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125:583 Molecular Modeling I Prof. William Welsh November 2, 2006

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

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125:583 Molecular Modeling I Prof. William Welsh November 2, 2006

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  1. 125:583Molecular Modeling IProf. William WelshNovember 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 • 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 WN292 PP063

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

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

  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 (Xi) QSPR Y = f(Xi) Prediction Interpretation

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

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

  15. Simple (Univariate) Linear Regression Hammett, 1939 pKi = ao + a1 (Mol Voli) Hansch, 1969 Multiple Linear Regression (MLR) pKi = ao + a1 (Mol Voli) + a2 (logP) + a3 (i) + ... Wold, et al. 1984 Partial Least-Squares (PLS) Regression pKi = ao + a1 (PC1) + a2 (PC2) + a3 (PC3) + ... Building QSAR Models (obs. property or activity)  (molecular descriptors) Y = f(Xi)

  16. extract descriptors Predicted activity of untested polymer Predicting Activities of Untested Compounds Untested polymer: O H H O  logP V Validated QSPR model:Yi = 0.52 (Vi) + 0.27 (logPi) - 0.38 (i)

  17. Input Input Input Input Input Input Input Input Input Artificial Neural Network (ANN) 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. Hidden Layer Output A set of weighed linear regressions or other functions Any measured parameter or observation Prediction of the model

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

  19. Combinatorial Polymer Libraries diacid component O O O C Y C O C NH CH O C O O R n diphenol component CH2 CH2 CH2 Combinatorial Explosion!!! Size of library Y or R

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

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

  23. Computational Procedure • 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

  24. List of Molecular Descriptors FUNCTIONAL GROUPS EMPIRICAL DESCRIPTORS 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 MOLECULAR PROPERTIES Molar refractivity Polar surface area Octanol-water partition coefficient (logP)

  25. Normalized Metabolic Activity 150 2 R =0.75 2 R =0.55 cv PLS 100 Predicted 50 0 0 25 50 75 100 125 Experimental Loadings: Decompose PCs into Constituent Molecular Descriptors nBRs nBRp PC1 PC3 PC5 PC2 PC4 Set of 62 polyarylates & their calculated descriptors

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

  27. (53.2±10.1) (62.6±11.9) (41.4±7.9) (67.1±12.7) (63.7±12.3) (101.5±19.3) Predicted NMA for Untested Polyarylates Polymer code: DTiB_DGA Predicted NMA: 55.0 Polymer code: HTH_GLA Predicted NMA: 59.5 Polymer code: DTiB_AA Predicted NMA: 40.9 Polymer code: HTH_AA Predicted NMA: 69.7 Polymer code: HTH_MAA Predicted NMA: 33.7 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

  28. FIBRINOGEN ADSORPTION FRLF NMA Y R Y R

  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. Relevant Papers 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  Polymer45(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 ApproachPolymer 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)

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