125:583125:583Molecular Modeling IMolecular Modeling IProf. William WelshProf. William WelshNovember 2, 2006November 2, 2006

Norman H. Edelman Professor in BioinformaticsDepartment of Pharmacology

Robert Wood Johnson Medical SchoolUniversity of Medicine & Dentistry of New Jersey (UMDNJ)

& Director, The UMDNJ Informatics Institute675 Hoes Lane

Piscataway, NJ 08854

Applying the Drug Discovery Applying the Drug Discovery

Paradigm to BiomaterialsParadigm to Biomaterials

Bill WelshRobert Wood Johnson Medical School & Informatics Institute

University of Medicine & Dentistry of New Jersey (USA)

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

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

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

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

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

The Modern Drug Discovery Paradigm: The Modern Drug Discovery Paradigm:

RationalRational Drug Design Drug Designgenes

proteins

small molecules

drug candidates

Combinational Chemistry (CombiChem)Combinational Chemistry (CombiChem)

small-moleculelibraries

HO

OH

A B

C D

OH

O

OH O

O

O

HO

OH

OH

O

A

C D

HO

OH

ON

HO

OH

O

HO

OH

OH

O

A

C D

Parallel Chemical Synthesis

polymer libraries

Parallel Chemical Synthesis

O

O

OO

N

O

O

O

O

O

O

O

O

O

CH3

O

O

OO O

ON

O

O

O

CH3

O

O

O

O

O

ON

O

O

O

CH3

O

O

Focal AreasSurrogate 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

Quantitative Structure-Performance Relationship (QSPR) Models

• Find correlations between chemical structure and performance

• Predict complex polymer performance characteristics from simple structure and material properties

Quantitative Structure-Performance Relationship (QSPR) Models

Set of Polymers

In vitro/In vivo Data (Y) Molecular Descriptors (Xi)

QSPRY = f(Xi)

InterpretationPrediction

Types of Molecular Descriptors

*

O

CH2 CH2

O

NH CH CH2

O

O

O

O

CH2 O

CH2

OH

CH2 *n 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)

Extract and Tabulate DescriptorsExtract and Tabulate Descriptors

POLYMER NMA "Y"

Descriptors (X i) Mol. Vol. ( Å 3) LogP Hydrophilicity

1 32 420 3. 31 0. 14 2 52 332 3.92 0.11 3 2 4 98 4.57 0.07 4 75 467 2.93 0.16 5 16 359 3.68 0.12

etc. etc. etc. etc. etc.

Quantitative Structure-Performance Quantitative Structure-Performance Relationship (QSPR) ModelsRelationship (QSPR) Models

Polymer Data SetPolymer Data Set

Building QSAR ModelsBuilding QSAR Models

Multiple Linear Regression (MLR)

pKi = ao + a1 (Mol Voli) + a2 (logP) + a3 (i) + ...

Hansch, 1969

Partial Least-Squares (PLS) Regression

pKi = ao + a1 (PC1) + a2 (PC2) + a3 (PC3) + ...

Wold, et al. 1984

(obs. property or activity) (molecular descriptors)

Y = f(Xi)

Simple (Univariate) Linear Regression Hammett, 1939

pKi = ao + a1 (Mol Voli)

Predicting Activities of Untested CompoundsPredicting Activities of Untested Compounds

Untested polymer:

extract

descriptors

Predicted activity of untested polymer

Validated QSPR model: Yi = 0.52 (Vi) + 0.27 (logPi) - 0.38 (i)

V logP HO

OH

Artificial Neural Network (ANN)

Input

Input

Input

Input

Input

Input

Input

Input

Input

Hidden Layer Output

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.

Combinatorial Polymer Libraries

OH OH

OH OH

OH

OO

OH

OH

OH

OH

OH

OH

HO CH2

C

C

O

NH CH CH2

O

OR

OH

Isopropanol

Benzyl Alcohol

Butanol

Hexanol

iso-Butanol

2-(2-Ethoxyethoxy)ethanol

sec-ButanolEthanolMethanol

Dodecanol

Octanol

n=1,2

n

diacid component

diphenol component

R

O

C

C NH OO CH2CH2C

O

CH2

O

CHC

O

Y

O

HO2CCO2H

HO2C CO2H

HO2CCO2H

HO2CCO2H

HO2CCO2H

HO2C O CO2H

HO2CCO2H

HO2C OO CO2H

3-Methyl-Adipic Acid

Diglycolic AcidGlutaric Acid

Sebacic Acid

Adipic Acid

Suberic Acid Dioxaoctanedioic Acid

Succinic Acid

C

O

OHYC

O

HO

Combinatorial Polymer Libraries

OH OH

OH OH

OH

OO

OH

OH

OH

OH

OH

OH

HO CH2

C

C

O

NH CH CH2

O

OR

OH

Isopropanol

Benzyl Alcohol

Butanol

Hexanol

iso-Butanol

2-(2-Ethoxyethoxy)ethanol

sec-ButanolEthanolMethanol

Dodecanol

Octanol

n=1,2

n

diacid component

diphenol component

R

O

C

C NH OO CH2CH2C

O

CH2

O

CHCO

YO

25 100

400

2500

0

500

1000

1500

2000

2500

5 10 20 50 Y or R

Siz

e o

f lib

rary

Combinatorial Explosion!!!

Deploy Rational Drug Design Deploy Rational Drug Design Approaches Approaches

to Biomaterials Designto 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

Cluster representatives

Pre

dic

ted v

alu

e Synthesis-> Biol. testing-> QSPR model

Dipole

Molecular volume

Rotatable bonds

Good diversity

Double bonds

Moment of inertia

Density

Poordiversity

HO2CCO2H

HO2C CO2H

HO2CCO2H

HO2CCO2H

HO2CCO2H

HO2C O CO2H

HO2CCO2H

HO2C OO CO2H

3-Methyl-Adipic Acid

Diglycolic AcidGlutaric Acid

Sebacic Acid

Adipic Acid

Suberic Acid Dioxaoctanedioic Acid

Succinic Acid

C

O

OHYC

O

HO

OH OH

OH OH

OH

OO

OH

OH

OH

OH

OH

OH

HO CH2

C

C

O

NH CH CH2

O

OR

OH

Isopropanol

Benzyl Alcohol

Butanol

Hexanol

iso-Butanol

2-(2-Ethoxyethoxy)ethanol

sec-ButanolEthanolMethanol

Dodecanol

Octanol

n=1,2

n

diacid component

diphenol component

R

OC

C NH OO CH2CH2CO

CH2

O

CHCO

YO

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

• 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 ProcedureComputational Procedure

List of Molecular DescriptorsList 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 chainNumber of ethers (aliphatic)Number of H-bond acceptor atoms (N, O, F)

Unsaturation indexHydrophilic factorAromatic ratio

Molar refractivityPolar surface areaOctanol-water partition coefficient (logP)

Set of 62 polyarylatesSet of 62 polyarylates

& their calculated descriptors& their calculated descriptors

0 25 50 75 100 1250

50

100

150R2 = 0.75

Rcv2 = 0.55

Experimental

Pre

dic

ted

Normalized Metabolic Activity

PLS

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

MLOGP

PSA

MR

ARR

Hy

Ui

nHAcc

nROR

nOHs

nOHp

nCaR

nCaH

nCt

nCs

nCp

Loadings: Decompose PCs into Constituent Molecular Descriptors

PC1 PC4 PC5PC2 PC3

nBRs

nBRp

Key Descriptors Associated With Key Descriptors Associated With (NMA)(NMA)

Hydrophilic factor: # hydrophilic groups

Octanol-water partition coefficient logP

Number of secondary C (sp3)

Molar refractivity

PC1

0%

20%

40%

60%

80%

100%

PC1 PC2 PC3 PC4 PC5

MLOGP

PSA

MR

nOHs

nCt

SIMPLIFY the model

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

MLOGP

PSA

MR

ARR

Hy

Ui

nHAcc

nROR

nOHs

nOHp

nCaR

nCaH

nCt

nCs

nCp

nBRs

nBRp

nBRs

Predicted NMA for Untested PolyarylatesPredicted 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

OO

NH

OO

O

OCH

2

O

OO

NH

OO

O

OCH2

2

O

O OO

NH

OO

O

OCH

22O

Polymer code: DTiB_DGA

Predicted NMA: 55.0

OO

NH

OO

O

OCH

2O

OO

NH

OO

O

OCH2

O

Polymer code: THE_DGA

Predicted NMA: 82.6

O OO

NH

OO

O

OCH2

O

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)

FIBRINOGEN ADSORPTION FRLF NMA

YY YY RRRR

Summary & Conclusions Summary & Conclusions Computational molecular modeling represents a Computational molecular modeling represents a

powerful tool for accelerating optimal powerful tool for accelerating optimal biomaterial designbiomaterial design

QSPR models are useful for predicting, and QSPR models are useful for predicting, and interpreting, biomaterials' performance interpreting, biomaterials' performance propertiesproperties

QSPR-based approaches are complementary to QSPR-based approaches are complementary to atomistic simulation models (Knight, Latour, atomistic simulation models (Knight, Latour, Welsh)Welsh)

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 PapersRelevant Papers