Multi-Criteria Drug Discovery: using CoMFA models to drive target specificity. Lei Wang, Ph.D.

Transcription

1 Multi-Criteria Drug Discovery: using CoMFA models to drive target specificity Lei Wang, Ph.D.

2 The Problem Compounds exist that have some desirable properties to become a drug but The known compounds are not suitable due to: Intellectual Property Physical Properties DMPK Properties Toxicological Properties Selectivity Profile 2

3 Blood coagulation cascade FactorXa, Thrombin occupy central positions in the blood coagulation cascade. Various FactorXa and Thrombin direct inhibitors are developed where selectivity is highly desirable. 3

4 The Design Challenge Selectivity Profile What compound should I make next with the right selectivity profile? generate ideas that will satisfy ALL the criteria for a successful drug candidate? Must have design ideas that can be pursued in the lab 4

5 The Design Challenge Selectivity Profile What compound should I make next with the right selectivity profile? generate new ideas? 5

6 The Design Challenge Selectivity Profile De novo engine generate new ideas? 6

7 De novo engine Identify novel structures, scaffolds, or side-chains that meet specific design objectives. Potential access to a universe of chemistry Focus on medicinally relevant structures with high synthetic feasibility 7

8 Molecular Invention Process Seed Structures Result Structures Population of Parents (With Scores) Select Invent Repeat for Number of Generations Population f Parents & Children (With Scores) Population f Children Score Me H N Muse Invent Engine H N N N N N N Multiple Criteria HN 8 Denovo Design of a Picomolar Nonbasic 5-HT1b Receptor Antagonist D.A. Nugiel, et. al., J. Med. Chem., 2010, 53,

9 A Success Story - Neurocrine Invention of Selective Norephinephrine Re-uptake Inhibitors N H H Training Set H N N H N H Atomoxetine NH N H Reboxetine NH MDL 27777A NH CF 3 Muse Invent Engine ES H N W W Cl Multiple Criteria ES DE HN US HN Cl trans-sertaline Cl Cl Indatraline Representative examples of known inhibitors identified ne of the top scoring compounds (not shown) was found to be highly active and was selected as lead compound in the project at Neurocrine M. Feher, et. al. The use of ligand-based de novo design for scaffold hopping and sidechain optimization: two case studies Bioorg. Med. Chem. 2008, 16,

10 The Design Challenge Selectivity Profile generate new ideas? predict activities for the ideas 10

11 The Design Challenge Selectivity Profile generate new ideas? predict activities for the ideas 11

12 QSAR - 3D QSAR - CoMFA Comparative Molecular Field Analysis Descriptors are field strengths around molecules - electrostatic, steric, H-bond.. PLS pki = A + B(D 1 ) + C(D 2 ) +...

13 CoMFA - Interpretation High Coefficient (important) lattice points can be plotted around molecular structures More positive charge Less steric bulk

14 The Design Challenge Selectivity Profile predict activities for the ideas? generate new ideas? automatically align new ideas? 14

15 The Design Challenge Selectivity Profile predict activities for the ideas? generate new ideas? automatically align new ideas? 15

16 Template CoMFA 16

17 Template CoMFA Template CoMFA empowers you to use all your available information to make design decisions Ligand SAR information Target/Ligand structure (all the multi-.pdb data sets currently found in Cramer. R.D. Template CoMFA: The 3D-QSAR Grail? J. Chem Inf. Model. 2014, 54(2), pp

18 How Template CoMFA uses all your info: X-ray and SAR Templates are ligands superimposed in their binding conformations To align a training or test set ( candidate ) structure: Position it overall by identifying the bond having the highest similarity to any of the bonds in any of the templates verlaying this candidate bond onto that template bond Its individual atoms are then positioned by Wherever candidate and template atoms match, coordinates are simply copied Wherever candidate and template atoms do not match, use the topomer protocol Classical CoMFA on these aligned structures with all your info meaning: all your info in one 3D-QSAR model DI /ci400696v

19 Template CoMFA validation studies Wendt, B. Cramer. R.D. Challenging the gold standard for 3D QSAR: template CoMFA versus X-ray alignment. J. Comput Aided Mol. Des 2014, June 17. Cramer. R.D., Wendt, B Template CoMFA: The 3D-QSAR Grail? J. Chem Inf. Model. 2014, 54(2), pp Cramer. R.D. Template CoMFA applied to 116 biological targets. J. Chem Inf. Model. 2014, June 9 ASAP Tripos, L.P All Rights Reserved

20 The Design Challenge Selectivity Profile predict activities for the ideas? generate new ideas? automatically align new ideas? combine different SARs? 20

21 The Design Challenge Selectivity Profile predict activities for the ideas? generate new ideas? automatically align new ideas? combine different SARs? 21

22 Using Composite score in Muse to drive target specificity Combine different CoMFA model values together with +/- weighting for selectivity Decouple Invention from Scoring Scoring can be literally any CADD design criteria 22

23 Datasets Böhm M 1, St rzebecher J, Klebe G. Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J Med Chem Feb 11;42(3): training set molecules 16 test set molecules Chembl Database search, molecules with FactorXa, Thrombin and Trypsin activities 73 molecules Total of 161 molecules 50 diverse molecules -> training set. 111 validation set

24 Muse Template CoMFA workflow Crystal templates were found for FactorXa, Thrombin and Trypsin Three Template CoMFA models for FactorXa, Thrombin and Trypsin De novo engine generate ideas Template CoMFA auto-align molecules New Molecules with activity combination score 24

25 BIBR1109 with FactorXa, Thrombin and Trypsin Blue: FactorXa, Yellow: Thrombin, White: Trypsin Nar, H. et al. Structural Basis for Inhibition Promiscuity of Dual Specific Thrombin and FactorXa Blood Coagulation inhibitors? Structure, Vol

26 FactorXa templates 26

27 Thrombin templates 27

28 Trypsin templates (added two factorxa templates) 28

29 Muse Template CoMFA workflow Crystal templates were found for FactorXa, Thrombin and Trypsin Three Template CoMFA models for FactorXa, Thrombin and Trypsin De novo engine generate ideas Template CoMFA auto-align molecules New Molecules with activity combination score 29

30 Template CoMFA models for Thrombin, FactorXa and Trypsin q2 SDEP #Cmp r2 # Tmpl Thrombin FactorXa Trypsin FactorXa CoMFA model shown: Left (Steric) Right(Electrostatic) Thrombin models comparable to literature model (not shown)

31 Validation steps A seed structure with relative low FactorXa and high thrombin from training set Seed Structures Invent High FactorXa and low Thrombin, compare to training and test dataset Result Structures Population of Parents (With Scores) Select Repeat for Number of Generations Population f Parents & Children (With Scores) Population f Children Score Three template CoMFA models combined Trypsin and Thrombin FactorXa 31

32 Validation runs Invented Trypsin: 6.1 Thrombin: 6.64 FactorXa: 5.82 Test Trypsin: 6.51 Thrombin: 6.63 FactorXa: 5.59 Trypsin: 6.57 Thrombin: 6.82 FactorXa: 5.59 Invented Starting molecule Trypsin: 6.56 Thrombin: 6.47 FactorXa: 5.47 Trypsin: 6.54 Thrombin: 6.5 FactorXa: 5.54 Trypsin: 6.16 Thrombin: 7.13 FactorXa: 5.7 Trypsin: 6.82 Thrombin: 6.56 FactorXa: 5.71 Trypsin: 6.07 Thrombin: 6.05 FactorXa: 6.05 Train Invented Invented Test

33 Validation runs Invented Same structure Trypsin: 6.22 Thrombin: 6.92 FactorXa: 9.3 Test Trypsin: 6.11 Thrombin: 6.44 FactorXa: 8.86 Trypsin: 4.38 Thrombin: 5.18 FactorXa: 5.12 Trypsin: 6.19 Thrombin: 6.44 FactorXa: 7.28 Trypsin: 6.12 Thrombin: 6.22 FactorXa: 8.35 Same structure Invented Starting molecule Test Invented Trypsin: 6.02 Thrombin: 5.97 FactorXa: 8.44 Trypsin: 6.15 Thrombin: 5.85 FactorXa: 6.68 Trypsin: 5.47 Thrombin: 6.15 FactorXa: 6.46 Test Trypsin: 6.04 Thrombin: 5.98 FactorXa: 8.34 Invented Test

34 Validation runs Starting molecule Trypsin: 7.17 Thrombin: 6.09 FactorXa: 7.45 Trypsin: 6.2 Thrombin: 5.77 FactorXa: 8.13 Invented Trypsin: 6.16 Thrombin: 5.68 FactorXa: 8.02 Invented Trypsin: 6.18 Thrombin: 5.63 FactorXa: 8.12 Invented Trypsin: 7.42 Thrombin: 6.0 FactorXa: 9.4 Test Test Trypsin: 7.44 Thrombin: 6.19 FactorXa: 8.4 Trypsin: 7.16 Thrombin: 5.49 FactorXa: 8.28 Train Same structure

35 Validation runs Muse invented the same structure as training set molecule CHEMBL Shown with the factorxa template CoMFA model Seed Muse with template CoMFA Trypsin: 6.18 Thrombin: 5.63 FactorXa: 8.12 Experimental: Trypsin: 7.16 Thrombin: 5.49 FactorXa: 8.28 Trypsin: 7.17 Thrombin: 6.09 FactorXa:

36 Conclusion What compound should I make next with the right selectivity profile? predict activities for the ideas? generate new ideas? automatically align new ideas? combine different SARs? 36

37 References 1. Cramer. R.D. Template CoMFA applied to 116 biological targets. J. Chem Inf. Model. 2014, June 9 ASAP 2. Cramer. R.D. Template CoMFA: The 3D-QSAR Grail? J. Chem Inf. Model. 2014, 54(2), pp Wendt, B. Cramer. R.D. Challenging the gold standard for 3D QSAR: template CoMFA versus X-ray alignment. J. Comput Aided Mol. Des 2014, June J.R. Damewood, Jr., C.L. Lerman and B.B. Masek NovoFLAP: A Ligand-Based De Novo Design Approach for the Generation of Medicinally Relevant Ideas. J. Chem. Inf. Model., 2010, 50 (7), D.A. Nugiel, et. al., Denovo Design of a Picomolar Nonbasic 5-HT1b Receptor AntagonistJ. Med. Chem., 2010, 53 (4), Böhm M 1, St rzebecher J, Klebe G. Three-dimensional quantitative structureactivity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J Med Chem Feb 11;42(3):

38 Acknowledgement Fred Soltanshahi Brian Masek Bernd Wendt Stephan Nagy Dick Cramer 38