Towards Physics-based Models for ADME/Tox. Tyler Day

Transcription

1 Towards Physics-based Models for ADME/Tox Tyler Day

2 Overview Motivation Application: P450 Site of Metabolism Application: Membrane Permeability Future Directions and Applications

3 Motivation Advantages over traditional ligand-based informatics methods

4 Motivation Advantages over traditional ligand-based informatics methods Broad domain of applicability Reduced need for training

5 Motivation Advantages over traditional ligand-based informatics methods Broad domain of applicability Reduced need for training Intuitive physics-based rationale for the prediction More informed lead optimization decisions

6 Disadvantages

7 Computational speed Disadvantages Ligand-based methods: < 1 second per ligand Physics-based methods: minutes or hours per ligand

8 Computational speed Disadvantages Ligand-based methods: < 1 second per ligand Physics-based methods: minutes or hours per ligand Longer development time required

9 Computational speed Disadvantages Ligand-based methods: < 1 second per ligand Physics-based methods: minutes or hours per ligand Longer development time required Requires careful preparation of structures Protonation states of ligands and proteins Tautomers

10 Computational speed Disadvantages Ligand-based methods: < 1 second per ligand Physics-based methods: minutes or hours per ligand Longer development time required Requires careful preparation of structures Protonation states of ligands and proteins Tautomers Susceptibility to physical artifacts Large induced fit effects required

11 P450 Site of Metabolism: Methods Ligand-based reactivity estimation methods Semi-empirical (AM1) Trend-vector model Atom-neighborhood encoding fingerprints (Sheridan et al.) MetaSite Reactivity + GRID representation of the active site QM or QM/MM calculations Molecular dynamics simulations Docking methods Rigid receptor model Docking constraints

12 P450 Site of Metabolism: Methods For the metabolic reaction to occur at a given site: 1) The site should have sufficiently high intrinsic reactivity 2) The site should be able to approach the heme-fe within a sufficiently close distance

13 P450 Site of Metabolism: Methods For the metabolic reaction to occur at a given site: 1) The site should have sufficiently high intrinsic reactivity 2) The site should be able to approach the heme-fe within a sufficiently close distance Our approach: Combine structure-based method with intrinsic reactivity Intrinsic reactivity of a site estimated using Hammett Taft method Accessibility of a site evaluated using Induced Fit Docking Produce a combined score to predict the likelihood of a given atom being a site of metabolism

14 Example of reactivity estimation Pattern: Aromatic G 0 = 3.10 Substituent contributions: Methoxy C 0.43 Ethyl Methylammonium 0.10 * t-butyl * Aromatic heteroatom contributions: Oxygen in a 5-membered ring 1.75 Estimated Total G = 1.95 * Substituent effect significantly dampened by intervening atoms

15 Intrinsic Reactivity Estimation: Training Set Training set 281 structures (MW < 600) Known substrates of 3A4 270 from Sheridan et al. J Med Chem 2007, from Brown et al. Drug Metabolism Reviews 2008, Success rate a true site within: Top 1: 67.6% Top 2: 83.6% Top 3: 91.5%

16 Flexibility of CYP2D6 active site Displayed residues treated flexibly in Induced Fit Docking (except Heme)

17 CYP2D6 active site (Binding mode A of GBR-12909) GBR-12909

18 CYP2D6 active site (Binding mode B of GBR-12909) GBR-12909

19 Nortriptyline in CYP2D6 Value of IFD Cyan atom is expt assigned SOM, but has poor reactivity (- 0.9) vs non-som in grey (-5.5)

20 Nortriptyline in CYP2D6 Value of IFD Cyan atom is expt assigned SOM, but has poor reactivity (- 0.9) vs non-som in grey (-5.5) Several IFD derived poses have the SOM close to the heme It has the best overall score The non-som is far from the heme in all poses

21 P450 SoM: IFD + Intrinsic Reactivity (IR) IFD: Receptor + Ligand Glide Docking (ala mutation, reduced vdw scaling) Prime Refinement (side chains and minimization) Epik: Ligand # of contacts Glide Redocking Epik: IR IR CYP Filtering & Scoring

22 P450: 2D6 / 2C9 Summary 2D6 37 ligands [Li et al, JCTC 2011] 2C9 40 ligands [Columbia] 2D6 TP TN FP FN Suite C9 TP TN FP FN Suite

23 P450: 2D6 / 2C9 Summary 2D6 37 ligands [Li et al, JCTC 2011] 2C9 40 ligands [Columbia] 2D6 TP TN FP FN Suite Suite C9 TP TN FP FN Suite Suite

24 P450: 2D6 / 2C9 Summary 24/41 (59%) FP come from 4 ligands Carvedilol_R/S: 9 FP Tamoxifen Amitryptyline Nortriptyline

25 P450: 2D6 / 2C9 Summary Carbons in saturated rings: FN with low IR Perhexiline (2D6) Accessibility: 4.1 Reactivity: -7.6 (<< cutoff: -2.0)

26 P450 Site of Metabolism Results GUI Potential SOMs (atoms which pass the IFD and reactivity filters) are ranked according to the overall score and grouped into 3 classes Circles surrounding the top 3 scoring sites have the largest radii and are the most likely SOMs Next 2 scoring sites, and the remaining sites have the smallest radii and are the least likely SOMs

27 P450 Site of Metabolism Results GUI Intrinsic reactivity: The higher the value, the more likely the atom is to be attacked by the heme Fe Fe-accessibility: Log of no. of IFD poses that atom is within 5Å of heme Fe Overall SOM Score: Atoms are ranked by overall score. The top scoring sites have larger radii, and are more likely SOMs.

28 Application: Membrane Permeability A molecular mechanics-based model of passive membrane permeability. Only consider the desolvation penalty of the lowest energy conformation in low ε medium, plus the de-ionization penalty: ΔG transfer = E CHCl3 - E H2O ΔG Insert = ΔG transfer + ΔG de-ionization

29 Membrane Permeability: Methodology Neutralize ligand (Epik) Free energy penalty for neutralization Conformational search in membrane environment 20k+ conformations, clustered, minimized and scored Rescore in the aqueous environment Specialized solvation model for accurate desolvation energies Identify conformation with the lowest free energy difference between the two environments Add in the penalty for neutralization ~ 10 min. per molecule

30 Membrane Permeability: Comparison SGB/NP-E: Prime with specialized solvation model SGB/NP: Prime with legacy solvation model VSGB: Prime with current default solvation model SAMM: Swift-Amaro MacroModel protocol QP: QikProp

31 Membrane Permeability: Other Methods Dataset N Method SGBNPE SGBNP VSGB Adveef 11 PAMPA Caco Balamine 22 PAMPA Caco % Abs SGB/NP-E: Specialized solvation model SGB/NP: Legacy solvation model VSGB: Current default solvation model Bermejo 16 PAMPA Caco Di 27 PAMPA Fujikawa 74 PAMPA Caco Goodwin 12 Caco Irvine 53 Caco % Abs Highlighted cells indicate predictions that are within 0.10 units of the best prediction MDCK Li 25 PAMPA Caco Whitlock 9 PAMPA Average

32 Membrane Permeability: Other Methods Dataset N Method SGBNPE SAMM QP Adveef 11 PAMPA Caco Balamine 22 PAMPA Caco % Abs SGB/NP-E: Specialized solvation model SAMM: Swift-Amaro MacroModel QP: QikProp Bermejo 16 PAMPA Caco Di 27 PAMPA Fujikawa 74 PAMPA Caco Goodwin 12 Caco Irvine 53 Caco % Abs Highlighted cells indicate predictions that are within 0.10 units of the best prediction MDCK Li 25 PAMPA Caco Whitlock 9 PAMPA Average

33 Membrane Permeability: Example Advantages of a physics-based model: Less dependent on a training set. Identifies internal H-bonds which reduce the free energy of de-solvation

34 Membrane Permeability: Example Advantages of a physics-based model: Less dependent on a training set. Identifies internal H-bonds which reduce the free energy of de-solvation

35 Membrane Permeability: Results Blind test on actual lead optimization series

36 Membrane Permeability: Results Blind test on actual lead optimization series R 2 = 0.48

37 Membrane Permeability: Results Blind test on actual lead optimization series R 2 = 0.48 R 2 = 0.74

38 Membrane Permeability: Results Blind test on actual lead optimization series R 2 = 0.04 R 2 = 0.55

39 Membrane Permeability: Visualization

40 Membrane Permeability: Visualization

41 dg(insert) vs logd 736 compounds curated from the literature

42 dg(insert) vs logd 736 compounds curated from the literature R 2 = 0.20

43 dg(insert) vs logd 736 compounds curated from the literature R 2 = 0.20 What if we cluster into congeneric series? Use Canvas K-means clustering based on fingerprints 20 clusters Ignore any cluster with < 5 members 10 clusters remain

44 dg(insert) vs logd by congeneric series R 2 = 0.74 R 2 = 0.68 R 2 = 0.90 R 2 = 0.44 R 2 = 0.92 R 2 = 0.86

45 dg(insert) vs logd by congeneric series R 2 = 0.00 R 2 = 0.10 R 2 = 0.09

46 dg(insert) vs logd by congeneric series R 2 = 0.00 R 2 = 0.10 R 2 = 0.09 R 2 = 0.64

47 dg(insert) vs logd by congeneric series R 2 = 0.00 R 2 = 0.10 R 2 = 0.09 R 2 = 0.64

48 dg(insert) vs logd by congeneric series R 2 = 0.00 R 2 = 0.10 R 2 = 0.09 R 2 = 0.64

49 dg(insert) vs logd by congeneric series R 2 = 0.32

50 Future Directions P450 Site of Metabolism Further enhancements to 2D6 and 2C9 models Structure-based 3A4 model Additional isoforms (1A2, etc.) Membrane Permeability Macrocycles Global logd/logp Additional targets of ADME/Tox interest

51 Accessing the Models Task View: Tasks -> ADME/Tox -> Structure-based P450 SoM Tasks -> ADME/Tox -> Physics-based Membrane Permeability Application View: Applications -> Physics-based ADME/Tox -> P450 SoM Applications -> Physics-based ADME/Tox -> Membrane Permeability