Automated Compound Collection Enhancement: how Pipeline Pilot preserved our sanity. Darren Green GSK

Transcription

1 Automated Compound Collection Enhancement: how Pipeline Pilot preserved our sanity Darren Green GSK

2 The Compound Collection Enhancement Challenge A diversity of ideas Which are novel Which have the desired properties Which can be synthesised without difficulty Which give rise to a diverse collection of scaffolds & molecules Which can be accessed in required numbers Which can be accessed within budget and resource limits ( Which deliver quality leads for GSK programs and increase the % of targets which find a lead

3 The Compound Collection Enhancement Challenge The difficulty lies in the AND A diversity of ideas Which are novel AND Which have the desired properties AND Which can be synthesised without difficulty AND Which give rise to a diverse collection of scaffolds & molecules AND Which can be accessed in required numbers AND Which can be accessed within budget and resource limits AND Which deliver quality leads for GSK programs and increase the % of targets which find a lead

4 What is the GSK Screening Collection? GSK Compound Collection All GSK samples Natural Products Electrophile set Steroids GSK Screening Collection Samples One sample per structure Volume QC Purity and surety Compounds Property constraints Sub-structure filters Collection Model Reagents FBDD

5 Goals for the Screening Collection: 2002 To contain at least one molecule, preferably multiple series of molecules, which exhibits the required biological response against any target presented for screening. To contain only those molecules that are deemed to form a good start point for medicinal chemistry. To contain an ever increasing proportion of molecules which are amenable to array synthesis. To contain an ever increasing proportion of molecules which have no undesirable developability features.

6 Historical overview of GSK In Silico Filters Undesirable substructures (legacy) Hann et al. J. Chem. Inf. Comput. Sci., 1999, 39 (5), pp (Hard filters); S. Garland and D. Hickey (Don t buy filters); D. Green, A. Whittington (Fierce Filters); G. Seibel, S. Hung (Hits Manager Filters) All in silico filters widely agreed upon (2002) Global review team from medicinal and computational chemistry. Major revision in 2009 Global Med. Chem. review of existing filters MDR initiatives to refine and evolve filters Cleaning The Collection (CTC) Compound stability analysis Comparison with external literature Filters continually evolve with chemist feedback

7 Different classes of substructure filters Interference Compounds Reactivity functionality Fluorescent Aggregate Poor start points Beta lactams, steroids, complex natural products Too flexible Poly aromatics. Controlled substances Uninteresting Too few heteroatoms Functionality locked up in one group Adequately covered already HARD filters No interest in these compounds Remove from collection Don t purchase SOFT filters Chemists equivocal Have them so screen Two strikes and you re out Focus eyeballing on these compounds if capacity. Acquisition only filters Can do better e.g. Multiple acyclic amides

8 Attributes of a compound collection Goal GSK standard Descriptor Example Diverse chemotypes Chemical Structure Scaffold & Fingerprints Ligands for diverse proteins Pharmacophore (Reduced Graphs) R P A Ar Ar/A Desired property space: Current Lead like emphasis Molecule Complexity Physicochemical Chirality Desirability function logp MWt Solubility

9 GSK Screening Collection Selection Strategy Derived from a mathematical model developed at GSK parameterised with actual screening data Enables us to ask questions such as given this portfolio of targets and these possible compounds, which ones should be sourced to give the maximum return (leads) Selection/Design strategies can be derived from the model and these underpin the strategy: A breadth-first sampling is optimal: sampling a new cluster of chemical space is more valuable than expanding on existing clusters Proactive seeking of novel chemical templates Large combinatorial libraries, which contain many similar molecules, are particularly damaging to the performance of a screening library Use non-combinatorial design methods and limit the number of compounds per library clusters containing leads Hit Non-Hit Lead α i Probability that a compound is active given that i contains a lead Harper, G.; Pickett, S. D.; Green, D. V. S. Design of a compound screening collection for use in high throughput screening. Comb. Chem. HTS (2004), 7(1),

10 GSK is Highly Selective- example from 2010 All new compounds for potential purchase are filtered through three phases: i) focus on lead-like physicochemical properties ii) removal of molecules with undesirable functional groups iii) requirement for adding diversity to existing set Commercially available compounds from selected suppliers Filter on lead-like properties (clogp<3, mw<360) Remove undesirable functional groups Diversity selection to avoid oversampling areas of chemical space 4,600,000 (100%) 530,000 (11%) 130,000 (2.8%) 42,000 (0.9%) Identified for priority purchase by med chem Check supplier availability Pass GSK QC 26,000 (0.6%) 18,000 (0.4%) ~16,000 (0.35%) Enter GSK collection If molecules don t meet our criteria we don t acquire them!

11 Philosophy: Diverse Compound Streams yield a Diverse Screening Collection Portfolio Focussed Chemistry Chemical Diversity GSK Project compounds GSK Off the Shelf or Commissioned GSK Scientist Proposals Natural Products/TCM External Companies A 2 Z

12 in GSK circa 2006 Chemist s ideas 2 automation facilities ~70 chemists ~5 comp chemists AC/SMTech etc CIX dept. to write/maintain end user tools Design Tools Robust reactions QC systems Up to ~80K molecules p.a. >>$20 million p.a. High quality molecules

13 Medicinal Chemists have bias and this bias is not consistent Assessment of the Consistency of Medicinal Chemists in Reviewing Sets of Compounds Michael S. Lajiness,* Gerald M. Maggiora, and Veerabahu Shanmugasundaram# Computer-Aided Drug Discovery, Pharmacia Corporation, Kalamazoo, Michigan 49008

14 Sampling chemical space: global selection vs individual ideation PC2 Lessons learnt - diversity depends on the number and quality of ideas - people do have the same/similar ideas - unless you can drive ideation to very novel places, you spend too much time arguing if similar ideas are different PC1

15 An imperfect process All ideas are not equal Chemist s ideas Ideas have champions that have invested time & intellect to make them work - mothers love Design Tools Robust reactions QC systems Lowe D., Chemistry World; 6: 22, 2009 A high quality COLLECTION?

16 in GSK 2017 Internal design & analysis team No full time staff All synthetic chemistry conducted outside Sample management and Q/A internal Up to 150K compounds p.a. << $20 million p.a. How?

17 Efficient/effective access to chemical diversity using minimal GSK FTE: Tangible Compounds Templates and exemplars Filter Attractiveness assessment Prioritise Design/ selection Synthesis Suppliers Team Med Chem Panel Team Team Suppliers 20-30K templates novel to GSK Predicable syntheses from 100s of chemists 1K-10K templates for prioritisation templates ~ 300 exemplars /template Competitive pricing includes: - Route development - Synthesis - Purification Investigation, tractability, failures, success, unsold ideas ALL INCLUDED IN COMPOUND PRICE

18 Medicinal Chemistry Input into Template Selection Get input from Med Chem WW but try to minimise the pain...! Combine ideas from two different books: The Wisdom of Crowds by James Surowiecki Why the Many are Smarter than the Few =>The Med Chem Panel Blink by Malcolm Gladwell The Power of Thinking Without Thinking => Harnessing Intuition

19 The Survey Tool

20 Selection process overview New compounds Remove duplicates with GSK Remove undesirable structures Lead like properties, Solubility prediction co-cluster with GSK By supplier with lowest number of target compounds - Order template by #molecules - Run Greedy algorithm to select target # - Add to GSK & selected - Next template Next supplier Selection algorithm Yields compounds that couldadd value to GSK collection Picks the mostvaluable compounds for GSK

21 Timing and work patterns October: November: December-January: February: March: Sept-Dec: October: Call for proposals (templates) Triage and Survey Vendor enumeration GSK selections Commission synthesis Compound delivery Start all over again

22 How long before you begin to lose your sanity? Foreach year Analyse & prioritse 30K templates 1K virtual library enumeration/profiling/triaging 300 designs Ordering, tracking, duplicate checking, inventory, QA failures Metrics Repeat Pressure => stress, mistakes Repetition => boredom, mistakes, staff attrition

23 GSK acquisition process

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26 43 Protocols Most address admin functions and process!!

27 Partnership vs Off the Shelf example Off-the-Shelf 4,600,000 (100%) Preferred Suppliers 482,405 (100%) 530,000 (11%) 130,000 (2.8%) 42,000 (0.9%) Lead-like Physicochemical properties Eliminate undesirable functional groups Compounds adding diversity to existing set 446,886 (93%) 394,629 (81%) 200,021 (41%) ~16,000 (0.35%) Enter GSK Collection 128,561 (27%)

28 Acknowledgements Stephen Pickett Siobhan Clancy Darren Rimmer Steve Besley Omar Rahman Tony Dean Biovia Tessella