integrated computational and experimental approach to ... · pdf fileoctanol/water partition...

• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •

Integrated Computational andExperimental Approach to

Characterization and Modeling ofPolymeric Biomaterials

Prof. Doyle Knight

Dept. of Mechanical and AerospaceEngineering , Rutgers University

October 31, 2008

The 9th New Jersey Symposium on Biomaterials Sciences and Regenerative Medicine


Prof. Doyle KnightMechanicalEngineering

Prof. Robert LatourBioengineering

Protein-SurfaceInteractions

Dr. Xianfeng LiPolymer Science& Computational

Chemistry

Prof. Alan WindleMaterials Science

Dr. James ElliottMaterials Science

Dr. Aurora CostacheComputational

Chemistry

RESBIO Core C – Computational Modeling

Dr. Mathieu BouvilleMaterials Science


RESBIO-Core C


Outline

Motivation, Goal and Approach

The Combinatorial-Computational Method

Semi-empirical models

Biomaterials Store


ChemspeedSLT100

BiomaterialsCluster

Motivation, Goal and Approach

Motivation– Modern synthesis techniques can produce

polymer libraries of extraordinary size (~1000sto ~10,000s)

– Characterization of the bioresponse to suchlarge libraries of polymers by experimentalmethods is infeasible

Goal– Develop efficient methods for identifying lead

polymers for specific biomedical research orclinical application

Approach– Combinatorial Computational Method (CCM)


The Combinatorial Computational Method(CCM)

Synthesis of arepresentativesubset fromthe library

CCM

Descriptor Generation-physical properties-biological properties-calculated descriptors

Experimental validationof model predictions,evaluation of leadpolymers

Librarydesign

ResultorProduct

A materialsrequirement

Computational modeling,development ofcorrelations and identifylead polymers


Semi-empirical models

Predict all stages of bioresponse to polymericbiomaterial, e.g.,– Protein adsorption– Cellular response– Degradation

using the descriptors generated by– Rapid screening and high throughput characterization– Mechanical interactions between cells and substratum– Biological response of polymeric materials– All-atom and mesoscale molecular dynamics simulations

Use the semi-empirical models in theCombinatorial Computational Method

Sawyer, A. et al,Biomaterials, 2007

Beyer, M. et al,Biomaterials, 2006

Zhang, Z. et al, Biomaterials,2006

CCM


2D and 3DDescriptorsOf Polymer

• Chemical Structure Prediction forENTIRE Library• Rank ordering

Main Steps to Build Semi-empirical Models

Experimental Data•SUBSET ofLibrary

Example:•Protein adsorption•Cell proliferation


TopologicalTopological

2-D structural formula

(Kier & Hall indices)

ElectrostaticElectrostatic

Charge distribution (partialcharges, PCSA)

GeometricGeometric

3-D structure of molecule(SA, Molecular Volume)

Quantum-chemicalQuantum-chemical

Molecular orbital structure (HOMO-LUMO energies, dipole moment)

*

O

CH2

CH2

O

NH CH CH2

O

O

O

O

CH2 O

CH2

OH

CH2 *

n

Constitutional descriptors

Molecular composition(Mw, # of atoms and bonds)

Calculated Molecular Descriptors –2D and 3D


• Examples of 2D and 3Ddescriptors

Starting from 3D structure ofpolymer, not relaxed-12 repeatunits• Simplest model• Fast• Computationally less expensive

Calculated molecular descriptors - 2D and 3D


3D conformation of polymer is important

Molecular Dynamics Simulations used prior 3D

descriptors calculation

Calculated molecular 3D descriptors –Molecular Dynamics Simulations

Final configurations of 1 ns MD simulations in implicit water. (a) Tetramer of poly(DTEglutarate) and (b) tetramer of poly(DTE dodecandioate).


Semi-empirical Models

Semi-empirical models are mathematicalcorrelations between inputs (descriptors) andoutcomes

Require experimental data for calibration

Examples

Decision Tree

Artificial Neural Network

Kriging function

Polynomial Neural Network

Radial Basis Functions

Support Vector Machines


Prediction of Fibrinogen Adsorption

Fibrinogen adsorption correctlypredicted for 38 of 45 polymers(I.e., within experimentaluncertainty)

Average rms percent error inprediction of validation set is 35%

Most significant descriptors

Tg: glass transition temperature

a_nH: number of hydrogen atoms in themolecule

logP(o/w): logarithm of theoctanol/water partition coefficient

Smith, J., Seyda, A., Weber, N., Knight, D., Abramson, S., and Kohn, J., Macromolecular RapidCommunications, Vol. 25, 2004, pp. 127-140.


Prediction of Rat Lung FibroblastProliferation

RLF proliferation correctlypredicted for 41 of 48 polymers(I.e., within experimentaluncertainty)

Average rms percent error inprediction of validation set is 28%

Most significant descriptors

SlogP_VSA9: Van der Waals surface arealogP(o/w) > 0.4

hydrophilic_factor: number of hydrophilicgroups

SlogP_VSA5: Van der Walls surface area

0.15 < logP(o/w) < 0.2

Smith, J., Seyda, A., Weber, N., Knight, D., Abramson, S., and Kohn, J., Macromolecular RapidCommunications, Vol. 25, 2004, pp. 127-140.


Fibrinogen adsorption to polyarylates

Gubskaya, A. et al, "The Prediction of Fibrinogen Adsorption for Biodegradable Polymers:Integration of Molecular Dynamics and Surrogate Modeling", Polymer, Vol. 48, 2007, pp. 5788-5801.

50

100

150

200

250

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45

experimental

predicted

Fib

rin

og

en

Ad

so

rp

tio

n

Polymer number

R

R

R

RR

DTH diglycolate

Fibrinogen Adsorption for PolyarylatesLibrary


20

70

120

170

220

20 70 120 170 220

Training setTest set

Actual

Predicted

R2=0.95

Cell growth

10

30

50

70

90

110

130

150

10 30 50 70 90 110 130 150

Training set

Test set

Actual

Predicted

R2=0.8

Cell attachment

100

120

140

160

180

200

220

100 120 140 160 180 200 220

R2 = 0.86

Training setTest set

Fibrinogen adsorptionActual

Predicted

3 models: were built using 20-23 experimental points they were validated using splitting data

protocolPrediction was generated for 1584 copolymers(for each type of bioresponse).

Polymethacrylates Library-CopolymersCopolymers


Data flowing from MySQL to WEKA environment

Easy to use GUI (Graphical User Interface) for doing modeling using WEKA –machine learning algorithms for solving real-world data mining problems

Biomaterials Store


Biomaterials Store


Crystal Structure ofFragment D of Fibrinogen

Applications of Biomaterials StorePrediction of Fibrinogen Adsorption

m

diacid component

diphenol component

R

O

C

C NH OO (CH2)nC

O

CH2

O

CHC

O

Y

O

n=1,2

PolymerPredicted Fibrinogen Adsorption

Experimental data

Descriptors for polymers


Applications of Biomaterials StorePrediction of Protein Retention

Hydrophobic Interaction Chromatography (HIC)

Apply Biomaterials Store to any kind of data

Accurate predictions of protein retention in HIC systems

Butyl Sepharose ValidationSet Predictions

Experimental

Predicted

Ladiwala’s predicted


Availability of Biomaterials Store

Ready to be used on our cluster

Soon available through our website

http://www.njbiomaterials.org/web/index.php?p=resbio&s=9757

Short courses will be available


Summary

Predict all stages of bioresponse to polymericbiomaterials:– Protein adsorption– Cellular response– Degradation – next step

Develop tools for identifying lead polymers forspecific biomedical research or clinicalapplication

- Semi-empirical Models

- Combinatorial - Computational Method

- Biomaterials Store


THANK YOU!


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 Today

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

Y

O

Siz

e o

f li

bra

ry

CombinatorialExplosion!!!


DECISION TREE

ANALYSISDescriptors generation…

P1

P2

Pn

ChemicalStructure ofn Polymers

2D and 3D molecular descriptors (Dn,1…Dn,102)+ two (Tg, ) experimentally measured quantities (Dn,103,Dn,104)+ TFI (Dn,105) for each of n polymers

D1,1…D1,105

D2,1…D2,105

Dn,1…Dn,105

…

…

E1 E2 En

ExperimentalDataset for npolymers

Identity of3 mostsignificantdescriptors:Dx,A,Dx,B,Dx,C



ANN

…D1,A, D1,B,D1,C

Three bestdescriptors foreach of the npolymers

Prediction ofbioresponse forn/2 polymers inthe validation set

Pn/2+1

Pn/2+2

Pn

…

…

E1 E2 En/2

Experimental Dataset forn/2 polymers (training set)

D2,A, D2,B,D2,C

Dn,A, Dn,B,Dn,C



Knowledge-Based Design of Polymeric

Biomaterials Beginning at the Atomic Level

Prof. Robert A. Latour

Dr. Xianfeng Li

Clemson University

Clemson, SC

NJ Biomaterials Symposium 2008


• Efficient searching over polymer compositional space

– Local vs. global compositional optimization

– Potential provided by molecular simulation

• Molecular simulation

– Need for advanced sampling methods

• Development of molecular models of RESBIO polymers

– Advanced sampling methods

– Use as predictive tool for both local and global optimization

Overview


Current Approach to Biomaterials Design

Local

parameter space

for biomaterials

design within a

polymer library

Optimal composition

Selected sampling within library


Local

parameter space

for biomaterials

design within a

polymer library

Optimal composition


Current Approach to Biomaterials Design


Optimal composition

Local

parameter space

for biomaterials

design within a

polymer library


RESBIO Approach – Interpolation within Polymer Library


Optimal composition

Local

parameter space

for biomaterials

design within a

polymer library


RESBIO Approach – Interpolation within Polymer Library

Local Optimization



Optimal composition – local space

1

Library – randomly selected

Global

Parameter Space

for Polymeric

Biomaterial Design

Optimal composition – global space

RESBIO Approach




1

II2


Global

Parameter Space

for Polymeric

Biomaterial Design


RESBIO Approach




1

II2


Global

Parameter Space

for Polymeric

Biomaterial Design


II3

RESBIO Approach


Molecular Simulation: Cause-and-Effect Relationships



1

II2


Global

Parameter Space

for Polymeric

Biomaterial Design


II3

RESBIO Approach


Vision for Future – Extrapolation to New Polymer Libraries



1

II2



Global

Parameter Space

for Polymeric

Biomaterial Design

II3

Molecular Simulation: Cause-and-Effect Relationships


Optimal composition

Library surrounding

parameter space

containing

global optimum

composition


Global Optimization

Vision for Future – Extrapolation to New Polymer Libraries


Objectives

• Development of molecular models of RESBIO polymers

– Bulk-phase models (water/drug transport and partitioning)

– Surface-phase models (protein adsorption and cellular response)

• Generate advanced descriptors from 3-D polymer structure

– Enhanced capabilities for local optimization

• Develop cause-and-effect understanding governing system

behavior

– Guide for global optimization


Molecular Simulation

• Basic equations– Force field equation

– Newton’s laws of motion

– Statistical mechanics {properties}

• Predict 3-D structure of polymers– Bulk-phase models

– Surface-phase models

• Advanced descriptors– Improved correlations with expt. data sets

• Investigate how functional groups of

polymer influence system behavior

)},({ bondingpositionfF =v

}{ amFvv

=


Molecular Simulation

• Basic equations– Force field equation

– Newton’s laws of motion

– Statistical mechanics

• Predict 3-D structure of polymers– Bulk-phase models


• Advanced descriptors– Improved correlations with expt. data sets

• Investigate how functional groups of

polymer influence system behavior


Molecular Simulation of Peptide-PLA Interactions

O’Brien, Langmuir, in press


O’Brien, Langmuir, in press

Molecular Simulation of Peptide-PLA Interactions


PDB: 1FIB

Macrophage binding site

PDB: 1M1J, fibrinogen, 340 kDa

Biomaterials Design to Control Cellular Response


PDB: 1FIB

Macrophage binding site

PDB: 1M1J, fibrinogen, 340 kDa

COOH / COO- Surface

CH3 Surface

COOH / COO- Surface

CH3 Surface

Agashe, Langmuir, 2005

Biomaterials Design to Control Cellular Response


Simulation of Fibrinogen – Polymer Interactions


Need for advanced sampling methods

Simulation of Fibrinogen – Polymer Interactions


• Development all-atom molecular models of RESBIOpolymers (existing all-atom polymer force field - PCFF)

• Application of advanced sampling methods• Translation of all-atom model into coarse-grained (CG) model

- Force field parameterization for CG models (Clemson Univ.)

• On-lattice configurational sampling (Cambridge Univ.)

• Reverse mapping back to all-atom polymer models

• Final equilibrated all-atom models• Bulk-phase models

• Surface-phase models

Approach: Molecular modeling of RESBIO polymers


Coarse-Grained Model of DTB Succinate

Bond Bond Angle Dihedral Angle

Connection Label Connection Label Connection Label

PhF–Et1 l1 PhF-Et1-nEF 1 PhF-Et1-nEF-MeF 1

Et1–nEF l2 Et1-nEF-MeF 2 PhF-Et1-nEF-PhF 2

nEF–MeF l3 Et1-nEF-PhF 3 Et1-nEF-MeF-Pro 3

MeF–Pro l4 nEF-MeF-Pro 4 Et1-nEF-PhF-Et2 4

nEF–PhF l5 nEF-PhF-Et2 5 MeF-nEF-PhF-Et2 5

PhF–Et2 l6 MeF-nEF-PhF 6 Pro-MeF-nEF-PhF 6

PhF-Et2-PhF 7 nEF-PhF-Et2-PhF 7

Et2-PhF-Et1 8 PhF-Et2-PhF-Et1 8

Et2-PhF-Et1-nEF 9


Mapping CG model onto Atomistic Model

DTB Succinate Tetramer

• Radius of gyrationModel Atomistic CG

<Rg> (Å) 7.66 ± 0.06 7.81 ± 1.49

• Displacement of the center of mass of as a function of time

0 2 4 6 8 10 120.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

D=0.302 (Å2/ns)

<[Rg

c(t

)-Rg

c(0

)]2

> (

Å2

)

Time (ns)0 2 4 6 8 10 12

0

100

200

300

400

D=36.444 (Å2/ns)

Time (ns)

Atomistic model CG model

[ ]2gcgc

t)0,z,y,x(R)t,z,y,x(R

t6

1 limD =

D=36.4 Å2/nsD=0.30 Å2/ns

Statistical Mechanics


DTB Succinate Tetramer

• Radius of gyrationModel Atomistic CG

<Rg> (Å) 7.66 ± 0.06 7.81 ± 1.49

• Displacement of the center of mass of as a function of time

0 2 4 6 8 10 120.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

D=0.302 (Å2/ns)

<[Rg

c(t

)-Rg

c(0

)]2

> (

Å2

)

Time (ns)0 2 4 6 8 10 12

0

100

200

300

400

D=36.444 (Å2/ns)

Time (ns)

Atomistic model CG model

1 CG time unit 120 atomistic time units

> 2 orders of magnitude acceleration

[ ]2gcgc

t)0,z,y,x(R)t,z,y,x(R

t6

1 limD =

Mapping CG model onto Atomistic Model

D=36.4 Å2/nsD=0.30 Å2/ns

Statistical Mechanics


• Development of equilibrated models of RESBIO polymers

– Bulk-phase models

– Water/drug transport and partitioning


– Protein adsorption and cellular response

• Advanced descriptors: 3-D structure

– Interpolation within existing library

– Enhanced local optimization

• Molecular-level cause-and-effect relationships

– Guide for design of new libraries

– Global optimization

Concluding Remarks


Knowledge-Based Design of Polymeric

Biomaterials Beginning at the Atomic Level


Npp

0 1 2 3 4 5 6 7 8 ...0 1 2 3 4 5 6 7 ...

Npw

free

adsorbed

Density of states matrix contains

probabilities of all possible

macrostates (Npp, Npw)


SEMIEMPIRICAL

MODEL

integrated computational and experimental approach to ... · pdf fileoctanol/water partition...

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