Computational decision support for drug design

1. Computational decision support for drug design Profiling of small molecule compound libraries Anne Marie Munk Jørgensen

2. Lundbeck • Lundbeck’s Vision is to become the world leader in psychiatry and neurology • Focus solely on treatment of diseases in the central nervous system (CNS) • depression • Psychoses • Migraine • Alzheimer • Sleep disorders 5000 people worldwide – app 800 in R & D

3. What is a small molecule drug? How can computational methods help during the drug discovery phase? Library profiling: overall characterisation of a large pool of structures. Prediction of more specific characteristics like biological activity and ADME properties Privileged structures…. Outline

4. A small molecule drug … is a compound (ligand) which binds to aprotein, often a receptor and in this wayeither initiates a process (agonists) or inhibits the natural signal transmittersin binding (antagonists) The structure/conformation of the ligand is complementary to the space defined by the proteins active site The binding is caused by favourable interactions between the ligand and the side chains of the amino acids in the active site. (Electrostatic interactions, hydrogen bonds, hydrophobic contacts…) The ligand binds in a low energy conformation < 3 kcal/mol

5. Binding site complementarity HIV-Portease inhibitor JACS,V.16,pp847 (1994) H-bond donating H-bond accepting Hydrophobic Flo98, Colin McMartin. J.Comp-Aided Mol. Design, V.11, pp 333-44 (1997)

6. Example of ligand binding 1UVT, Trombin Inhibitor

7. No vacancy!

8. Molecular factors Conformation Intramolecular interactions Ionization Intermolecular forces Electronic distribution Solubility, Partitioning Carrupt P-A., Testa B., Gailard P. Boyd D.B., Lipkowitz K.B., Reviews in Computational Chemistry, Vol. 11, 1997, pp. 241-304.

9. Compound library profiling Analyze a pool of structures to find out how attractive they are to us….. • 10 years ago: Diversity + HTS • Now: very high focus on how biologically relevant the screening collection is. • Computational methods to predict drug likeness, CNS likeness…. High throughput is not enough … to get high output…..

10. Compound analysis Chemical intuition Ideal 50.000 Structures:

11. Choosing the right descriptors is difficult Wolfgang Sauer, SMI 2004

12. Calculate a number of phys chem descriptors, like molecular weight, nhba, nhbd, logP, SASA….. Describe the structures by keys…. How we describe the structures in the computer

13. Lipinski statistics Rule of 5 References (1) Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev JID - 87105231997, 23, 3-25.

14. Diversity and "Chemical Space" PCA

15. Global Positioning System (GPS) Chemical space navigator Chem GPS (Oprea & Gottfries, J. Comb. Chem 2001) • We want to define the CNS ”world” – the space which • is biologically relevant when considering CNS drugs

16. CNS model PCA 12 descriptors  3 components, R2X=0.71 Blue dots define:: CNS drug space CNS ”World”

17. CNS ”world”sub classes

18. Model used to predict CNS-likeness

19. 1 3 6 13 19 26 31 38 0.349 1 Structural clustering based on keys C-N …01000100110001…. C=O C=C Similarity by Tanimoto: Tc= Bc/(B1 + B2 – Bc) clust_benzo (order)

20. Clustering Virtual screening – looking for structural similar compounds in a large pool of structures….. Structural analysis

21. I have talked about overall profiling of a large number of compounds…… in terms of CNS-likeness … now I will turn to talk about prediction of more specific characteristics like biological activity and ADME properties….. Quantitative Structure Activity Relationship or Quantitative Structure Property Relationship

22. In house QSAR study Correlation between Glyt-1 inhibitor activity and pi (lipophilicity) and SigmaP (electronic characteristics) for the R substituent

23. ADME property predictions Oral absorption …depends heavily on permeability and Solubility… high interest in predicting these things in silico… Other things: Blood-brain Barrier penetration, clearance, Metabolism, tox…..

24. Aqueous Solubility • QSRP model • n=775,R2=0.84, Q2=0.83 • 8 2D descriptors, Cerius2 • Most important descriptors: logP, hba*hbd, hba, hbd • Drugs: –6 < logS < 0; • If error of 1 log unit is OK  model predicts 60-80% of the compounds correctly Journal of Medicinal Chemistry, 2003, Vol. 46, No. 17

25. Permeability • QSRP • N= 13 • R2=0.93 Q2= 0.83 • Key descriptors: • PSA> Odbl >N-H > • ..NPSA >SA • Polar descriptors important and …. size matters…. • Simple Rule: PSA < 120 Å2 Journal of Medicinal Chemistry, 2003, Vol. 46, No. 4

26. Pharmacophore modelling ….. Another method of biological activity prediction… Observations that modification of some parts of a ligand results in minor changes of activity, whereas modifications of other parts of the ligand result in large change of activity. Pharmacophore element: Atom or functional group essential for biological activity 3D Pharmacophore mode: Collection of pharmacophore elements including their relative position in space

27. Selective Serotonin Reuptake Inhibitors (SSRIs) From TCAs to SSRIs and Beyond fluoxetine prozac/fontex 10.1.1974 First synt. May 1972 citalopram cipramil/celexa 14.1.1976 First synt. Aug 1972 sertraline zoloft 1.11.1979 zimelidine 28.04.1971 paroxetine paxil/seroxat 30.1.1973 fluvoxamine fevarin 20.3.1975 indalpine 12.12.1975

28. The mechanism of SSRI’s

29. Pharmacophore modelling example Fluoxetine Paroxetine Citalopram Sertraline Chapter 13. Pharmacophore Modeling by Automated Methods: Possibilities and Limitations M.Langgård, B.Bjørnholm, K.Gundertofte In "Pharmacophore Perception, Development, and use in Drug Design". Edited by Osman F. Güne International University Line (2000)

30. Privileged structures ……. are ligand substructures that can provide high-affinity ligands for more than one target…..

31. Privileged structures ”A single ring system, the 5-phenyl-1,4-benzodiazepine ring, provides ligands for a surprisingly diverse collection of receptors…..” Evans et al., J. Med.Chem 1988, 31, 2235-2246

32. G-protein coupledreceptors • 7 TM • Example:dopamine, serotonine, muscarinic, histamine, neurokinin • Family A, B, C, A = Rhodopsin like • In general low sequence homology even within each family, but highly conserved residues in the TM regions • Small molecule ligands bind wholly or partly within the transmembrane region mainly in the region flanked by helix 3,5,6 and 7 • From site-directed mutagenesis studies, side chains involved in binding has been characterised ChemBioChem 2002, 3, 928-944

33. GPCR Privileged structures type of receptor J. Med. Chem.,47 (4), 888 -899, 2004

34. Amino acid ”hot spots” Examine which amino acids are conserved in binding pocket for T1 and T2 Priviledged sub structure for target T1 and T2 Align T1 and T2 Amino acid ”HOT SPOTS” Look for these in other GPCR’s Linking target and ligand side….. Didier Rognan at the 5ht international workshop in New Approaches In drug design & discovery, Marburg 21-24 marts 2005

35. Fluoxetine scaffold common for SERT and GLYT-1 Gibson et al, Biorg. Med. Chem Letters 2001 (11), 2007-2009 Atkinson et al, Mol. Pharm. 2001 (60), 1414-1420

36. Comparison between SERT and GLYT-1 Y102 Y310 F288 GLYT1 sequence; RED: conserved residues GREY:conservative mutations SERT model From Na+/H+ antiporter, J. Pharmacol & Exp Therapeutics, 307, 34-41

37. Computational methods for Compound library profiling, Chem GPS activity QSAR prediction and pharmacophore modelling Solubility and permeability QSPR prediction Privileged structures of GPCR’s Resume

38. ”Hit finding” • Drug discovery ~ Looking for a needle in a haystack • Filtering of compounds ~ remove some of the hay

39. Serendipity “To look for the needle in the haystack - and coming out with the farmer’s daughter” Arvid Carlsson