Vanderwall cheminformatics Drexel Part 1

Cheminformatics & The evolving relationship between data in the public domain & pharma Dana Vanderwall Cheminformatics, Bristol-Myers Squibb

How do we start to find a new chemical that might be the next drug?
Typically - Need a specific protein to target that we think we can use to fix the problem that causes the disease
Caveat: emerging trends (&what’s old is new again)
Need to design experiments that test for chemicals that can fit that protein (lock & key)
Thousands to >2 million chemicals are tested with that protein to look for a starting point

This is where drug discovery gets really modern
Highly automated robots and infromatics can do work that used to take years in 1 week

Compound optimization
Compounds are optimized for many parameters including
Potency, selectivity, oral bioavailability, safety
1-3 years >2 million compounds tested in primary assay Make another 2-10,000

Getting ready for the clinic
All compounds are tested for safety in animals
Need to prove we can give enough to get the positive benefit without side effects
We have to be able to make it on a scale and form suitable for dosing in the clinic
The early stages need milligrams or grams (tablespoons)
To start testing in humans requires many kilograms of very, very pure material
1-3 years 2-3 years

Profiling Assays and Lead Op Progression 2M 2K 200 1-10 1 2-5 10-50 300-600 HTS Hit Triage Early Lead Op Late Lead Op Target to Hit Hit to Lead Lead to Candidate # Compounds # Assays

Chemical Structures are the Intellectual Property
The targets exist in nature- chemical structures are the unique component that pharma & biotech can bring to the table
(Biologicals are increasing in importance)
As such, the structures, and their biological activity, are extremely sensitive
Captured in the patents filed
Never disclosed until protected
Even similarity/sub-structure searches on public sites are treated cautiously

GlaxoSmithKline moves to stimulate public-private partnerships for R&D in neglected tropical diseases
http://www.gsk.com/responsibility/access/rnd-neglected-tropical-diseases.htm
GSK launched the open lab at Tres Cantos as one way in which to share our expertise and seek to stimulate open innovation in drug discovery into diseases of the developing world
60 slots for scientists
Access to screening facility & TC staff scientists to support collaborations
$5M GBP facilities expansion
Committed to sharing data & IP on GSK research in DDW
Starting with recently generated novel anti-malarial hits

Malaria
Mosquito-borne infectious disease, caused by the plasmodium parasite
250 M cases/annum, 1-3 M deaths
Variety of drugs available, but resistance is a constant problem
http://www.mcwhealthcare.com/malaria_drugs_medicines/life_cycle_of_plasmodium.htm

The Complexity of Cell Biology The assumption is: One target One consequence Target
In reality:
This target is one component of a complicated biochemical network.
A selective probe may influence many pathways.
Probes can interact with multiple targets.
Network interactions can be redundant.
Biological effects are often a consequence of interaction with multiple targets.
Target

Emerging paradigm- look for the cellular activity first
Advances in cell biology & the HTS platforms are enabling HTS screening for a cellular phenotype
Start with something that works in a cellular model for disease phenotype (a.k.a. black box), then figure out how it works
Target deconvolution

Supporting black-box HTS for anti-malarials
2M compound GSK HTS collection screened @ 2  M vs. P. falciparum (3D7) infected human erythrocytes
12 mos. Screening in biohazard lab
Avg. z’ = 0.7
19,451 primary hits; inh. parasite growth >80%; 13,533 confirmed in via retests
1,982 showed cytotox in HepG2s @10  M
None active in cell background control
8,000 also active against DD2 (multi-drug resistant strain) >50%
F-J Gamo et al. Nature 465 , 305-310 (2010) doi:10.1038/nature09107

Characterizing the hits
Clustering was used to help characterize chemical space
416 “molecular frameworks” Bemis & Murcko J. Med. Chem. 39 2887 (1996)
857 clusters/1978 singletons by Daylight FP/Tanimoto (.85)

Three-dimensional plot of some of the novel chemical diversity present in TCAMS F-J Gamo et al. Nature 465 , 305-310 (2010) doi:10.1038/nature09107

Characterizing the hits
Compounds with an abnormally high frequency of activity across HTS campaigns were filtered out
Excluded where IFI=5% where tested in >100 HTS to 20% where tested in >25 HTS (~1800 cmpds.)
~70 compounds that clustered with know anti-malarials
How are these rest of these compounds working???

Can we leverage the historical target data on compounds?
Target assays
Clear relationship between interactions and measurements, but what does it mean biologically?
Can we use the data to figure out which targets lead to which biological response? ? stimulant readout
Phenotypic assays
Clear biological result associated with readout, but from which interaction(s)?
kinase_1 kinase_2 kinase_3 kinase_4 7TM_1 7TM_2 NR_3 NR_2 NR_1

Can we leverage the historical target data on compounds?
Find all target assay data for compounds tested in anti-malarial screen
Aggregate at the target-result type level (max pIC50/pEC50)
Of the 2M tested, 130K had some associated target assay data
Incl. 3,435 of the 13,500 ‘actives’
“ Hits”* at 413 targets
*pIC50 >7.0 for antag/inh/blocker
*pEC50 >6.5 for ag/activation/opener
Given that some targets are screened in 2-3 modes, >650 target-result type combinations
Surely not all 400 targets are significant
Data very sparse, avg. ~2 pXC50s per compound that had data

Finding targets ‘enriched’ among the anti-malarials
An ‘enrichment’ was calculated for each possible target-result type combination
Are compounds active at target X more prevalent amongst the compounds that inhibited P. falciparum, o r equally distributed across all screened compounds?
For each target –result type, calculate:

Narrowing down the possible candidates
~140 targets @ ≥2 fold enrichment
~50 with homologues in P. falciparum
400 targets
>2 fold enrichment F-J Gamo et al. Nature 465 , 305-310 (2010) doi:10.1038/nature09107

Targets with homologues in P. falciparum genome Aspartic protease Methionyl-tRNA synthetase b-Ketoacid reductase Phenylalanyl-tRNA synthetase Calcium/calmodulin-dependent kinase Phosphatidylinositol 3-kinase Cysteine protease Plasmodium electron transport chain Dihydrofolate reductase Ribosome Dihydroorotate dehydrogenase Ser/Thr protein kinase DNA gyrase Tyrosyl-tRNA synthetase Isoleucyl-tRNA synthetase

Targets with NO homologues in P. falciparum genome GPCR: Adrenergic antag Nuclear Receptor ag/antag GPCR: Cannabanoid antag Ion Channel inh GPCR: Chemokine antag Phospholipse inh GPCR: Cholinergic ag Lipid amide hydrolase inh GPCR: Free Fatty Acid ag Serine protease inh GPCR: Serotonin ag/antag Toll-like receptor ag GPCR: Opiod ag/antag GPCR: Peptide hormone receptor ag/antag

Data publicly available
All chemical structures and exp. data for compounds available@http://www.ebi.ac.uk/chemblntd
EXT_CMPD_NUMBER
SMILES
Percentage_inhibition_3D7
Percentage_inhibition_DD2
Percentage_inhibition_3D7_PFLDH
XC50_MOD_3D7
XC50_3D7 (µM)
Percentage_inhibition_HEPG2
Chemical cluster Nr
IFI
Graph_Frame_Cluster
Target_Hypothesis
P. falciparum locus
Commercial Supplier_Reference
Additional information & interest in additional collaborations contact:
[email_address]

And the raw target data used to develop hypotheses?
That was trickier
Release the list of 400 targets & all the inactive compounds would
Reveal our whole compound collection
All the targets in the current (and past) portfolio
Needed some level of validation for analysis to publish

Surrogates for internal data
Chemical structures associated with a particular target hypothesis were used as ‘bait’ to find published structures & data that validate proposed MOA for each chemotype
Similarity & SSS in Aureus DBs & SciFinder
Exemplars and their similarity to original hits published in Suppl. Material with reference
We often found our own compounds and data in J Med Chem and Patent literature.

Acknowledgements Anti-malarial HTS
Tres Cantos Medicines Development Campus, Tres Cantos Spain
Medicines Research Centre, Stevenage, UK
Darren VS Green
Collegeville & King or Prussia, PA, USA
Vinod Kumar
Samiul Hasan
James Brown
Catherine Peishoff
Lon Cardon
Francisco-Javier Gamo
Laura Sanz
Jaume Vidal
Cristina de Cozar
Emilio Alvarez
Jose-Luis Lavandera
Jose Garcia-Bustos

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Vanderwall cheminformatics Drexel Part 1

by Jean-Claude Bradley on Dec 05, 2010

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Vanderwall cheminformatics Drexel Part 1 — Presentation Transcript