David KimAllergan Inc.SoCalBSICalifornia State University, Los Angeles

ObjectiveDevelop a model to predict corneal permeability based on literature compounds

IntroductionOcular drug delivery mechanism (through cornea and/or conjunctiva)

Focus of the project is the corneal route

Three major cell layers of the Cornea

                                                                                             

Why predict corneal permeability?Allergan, Inc. develops drugs which

are administered through the eyeA drug is only effective if it can

reach its target tissueCan save company time and money

in determining if the drug can pass through the cornea before the drug is synthesized

IntroductionFew models have been developed to predict corneal permeabilityCongeneric model (one class of compounds)Non-congeneric model (mutiple class of

compounds)

Develop non-congeneric model focused on drug-like compounds

Find optimal training and testing set percentage

Final Model

Statistical analysis

Literature• Compound names• logPC and logD• structure of compounds

Run Partial Least Squares modeling

Pick best model

Remove descriptor with the lowest importance Rebuild model

Filter descriptors (intuitively)

Generate descriptor values

Partition Coefficient:

Log D = log of the Distribution Coefficient (pH 7.65)

Log PC = log of the Permeability Coefficient (cm/s)

Yoshida, F., Topliss, J.G., J. Pharm. Sci. 85, 819-823 (1996)

Yoshida, F., Topliss, J.G., J. Pharm. Sci. 85, 819-823 (1996)

Compounds in LiteratureWent through published literatureFiltered compounds to look only for

drug like compoundsCame up with 30 compounds and

their measured permeabilityNext step in our model building

process is to produce descriptors for each of our compounds

Descriptors

Molecular weight or volumeDegree of ionizationAqueous solubilityHydrogen-bondingLog DPolar surface area (PSA)pKaSolvent accessible surface area

Schrödinger SoftwareNamed after Erwin Schrödinger –

Nobel prize winner for the Schrödinger equation which deals with quantum mechanics

Suite of various programs dealing with computational chemistry

Two programs used: Maestro – calculate descriptor valuesCanvas – generate model

Maestro Program

Maestro ProgramCan generate 77 descriptorsCan manually input descriptors (eg. log

D)Filtered descriptors which do not deal

with permeability (intuitively) to reduce noise

Came up with 30 descriptors to useExport the 30 compounds and its 30

descriptors to Canvas

Canvas Program

Canvas ProgramPartial Least Squares (PLS)

modelingCan specify what descriptors to use

to build the modelCan specify the compounds used for

training and testing the modelModel assessment: corresponding

statistics of the model

StatisticsTraining Set

Standard deviation (SD) – lowCoefficient of determination (R2) – high close to

1Coefficient of determination, cross validation

(R2-CV) – high close to 1Stability – close to 1F-statistic (overall significance of the model) –

highP-value (probability that correlation happened

by chance) – low <0.01

StatisticsTesting Set

Root Mean Squared Error (RMSE) – lowQ2 – high close to 1Pearson correlation coefficient (r-Pearson) –

high close to 1

Important for the assessment of what percentage of the compounds we want to use for the training set

Important for the assessment of our model as we start to remove unnecessary descriptors

Finding the ideal training set percentageRan PLS modeling specifying various

percentages to use for the training set40%, 50%, 60%, 70%, 80%Looked at the statistics of each of the

models builtFound that using 80% of the compounds

for the training set was ideal30 compounds found in literature

24 in training set and 6 in the testing set

bx coefficientAfter the PLS model is built, it gives the bx

coefficient for each descriptor in order to predict permeability

The bx coefficient is the weight that the model puts on the descriptor after the descriptor values have been scaled

Example:log PC = 0.348(scaled MW) –0.221(scaled log D) -

0.002(scaled log P)……

Removal of descriptorsStarted with 30 descriptors and built a modelIdentified the descriptor with the lowest bx

coefficient and removed itRebuilt model with 29 descriptorsRepeat…. while keeping track of the statisticsWant to keep track of statistics to know when

to stop

Example:log PC = 0.348(scaled MW) –0.221(scaled log D) -0.002(scaled log P)………….(30)log PC = 0.392(scaled MW) –0.183(scaled log D)……………………………….………….(29)

Test Statistics

Training Statistics

Remaining 8 DescriptorsCIQPlogS – conformation independent

predicted aqueous solubilityQPlogS - predicted aqueous solubilityFOSA – hydrophobic component of the

total solvent accessible surface areaPISA - (carbon and attached hydrogen)

component of the total solvent accessible surface area

Remaining 8 DescriptorsQPlogKp - predicted skin permeabilityQPlogBB – predicted blood/brain partition

coefficientdonorHB - Estimated number of hydrogen

bonds that would be donated by the solute to water molecules in an aqueous solution

log D – Distribution coefficient

Permeability Model Function

log PC = -0.1371(scaledCIQPlogS ) - 0.1383(scaledFOSA) + 0.1792(scaledPISA) + 0.1558(scaledQPlogBB) + 0.2815(scaledQPlogKp) - 0.1451(scaledQPlogS) - 0.2242(scaleddonorHB) + 0.2646(scaledlogD)

SD = 0.460791 R2 = 0.814213 F = 46.0162 (p < 0.0000001)

Predicted vs Observed Permeability

ConclusionSuccessfully created a model to predict the

corneal permeability of compounds

Showed that the Schrödinger software generates significant descriptors to build a permeability model

Potential Future WorkApply the model to external training set to

asses its predictability powerBuild a more refined model with more

compoundsFind other descriptors other than the ones

generated by Maestro and use them in the model building

Acknowledgments

Dr. Ping DuDr. Chungping YuPushpa ChandrasekarNoeris SalemAllerganSoCalBSI