Towards a comprehensive

computational platform for next

generation drug development –

A Russian‐German joint venture

Edgar Wingender CEO

Wolfenbüttel, Am Exer 10b http://www.genexplain.com GmbH

We aim to provide a comprehensive platform of

bioinformatics, systems biological and cheminformatics

tools for a

personalized medicine and pharmacogenomics

Some facts about geneXplain:

Founded in April 2010, starting active business July 2010

International (German-Russian) shareholder structure

Managing directors: E. Wingender (CEO), A. Kel (CSO)

Product portfolio in bioinformatics, systems biology,

cheminformatics

Development close to science and research

Participation in international and national research consortia

- SYSCOL (EU FP7)

- GERONTOSHIELDS (BMBF)

proteins

compounds

genes

networks

Some facts about geneXplain:

Founded in April 2010, starting active business July 2010

International (German-Russian) shareholder structure

Managing directors: E. Wingender (CEO), A. Kel (CSO)

Product portfolio in bioinformatics, systems biology,

cheminformatics

Close to science and research

Participation in international and national research

consortia

- SYSCOL (EU FP7)

- GERONTOSHIELDS (BMBF)

- TEMPUS (EU)

The idea:

Providing a platform of methods for

Biomedical research

Focus: drug development

Complete pipeline from high-throughput data to a lead structure

High-throughput data:

Genomics

Transkriptomics

Proteomics

Public private partnership

GeneXplainTM Platform: A Workflow for Drug Discovery

The geneXplain platformTM is a new product integrating bio- and cheminformatics tools for pharmacogenomics. It provides a drug discovery workflow that guides from the statistical analysis of biological high-throughput data to a panel of potential lead compounds for further validation.

Within the geneXplain platformTM, identification of drug target protein molecules by bioinformatics and systems biology methods, is complemented by prediction of biological activities and adverse effects for chemical compounds, based on multilevel neighborhoods of atoms (MNA) descriptors.

Statistics Input: High-throughput data

from patients (genomics, transcriptomics, ChIP-seq,

proteomics, etc.) Output: List of relevant genes or

proteins

Bioinformatics Search for regulatory modules in any

genomic regions Output: List of transcription factors

potentially responsible for the observed (co-)regulation of genes

Systems Biology Topological analysis of the networks upstream of transcription factors,

simulation of the network behavior, patient stratification

Output: List of potential master regulators

Cheminformatics Prediction of biological activities of the

compounds, selection of compounds with required effects and without adverse or

toxic effects. Output: List of potential lead structures

for validation

Any pre-processed list of genes or proteins from own experiments, from literature or databases

Any list of transcription factors; any list of genes or proteins from own experiments, from literature or databases to be mapped on known pathways

The workflow The incorporated statistical analyses help to identify relevant genes or proteins in the raw data, e.g. those that are differentially expressed. The Bioinformatics block allows to reveal potential regulation of genes by transcription factors or miRNAs. Systems biology approaches analyze networks of molecular events and suggest promising drug target molecules and their mechanisms of action. The integrated PASS tool enables to direct compound screening by pre-selection of chemicals with desirable and without adverse or toxic effects.

Hypotheses about gene regulators essential for the studied process

Hypotheses about target molecules and their role in the studied process

Hypotheses for validations and clinical

trials Systematic generation of

statistically significant hypotheses

Proof of concept:

Transcriptomics breast cancer cell line

Statistical evaluation

Integrated bioinformatic analysis (promoter & pathway analysis)

Systems biological simulation

Cheminformatic identification of candidate drugs

Net2Drug consortium

EU FP6, Coordinator: A. Kel

Proof of concept:

Transcriptomics breast cancer cell line

Statistical evaluation

Integrated bioinformatic analysis (promoter & pathway analysis)

Systems biological simulation

Cheminformatic identification of candidate drugs

Net2Drug consortium

EU FP6, Coordinator: A. Kel

Results:

Out of 24 million compounds, 16 substances turned out

to be feasible for experimental testing.

For 2 compounds, highly specific activities were found.

GeneXplainTM Platform: A Workflow for Drug Discovery

The geneXplain platformTM is a new product integrating bio- and cheminformatics tools for pharmacogenomics. It provides a drug discovery workflow that guides from the statistical analysis of biological high-throughput data to a panel of potential lead compounds for further validation.

Within the geneXplain platformTM, identification of drug target protein molecules by bioinformatics and systems biology methods, is complemented by prediction of biological activities and adverse effects for chemical compounds, based on multilevel neighborhoods of atoms (MNA) descriptors.

Statistics Input: High-throughput data

from patients (genomics, transcriptomics, ChIP-seq,

proteomics, etc.) Output: List of relevant genes or

proteins

Bioinformatics Search for regulatory modules in any

genomic regions Output: List of transcription factors

potentially responsible for the observed (co-)regulation of genes

Systems Biology Topological analysis of the networks upstream of transcription factors,

simulation of the network behavior, patient stratification

Output: List of potential master regulators

Cheminformatics Prediction of biological activities of the

compounds, selection of compounds with required effects and without adverse or

toxic effects. Output: List of potential lead structures

for validation

Any pre-processed list of genes or proteins from own experiments, from literature or databases

Any list of transcription factors; any list of genes or proteins from own experiments, from literature or databases to be mapped on known pathways

The workflow The incorporated statistical analyses help to identify relevant genes or proteins in the raw data, e.g. those that are differentially expressed. The Bioinformatics block allows to reveal potential regulation of genes by transcription factors or miRNAs. Systems biology approaches analyze networks of molecular events and suggest promising drug target molecules and their mechanisms of action. The integrated PASS tool enables to direct compound screening by pre-selection of chemicals with desirable and without adverse or toxic effects.

Hypotheses about gene regulators essential for the studied process

Hypotheses about target molecules and their role in the studied process

Hypotheses for validations and clinical

trials Systematic generation of

statistically significant hypotheses

The cheminformatics portfolio:

PASS predicts biological activities of chemical compounds from their structural formulae; assigns

probability values to each activity and identifies those parts of the molecule that are responsible

for this activitiy

PharmaExpert mines large amounts of predictions generated by PASS to filter out those compounds that

optimaly fit user-defined requirements

GUSAR generates quantitative structure-activity relationship (QSAR) models

GeneXplainTM Platform: A Workflow for Drug Discovery

The geneXplain platformTM is a new product integrating bio- and cheminformatics tools for pharmacogenomics. It provides a drug discovery workflow that guides from the statistical analysis of biological high-throughput data to a panel of potential lead compounds for further validation.

Within the geneXplain platformTM, identification of drug target protein molecules by bioinformatics and systems biology methods, is complemented by prediction of biological activities and adverse effects for chemical compounds, based on multilevel neighborhoods of atoms (MNA) descriptors.

Statistics Input: High-throughput data

from patients (genomics, transcriptomics, ChIP-seq,

proteomics, etc.) Output: List of relevant genes or

proteins

Bioinformatics Search for regulatory modules in any

genomic regions Output: List of transcription factors

potentially responsible for the observed (co-)regulation of genes

Systems Biology Topological analysis of the networks upstream of transcription factors,

simulation of the network behavior, patient stratification

Output: List of potential master regulators

Cheminformatics Prediction of biological activities of the

compounds, selection of compounds with required effects and without adverse or

toxic effects. Output: List of potential lead structures

for validation

Any pre-processed list of genes or proteins from own experiments, from literature or databases

Any list of transcription factors; any list of genes or proteins from own experiments, from literature or databases to be mapped on known pathways

The workflow The incorporated statistical analyses help to identify relevant genes or proteins in the raw data, e.g. those that are differentially expressed. The Bioinformatics block allows to reveal potential regulation of genes by transcription factors or miRNAs. Systems biology approaches analyze networks of molecular events and suggest promising drug target molecules and their mechanisms of action. The integrated PASS tool enables to direct compound screening by pre-selection of chemicals with desirable and without adverse or toxic effects.

Hypotheses about gene regulators essential for the studied process

Hypotheses about target molecules and their role in the studied process

Hypotheses for validations and clinical

trials Systematic generation of

statistically significant hypotheses

How to get there:

The way:

Integrated collection of bioinformatic and systems

biological program modules („Bricks“)

Based on proven BioUML technology

Statistical analysis of high-throughput data

Integrated bioinformatic promoter- and network analysis

Systems biological simulation

Unified look-and-feel

Workflow management system

Pre-defined standard workflows

Easy integration of own tools and scripts

The geneXplain platform

Upstream analysis of causes

Key node

The way:

Integrated collection of bioinformatic and systems biological

program modules („Bricks“)

Based on proven BioUML technology

Statistical analysis of high-throughput data

Integrated bioinformatic promoter- and network analysis

Systems biological simulation

Unified look-and-feel

Workflow management system

Pre-defined standard workflows

Easy integration of own tools and scripts

The geneXplain platform

The geneXplain platform

The geneXplain platform

The geneXplain platform

Clash of cultures:

Cheminformatics: commercial approaches accepted

Bioinformatics: public domain prevalent (Internet culture)

Advantages of public-domain services:

Latest state of the art

Visibility („marketing“ through publications, conference talks, etc.)

High acceptance

Disadvantages of public-domain services:

No unified look-and-feel

Low user-friendliness

Poor support

Uncertainty on side of users without expertise

Unsure long-term perspective

Public Private Partnership

The geneXplain platform

The disadvantages of the public domain are advantages of a

commercial offer

Optimal: combination of free and commercial tools

Business model:

Platform with integrated free and proprietary offerings

Payable access

Payable support

Public Private Partnership

The geneXplain platform

Advantages for the user

Standardized interface

Integrated workflows

Default parametrizations byexperts

Selection of free modules by experts in the field

Selection of proprietary, uszually low-price modules by the user

Full cost-control by the user

Public Private Partnership

The geneXplain platform

Contact: Edgar Wingender edgar.wingender@genexplain.com

www.genexplain.com