“Making Lead

1. “Making Lead Discovey less Complex?” Mike Hann, Andrew Leach & Gavin Harper. Discovery Research GlaxoSmithKline Medicines Research Centre Gunnels Wood Rd Stevenage SG1 2NY email MMH1203@gsk.com

2. Introduction • A simple model of molecular recognition and it’s implications • Experimental data • An extreme example • Conclusions

3. HTS & 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 resulting HTS screening paradigms have not delivered the results that were initially anticipated. • Why? • immaturity of the technology, • lack of understanding of what the right types of molecule to make actually are . (design problem) • the inability to make the right types of molecules with the technology . (synthesis problem)

4. The Right Type of Molecules? • Drug likeness • Lipinski for oral absorption • Models (eg Mike Abrahams) for BBB penetration • But all these address the properties required for the final candidate drug • Lead Likeness • What should we be seeking as good molecules as starting points for drug discovery programs? • A theoretical analysis of why they need to be different to drug like molecules • Some practical data

5. Feature Position 1 2 3 4 5 6 7 8 9 10 11 12 Binding site features - - + - + - - + - + + + A very simple model of Molecular Recognition • Define a linear pattern of +’s and -’s to represent the recognition features of a binding site • these are generic descriptors of recognition (shape, charge, etc) • Vary the Length (= Complexity) of this linear Binding site as +’s and -’s • Vary the Length (= Complexity) of this linear Ligand up to that of the Binding site • Calculate probabilities of number of matches as ligand complexity varies. • Example for binding site of 12 features and ligand of 4 features: Ligand mode 1 + + - + Ligand mode 2 + + - +

6. 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! Probabilities of ligands of varying complexity (i.e. number of features) matching a binding site of complexity 12 Example from last slide

7. The effect of potency (binding site 12; ligand complexity </=12) • P (useful event) = P(measure binding) x P(ligand matches)

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

9. 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)

10. Sneader LeadSneader DrugWDI 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 Leads are less complex than drugs!!

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

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

13. In terms of numbers • Astra Zeneca data similar using hand picked data from literature • AZ increases typically even larger • RSC/SCI Medchem conference Cambridge 2001. MW increase ca. 70-90 depending on starting definitions

14. 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

15. Catch 21 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 appropriate assay and ligands • e.g the extreme Mulbits (Multiple Bits) approach • Mulbits are molecules of MW < 150 and highly soluble. • Screen at up to 1mM • An example indicating how far this can be taken • 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.

16. Thrombin Mulbit to “drug”

17. 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.

18. 3 2 1 0 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 Log Enzyme inhibition -1 -2 -3 -4 -5 MW of inhibitor Enzyme target - bangs per bucks Plot of Log Enzyme activity vs MW for “Interesting monomer” containing inhibitors Interesting monomer mM MW nM Most interesting lead

19. 350 Mwt >500 Mwt <200 Non-HTS Shapes (Vertex ) Needles(Roche) MULBITS(GSK) Crystallead(Abbott) SARbyNMR(Abbott) Lead Continuum H2L problems ? Lipinski Data zone Leadlike Drug-like HTS screening Slide adapted from Andy Davis @ AZ

20. In conclusion Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery Michael M. Hann,*Andrew R. Leach, andGavin Harper J. Chem. Inf. Comput. Sci.,41 (3), 856 -864, 2001. Is There a Difference between Leads and Drugs? A Historical Perspective Tudor I. Oprea,*Andrew M. Davis,Simon J. Teague, andPaul D. LeesonJ. Chem. Inf. Comput. Sci.,ASAP Articles • 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 to: Andrew Leach, Gavin Harper. Darren Green, Craig Jamieson, Rich Green, Giampa Bravi, Andy Brewster, Robin Carr, Miles Congreve,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. Andy Davis and Tudor Oprea at AZ