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

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

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

1

2

5

4

3

1 5

3

2 4

RINGMASTER: atom/bond types, size, connections, conformation

RINGMAKER: 3D molecular coordinates built in 2D projection

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

SAVOL2: Analytic Surface Area and Volume

+

G gas -> solution

Thermodynamic Free-Energy Analysis

Theoretically Based Semi-Empirical Models of

Solute-Solvent Interactions

+

+

G cavity G ssi

G gas -> solution

27 experimental ocular corneum permeabilities

QSPR Model

• Cavity

• Dispersion

• Proximity

• Electrostatic

• H-Bond

Empirical Model

•Log P

•MW

1987 JUC Pharm. Sci Meeting in Honolulu!

1987 JUC Pharm. Sci Meeting in Honolulu!

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

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

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

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

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

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

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

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

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

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

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

Then Now

Discovery Kill them fast

Kill them early

Make them hardier

Development More shots on goal

Better shots on goal

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