1. In memory of Rich Green 1947 - 2001 An Outstanding Medicinal Chemist and Colleague

2. “Making LeadDiscoveyless complex?” Mike Hann, Andrew Leach & Gavin Harper. Computational Chemistry and Informatics Unit GlaxoSmithKline Medicines Research Centre Gunnels Wood Rd Stevenage SG1 2NY email MMH1203@gsk.com Subtitle: Molecular Recognition versus the gambling game that we play in using HTS and libraries to discover new leads

3. Libraries - have they been successful at revolutionising the drug discovery business?  Despite some successes, it is clear that the high throughput synthesis of libraries and the HTS screening paradigms have not delivered the results that were initially anticipated.  Why –immaturity of the technology, –the inability to make the right types of molecules with the technology –lack of understanding of what the right types of molecule to make actually are  drug likeness, Lipinski,etc

4. An additional reason exemplified by a very simple model of Molecular Recognition  Define a linear pattern of +’s and -’s to represent the recognition features of a binding site  Vary the Length/Complexity of a linear Binding site as +’s and -’s  Vary the Length/Complexity of a linear Ligand up to that of the Binding site  Calculate probabilities of number of matches as ligand complexity varies.  Example for binding site of 9 features: Feature Position1 2 3 4 5 6 7 8 9 Binding site features - - + - +- - + - Ligand mode 1 + + - Ligand mode 2+ + -

5. Probabilities of ligands of varying complexity (i.e. number of features) matching a binding site of complexity 12 As the ligand/receptor match becomes more complex the probability of any given molecule matching falls to zero. i.e. there are many more ways of getting it wrong than right!

6. The effect of potency  P (useful event) = P(measure binding) x P(ligand matches)

7. Too simple. Low probability of measuring affinity even if there is a unique mode Too complex. Low probability of finding lead even if it has high affinity Optimal. But where is it for any given system?

8. Limitations of the model  Linear representation of complex events  No chance for mismatches - ie harsh model  No flexibility  only + and - considered  But the characteristics of any model will be the same  Real data to support this hypothesis!! P (useful event) = P(measure binding) x P(ligand matches)

9. Leads vs Drugs  Data taken from W. Sneader’s book “Drug Prototypes and their exploitation”  Converted to Daylight Database and then profiled with ADEPT  480 drug case histories in the following plots Sneader Lead Sneader Drug WDI Leads are less complex than drugs!!

10. Change in MW on going from Lead to Drug for 470 drugs Average MW increase = 42

11. ADEPT plots for WDI & a variety of GW libraries  Molecules in libraries are still even more complex than WDI drugs, let alone Sneader Leads WDI Library compounds are often far too complex to be found as leads !!

12. In terms of numbers  Astra Zeneca data similar using hand picked data from literature  AZ increases typically even larger (because of data picking?)

13. Catch 22 problem  We are dealing with probabilities so increasing the number of samples assayed will increase the number of hits (=HTS).  We have been increasing the number of samples by making big libraries (=combichem)  And to make big libraries you have to have many points of diversity  Which leads to greater complexity  Which decreases the probability of a given molecule being a hit

14. Concentration as the escape route  Screen less complex molecules to find more hits –Less potent but higher chance of getting on to the success landscape –Opportunity for medicinal chemists to then optimise by adding back complexity and properties  Need for it to be the right sort of molecules –the Mulbits (Mul tiple Bits) approach –Mulbits are molecules of MW < 150 and highly soluble. –Screen at up to 1mM  Extreme example from 5 years ago - Thrombin: –Screen preselected (in silico) basic mulbits in a Proflavin displacement assay specific –known to be be specific for P1 pocket. Catch 21

15. Thrombin Mulbit to “drug”

16. Related Literature examples of Mulbits type methods  Needles method in use at Roche .Boehm, H-J.; et al Novel Inhibitors of DNA Gyrase: 3D Structure Based Biased Needle Screening, Hit Validation by Biophysical Methods, and 3D Guided Optimization. A Promising Alternative to Random Screening. J. Med. Chem., 2000, 43 (14), 2664 -2674.  NMR by SAR method in use at Abbott  Hajduk, P. J.; Meadows, R. P.; Fesik, S. W.. Discovering high-affinity ligands for proteins. Science, 1997, 278(5337), 497-499.  Ellman method at Sunesis  Maly, D. J.; Choong, I. C.; Ellman, J. A.. Combinatorial target-guided ligand assembly: identification of potent subtype-selective c-Src inhibitors. Proc. Natl. Acad. Sci. U. S. A., 2000, 97(6), 2419-2424.

17. In conclusion  Lipinski etc does not go far enough in directing us to leads.  We have provided a model which explains why.  “Everything should be made as simple as possible but no simpler.” Einstein –Simple is a relative not absolute term  where is that optimal peak in the plot for each target? –Simple does not mean easy!! Thanks: Rich Green, Giampa Bravi, Andy Brewster, Robin Carr, Miles Congreve, Darren Green, Brian Evans, Albert Jaxa-Chamiec, Duncan Judd, Xiao Lewell, Mika Lindvall, Steve McKeown, Adrian Pipe, Nigel Ramsden, Derek Reynolds, Barry Ross, Nigel Watson, Steve Watson, Malcolm Weir, John Bradshaw, Colin Grey, Vipal Patel, Sue Bethell, Charlie Nichols, Chun-wa Chun and Terry Haley