David Kim Allergan Inc. SoCalBSI California State University, Los Angeles.
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Transcript of David Kim Allergan Inc. SoCalBSI California State University, Los Angeles.
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