Prof. Samir K. Brahmachari

Former Director General, CSIR India

Chief Mentor, Open Source Drug Discovery

Academy Professor, AcSIR

J C Bose National Fellow

CSIR-Institute of Genomics and Integrative Biology

New Delhi, India(http://samirbrahmachari.rnabiology.org/)

Basel

07 September 2015

The Open Source Drug Discovery Project:

Need for Global Collaboration

9th European Congress on Tropical Medicine and International Health

Breakthrough

Science

New

product

Return on Investment

From Market

Increased

Involvement

in R & D

Patent

Protection

Virtual Cycle

Investment

Investment

Traditional Innovation Model

Lack of market incentives for neglected diseases break the traditional innovation cycle

Why Open Source Drug discovery ?

Drug at affordable cost is the right of all.

Present drug discovery is driven by market size.

For infectious diseases like MTb market size is only US$ ~300m and

are not profitable for major Pharma Companies to invest.

Successful Open Source Models

o Human Genome Sequencing Initiative

o Open Source Software Initiative (eg: Linux OS)

o The WWW

Confidentiality and IPR Protection increases cost and decreases free

knowledge sharing for drug discovery.

Why Open Source Drug discovery ?

“Drug Discovery” is not easily reproducible.

Drug Discovery need to move out of indoors of Pharma Companies to

Open Sky for young generation globally to participate.

Present IT infrastructure, connectivity and high throughput analysis

capability makes OSDD possible.

The work can be done by Academia, University students and CRO’s at much lower cost.

NCE will become Generic as soon as it is discovered.

Protection of IPR (only through “Click Wrap” protection of database)

least required as major Pharmas will not work on Generic products.

Credits and Intellectual Property

Open Source approach will allow National as well as Global participation without confidentiality barrier.

Web based submission and collaboration.

Every submission would be provided a unique submission ID.

“Microattribution” – A novel credit system for contributors.

Web based data will be “Click-Wrap” protected to avoid creation of

private good from public good.

From: William A. Haseltine] Sent: Thursday, September 20, 2007 6:01 PM

Samir, I read an article in today's Mint reg virtual drug discovery. What a great idea. I am hosting a special session at the Aspen Institutes’ Health Forum Oct 4-6. My session is on The 5th, New Approaches to Drug. Development. Your idea would fit right in.

I would like to invite you to present at the conference. Look at the website and see how interesting the entire conference is. We pay your expenses including travel. I know it is last minute but I didn't know you were thinking this way until now. I do hope you can attend.

All the best, Bill

Mail From:

William HaseltinePresident, Haseltine Associates, Ltd.; former CEO, Human Genome Sciences

M. tuberculosis SysBorgA rational platform for drug target identification and assessment studies for infectious diseases using Systems Biology of whole organism

M. tuberculosis

entering a macrophage

Latent stageLatency?

Questions in Tuberculosis

Fumarate

ATP

Modelling Metabolic and Signalling Network

Need for Open Source Drug Discovery Foundation

• A CSIR Team India Consortium with Global Partnership with a vision

to provide affordable healthcare to the developing world.

• The concept is to collaboratively aggregate the available biological

and genetic information to facilitate use and hasten the drug

discovery process.

• Inspired by the success of Open Source models in Information

Technology (Web Technology, Linux) and Biotechnology (Human

Genome Sequencing); OSDD too works in a virtual, distributed whole-

Earth “macroscope”.

• OSDD provides a global platform where the best minds can

collaborate and collectively contribute to solve the complex problems

associated with discovering novel therapies for neglected tropical

diseases.

• On date OSDD has >8000 registered participants from 130 countries.

Untapped Pools of Scientific Talent

•Traditional Talent Pools

•Opportunity pools of intellectual capacity

Open Innovation reaches outside the four walls and literally attracts everyone those who are willing to solve problems

Author, Angela Saini

Geek Nation: How Indian Science Is Taking Over The World

http://www.sunday-guardian.com/bookbeat/tour-of-indian-science-that-fails-to-see-full-picture

OSDD is now an internationally reputed drug discovery initiative pioneered by

Government of India: Gone Global as OPEN SOURCE PHARMA(2015)

Report of the CEWG of WHO

Recognised OSDD as an Open

Innovation Model5 April 2012 | Geneva

How Open Source Drug

Discovery Is Helping

India Develop New DrugsApr 9, 2012

DNDi POLICY BRIEF recognised

OSDD as part of Global Landscape

for Neglected Diseases R&DApril 2012

Crowd Sourcing

Innovation:

CSIR portal for OSDD2011

Crowd-Sourcing Drug Discovery24 February 2012

Vol. 335 no. 6071 p. 909

OSDD Innovation Model Recognised Globally…

Editorial dated: April 17, 2010

OSDD Distributed Virtual Laboratory &

Some Current Partners

NIIST

CLRI

Compound Repository

TB Screening

Compound

Repository

Screening for TB &

Malaria @CDRI

Compound

synthesis

Screening

Clinical Trials

LRS

Screening

Screening

IICB

IIT K

Platform to assess & estimate the property of molecule(s) using

chemoinformatics approaches to diagnose their potential as drug

Molecules

•Classify the compound

•Compare with compounds in

existing databases

•Property calculation

•QM calculations

•DFT based properties

•Calculation of

conventional descriptors

•Calculation of drug-like

properties

•Fragmentation of compound

•Docking with possible targets

•MD Simulations

•QM/MM

•FEP Calculations

Compound class

and type

Property Profile

Constituent

fragments , their

properties

Interactions

with targets

Interactions

with CyP450

Possible reaction

intermediates and

toxicity profile

Molecular Property Diagnostic Suite (MPDS)

CSIR-IICT CSIR-NCL CSIR-IMT BBAU CSIR-CLRI JNU NIPER

Dr(s) Sastry Karthikeyan Raghava

OSDD Outreach

Programme

Chemically Diverse

Compound Library

Initiative (CDCLi)

Individual Driven

Projects

Fund US $ 0.9 Million

• 120 Compounds

Deposited in Mol Bank

• 120 Screened against

TB

• 47 Screened against

Malaria

• 2 Found Active in

Malaria Screen

Started in September 2011 Started in May 2012 Started in September 2008

Fund US $ 1.2 MillionFund US $ 0.4 Million

• 57 Projects

• 59 PIs Involved

• 45 Projects

• 29 PIs Involved

• 64 Students

• 7 Projects

• 12 PIs Involved

• 27 Students

• 40,000 Compounds

Screened against M.

tuberculosis at CSIR-

IIIM and CSIR-CDRI.

• 7 New Active Scaffolds,

2 Hit to lead

• 70 Actives and 1 in Lead

Optimization.

• 1100 Compounds

Deposited,

• 1100 Screened against

M. Smegmatis

• 28 Found Active

Crowd Sourcing Chemical Synthesis & Screening for OSDD

OSDD: Distributed Collaborative

Open Source Drug Discovery Platform

and More…

Open Innovation from

Best Minds in Academia

& Industry

Industry / CROs

Open Data for

Community Access &

Inputs

First Time in India:

Clinical Trial of Anti TB

Novel Multi Drug

Regimen.

DiscoveryDevelopment

Clinical Trial

Over 100 Labs 15 CROs/ConsultantsPartner: LRS TB Hospital,Ministry of Health & Family Welfare

CRO Partner: GVK Biosciences

OSDD URL : www.osdd.net Sysborg URL: http://sysborg2.osdd.net

OSDD Community

>8000 Members 130 Countries

Tuberculosis Malaria Leishmaniasis

OSDD-TB Alliance Phase IIb Clinical Trial

In MDR Tuberculosis Patients

To evaluate the anti-mycobacterial activity, safety, tolerability and

pharmcokinetics of drugs/regimens under evaluation

• Trial Center: LRS Institute of Tuberculosis (a tertiary care hospital),New Delhi

• Trial Size: ~80 patients in each arm

Recruitment has been initiatedTrial data to be made open without comprising

patient confidentiality

Pa+ Cat IV regimen 2 months of treatment

Cat IV regimen

Pa-M-Z

Cat IV treatment

Pa = PA-824; M = moxifloxacin; Z = pyrazinamide

Hospitalization

With TB ALLIANCE

8100 members from over 130 countries

Prof. Samir BrahmachariChief Mentor, OSDD

Dr. T. S BalganeshFormer Head, OSDD

Dr. Sarla Balachandran Project Coordinator

CSIR-OSDD

Dr. Geetha Vani RayasamCSIR-OSDD

Zakir ThomasFormer Project Director

CSIR- OSDD

Dr. Anshu BhardwajCSIR-OSDD

Dr. U.C Jaleel OSDD

Systems Level Approaches to Identify Non-toxic Targets & Inhibitors

Problem Approach

Results Significance

From systems level analysis:

Potential Non-toxic targets based on Interactome data

Drug like inhibitors design based on Metabolome data

Prioritize candidates for validation Provides mechanistic understanding

at systems level Has a potential to reduce attrition

Identify potential choke points & back ups From random prototypes to simulated designs

Systems Biology is the integrated approach to studying biological systems— intracellular networks, cells, organs, and any biological entity—by measuring and integrating genetic, proteomic and metabolic data. This approach involves cellular and pathway events that are in flux and interdependent. Its application to drug discovery includes utilizing clinical samples from diseased and healthy (normal) patients to uncover System Biology Markers and Pathways Targets, which are indicators of disease and potential targets for therapeutic intervention.

Need for System Biology Approach :

Literature

Annotation Tools

Genomic Databases

Curated Annotations

Raw Annotations

OSDD C2DCommunity

800+ Student Researchers

Collaborative Curation

Gene Ontology |Protein Interaction | Protein Structure/Fold |Metabolic Pathway|

Mtb Genome Annotation Through Crowd Sourcing

99.5% Annotated >28,000 Publications

www.osdd.net

Annotating on a Virtual Cloud Workspace

ANNOTATORS

DISCUSSIONDATA

4000 Genes Annotated in 10 month

“Given enough eyeballs, all bugs are shallow” - Linus Torvalds

Errors were discussed, marked and corrected by the OSDD community

Social Networks to Create Biological Networks

Community Operated Web 2.0 Channels

MicroBlogs for Sharing and Collecting Literature

Online Thematic Discussion Groups

OSDD Platform: Sysborg 2.0System Architecture

Collaborative tools to accelerate neglected diseases research” in the book “Collaborative Computational Technologies for Biomedical Research”. Wiley and Sons. May 2011

Plenty of experimental studies are buried in literature over years…

Challenge was to manually

mine literature to understand each protein

Collaborative literature mining/analysis to

unlock relevant information

Generated the largest protein-protein functional interaction network for

Mycobacterium tuberculosis

Sequence and Structural Level Analysisof 73 Central Proteins

Sequence Level• With Human Genome.• Human Gut and Oral Flora.

Structural Level• Peptide conformational analysis

.• Protein Binding site level

analysis.

17 Central Proteins asTargets (Interactome)Non-Synonyms variations in Mtb Genomes (1620 clinical isolates)

PresentAbsent

Variations

Number of Genomes

Targets Targets

Building Biological Network with Human Network1 Council of Scientific and Industrial Research (CSIR), Delhi, India2 CSIR- Institute of Genomics and Integrative Biology, Delhi, India3 Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka, India4 Acharya Narendra Dev College, University of Delhi, India5 Goethe University, Frankfurt, Germany6 PSG College of Technology, Peelamedu, Coimbatore, Tamil Nadu, India7 SASTRA University, Tirumalaisamudram, Thanjavur, Tamilnadu, India8 SDM College, Ujire, Karnataka, India9 Sree Narayan Guru College, Coimbatore, Tamil Nadu, India10 Maharshi Dayanand University, Rohtak, Haryana, India11 Amity Institute of Biotechnology, Amity University, Lucknow, Uttar Pradesh, India12 Bharathiar University, Coimbatore, Tamil Nadu, India13 Bharathidasan University, Palkalaiperur, Tiruchirappall, Tamil Nadu, India14 Bitvirtual patan Node, Hem. North Gujarat University, Patan, Gujarat, India15 Business Intelligence Technologies India Pvt Ltd., Bangalore, Karnataka, India16 Christ College, Vidya Niketan, Saurashtra University, Rajkot, Gujarat, India17 Department of Life Sciences, Hemchandracharya North Gujarat University, Gujarat, India18 Department of Biotechnology, University of Pune, Maharashtra State, India19 CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India20 Indian Statistical Institute, Kolkata, West Bengal, India21 Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India22 Shri Ram College of Pharmacy, Karnal, Haryana, India23 The Maharaj Sayajirao University of Baroda, Gujarat, India24 Pathogen Biology Laboratory, Department of Biotechnology, School of Life Sciences, HCU, Hyderabad, Andhra Pradesh, India25 University of Kerala, Thiruvananthapuram, Kerala, India26 All India Institute of Medical Sciences, New Delhi, India27 Department of Biotechnology, Delhi Technological University, Delhi, India28 Bioinformatics Centre, CSIR-Institute of Microbial Technology, CSIR, Chandigarh, India29 Faculty of Science, Institute of Biological Sciences, University of Malaya, Malaysia

Reference: Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacteriumtuberculosis. PLoS One. 2012;7(7):e39808. PubMed PMID: 22808064; PMCID: PMC3395720.

Over View of Approach

Metabolites 961

Genes 890

Reactions 1152

Pathways 50

Reactions catalyzed by single gene 546

Exchange Reactions 97

Structured Knowledge Base of Metabolism in Mtb

Genomic, Genetic and Biochemical Knowledge of Mtb

Metabolites(284/961) Genes (Genomic order)(400/890)

Exchange

Reactions

Systems Biology Spindle Map of Metabolism (SBSM)Simplifying metabolic complexity, enabling computational analysis

Active reactions and gene as obtained from simulation on Middlebrook media

Reactions/Pathways

(439/1152/50)

Metabolites 284(961) Genes (Genomic order) 400(890)

Rv1484

Exchange

Reactions

Systems Biology Spindle Map of Metabolism (SBSM)Simplifying metabolic complexity, enabling computational analysis

Rv1484, Target of drug Isoniazed

Reactions/Pathways

439(1152/50)

Metabolites 108(961) Genes (Genomic order) 168(890)

Rv1484

Exchange

Reactions

Systems Biology Spindle Map of Metabolism (SBSM)Simplifying metabolic complexity, enabling computational analysis

Rv1484 knock out resulting into lose of information

Reactions/Pathways

172(1152/50)

Metabolites 91(961) Genes (Genomic order) 60(890)

Rv1484

Exchange

Reactions

Systems Biology Spindle Map of Metabolism (SBSM)Simplifying metabolic complexity, enabling computational analysis

Metabolites, genes and reactions became active upon Rv1484 knockout

Reactions/Pathways

57(1152/50)

Deciphering Alternate Metabolic Routs upon Drug Target Knock-out

Rv1484(inhA)

Absolute Intracellular Abundance of Predicted Essential Genes

(Single-Gene Knock Out) 75% are experimentally validated

Of the total 116 single knockout lethal genes, 97 genes were expressed,

resulting in enzymes of measurable concentration. 75 of these expressed

genes resulted in protein/enzyme concentrations lower than the total mean

concentration of Mtb proteome.

• nadA~E: Involved in the directional re-routing of metabolic fluxes – can potentiate persister formation

• MurA~E: Peptidoglycan biosynthesis – Nacetylglucosamine-N acetylmuramyl – ATP-dependent amino acid ligase required for stepwise synthesis of pentapeptide side chain

• ppsA~E: type-I polyketide synthase – production of phthiocerols and phenolphthiocerols

• kapA~C: Involved in Host-Pathogen interaction, through a calcium channel

Few Potential Metabolic Targets

Metabolic adaptation:

Mechanism: The activation of nadA~E operon lead to de novo biosynthesis of NADH, pool which is then reduced to NAD following the activation of the nuoA~Eoperon coding for NDH-I. This maintains the electron flow with proton translocation, which increases the potential difference across cell membrane, and can potentiate ATP production, thereby providing the necessary energy when challenged with antibiotic stress.

Mycolic AcidBiosynthesis

de novo synthesis Of NAD

Isoniazid BlocksMycolic AcidProduction

ReductionIn Glycolysis &TCA

Activation ofde novo biosynthesis & NDH-I

Metformin as Inhibitor of NDH-I !!

Metformin as Combination Therapy for TB

890 Metabolic Genes

Genes with PDB ID

Variations in the

Genome

890 genes of Mtb with available PDB structures and Non-Syn variation data from 1620 clinical isolates

Invariant Genes out of 890 Metabolic Genes

Gene Name Reaction ID Function PDB-ID GSK Inhibitor*

Rv2361c # ** Rv2361c

Long (C50) chain Z-isoprenyl diphosphatesynthase (Z-decaprenyl diphosphate

synthase) 2VG2 2VG3 2VG4 No

Rv2965c coaD phosphopantetheine adenylyltransferase

1TFU 3PNB 3LCJ 3NBA 3NBK SKF-67461

Rv3588c H2CO3DCatalyzes reversible dehydration of CO2

to form bicarbonate 1YM3, 2A5VTotal= 35 reported

inhibitors

Rv3048c # RNDR2 Involved in the DNA replication pathway 1UZR GR119270B

Rv0865 MODAT Involved in molybdopterin biosynthesis 2G4R No

Rv3607c # ** DHNPA dihydroneopterin aldolase 1NBU GSK2168465A

Rv0321 dcd interconversion of dCTP and dUTP 2QLP, 2QXX No

Rv0156 pntAbNAD(P) transhydrogenase (subunit alpha)

PntAb No No

Rv0763c Rv0763c Ferredoxin No No

Rv1305 # ** ⌘ atpE F0F1 ATP synthase subunit C No No

Rv1508A GFUCS Function unknown No No

* 177 ( open access) GSK compounds active (in vitro analysis) against M.tb# Essentiality based on experimental results ** Metabolic Persister Genes⌘ Target for Bedaquiline; molecule cleared phase III clinical trial

1NBU

Folate Biosynthesis Pathway involving an invariant gene Rv3607c (folB)

Improved docking of compounds with structure similarity to GSK reported compound

GSK, original compound docking score = -4.31251

New Molecule (NC1), improved docking

= -5.07758

New Molecule (NC2), improved

docking = -7.0281

Thanks to …

Dr. Divneet KaurPost Doc Associate

divneet.k@gmail.com

Rohit VashishtPhD Student

vashishtrv@gmail.com