thanks! · 3d searching, pharmacophores and 3d target structures • literature search on...
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Thanks!
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At Yvonne’s Retirement from Abbott
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Group Members Soaring Bear Rob Brown Mark Bures Cary Chabalowski Eugene Coats Elizabeth Danaher Jerry DeLazzer Rob DeWitte Giovanni Greco James Hackbarth
Ki Kim Isabella Lico Haight Cathy May Ingo Mugge T.J. O'Donnell Helen Panas Patricia Pavlik Hutchins Kathleen Riley Lynn John Rys John van Drie
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Other Abbott Colleagues Derek Debe J. D. Taylor Mark Holladay
Steve Fesik Ron Wiegand P. Hal Jones
Jonathan Greer George Chao Tom Perun
Philip Hajduk Eugene Gady Robert Schoenleber
Steve Muchmore C. Thomas Lin
Kent Stewart Lilly Sanathan
Herschel Weintraub Paul Sanders
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Ligand-Based Drug Design
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Hansch: QSAR log 1/C = a + b log P + c Es + ρσ … • Potency may be influenced by more than one physical property.
• Statistical evaluation explores the possible significance of each property to activity.
• logP is an additive constitutive property; that it can be calculated.
Hansch, C.; Leo, A. Exploring QSAR: Fundamentals and Applications in Chemistry and Biology; American Chemical Society: Washington DC, 1995.
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Alkyl Erythromycin Esters
Martin, Jones, Perun, Grundy, Bell, Bower, Shipkowitz. J. Med. Chem. 15, (1972) 635-638
Binding to the target is constant for this series, except for the 2’ esters, which are rapidly hydrolyzed. 4”
11
A
2’
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Antibacterial Potency of Alkyl Erythromycin Esters
Martin, Jones, Perun, Grundy, Bell, Bower, Shipkowitz. J. Med. Chem. 15, (1972) 635-638
Log (1/C) = 6.89 -1.36 log P -0.29 D4” -0.17 D11 -0.36 A
R2 = 0.945, s = 0.127, n = 28
4”
11
A
2’
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Back to Erythromycin Analogues
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Predictions of Antibacterial Potency of Diverse Erythromycin Analogues
Martin, Quantitative Drug Design, Second Edition, in press
Model Based on 28 Analogues # Predictions Standard Deviation of Predictions
Alkyl esters, physical properties R2 = 0.94
44 0.91
Esters plus five more polar R2 = 0.93
39 0.45
Alkyl esters, 1 component CoMFA R2 = 0.90, Q2 = 0.86
44 0.51
Alkyl esters, 2 component CoMFA R2 = 0.94, Q2 = 0.91 44 0.53
Alkyl esters, 6 component CoMFA R2 = 0.98, Q2 = 0.96 44 0.56
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Predictions of Antibacterial Potency of Diverse Erythromycin Analogues
Model Based on 28 Analogues # Predictions Standard Deviation of Predictions
Alkyl esters, physical properties R2 = 0.94 (negative log P)
44 0.91
Esters plus five more polar R2 = 0.93 (optimum log P)
39 0.45
Alkyl esters, 1 component CoMFA R2 = 0.90, Q2 = 0.86
44 0.51
Alkyl esters, 2 component CoMFA R2 = 0.94, Q2 = 0.91 44 0.53
Alkyl esters, 6 component CoMFA R2 = 0.98, Q2 = 0.96 44 0.56
Martin, Quantitative Drug Design, Second Edition, in press
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D1 CoMFA Model on 61 Compounds
Martin & Pavlik, unpublished / ACS telesymposium. Schoenleber & Lin. co-workers
Positive & negative steric CoMFA contours Model based on 61 full agonists and
antagonists of very diverse structures: CH3 probe, 2A spacing.
Leave-one-out cross-validation • Q2 = 0.51 • RMSE = 0.79
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Predictions from the D1 CoMFA Model
LOO RMSE = 0.79
Predicted to be potent: 6/11 have pKi > 7. Predicted not to be potent: 6/9 have pKi < 6.
Random expectation: 15% pKi > 7 Observed: 54% of those predicted to have pKi >7
3.6 X enhancement
True forecasts of affinity of 201 compounds proposed for synthesis: full conformational and stereochemical searches.
Pred. No. No. Observed. pKi pKi modeled made <6 6-7 >7 <6.5 146 9 6 3 0 >6.5 55 11 1 4 6
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Does Using Structures Increase the Precision of QSARs?
Enzyme N Literature method Author carbonic anhydrase 20 Simple interaction E Menziani, et al.(1988)
HIV protease 33 Relax ligand, E from Merck Holloway et al. (1995) force field
Thrombin 35 Relax ligand with CHARMm Grootenhuis & vanGalen dynamics, E decomposed (1994
& 5)
Thrombin 7 MC Linear Response Jones-Hertzog & OPL
S Jorgensen(1997)
Neuraminidase 24 Molecular dynamics ligand Taylor & von Itzstein CVFF, decompose energy (1996)
Herpes TK 18 Relax ligand with Amber, De Winter & Herdewijn decompose energy DELPHI (1996) Martin & Bear, 1997 unpublished.
Martin, Quantitative Drug Design, Ed2, in press
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Fits Using 3D Structures versus CoMFA
Martin & Bear, 1997 unpublished. Martin, Quantitative Drug Design, Ed2, in press
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Cross-validated standard error
target-based CoMFA
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CoMFA versus Structure-Based 18 Inhibitors of Urokinase
Molecular Mechanics with Poisson‑Boltzmann Surface Area (MMPBSA) analysis the affinity of 18 inhibitors of urokinase with an R of 0.90, R2 of 0.81.
Brown, Muchmore. J. Chem. Inf. Model. 2007, 47, 1493-503
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Latent variables 1 2 3 R2 0.80 0.92 0.97
s 0.44 0.30 0.18
loo q2 0.59 0.71 0.75
loo spred 0.62 0.54 0.53
CoMFA versus Structure-Based 18 Inhibitors of Urokinase
Molecular Mechanics with Poisson‑Boltzmann Surface Area (MMPBSA) analysis the affinity of 18 inhibitors of urokinase with an R of 0.90, R2 of 0.81.
CoMFA 3D QSAR Models of the Urokinase Inhibitors
Martin, Quantitative Drug Design, 2 ed. in press
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Further Comparisons of QSAR with MMPBSA
Brown and Muchmore. J. Med. Chem. 52 (2009) 3159-3165.
Enzyme Number R
MM-PBSA R
MW Urokinase 75 0.78 0.61
PTP-1B 110 0.83 0.69
Chk-1 123 0.72 0.59
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Physical properties considered: – Molecular weight – CMR – Number of rotatable bonds – Log P from CLOGP – Log P from KowWin – Polar surface area
Further Comparisons of QSAR with MMPBSA
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Brown and Muchmore. J. Med. Chem. 52 (2009) 3159-3165. QSAR
Enzyme Number R
(MM-PBSA) R
(MW) R
(physical props) Urokinase 75 0.78 0.61 0.74
-CLOGP CMR
PTP-1B 110 0.83 0.69 0.86 PSA -rtbnd CMR
Chk-1 123 0.72 0.59 0.64 KowWin PSA
Further Comparisons of QSAR with MMPBSA
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Brown and Muchmore. J. Med. Chem. 52 (2009) 3159-3165. QSAR
Enzyme Number R
MM-PBSA R
physical properties
R both
Urokinase 75 0.78 0.74 0.82 -CLOGP -rtbnd
CMR
PTP-1B 110 0.83 0.86 0.88 CMR
Chk-1 123 0.72 0.64 0.76 KowWin PSA
Further Comparisons of QSAR with MMPBSA
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Graphics Modelling D1 Agonists
O
N
O O
N
O
Two low energy conformations Bioactive conformation
O
N
O O
N
O
Martin, Kebabian, MacKenzie, & Schoenleber. In QSAR: Rational Approaches in the Design of Bioactive Compounds; C. Silipo and A. Vittoria, Ed.; Elsevier: Amsterdam, 1991; pp 469-482.
Synthesis of analogues
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Aladdin 3D Search
Pharmacophore mapping
7.24
3D Searching 4.88
Pharmacophore mapping
6.82
Van Drie, Weininger, Martin. (1989) J. Comput-Aided Mol. Des. 3, 225-251.
Martin, Kebabian, MacKenzie, & Schoenleber. In QSAR: Rational Approaches in the Design of Bioactive Compounds; C. Silipo and A. Vittoria, Ed.; Elsevier: Amsterdam, 1991; pp 469-482.
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3D Searching, Pharmacophores and 3D Target Structures
• Literature search on “pharmacophore” from 2000-2008. • 76 publications in which novel active compounds were
identified. • Half of these reports involved targets for which the
investigator knew the structure of a protein-ligand complex. The most common use of pharmacophores was with 3D searching, either before or after docking.
• Half of these reports were ligand-based with no use of the structure of a complex.
Martin, in press, Burger’s Medicinal Chemistry
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Lead-hopping from One or More Ligand Structures
Methods tested: Adriana 2D, various endpoints
Adriana 3D, various endpoints
Daylight fingerprints
ECFP_2, 4, and 6 fingerprints
FCFP_2, 4, 6 fingerprints
FeatureTree
ISIS fragments
Pharmacophore, various comparisons
ROCS, shape and combo
Topological torsions
Martin & Muchmore. QSAR & Combinatorial Science 2009, 28, 797-801
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ISIS
EC
FP_6
FCFP
_6
Any
Day
light
RO
CS
Methods tested: Adriana 2D, various endpoints
Adriana 3D, various endpoints
Daylight fingerprints ECFP_2, 4, and 6 fingerprints FCFP_2, 4, 6 fingerprints FeatureTree
ISIS fragments Pharmacophore, various comparisons
ROCS, shape and combo TOTO
Relationships between hits of the best methods:
Martin & Muchmore. QSAR & Combinatorial Science 2009, 28, 797-801
Lead-hopping from One or More Ligand Structures
Each row is a compound, it is shaded if the method found it in top 5%.
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Lead-hopping from One or More Ligand Structures
ISIS
EC
FP_6
FCFP
_6
Any
Day
light
RO
CS
Methods tested: Adriana 2D, various endpoints
Adriana 3D, various endpoints
Daylight fingerprints ECFP_2, 4, and 6 fingerprints FCFP_2, 4, 6 fingerprints FeatureTree
ISIS fragments Pharmacophore, various comparisons
ROCS, shape and combo TOTO
Relationships between hits of the best methods:
Combine scores with belief theory
Muchmore, Debe, Metz, Brown, Martin, Hajduk. J. Chem. Inf. Model., 2008, 48 941–94
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Ligand-Based Methods Are Useful
• Lead-hopping • Pharmacophore-based enhancement of affinity • Pharmacophore-based 3D identification of novel cores • Potency predictions
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Challenges to Predicting Potency
Water energetics
Energetics of conformers, tautomers, ionization states
Energetic consequences of subtle changes in 3D structure of target in response to ligand binding or changes in the environment
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Thank you for your attention