1. Drug Design / drug discovery Jerome Baudry Assistant Professor BCMB UT/ORNL Center for Molecular Biophysics 2 previous incarnations: Research faculty at UIUC Research scientist at Transtech Pharma, Inc.

2. Drug Design / drug discovery What’s a drug? A substance that treats/cure a disease. A small molecule that interacts with a target, (often protein involved in the disease process; activator/inhibitor) Drug discovery: The process of finding such a small molecule – combination of approaches Drug discovery or drug design? In principle: “Design” is more rational and targeted, and “discovery” is more serendipitous. But design and discovery share a lot and are ~ synonymous in a pharmaceutical context. Hoopkins, Groom, Nat Rev Drug Discov. 2002 1(9):727-30. 5% of human genome is “druggeable”

3. Gigantic economic importance: 10 years & $200 to $1,900 million to develop a drug 25 new molecules /year Intense scientific activity: very interdisciplinary approach > $340 billion Drug discovery market in millions US$RevenueR&Dincome Johnson & Johnson53,3247,12511,053 Pfizer48,3717,59919,337 GlaxoSmithKline42,8136,37310,135 Novartis37,0205,3497,202 Sanofi-Aventis35,6455,5655,033 Hoffmann–La Roche33,5475,2587,318 AstraZeneca26,4753,9026,063 Merck & Co.22,6364,7834,434 Abbott Laboratories22,4762,2551,717 Wyeth20,3513,1094,197 http://en.wikipedia.org/wiki/List_of_pharmaceutical_companies

4. Chemistry: synthesis Discovery and design (hit/lead/optimisation) Biology: assay (binding/activity; in vitro / in vivo,) Target identification The drug discovery and design workflow: drug development: Pharmacology / testing

5. The long and winding road to drug discovery Computational chemistry /Molecular modeling useful across the pipeline, but very different techniques aim for success, but if not: fail early, fail cheap

6. Structure-based know receptor, don’t known ligands Two pathways to drug discovery / drug seign ? What will be happy in there? Structure-based don’t know receptor, known ligands Protein/ligand interactions structure/biophysics docking Statistical analysis of what group(s) are important for biological activity

7. structure modeling (homology/experimental X- ray/NMR/neutron) Get a structure high-throughput docking/screening Get a “hit” (anything at all) Structure-based approaches Use knowledge of structure to find something that 1) binds, and 2) does the desired biological activity focused library docking fragment-based growth ‘individual’ molecules simulations

8. Structure-based library screening What do we need: 1) Compounds libraries 2) Protein target 3) Binding site in the protein 4) Docking: generate different (many) possible conformations of the compounds in the binding site 5) Scoring: evaluate the strength of the protein/ligand interactions (score). 6) Select preferred ligands to propose a list of prioritized compounds for experimental screening.

9. Best case scenario, a high-quality experimental structure exists: PDB: http://www.rcsb.org/pdb/http://www.rcsb.org/pdb/ - experimental collection of (49 295) structures, ~18 000 non-redundant sequences - X-Ray & NMR, - nucleic acids, proteins, carbohydrates Structure-based approaches Structure modeling

10. that’s ~1% of the 5.5 million protein sequences in swissprot (http://www.ebi.ac.uk/swissprot/sptr_stats/index.html) and < ~0.00007% of earth’s proteins, (5E6 organisms, 5K genes/genome, low-end estimate.) ~50,000 non-redundant protein structures in the PDB: is that a lot? Structure-based drug discovery = “Post genomics challenge”: structural biology, functional genomics, chemical biology… …AQRTEVYTYRRS… protein sequence protein structure Must do for new pharmaceutical target ( homology, ab-initio folding…) Structure-based approaches Structure modeling

11. Structure-based approaches Structure modeling If no available experimental structure – work on that, and in the meantime: Homology modeling: use structure of close (sequence-wise) proteins to build, by analogy, a new protein.

12. R1R1 R2R2 R4R4 R3R3 http://blaster.docking.org/zinc/ Databases of compounds - vendors - literature - corporate/laboratory - virtual compounds -A priori anything, but we can be smarter than that http://nihroadmap.nih.gov/molecularlibraries/ Library designed against protein target, - based on hits from previous database screening Millions of cmpds’ structures are available from public databases. Major NIH effort to fund & develop libraries: more exploratory more focused Structure-based approaches Compound selection

13. outside inside deleted When site is not known, eraser/flooding techniques binding site (3D) Or…make your life easier and build the site around a co-crystallized ligand If available… Locate cavities in a protein Structure-based approaches Binding Site

14. save HIGH-THROUGHPUT OR LOW-THROUGHPUT ? fast (initial) accurate (on best cmpds from initial) Choices based on the desired throughput from 10 seconds to 10 minutes / compound 650,000 cmpds library, on 10 processors: from 3 days to 6 months Most time-consuming part (by far) YES NO OK BETTER Structure-based approaches docking

15. Scoring functions. Quantify the energy of protein/ligand interactions such as: hydrogen bond electrostatics van der Waals hydrophobic  etc … Several scoring functions exist, more/less specialized, fast etc… PROTEIN LIGAND Structure-based approaches scoring

16. scoring functions: Force-field based: (CHARMM, AMBER etc). MMFF: very popular one because of “modular parametrisation”: easy to derive parameters from functional groups, well adapted to organic molecules. Physically ‘accurate’ but slow, parametrisation issues. Empirical – count the number of interactions and assign a score based on the # of occurrences. E.g. : H-bonds, ionic interactions (easy because very directional and well quantified) Hydrophobic interactions (more difficult to assess and quantify) Number of rotatable bonds frozen (link to entropic cost of binding, quite difficult to estimate) Knowledge-based – observe known protein/ligand structures, and favor interactions and geometries that are seen often. Idea: directly link to free energy because “real life” distribution (potential of mean force). But: based on small # of entries. Intense competition “my scoring function is better than yours” Future: force-field based / even QM-based Different approaches depending on size

17. Structure-based approaches scoring Enrichment factor = (5/30) / (30/ 1000000) = 166 HUGE SUCCESS Often: consensus scoring: choose the few molecules that are ranked consistently well among many docking function 1,000,000 molecules, 30 actives.  1000 selected, 5 actives

18. Enrichment factor = (3/1000) / (30/1,000,000) = 100 HUGE SUCCESS 1,000,000 molecules, 30 actives.  1000 selected, 3 actives Structure-based approaches scoring R1R1 R2R2 R4R4 R3R3 COMPUTATIONAL DOCKING: GENERATE TESTABLE IDEAS Chemistry: synthesis Discovery and design (hit/lead/optimisation) Biology: assay (binding/activity; in vitro / in vivo,) Possible to start next round of iteration (or do ‘traditional’ modeling). Redock with improved accuracy (e.g QMMM)

19. Reproduce know xtal structure HIV protease and inhibitor Examples (low-throughput) Works great … in most publications crystal structure first round of docking (shape only) final result (after rigid-body minimizations: energetics taken into account) Ligand-based siteFlood-based site Venkatachalam, et al.; J. Mol. Graph. Model. 2003, 289-307

20. But also… fails miserably (rarely in publications !) crystal structure final results (rigid-body minimizations) Illustrate issues with binding site’s shape (there are workarounds) Examples (low-throughput) Venkatachalam, et al.; J. Mol. Graph. Model. 2003, 289-307

21. Ke et al, Archives of Biochemistry and Biophysics 436 (2005) 110–120 Example II): discovery of ligand/function for a new P450

22. Development of a database of bio and agrochemical compounds of relevance for P450 (currently ~ 14,000 structures). In-house compounds, KEGG database: (http://www.genome.jp/kegg/ligand.html), Compendium of Pesticide Common Names: (http://www.alanwood.net/pesticides/index.html).http://www.genome.jp/kegg/ligand.htmlhttp://www.alanwood.net/pesticides/index.html Development of CYP120A1 model from CYP107A template (23.6% identity) HT-docking (LigandFit). identify 99 compounds consistently predicted to be good binders. Confirmed: retinoic acid ~14,000 structures Ke et al.. Arch. Biochem. Biophys. 2005 high-throughput docking Get a “hit” (anything at all)

23. CONCLUSIONS In-silico combinatorial library design & structure-based screening: fast, efficient and inexpensive tool to : - discover new possible ligands against a macromolecular target - test library design ideas - identify most promising scaffolds and R groups prior to synthesis Baudry, J.; Hergenrother, P. J. "Structure-based Design and In-Silico Virtual Screening of Combinatorial Libraries. A Combined Chemical/Computational Laboratory Assignment" J. Chem. Ed. 2005, 82, 890-894. http://www.scs.uiuc.edu/~phgroup/pdfs/2005PJHchemed.pdf HT-DOCKING SUCCESS IF: i) FIND A FEW MOLECULES OF INTEREST ii) MUCH QUICKER AND CHEAPER THAN “real” screening

24. Comparison model / crystal structure residues within 4 Å of heme Green/blue: model, red/orange:crystal

25. Residues around the ligand’s  -ionone ring are very close in both structures (phe182 & Trp76 same pharmacophore) Green/blue: model, red/orange:crystal Comparison model / crystal structure

26. De novo design Fragment-based “inside-out” approach Put functional groups in binding site (docking or manually, or combination) Link these groups (docking or manual, or combination): *must* be able to synthesize it – no molecular monsters Caflish, Miranker, Karplus J.Med.Chem. 36, 2142-2167 (1993) Eisen, Wiley, Karplus, Hubbard Proteins Structure, Function and Genetics 19, 199-221 (1994). i)dock functional groups ii)keep low energy groups link with scaffolds iii) correct binding site, but ≠ too; “lead hopping”