234th acs national meetingpaper id: 1121959 division of chemical information herman skolnik award...
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234th ACS National Meeting PAPER ID: 1121959Division of Chemical InformationHerman Skolnik Award Symposium
Bridging the gap between discovery data and development decisions
Jeffrey M. Skell, Ph.D.Scientific DirectorGenzyme Drug and Biomaterial R&DDMPK & Pharmaceutics
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SOFTWARE TOOLS FOR COMPUTER-ASSISTED MOLECULAR DESIGN
by
JEFFREY M. SKELL, B.S.,B.S.
DISSERTATION
Presented to the Faculty of the Graduate School of
The University of Texas at Austin
In Partial Fulfillment
Of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
THE UNIVERSITY OF TEXAS AT AUSTIN
December, 1993
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Collision cross-sections: 2D molecular projections
Gas-Phase Molecular Ion Mobility of Polycyclic Aromatic Hydrocarbons in an Inert Carrier Gas
Model 1
• Silhouette
• TSA
• Vol
Empirical Model
• RMS Cross-section
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RINGMASTER: atom/bond types, size, connections, conformation
RINGMAKER: 3D molecular coordinates built in 2D projection
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Z-Coordinate Strain as a Function of Deviation from Ideal Bond Angle
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SAVOL2: Analytic Surface Area and Volume
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G gas -> solution
Thermodynamic Free-Energy Analysis
Theoretically Based Semi-Empirical Models of
Solute-Solvent Interactions
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G cavity G ssi
G gas -> solution
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27 experimental ocular corneum permeabilities
QSPR Model
• Cavity
• Dispersion
• Proximity
• Electrostatic
• H-Bond
Empirical Model
•Log P
•MW
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1987 JUC Pharm. Sci Meeting in Honolulu!
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1987 JUC Pharm. Sci Meeting in Honolulu!
“What was I thinking? I’ll never do that again!”
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1,500 hits on
“Polar Molecular Surface Properties Predict the Intestinal Absorption of Drugs in Humans”
Polar Molecular Surface Properties Predict the ...
- Palm 1997 - Cited by 159
Rapid calculation of polar molecular surface area and ...
- Clark 1999 - Cited by 184
Molecular properties that influence the oral ...
- Veber 2002 - Cited by 224
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Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties
P. Ertl,* B. Rohde, and P. Selzer J. Med. Chem., 2000, 43 (20), 3714 -3717
Figure 1: Comparison of the new methodology with the traditional way to calculate PSA
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GSSI, a General Model for Solute-Solvent Interactions. 1. Description of the Model
A novel, semiempirical approach for the general treatment of solute-solvent interactions (GSSI) was developed to enable the prediction of solution-phase properties (e.g., free energies of desolvation, partition coefficients, and membrane permeabilities).
Felix Deanda, Karl M. Smith, Jie Liu, and Robert S. PearlmanMol. Pharmaceutics, 2004, 1 (1), 23–39
G gas -> solution
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A Theoretical Basis for a Biopharmaceutical Drug Classification:The Correlation of in Vitro Drug Product Dissolution and in Vivo
Bioavailability
30,000 references to
“Predicting Human Absorption” FDA Guidance issued in 2000
G.L. Amidon, H. Lennernas, V. P. Shah, and J. R. Crison
Pharm. Res., 12(3), 1995, 413-420
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Recent Progress in the Computational Prediction of Aqueous Solubility and Absorption
Selected Rules or Alerts Derived Statistically for Absorption/Bioavailability
---------------------------------------------------------------------------------------------------
Palm et al 119 high for PSA ≤ 60; low for PSA ≥ 140
Lipinski et al 104 logP ≤ 5; HBD ≤ 5; HBA ≤ 10; MW ≤ 500
Veber et al 108 rotatable ≤ 10; PSA ≤ 140 Å2 or HB ≤ 12
Martin 111 anions: high PSA is < 75; low PSA >150
cations: and neutrals: pass/fail on Lipinski’s rules
-------------------------------------------------------------------------------------------------------------S.R. Johnson, W. Zheng,
AAPS Journal. 2006; 8(1): E27-E40
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Classification of Membrane Permeability of Drug Candidates:A Methodological Investigation
1040 drug candidates: training set 832; test set 208 compounds
High (>4 * 106 cm/s) and Low (<4 * 106 cm/s) membrane permeation in a cell based assay
The best model: flexible bonds, HBD, MW, PSA
In the test set of 208 compounds 9% were not classified. False positive rate was 0.08 and the sensitivity was 0.76.
B.F. Jensen, H.H.F. Refsgaard, R. Bro, Per B. Brockhoff*QSAR Comb. Sci. 2005, 24, 449-457
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In Silico Classification of Solubility using Binary k-Nearest Neighbor and Physicochemical Descriptors
Turbidimetric on 518 drug candidates: training set 389; test set 129
Solubility: Low <0.02 mg/mL and High >0.02 mg/mL
clog D was found to be the descriptor that separated the two solubility classes most efficiently
…the solubility model could be used to flag molecules with low solubility in an early stage of discovery projects.
B. Fredsted, P.B. Brockhoff, C. Vind, S.B. Padkjaer, H.H.F. RefsgaardQSAR Comb. Sci. 2007, 26, 452-459
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In Silico Classification of Solubility using Binary k-Nearest Neighbor and Phyiscochemical Descriptors
Turbidimetric on 518 drug candidates: training set 389; test set 129
Solubility: Low <0.02 mg/mL and High >0.02 mg/mL
clog D was found to be the descriptor that separated the two solubility classes most efficiently
…the solubility model could be used to flag molecules with low solubility in an early stage of discovery projects.
B. Fredsted, P.B. Brockhoff, C. Vind, S.B. Padkjaer, H.H.F. RefsgaardQSAR Comb. Sci. 2007, 26, 452-459
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Pursuing the leadlikeness concept in pharmaceutical research
…what makes a good lead has been recognised with the concept of leadlikeness. Leadlikeness implies cut-off values in the physico-chemical profile of chemical libraries such that they have reduced complexity (e.g. MW below <400) and other more restricted properties.
This supports the design and screening of ‘reduced complexity’ (leadlike) compound libraries…
M.M. Hann, and T.I. OpreaCurrent Opinion in Chemical Biology, 2004, 8(3), 255-263
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Pursuing the leadlikeness concept in pharmaceutical research
…what makes a good lead has been recognised with the concept of leadlikeness. Leadlikeness implies cut-off values in the physico-chemical profile of chemical libraries such that they have reduced complexity (e.g. MW below <400) and other more restricted properties.
This supports the design and screening of ‘reduced complexity’ (leadlike) compound libraries…
M.M. Hann, and T.I. OpreaCurrent Opinion in Chemical Biology, 2004, 8(3), 255-263
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Then Now
Discovery Kill them fast
Kill them early
Make them hardier
Development More shots on goal
Better shots on goal
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Then How Now
Discovery Kill them fast
Kill them early
Integrate leadlikeness
Make them hardier
Development More shots on goal
Improve human PK prediction
Better shots on goal