1. Lecture Contents -- Unit 3 Drug Discovery –Basic objectives and problems –Screening approach vs. rational design –Phytopharmacology –Databases, QSAR, and CoMFA –“Pharmacogenomics” and “proteomics” –Case study: GV 150526A

2. Basic Facts About Drug Discovery Almost any metabolic pathway with all it’s adjuncts (receptors, enzymes, genes therefor, and regulatory elements) is a potential drug target During the past century, pharmacology has identified some 400 such targets; the human genome project confirms that thousands must exist Independent of this, the present rate of drug discovery is insufficient; new strategies are required

3. Some Companies Specialize in Drug Discovery

4. Drug Discovery Strategies Screening-based: –Traditional medicine –Bioprospecting –Mass screening of microbial strains –Combinatorial chemistry Rational Drug Design –Target interaction analysis and molecular modeling

5. Natural Product-Based Drug Discovery

6. Natural Product Success Stories Microorganisms: Antibiotics Plants: –Taxoids for cancer –Artemisinin for malaria –Huperzine A and galanthamine for Alzheimer Animals: Conotoxins as ultra-high potency analgetics

7. Phytopharmacology: Decision Tree

8. „Microbial Pharmacology:“ Penicillin And Other ß-Lactames Fleming (1928): Growth of bacterial cultures inhibited by co-infection with Penicillium notatum  “penicillin” postulated as a secreted molecule 1938: Penicillin isolated and characterized as part of British war preparations Beta-lactames became most important lead structure ever since then Benzylpenicillin (Penicillin V)

9. Phytopharmacology: Taxoids Diterpene from Taxus brevifolia Most significant anticancer agent developed in the past two decades (“mitotic poison”)

10. Phytopharmacology: Artemisinin Unusual sesquiterpene endoperoxide from Artemisia annua (Quinghaosu in Chinese traditional medicine) Lead compound for new generation of malaria therapeutics (including chloroquine- resistant and cerebral malaria) C 15 H 22 O 5 MW = 282.3

11. Marine Pharmacology: Conotoxins Peptide neurotoxins (receptor channel blockers) from molluscs (snails and shells)  -conotoxin PnIa: nicotinic receptor blocker  -conotoxin MVIIc: P-type Ca-channel blocker

12. The Ideal Combinatorial Library Made by forming all possible combinations of a series of sets of precursor molecules, and applying the same sequence of reactions to each combination

13. Combinatorial Chemistry: Basic Theoretical Approach TEMPLATE R1 R2 R3

14. Combinatorial Chemistry: Detection of Hits

15. Obstacles to Combinatorial Chemistry Restricted and specialized chemistry, needs training Not yet suitable for large molecules Automated synthesis needs to be installed and integrated with the laboratory workflow Equipment AND organization must be tightly integrated with a tailored data management infrastructure

16. A Well-Designed Library Can Mean BIG Money... 1995: Schering-Plough pays $3 million for access to certain parts of the Neurogen compound library Payment estimates for unrestricted access to targeted libraries run up to $15 million Construction of large (diverse or targeted) combinatorial libraries) has become a significant outsourcing business

17. Combinatorial Chemistry: SAR By NMR

18. New Frontiers in Receptor Ligand Screening

19. Databases In Drug Discovery Employ advanced search algorithms including artificial intelligence (AI) systems “Data Mining” -- knowledge discovery in databases: –Fuzzy logic -- “soft” search criteria –Structural similarity searches –Retrieve implicit information –Link structural information with bio-informatics

20. Tools for Rational Drug Design (Q)SAR: (Quantitative) Structure-Activity Relationships SAFIR: Structure-Affinity Relationships SPAS: Structure-Property/Affinity Studies CoMFA: Comparative Molecular Field Analysis

21. SARs, Easy and Obvious? Stimulants/Anorectics in Medicine

22. SARs, Easy and Obvious? Stimulant Drugs of Addiction

23. Can „Drug-Like“ Structures Be Predicted? Only 32 basic templates describe half of all known drugs (Bemis et al. 1996) Medicinal chemists essentially use their intuition (“expert rules”) to gauge drug structures  emulation by trainable (and self-entraining) neuronal networks working from relatively few molecular descriptors If “drug-likeness” can be quantified  targeted design of combinatorial libraries

24. Comparative Molecular Field Analysis CoMFA: Method to analyze and predict structure-activity relationships (Cramer 1988) Based on superimposition techniques: –Steric overlap (“distance geometry”) –Crystallographic data –Pharmacophore theory –Steric and electrostatic alignment algorithms –„Automated field fit“ Further reading: http://www.netsci.org/Science/Compchem/feature11.html ; http://cmcind.far.ruu.nl/webcmc/camd/3dqsar.html

25. The Essence of CoMFA Superpose active and inactive analogues; calculate the “receptor excluded volume,” the occupancy of which would result in loss of activity Use ligand binding points and conformational restraints to decompose the distance matrix into differences and similarities © Tripos Software

26. Somatostatin Receptor Ligand Modeling Science 282, 737-9 (23 Oct 98)

27. New Buzzwords in Drug Discovery

28. A Case Study In Drug Discovery GV-150526A (CAS: 153436-38-5) 3-[2-phenylaminocarbonyl)ethenyl]-4,6-dichloroindole-2-carboxylate, a glycine antagonist currently completing Phase III studies for stroke

29. Glutamate, Receptors, And Stroke

30. The NMDA Receptor Complex

31. Starting Point: Known Antagonists of Glycine Site at the NMDA Receptor Kynureic acid (R1 and R1 can be H or Cl) Nanomolar in vitro affinity but poor in vivo activity due to insufficient CNS penetration Improved CNS penetration but lack of receptor selectivity 4,6-dichloroindole-2-carboxylate: Good receptor selectivity and CNS penetration, but in vitro affinity for glycine site (pKi=5.7) needs to be improved; however: A NEW LEAD STRUCTURE IS IDENTIFIED! !

32. Input From Theory Comparison with receptor model predicts that a hydrogen bond accepting group in the “northeast” of the template is required for optimal binding   C-3 unsaturated side chains should be able to considerably enhance the affinity to the glycine binding site

33. Template Derivatization At C-3 PRIMARY SCREENING SYSTEM: In vitro binding inhibition of [ 3 H]-glycine to crude synaptic membrane preparations from adult rat cerebral cortex

34. SARs From Primary Screening RpKi H5.7 CH2-CH2-COOH7.4 CH2-CH2-CONH-Ph7.6 CH=CH-COOH7.7 CH=CH-COO-tBu6.3 CH=CH-CONH-Ph8.5 CH=CH-CONH-C10H77.4 CH=CH-CONH-CH2-Ph6.9 CH=CH-SO2NH-Ph6.1 pKi = inverse logarithm of binding constant to the glycine site of the NMDA receptor

35. Can The in vitro Characteristics of the Refined Lead Be Improved Further? RoRmRppKi HHH8.5 HHNH28.9 HNH2H8.3 NH2HH8.5 HHOH8.7 NO2HH7.6 HOCH3OCH38.1 CH3HOCH37.7 NO2HF7.5 HHCOOH7.2 HHN(CH3)27.9 HHO-CH2-CH38.3 HNO2Cl6.9 HHCF36.8

36. The Glycine Site of the NMDA Receptor