts system can be used to predict the outcome of a bacterial mutagenicity assay to support hazard assessment.

8. High Content Screening is being used for predictive toxicology.

9. 2013 Nobel prize for Chemistry was given in the field of simulating chemical reactions on computers. Even predictive toxicology can immensely benefit from this finding.

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Question that predictive toxicology could answer

If weathered “toxaphene” is toxicologically equivalent to the product that was originally released into the environment.?

Which diseases are associated with the chemical: bisphenol A (BPA), which BPA-induced genes function during development, which biologic functions and molecular pathways BPA affects, and which chemicals have interaction profiles similar to those of BPA.


Questions that common toxicological DB would answer

Prediction Algorithms


1. Multiple Linear Regression:is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X.

2. Bayesian Techniques: A compound is classified as toxic if the probability for toxicity exceeds the probability for non-toxicity. Naive Bayes can rapidly generate models because it requires only a single scan through the database to count the occurrences of features in each class. Predictions are also fast because of the simplicity of the classification model.

3. Recursive Partitioning: Uses a divide and conquer approach that starts with a search for the substructure that provides the best separation between toxic and nontoxic compounds.

4. Feed forward Neural Networks: is an artificial neural network where connections between the units do not form a directed cycle.

5. Support Vector: Machines:are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.

6. K Nearest Neighbor Method: k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.

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Toxicogenomics Toxicogenomics is a field of science that deals with the

collection, interpretation, and storage of information about gene and protein activity within particular cell or tissue of an organism in response to toxic substances

It combines toxicology with genomics or other high throughput molecular profiling technologies such as transcriptomics, proteomics and metabolomics.[1][2] Toxicogenomics endeavors to elucidate molecular mechanisms evolved in the expression of toxicity, and to derive molecular expression patterns (i.e., molecular biomarkers) that predict toxicity or the genetic susceptibility to it.

The nature and complexity of the data (in volume and variability) demands highly developed processes of automated handling and storage. The analysis usually involves a wide array of bioinformatics and statistics,[3] regularly involving classification approaches.


Why genomics According to new draft policy by Dept. of

Biotechnology, Govt. of India, genome based prescription and treatment will be top priority in next few years.

The draft policy envisages converting half of hospitals currently engaged in treatment of human diseases to that of prediction and prevention of diseases using genomic tools.

It also aims to provide all available genetic screening tests to general public at affordable prices.

As a result, effect of toxicity on genes would go hand in hand with genome based prescription and treatment.

A small investment today in this area would go a long way in the future.


ACS offerings available 1. Toxicogenomics - This combines toxicology with genomics or other high throughput

molecular profiling technologies such as transcriptomics, proteomics and metabolomics.2. Pre-processing of assay data - Data quality services to extract, clean, analyze and integrate

toxicological data sourced internally. 3. Leverage toxicity data available publicly - Suggest, download, centralize all available

external toxicity data and integrating with internal assay data for creating practical and useful QSAR and structure models.

4. Text Mining – Automated downloading, trend analysis and reporting of newest patents, publications, and developments pertaining to toxicology worldwide, even social media posts are included for toxic substances.

5. Data Mining - Setting up Data mining of experimental data after pre processing to solve specific business problems.

6. Develop Expert System – work with experts to develop prediction models. Its like incorporating all the knowledge gained so far into a big software program that can be applied on new compounds.

7. Undertake general projects - Propose project for prioritization of lead compounds based on predictive toxicology during initial phase before its is too late.

8. Big Data - Set the Big Data ball rolling by offering consultancy on how to hook onto existing systems for collaborative research. Environmental science is the most suitable candidate for big data application use.

9. Carcinogenicity Tests - Assist with their in vivo carcinogenicity tests.10. Expand services by virtue of collaboration – In the field of data and predictive analytics

and cancer related services, facilitate expansion of your service portfolio.Accommodator Consultancy Services

Lucknow

Value we would add


We have vast experience in database development, data analysis, text & data mining and other programming languages. We will take the IT and statistics worries away from you so you can concentrate on pure research.

Vast experience on a number of software platforms. Team consists of chemist, data warehouse and data mining

professional and senior cancer surgeon. SME’s will act as bridge between IT developers and scientists.

Able guidance of Dr. Naresh Kumar, with over 3 decades of relevant industry experience is available.

We are based in Lucknow and will give you the attention you deserve.

We offer exceptional value for money with a number of flexible project development, execution and tracking models.

Questions/Comments?


In the interest of keeping material short, only a simple summary has been provided. Please do not hesitate to ask any questions/clarification for further details.

Our contact details:

Ankur Khanna: Director Technical 945 166 8432

Dr Vibhor Mahendru: Director Business Development 800 536 5132

THANK YOU