Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Optimizing Dosing Strategies for Defined Therapeutic Targets Mats O. Karlsson and Siv Jönsson Dept. of Pharmaceutical Biosciences Uppsala University Sweden

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Overview Target definition and dose finding Application to real drugs –Posterior definition of utility function –Dose optimization based on Responder criteria Utility functions Concentration Simulations –A priori individualization based on concentration –A posteriori individualization based on biomarker

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Dosing strategy alternatives The single, one-size-fits-all-always, dose A priori individualization, based on Patient characteristics A posteriori individualization, based on –Effects –Side-effects –Biomarker –Drug concentration –A mixture of the above Combination of a priori and a posteriori

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Target The selection of any dosing strategy requires a target concept The target could be based on –The weighted balance between beneficial effect(s) and side- effects (utility) –Beneficial clinical endpoint(s) –Side-effect(s) –Drug concentration –Biomarker(s) Target can differ between subjects based on patient characteristics

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Seriousness of devation from the target All-or-none criteria –Therapeutic window All values within the window are equally desireable All values outside of the window are equally undesirable –Responder/non-responder concepts Penalty (loss) gradually increasing with increasing deviation from target, e.g. –Quadratic loss (  (observed i -target i ) 2 ) –Absolute loss (  |observed i -target i |) –Non-symmetric loss Seriousness of target deviation may vary between patients

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Scenario 1 1.A dose (dosing strategy) is selected based on some implicit criteria 2.Stop

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Scenario 2 1.A dose (dosing strategy) is selected based on some implicit criteria 2.Population model for dose – target variable 3.Estimation of the target and penalty function based on model and selected dose (dosing strategy) 4.Assessment of whether target and penalty function are appropriate 1.If they are, stop 2.If they are not, revise dosing strategy in light of a more appropriate target/penalty function

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Exposure Drug side-effect Frequency Target Specification Drug effect

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Optimal dose versus weighting of events

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Define target and penalty functions Ask a clinician Consult preclinical and phase I data on drug Consult literature Consult marketing Develop a few alternative targets / penalty functions Apply to historical data, if available Ask a few (many) clinicians Revise Include inter-clinician (-patient) variability in target definition

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Scenario 3 1.Formal target and penalty functions are defined 2.Population model for dose – target variable 3.Estimate the best dosing strategies given different constraints 1.One-dose-fits-all 2.Two-dose individualization based on covariate 3.Two-dose individualization based on feedback 4.Etc 4.Select dosing strategy based on target fulfillment and practical considerations

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Optimizing one-size-fits-all dose Define –Target and penalty function –Population PK and PD models Only partial models may be needed Covariate models essentially superfluous PK and PD parameters are simulated for a large number (>1000) of patients Obtain a prediction of each individual’s deviation from the target for a certain dose Obtain the optimal dose by minimising overall loss –Repeated simulations –Estimation

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Target = individual’s prediction + deviation from target Approach

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Optimal a posteriori dosing strategies 1.Questions 1.What dose strengths should be made available? 2.When should an observation for individualization be made? 3.At what value(s) should a dose change be made? 4.What is the best starting dose? 5....

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Example: a posteriori optimal dosing Question: Is allowing individualization by two doses better than one-dose-fits-all? Responder / non-responder concept Obtain optimal one dose –Estimate a single dose size Obtain optimal a posteriori two dose strategy –Estimate simultaneously lower dose size higher dose size fraction of patients that preferentially should be treated by the lower (higher) dose –Use $MIXTURE function in NONMEM

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Satisfactory effect + Acceptable side-effects = Responder

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Estimation of optimal individualised dosing strategy Dose % Acceptable side-effect Responders Satisfactory effect 47% 74% 61%

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Simulation Studies - Comments Robustness of dosing strategy to variation in utility definition between clinicians/patients desireable All-or-none type responder definitions favour individualization to a higher degree than gradual utility functions

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Optimal a priori dosing strategies 1.Questions 1.What is the best covariate to base dosing on? 2.Number of doses sizes (subpopulations)? 3.What covariate intervals should each dose size be applied to? 2 dose groups: Cut-off value Higher dose Lower dose = 3 parameters 3 dose groups: = 5 parameters

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Optimizing covariate-based dosing Define –Target and penalty function –Population PK and PD models Covariate models essential Population distribution of covariate(s) simulated or obtained from empirical data base PK and PD parameters are simulated for a large number of patients Obtain a prediction of each individual’s deviation from the target for a certain dose Obtain the optimal dosing strategy by estimation of dose sizes and cut-off value(s)

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Individualisation based on a covariate NXY-059 Drug for usage during acute hospitalisation following stroke Two-step infusion (1-h loading, 71-h maintenance)

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Individualisation based on a covariate NXY-059 Target: Cu = 100  M Penalty: Quadratic loss in log domain Pop PK model –CL ~CLCR –V ~WT Empirical CLCR and WT distributions Loading infusion: 1-2 dose groups, CLCR or WT Maintenance: 2-4 dose groups, CLCR Selection of dosing strategy –>90% of patients above 70  M –<5% of patients above 150  M

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences One loading dose 2400 units/h Three maintenance infusion levels –CLCR > 80 ml/min1110 units/h –CLCR ml/min681 units/h –CLCR ml/min426 units/h Individualisation based on a covariate NXY-059

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Target fulfillment in prospective study 92% > lower limit 7% > upper limit

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Simulations I – A priori dosing Dosing based on CRCL –Standard approach often uses Predetermined cut-off values Large dose decrements (often a factor 2 or higher) –Optimal approach depending on Drug characteristics –Fraction excreted unchanged (fe) –Interindividual variability in CL R and CL NR Penalty function shape CRCL distribution in target population –Parametric simulation –Empirical databases

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Individualisation based on a covariate Simulations – main results

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Target definition –To aid data collection –To guide modeling efforts –To improve communication within project team –To appropriately value the drug compared to competitors –To assess (or claim) whether the dose is right

Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Dosing strategy estimation –To motivate choice of dose –To obtain conditions for optimal individualization –To assess maximal potential value of individualization –To justify individualization (or lack thereof) –Simplicity in dosing can be directly offset against decrease in target fulfillment –Is easy!