the trend to outsource computational chemistry€¦ · > cresset has been offering computational...

The trend to outsource computational chemistry Dr Martin Slater Director of Consulting

© 2013 Cresset Group Ltd.

> Unprecedented change over the last 5 years > Turbulent times for the pharma industry > Trend for outsourcing chemistry – 2007-2012

A historical perspective on drug R&D

Areas / companies benefiting: Asia/US AMRI WuXi EU Evotec BioFocus/Argenta


R&D budgets in decline

> R&D spend for pharmaceutical companies historically a larger proportion of sales than any other industry – including high tec - software, Aerospace

> Limited protection for the ROI > Patent cliff for several high earning drugs > Revenue swallowed up by the generics market

> ROI, now low hanging fruit gone, poor prospect for investors – perceived innovation deficit

> …….something had to give…………


Drug discovery must continue…but…...how? > Further fragmentation of the drug the discovery process: > More focus given to collaborative working

> Open innovation - Eli Lilly > Virtual company models > Outsourcing / Offshoring – eg. ASIA

> Drug discovery elsewhere …..

> Not-for-profit organisations / Academia/ government sponsored ……..Quicker?……. Cheaper?..........Smarter!!

New opportunities - New models

© 2013 Cresset Group Ltd. 5

Where is comp. chem. useful in the DD process?

Drugability Lit.

precedence

Sequence analysis

chemogenomics

Protein modelling

3DQSAR target

3DQSAR off target Tox target

Optimisation selectivity properties

De-novo Ligand design

scaffold hopping

Bioisosteres Patent scope

Bioactive conformation Binding mode

Library design

Compound selection

Virtual screening

FBDD Fragment

joining

Peptide mimetic design

HTS rescue hit

expansion Thematic Analysis™: a chemogenomic approach to GPCR drug discovery. Crossley R , Macritchie JA , SlaterMJ . Current topics in medicinal chemistry 2011 ;11(15):1925-43

Sequence alignments

animal-human

De-novo Ligand design scaffold

hopping

Virtual screening


Working with Cresset BMD

> Cresset has been offering computational chemistry as a service for a decade!

> Over a100 projects completed for customers.

> Enzymes, Channels, Receptors, Anti-infective

> We are now expanding this service and are offering an extremely flexible ‘Comp. Chem. On-Demand’ service

> Powerful combination > Unique software > Software infrastructure > Expertise

> scientists with frontline experience in drug discovery


PDE5

11bHSD

CDK2

HIV NNRTI

CCK2

> Drug Discovery Projects • 100+ projects • 80%+ success rate • Wide range of targets

– GPCRs – Ion Channels – Kinases – Proteases – Protein-Protein Interactions – etc.

> Software Sales • Top 20 pharma comps • 20+ Biotechs • Top academics

Cresset’s Track Record


Work in 2012

Completed 14 projects:

> Protease X2 Virtual screen /de-novo design > Nuclear hormone Patent protection Receptor > Antibacterial X3 3DQSAR/modelling Med.Chem.Support > Oncology target X2 Virtual screen/Med.Chem. Support > Antiviral Med. Chem Support > GPCR X3 Virtual screen / SAR field mapping > Antifungal 3D QSAR / prop. optimisation > Ion channel Virtual screen


Typical tasks – 3D overlays of actives


Typical tasks – de novo design / scaffold hop


An Opportunity & a Challenge

> Provide a small diverse screening library 10K for a small biotech company > Diversity in potential biological targets to be hit

> Minimum redundancy in the set

> Maximum chance of success in finding a lead within

available budget and screening resources


Initial thoughts

> Compare 3D and 2D similarities for compound collections - are we wasting our time?

> Take a small compound collection > Full NxN calculation > 3D method = Fields & Shape > 2D method = atom pairs

> Compare and Contrast


Compound Collection

> BIONET 'Rule of Three' ('Ro3') Fragment Library: “7,907 'Ro3'-compliant fragments”

> Conformation hunt on every fragment Maximum of 5 conformations (!)

> Full N x N similarity matrix, 3D & 2D (60 Million data points)

> ~30 compounds failed conformation hunting


Problems

> 400Mb of data > Tedious to use and examine Pilot study just using the first 500 compounds

> Some chemical families in this area

> Still a large dataset to deal with (250,000 data points)

> 2D similarities and fragments > Small changes cause disproportionately high changes

> Atom pairs particularly bad

> Switch to KNIME fingerprints

All 2D values lower than ‘normal’


Comparing 2D and 3D metrics

Agreement


N NHO

O

Cl

Example - Similar Scores

N NHO

O Cl

Cl

101 104 2D sim = 0.9

3D field sim = 0.87


Example - Higher 3D Sim

2D sim = 0.1 (other methods=0.3)

3D field sim = 0.82

SO Br

N

HNO


O

O

O

O

HN

Example - Higher 3D Sim

141

2D sim = 0.2

3D sim = 0.7

454


So…

> Pilot study suggests some added value > Full matrix painful even if we could calculate it

> What about a reduced matrix?

> Use ‘Probe’ compounds to tease out molecules that are different in Field space

How many probes?

Across how many molecules

> We were running out of time…


Compound selection by Field Diversity

> Proposed workflow for generation of a field diverse library:

9M

commercial compounds

Calc. 200 X 200 2D similarity

matrix

Pick 20K sub-set

Pick 100 Diverse

Field probes

Calc. Shape Diversity by

PMI

Pick 200 sub-set

Property Filters

Calc. 20K X 100

Field similarity matrix

Pick 12K Field

Diverse set

3D PCA on Field matrix

1.2M


Field Diverse library: Outcome

12K ‘Field Diverse’ library mapped by 3D PCA on the 100 x 20,000 ‘Field Similarity Fingerprint’

Distinct separation of charged species within this space

Ammoniums Piperidines

….so what!!

Benzoic and aliphatic acids


Field Diverse library: Outcome

12K ‘Field Diverse’ library mapped by 3D PCA

Distinct separation of by molecules by size within this space

….so what!!

Decreasing Size


Deeper - Moderate ‘Field Similarity’

Alignment to ‘template1’



Alignment to ‘template1’ Random selection of mols



Alignment to ‘template’


Is the chemical space sensible?

Small sulphonamides

Large esters

Two example clusters


Conclusions

> Work in progress > Full similarity matrix shows potential of 3D sim to add value > Full matrix difficult to handle and possibly unnecessary > Using probes looks like a possible solution

> But how many – 100 arbitrary? Should they be field diverse?

> PMI – binned by mwt?

> Not a panacea - still need to play the numbers game > All methods have bias – strongly field distinct compounds are

discriminated well but…the rest? > Major issue not enough breadth to sample conformations


> We have excellent software > An excellent team of modellers > We are extremely flexible > We can deliver on diverse projects > Integrity

> If we don’t think we can help you we wont try and sell you a solution we don’t believe in

Conclusions – comp. chem. solutions


Acknowledgements

> Cresset > Tim Cheeseright

> Rob Scoffin

> Mark Mackey

> James Melville

> Mission Therapeutics > Keith Menear, Mark Kemp

the trend to outsource computational chemistry€¦ · > cresset has been offering computational...

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