1. 234th ACS National MeetingPAPER ID: 1121959 Division of Chemical Information Herman Skolnik Award Symposium Bridging the gap between discovery data and development decisions Jeffrey M. Skell, Ph.D. Scientific Director Genzyme Drug and Biomaterial R&D DMPK & Pharmaceutics

2. 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

3. 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

4. 1212 5454 3 15 3 24 RINGMASTER: atom/bond types, size, connections, conformation RINGMAKER: 3D molecular coordinates built in 2D projection

5. Z-Coordinate Strain as a Function of Deviation from Ideal Bond Angle

6. SAVOL2: Analytic Surface Area and Volume

7. +  G  gas -> solution Thermodynamic Free-Energy Analysis Theoretically Based Semi-Empirical Models of Solute-Solvent Interactions

8. + +  G  cavity  G  ssi  G  gas -> solution

9. 27 experimental ocular corneum permeabilities QSPR Model Cavity Dispersion Proximity Electrostatic H-Bond Empirical Model Log P MW

11. 1987JUC Pharm. Sci Meeting in Honolulu!

12. “What was I thinking? I’ll never do that again!”

13. 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

14. 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

15. 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. Pearlman Mol. Pharmaceutics, 2004, 1 (1), 23–39  G  gas -> solution

16. 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

17. Recent Progress in the Computational Prediction of Aqueous Solubility and Absorption Selected Rules or Alerts Derived Statistically for Absorption/Bioavailability --------------------------------------------------------------------------------------------------- Palm et al119 high for PSA ≤ 60; low for PSA ≥ 140 Lipinski et al104 logP ≤ 5; HBD ≤ 5; HBA ≤ 10; MW ≤ 500 Veber et al108rotatable ≤ 10; PSA ≤ 140 Å 2 or HB ≤ 12 Martin111anions:high PSA is 150 cations: and neutrals: pass/fail on Lipinski’s rules ------------------------------------------------------------------------------------------------------------- S.R. Johnson, W. Zheng, AAPS Journal. 2006; 8(1): E27-E40

18. Classification of Membrane Permeability of Drug Candidates: A Methodological Investigation 1040 drug candidates: training set 832; test set 208 compounds High (>4 * 10 6 cm/s) and Low (<4 * 10 6 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

19. 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 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. Refsgaard QSAR Comb. Sci. 2007, 26, 452-459

20. 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 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. Refsgaard QSAR Comb. Sci. 2007, 26, 452-459

21. 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. Oprea Current Opinion in Chemical Biology, 2004, 8(3), 255-263

22. 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. Oprea Current Opinion in Chemical Biology, 2004, 8(3), 255-263

23. ThenNow DiscoveryKill them fast Kill them early Make them hardier DevelopmentMore shots on goal Better shots on goal

24. ThenHowNow DiscoveryKill them fast Kill them early Integrate leadlikeness Make them hardier DevelopmentMore shots on goal Improve human PK prediction Better shots on goal