1. 125:583 Molecular Modeling I Prof. William Welsh November 2, 2006 Norman H. Edelman Professor in Bioinformatics Department of Pharmacology Robert Wood Johnson Medical School University of Medicine & Dentistry of New Jersey (UMDNJ) & Director, The UMDNJ Informatics Institute 675 Hoes Lane Piscataway, NJ 08854

2. Applying the Drug Discovery Paradigm to Biomaterials Bill Welsh Robert Wood Johnson Medical School & Informatics Institute University of Medicine & Dentistry of New Jersey (USA)

3. Some Advanced Medical Applications of Implant Materials WN292 PP063 Tissue Engineering -requires degradable (bioactive) materials as temporary scaffolds for tissue remodeling -requires materials that elicit controllable and predictable cellular responses Implantable Drug Delivery Systems and Degradable Temporary Support Devices -require fine-tuning of multiple sets of properties

4. We don’t have the right materials The material base of the medical device industry is outmoded -The industry relies currently on industrial plastics from the 1940’s and 1950’s -Very few degradable biomaterials are available The lack of degradable biomaterials that elicit predictable and controllable cell and tissue responses is a “bottleneck” in bringing tissue-engineering based therapies into the clinic PP062

5. A New Approach: Combinatorial Chemistry in Materials Design Model: Drug Discovery -Very large libraries -Very specific bioassays looking for one particular bioactivity -Searching for a needle in a haystack Outcome -Dramatic acceleration of the pace in which lead compounds can be identified

6. Elements of a Biomaterials “Combi” Approach Parallel synthesis of a larger number of polymers Rapid screening assays for the characterization of bio-relevant material properties -e.g., protein surface adsorption, cell growth, gene expression in cells Data mining, computational design and modeling Reduced cost and risk, leading to greater willingness of industry to consider the commercialization of new biomaterials for specific applications

7. Screening for Fibrinogen Adsorption Major surface protein to initiate coagulation and inflammation Blood cells bind to fibrinogen Level of fibrinogen adsorption is commonly used as a blood compatibility indicator

8. The Modern Drug Discovery Paradigm: Rational Drug Design genes proteins small molecules drug candidates

9. Combinational Chemistry (CombiChem) small-molecule libraries Parallel Chemical Synthesis polymer libraries Parallel Chemical Synthesis

10. Focal Areas Surrogate Molecular Modeling to Accelerate Polymer Design and Optimization Virtual Combinatorial Chemistry: Compressing Large Polymer Libraries into Representative Subsets Quantitative Structure-Performance Relationship (QSPR) Models: Predicting Cell-Material Interactions from the Polymer’s Chemical Structure Atomistic Molecular Modeling to Explore Polymer Properties and Polymer-Protein Interactions Molecular Simulations of Water Transport Through Polymers Scoring Functions to Study Polymer-Protein Interactions

11. Quantitative Structure-Performance Relationship (QSPR) Models Find correlations between chemical structure and performance Predict complex polymer performance characteristics from simple structure and material properties

12. Quantitative Structure-Performance Relationship (QSPR) Models Set of Polymers In vitro/In vivo Data (Y) Molecular Descriptors (X i )  QSPR Y = f(X i ) InterpretationPrediction

13. Types of Molecular Descriptors Topological 2-D structural formula (Kier & Hall indices) Electrostatic Charge distribution (partial charges, H-bond donors/acceptors) Geometric 3-D structure of molecule (I, SA, Molecular Volume) Quantum-chemical Molecular orbital structure (HOMO- LUMO energies, dipole moment)

14. Extract and Tabulate Descriptors Extract and Tabulate Descriptors Quantitative Structure-Performance Relationship (QSPR) Models Polymer Data Set

15. Building QSAR Models Multiple Linear Regression (MLR) pK i = a o + a 1 (Mol Vol i ) + a 2 (logP) + a 3 (  i ) +... Hansch, 1969 Partial Least-Squares (PLS) Regression pK i = a o + a 1 (PC1) + a 2 (PC2) + a 3 (PC3) +... Wold, et al. 1984  (obs. property or activity)   (molecular descriptors) Y = f(X i ) Simple (Univariate) Linear Regression Hammett, 1939 pK i = a o + a 1 (Mol Vol i )

16. Predicting Activities of Untested Compounds Untested polymer: extract descriptors Predicted activity of untested polymer Validated QSPR model: Y i = 0.52 (V i ) + 0.27 (logP i ) - 0.38 (  i ) V logP  HO OH

17. Artificial Neural Network (ANN) Input Hidden LayerOutput Any measured parameter or observation A set of weighed linear regressions or other functions Prediction of the model The ANN needs a training set of data to determine the optimum value of the weighing functions in the hidden layer that lead to the closest match between an experimentally determined outcome and the prediction of the model. Thereafter the ANN can make empirical predictions of the outcome when presented with similar data sets.

18. Combinatorial Polymer Libraries n diacid component diphenol component R O C CNHOOCH 2 2 C O 2 O C O Y O

19. Combinatorial Polymer Libraries n diacid component diphenol component R O C CNHOOCH 2 C O O CHC O Y O Y or R Size of library Combinatorial Explosion!!!

20. Deploy Rational Drug Design Approaches to Biomaterials Design  Generate Virtual Combinatorial Libraries Compress large polymer libraries into representative subsets  Build Computational Models for these Subsets Predict bioresponse to the polymers based only the polymer’s molecular structure Make predictions for the entire polymer library and beyond

21. Cluster representatives Predicted value Synthesis-> Biol. testing-> QSPR model Dipole Molecular volume Rotatable bonds Good diversity Double bonds Moment of inertia Density Poor diversity

22. n diacid component diphenol component R O C CNHOOCH 2 2 C O 2 O C O Y O From QSPR models, select those descriptors and their values that are associated with optimal performance property Synthesize known polymers within cluster Design and synthesize new scaffolds within cluster 1 2 3 From Models to Rational Design and Synthesis

23. Calculate molecular descriptors for each polymer Generate QSPR models Compare predicted vs expt’l Normalized Metabolic Activity (NMA) Identify key descriptors associated with  (NMA) Predict NMA values for untested polymers Computational Procedure

24. List of Molecular Descriptors FUNCTIONAL GROUPS EMPIRICAL DESCRIPTORS MOLECULAR PROPERTIES Number of primary C (sp3) Number of secondary C (sp3) Number of tertiary C (sp3) Number of unsubstituted aromatic C (sp2) Number of substituted aromatic C (sp2) Number and position of branches in pendant chain Number of ethers (aliphatic) Number of H-bond acceptor atoms (N, O, F) Unsaturation index Hydrophilic factor Aromatic ratio Molar refractivity Polar surface area Octanol-water partition coefficient (logP)

25. Set of 62 polyarylates & their calculated descriptors 0255075100125 0 50 100 150 R 2 = 0.75 R cv 2 = 0.55 Experimental Predicted Normalized Metabolic Activity PLS Loadings: Decompose PCs into Constituent Molecular Descriptors PC1 PC4 PC5 PC2 PC3 nBRs nBRp

26. Key Descriptors Associated With  (NMA) Hydrophilic factor: # hydrophilic groups Octanol-water partition coefficient logP Number of secondary C (sp3) Molar refractivity PC1 SIMPLIFY the model nBRs nBRp nBRs

27. Predicted NMA for Untested Polyarylates Polymer code: DTiB_AA Predicted NMA: 40.9 Polymer code: HTH_AA Predicted NMA: 69.7 Polymer code: HTH_GLA Predicted NMA: 59.5 Polymer code: HTH_MAA Predicted NMA: 33.7 Polymer code: DTiB_DGA Predicted NMA: 55.0 Polymer code: THE_DGA Predicted NMA: 82.6 Kholodovych V, Smith JR, Knight D, Abramson S, Kohn J, Welsh WJ Polymer, 2004, 45, 7367-7379 (62.6±11.9) (41.4±7.9) (63.7±12.3) (53.2±10.1) (67.1±12.7) (101.5±19.3)

28. FIBRINOGEN ADSORPTIONFRLF NMA YYRR

29. Summary & Conclusions  Computational molecular modeling represents a powerful tool for accelerating optimal biomaterial design  QSPR models are useful for predicting, and interpreting, biomaterials' performance properties  QSPR-based approaches are complementary to atomistic simulation models (Knight, Latour, Welsh)

30. Smith JR, Knight D, Kohn J, Rasheed K, Weber N, Kholodovych V, Welsh WJ Using Surrogate Modeling in the Prediction of Fibrinogen Adsorption onto Polymer Surfaces Journal of Chemical Information & Computer Science 44(3): 1088-1097(2004) Kholodovych V, Smith JR, Knight D, Abramson S, Kohn J, Welsh WJ Accurate Predictions Of Cellular Response Using QSPR: A Feasibility Test Of Rational Design Of Polymeric Biomaterials Polymer 45(22):7367-7379 (2004) Smith JR, Kholodovych V, Knight D, Kohn J, Welsh WJ Predicting Fibrinogen Adsorption to Polymeric Surfaces In Silico: A Combined Method Approach Polymer 46: 4296 (2005) (Paper assigned for reading) Smith JR, Knight D, Kohn J, Kholodovych V, Welsh W J Using Surrogate Modeling In The Analysis of Bioresponse Data from Combinatorial Libraries of Polymers QSAR & Combinatorial Science (submitted) Relevant Papers