Using Modeling to Establish Clinical

Using Modeling to Establish Clinical

Role of Models in Design SpaceUsing Modeling to Establish Clinical Relevance in DesignSpaceKazuko Sagawa, PhDBiopharmaceutics Group

OutlineHow to build in silico modelsHow in silico models can be used to establishDesign SpaceExample: Particle size and BioavailabilityWhat numbers to use for in silico modeling in order to describeparticle size distributionA risk of using a single number such as D50Case example: clinical outcome prediction for a new lot with adifferent particle size distributionChallenges of in silico modelingSummary2

In silico ModelingINPUTSolubilityDissolutionPrecipitationSolubilizationPermeabilitychargesizeMetabolismintestinalhepaticTransporters & EffluxGI PhysiologyFluid volumepHTransit timeOUTPUT3

How to Build an in silico ModelBuild a model upon clinical data and in vitro data (notbased on first principles)Validation of the modelMay need to re-adjust compound specific parameters (such asPermeability, CL, Vd) to satisfy multiple data setsConfirm reliability of the model using clinical datawith input changes, such asDifferent dosage forms (solution, capsule, tablet)Different particle sizesDifferent processes (Direct compression, wet granulation, drygranulation etc.)Food effect4

In silico modelingVarious material attributes can affect oralbioavailabilityIn silico models are used in order tointerconnect various material attributes (prediction ofattribute interactions)identify attributes which influence oral bioavailabilityhelp understanding the relationship between theseattributes and oral bioavailability5

Dissolution: influence of particle sizeDissolution is related to particle size and particle surface area(smaller particle size, larger surface area, faster dissolution)dmdtdmdtkAC= dissolution rateCA = surface area of solidk = dissolution rate constantC s = solubility of drugC = concentration of drug in solutions“Particle size” will be used as a material attribute for this talk6

Cmax (ug/ml)Example: Establish a relationshipbetween particle size and Cmax0. BE range0.040.0200 5 10 15 20 25 30Mean Particle Radius (um)Current particle sizeMean particle size range which predicted to provide bioequivalent Cmax7

Cmax (ug/ml)Attributes that influence BioavailabilityIn silico model can identify attributes which influence oralbioavailabilityDrugs with low permeability are less impacted by dissolutionrate changesParticle size does not influence0.12bioavailability (within bioequivalence) PHigh P0 5 10 15 20 25 30Mean Particle Radius (um)8

Volume In (%)Volume ln (%)But particle size is not a single number…Particle distribution data are used as an input for in silicomodeling instead of a single number such as D50 or D905D10 (um) 1D50 (um) 11D90 (um) 47D[4,3] (um) 186D10 (um) 2D50 (um) 12D90 (um) 36D[4,3] (um) 1645321432100.1 1 10 100 1000Size (um)00.1 1 10 100Size (um)9

Example: A Risk of using a singlenumber…Log normal cummulativedistributionSame D50 and different distributionsPSD 1 PSD 2D10 (um) 12 29D50 (um) 50 50D90 (um) 200 8610. 1PSD 201.0 10.0 100.0 1000.0Size (µm)10

Plasma conc (ng/ml)Example: A Risk of using a singlenumber…Clinical outcome can be significantly different withthe same D50 lotsPredicted PSD 1 PSD 2Cmax (ng/ml) 27 35AUCinf (ng*h/ml) 404 64040353025PSD 1PSD 2201510500 10 20 30Time (hour)11

Case Example: particle size changeBCS 2: Low solubility and High PermeabilitypKa: 4.2 (free base)Model was developed using Phase I dataModel validated with multiple clinical studies12

Volume In (%)Plasma conc (ng/ml)Case Example: particle size changeIn silico model was built and validated using clinicalstudy with “lot A”INPUTOUTPUT (model validation)Lot A: D[4,3] 7um8643530252015Lot A DataPredicted200.1 1 10 100Size (um)10500 5 10 15 20 25 30Time (hour)13

Volume ln (%)Plasma conc (ng/ml)Plasma conc (ng/ml)Case Example: particle size changePrediction of clinical performance with Lot BLot B: D[4,3] 16um86430252015Lot B DataPredictedPredicted200.1 1 10 100Size (um)10500 5 10 15 20 25 30Time (hour)14

VariabilityModels are built on averageVariability can be incorporated into modelsPhysiological variabilityGI transit timeGastric emptying timeGI pHGI fluid volumePermeability (transporters)Metabolism and volume of distribution (compound specific)15

Challenges of in silico modelingAssessment of precipitation time and precipitated formAssessment of solubility and particle size of the precipitatedformUnderstanding GI Physiology and population variabilityAvailable fluid volume and distributionGI transit timeMixing and grinding effectsEstimation of in vivo PermeabilityEstimation of variabilityValidation of in silico modelRequire multiple clinical data setsMore data are used to validate, more robust models become16

SummaryIn silico modeling to predict clinical outcome is avaluable tool to establish design space by bridgingproduct characteristics to in vivo performance.In silico models are not based on first principles.Models are built and validated with clinical data.In silico models can be very robust if there areenough data to validate the models and can handlecomplicated cases.In silico models can interconnect various materialattributes (prediction of attribute interactions).Variability can be incorporated into in silico models17

AcknowledgementsPfizer Global Research & DevelopmentJoe KushnerRong LiCindy OksanenKen WatermanWeili Yu18

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