An Introduction to Rational Drug Discovery: QSAR CADD

1. An Introduction to Rational Drug Discovery: QSAR & CADD

2. Goal: How to judge whether or not a molecule might be active, based on only structural info? Basics of Protein-Ligand Binding QSAR & Pharmacophore Modelling Building a model for ACE Inhibitors

3. Drug Discovery

4. Drug Discovery High attrition rate of compounds Far less than 1 percent of screened compounds enter preclinical testing For every 250 compounds in preclinical testing, only 5 survive to enter clinical testing With only 1 approved by the FDA after an average of 15 years of total R&D!!!! Costs run >$500,000,000

5. Integrated Drug Discovery Target Discovery & Validation Genomics Proteomics Lead Identification High Throughput Screening Diverse Compound Repository Lead Optimization Medicinal Chemistry/Targeted Synthesis Combinatorial Chemistry Computational Chemistry Lead/Pre-clinical Development Formulation & Process Chemistry In Vivo Optimization Pharmacology, PK, Toxicology

6. �Da Basics� :Ligand-Protein Binding where Ki is the kinetically derived inhibition constant and Kd is the dissociation constant (see MM kinetics)

7. �Da Basics� :DGb, the Free Energy of Binding

8. �Da Rules�:Protein-Ligand Binding Binding affinity: Predominantly determined by hydrophobic interactions Depends on �buried� non-polar surface area Entropy driven Depends on states (conformation, protonation, tautomeric, etc.) of unbound species Binding specificity: Structural complementarity (steric & electronic factors) Hydrogen bonding (enthalpy driven)

9. Hydrogen Bonding Hydrogen bonds contribute to specificity but not significantly to binding affinity. A desolvation penalty must be paid when water is �removed� from the ligand and the protein binding sites This results from the loss of H-bonds between the ligand or protein and solvent waters This penalty is approximately balanced by the H-bonding energy gained through ligand-protein interactions. A significant free-energy price is paid for unsatisfied H-bonds, which reduces the overall binding free energy. The desolvation penalty results from the loss of H-bonds between the ligand or protein and solvent waters

10. Binding Affinity & DGb A factor of 10 in binding affinity, usually measured as Kd , is equivalent to ~1.3 kcal mol-1 of binding free energy at room temperature

11. Rational Drug Discovery:Lead Optimization Goals ? Binding Affinity ? Specificity ? Novelty � Intellectual Property (IP) ? Synthetic Accessibility ? Cell Penetration ? ADME/Tox Prop�s. Bioavailability, Plasma Stability, pKa, pKD, etc. MEET PROJECT TEAM OBJECTIVES!

12. Rational Drug Discovery:Lead Optimization Have a �hit� and a validated �lead series: QSAR Pharmacophore Modelling Active Site Modelling (to be discussed another time) Docking Experiments �Virtual Screening� or �vUltra-HTS�

13. What is QSAR? (Quantitative Structure Activity Relationships) Activity = function (structural properties) Example*: Relate biological activity to electronics and hydrophobicity log(1/C) = k1logP � k2(logP)2 + k3s + k4 C = concentration of a compound that gives a response P = partition coefficient between water & 1-octanol K1,k2,k3,k4 = constants s = Hammett substituents parameter *C. Hansch. (1969) Accounts of Chemical Res. 2:232-239

14. QSAR Objective Correlate 3D structure of a ligand with its biological activity Problems: Frame of reference Unknown steric interactions Multiple binding modes Underdetermined system

15. QSAR Process Synthesize & test biological activity for diverse set of ligands (including actives and inactives). Clever experimental design maximizes information content from a series. Regression techniques to fit an equation to the data. Which properties are correlated with activity? Choose/calculate Descriptors Cross-validate. Predict, wary of extrapolation vs. interpolation

16. Assumptions/Requirements of QSAR Models All analogs belong to congeneric series. All analogs have the same mechanism of action. All analogs bind in a similar fashion. The effect of isosteric replacement can be predicted. Binding affinity is correlated with interaction energy (e.g., ionic effects are approx. const.) Biological activity is correlated with binding affinity (e.g., not with transport properties).

17. Types of QSAR Models What distinguishes various QSAR methods from one another is how the descriptors are estimated and how the equations are solved. �2-D QSAR� methods tend to use 2-D molecular information. �3-D QSAR� methods tend to use 3-D molecular information. Linear regression methods (LS, PLS, PCA, Neural nets,�) Nearest-neighbor estimation (Clustering, B-cuts)

18. What is a Descriptor? Molecular Descriptors: Calculated: Solution to a mathematical procedure that transforms chemical information into a number surface areas (polar, non-polar), dipole moment, volume Experimental: the result of some standardized experiment to measure a molecular attribute melting point, partition coeficients, refractive index, etc.

19. Why do we need Descriptors? Why do we need descriptors? Describe different aspects of molecules Compare different molecular structures Compare different conformation of same molecule Database storage Relate structure to activity (QSAR) Find best set of descriptors relevant to activity

20. Types of Descriptors 0D (zero dimensions) Chemical formula Atom counts 1D Fragment count 2D Topological representation 3D Geometrical representation 4D 3D + scalar field

21. Types of Descriptors Counts of features: For example HBAs, HBDs, aromatic ring systems, substructures/fragments ( e.g. , carbonyl groups, basic nitrogens, carboxyl groups,�),etc. Physicochemical Properties: LogP, solubility, MW, MP, BP, heat of sublimation, molar refractivity, Hammett parameters, etc. Topological Indices: Wiener index, branching indices, kappa shape indices, electrotopological state indices, atom-pairs, topological torsions, etc. BCUTs (3-D, 2-D, 2-T): Electrostatic, charge, and polarizability (hydrophobic). Others: Volsurf, polar surface area, etc.

22. Dimensionality Reduction Too many descriptors! Need to reduce to manageable size! PCA � Principle Component Analysis Choose those descriptors that correlate with activity

23. Lipinski�s �Rule of Five� LogP < 5 MW < 500 Number of HBD < 5 Number of HBA < 10

24. Why QSAR Fails

25. Pharmacophore Models Paul Erlich, early 1900: �a molecular framework that carries (phoros) the essential features responsible for a drug�s (pharmacon) biological activity� Peter Gund, 1977 �a set of structural features in a molecule that is recognized at a receptor site and is responsible for that molecule�s activity� Wikipedia, today A pharmacophore is a 3-dimensional substructure of a molecule that carries (phoros) the essential features responsible for a drug�s (pharmacon) biological activity. Alternatively described as an ensemble of interactive functional groups with a defined geometry. Basically, one tries to talk the protein language by finding the �structural and chemical complementaries� (aka pharmacophore hypothesis) to target receptors

26. Pharmacophore Modeling Analog-based pharmacophores Unknown receptor Information from active and inactive molecules 3D-QSAR Receptor-based pharmacophores Known (and characterized) receptor Docking

27. Pharmacophore Modeling Obtain activity data on an appropriate set of compounds (range of activities) Carry out conformational analysis (multiple conformations, ~5.0 kcal mol -1 energy window) Identify � pharmacophoric features� Identify common feature patterns(2-point, 3-point, 4-point,� pharmacophores; multiple solutions) Search compound collections with pharmacophore �models� Screen selected compounds �Structure-activity relationships without chemistry� Pharmacophore methods also provide guidance in synthesis & design

28. Visual Pattern Recognition

29. Visual Pattern Recognition Visual identification of common structural and chemical features among active molecules and those features that are missing in the inactive ones Measurement of the 3D aspects of the common features, w. r. t. each other Development of a draft pharmacophore Validation that the pharmacophore fits the active compounds and fails to fit the inactive ones Refinement of the model by applying it to a database of compounds with known activity, until the desired result is reproduced

30. Visual Pattern RecognitionOne aromatic ring: phenyl or pyridyl

31. Visual Pattern RecognitionSecond 5- or 6- member ring

32. Visual Pattern RecognitionUrea group or amide functionality in 2nd ring

33. Visual Pattern RecognitionRing systems are side-by-side

34. Measurements From the amide nitrogen to the center of the aromatic ring is approx 5-6 � The aromatic ring and amide group are within 0.5 � RMSD (Root Mean Square Difference) from planarity

35. Developing a pharmacophore model Phenyl or pyridyl ring 5- or 6- member ring with link option Additional nitrogen, various bond types Distance and planarity constraints

36. Phramacophore Features

37. �3�-Point Pharmacophore

38. �4�-Point Pharmacophore

39. Active Compounds in a Pocket

40. Search a Compound Library for �Hits� � e.g. matches to Pharmacophore Model

41. Pharmacophore Based Screening Hits

42. 3D-QSAR: CoMFAComparative Molecular Field Analysis Select test and training sets of comparable diversity. Generate 3-D structures (single conformations). Structurally Align and place in a box with grid

43. 3D-QSAR: CoMFA1st need to structurally align 2-D Alignment Methods Maximum common substructure based methods Feature-Based Vector Methods Discrete feature values (�Bit Strings�) Continuous feature values 3-D Alignment Methods Field-based methods Structure-based methods (generalized RMSD approaches) Key Assumption All molecules bind to the same region of the target binding site in essentially the same way!

44. 2-D & 3-D AlignmentHIV-1 RT Inhibitors

45. 2-D & 3-D AlignmentHIV-1 RT Inhibitors

46. 3D-QSAR: CoMFAComparative Molecular Field Analysis Calculate property fields for each molecule at every grid point (training set) Property value at each grid point is equivalent to a descriptor value in 2-D QSAR Grid points with low variance may be neglected; nevertheless this may result in hundreds of grid points Many more descriptor values than experimental data points, thus �traditional� least-squares approach cannot be used Perform partial least squares (PLS) analysis Validate model (test set) Predict activities of new molecules

47. 3D-QSAR: CoMFA

48. Angiotensin Converting Enzyme (ACE)* Overall pressor (pressure-raising) effect Converts deca-peptide Angiotensin I to Angiotensin II AII = potent vasoconstrictor Stimulates release of steroid aldosterone Results in increased sodium retention Hydrolyzes C-terminal peptide from the hypotensive (vasodilator) nona-peptide bradykinin Stimulates antidiuretic hormone (ADH) release ACE action increases both vascular resistance And blood volume Inhibiting ACE results in antihypertensive effect Slides from Modeling Biosystems I Lectures by Dr. Dan Kuster (Marshall lab) Washington State University @ St. Louis

49. Angiotensin Converting Enzyme (ACE) ACE �makes� Angiotensin II in vivo

50. Angiotensin Converting Enzyme (ACE)

51. How to inhibit ACE? Bradykinin-Potentiating Peptide (BPP) Naturally occurring nonapeptide in Bothrops jararaca venom BPP5a = Glu-Lys-Phe-Ala-Pro Phe-Ala-Pro also inhibits ACE Many studies to deduce structure-activity relationship with Phe-Ala-Pro mimics Complicated by pKa at titratable groups, etc Simple computer models Several inhibitors emerge as potent Good peptide-analog correlation for captopril Poor peptide-analog correlation for enalapril How to Inhibit ACE?

52. Captopril Designed w.r.t. BPP5a First ACE inhibitor drug Orally available Potent Undesirable clinical side effects Loss of taste Rashes At therapeutic doses, does not interact with nervous system Similar sulfhydryl moeity as penicillamine, similar side effects

53. Find an Active Conformation Apply the active analogue hypothesis Choose a potent inhibitor (IC50<50nm) Scan systematically through all torsions Generate many conformations Reject those with improbably large energy (VdW criterion) Multiple-Minima Problem # of conformers = (360/angle) N-rejected N= # of rotatable bonds Small angles or large # of bonds ? Combinatorial explosion! Create distance map (DMAP) relating structural features relevant to binding site

54. Distance MAPs (DMAPs) Du = Zn DMAP valid for all active analogs DMAP can become many dimensional for some systems

55. Grids How know if two DMAPs are equal? Define a 3D grid around molecule Two conformers evaluate to same grid points? Effective resolution of predicted DMAP ~0.1- 0.5 angstrom Fine grid rejects more conformations Balance angle resolution with grid spacing!

56. Can this molecule place the right atoms in the necessary spacing?

57. Analogue Search Add fields and pharmacophore features to grid Screen for best fits.

58. 3D-QSAR: An �Active� Hit

59. 3D-QSAR: An �InActive� Hit

60. Summary Problems finding new drugs efficiently and cost-effectively! Computers may help the process! QSAR Pharmacophore Models CADD But still not enough known- the best you can do is enhance �hit-rate� The experiments still need to be done � no one will ever take or pay for a �virtual drug�!!!!

61. Acknowledgements Slides/Figures from: Dr. Dan Kuster (Marshall lab) Washington State University @ St. Louis Professor Gerry Maggiora Department of Pharmacology & Toxicology,College of Pharmacy College of Pharmacy University of Arizona University of Arizona