Dr. Nele Plock. Dr. N. Plock, March 11, 2008, American Conference on Pharmacometrics

Transcription

1 Parallel PKPD modeling of an endogenous agonist (A) and exogenous antagonist (B) based on research PKPD data to predict an effective first-in-man dose of B A combination of physiologically based PK and population PKPD Dr. Nele Plock

2 Overview Background and objectives Methods Available data Methodological approach Software Model validation Modeling results Model based predictions for humans Conclusion

3 Background and objectives Research compound B is a competitive antagonist of endogenous compound A Aim: prediction of an effective human oral dose of B based on animal data, taking concentrations of A into account Question: How much B is needed to antagonize a defined concentration of A? Model required that can describe pharmacodynamic effect of B in dependency of changing concentrations of A Model required to predict pharmacokinetic profile of B in humans

4 Methods Available data dense PK data of B from rat, dog and monkey after intravenous and oral dosing Physicochemical properties of B (logma, solubility, pka, molecular weight) In vitro data (gastrointestinal permeability, plasma protein binding) Built-in physiological parameters (e.g. organ volumes, blood flow ) Rat: PK data after administration of A Human: Concentrations of A Rat: Organ growth data after administration of A Rat: Organ growth inhibition data after administration of A and B

5 Methodological approach Develop a physiologically based model for B to make predictions about its pharmacokinetics in humans First step: describe iv-profiles Second step: describe oral profiles PKPD model: Sequential approach Sequential fitting of PK and PD (organ growth) data for A PK model for B Fix previously estimated parameters and model competitive antagonism of A and B based on PD data (organ growth) of B

6 PKPD model Dose B ka Baseline A: RATEA/CL2 Dose A k45 V3 Q V2 CL CL2 V5 Q5 V6 IC5 EC5 Emax K Organ size K Baseline value: E

7 Methods - Software Physiologically based modeling approach (PBPK) PK-Sim 3..6 (Bayer Technology Services, Germany) GastroPlus TM 5.2 (Simulations Plus, Lancaster, CA, USA) Population PKPD NONMEM V 1.1 Model validation Same data available for competitor compound (PK and PD) Develop analogous model Compare to clinical efficacy results (PK and PD available) Renders how much inhibition is needed to see a clinical effect

8 Results - PBPK Good results for rats and dogs (logma 5.8 instead of experimentally determined value of 4.8) Monkey: well predicted in terminal phase, overprediction of Cmax Decision: use data to predict iv-profiles in humans Concentration [ng/ml] Rat n=4 Concentration [ng/ml] Dog n=3 Concentration [ng/ml] Monkey n= [h] [h] [h]

9 Results - PBPK Good results for rats and dogs (solubility in FaSSIF) Monkey: well predicted using solubility in buffer Decision: use FaSSIF solubility to predict oral profiles in humans 4 35 Rat n=7 6 5 Dog n= Monkey n=3 Concentration [ng/ml] Concentration [ng/ml] Concentration [ng/ml] [h] [h] [h]

10 Results PopPKPD Compound A PK Two-compartment model, first order absorption and elimination Endogenous production rate baseline concentration of A Visual predictive check: 8 Baseline Low dose s.c. High dose s.c. 1,

11 Results PopPKPD Compound A Baseline PD Turnover model One control group, two groups after single dosing, one group after multiple daily dosing Visual predictive check: 15 Low SD dose s.c High SD dose s.c Multiple dose s.c Dr. N. Plock, March 11, 28, American 5 3 Conference 55 8 on 15Pharmacometrics

12 Results PopPKPD Compound B PK Two-compartment model, first order absorption and elimination iv single dosing and oral dosing (two doses, two occasions) Visual predictive check:.2 i.v. Low dose p.o. High dose p.o

13 Results PopPKPD Compound B PD Turnover model competitive antagonism of A, four dose levels Concentration-dependent growth inhibition well captured Visual predictive check: Dose 1 Dose Dose Ti Dose Dr. N. Plock, March 11, 28, American Conference on Pharmacometrics

14 Dose prediction for humans Simulation of % inhibition based on physiologic levels of A Combine with dose concentration prediction Dose inhibition relation inhibition, % B concentration [ng/ml] [h] Inhibition, % Concentration [ng/ml] Dose (mg)

15 Validation with competitor compound Visual predictive check of organ growth for different doses Dose 1 12 Dose Dose 2 12 Dose

16 Validation with competitor compound Effective concentrations in the range of ng/ml How much inhibition is achieved under physiological concentration of A? Use same level of inhibition for B Inhibition, % Inhibition, % Competitor concentration [ng/ml] Concentration B [ng/ml] Predicted human dose: 5 8 mg

17 Conclusion Successful combination of two modeling approaches differences in concentrations of A were taken into account would influence the overall activity of B obtained estimate of an effective human oral dose of B at a very early stage of drug development will help to plan dosing regimens for first-in-man studies

18 Thank you for your attention!

19 Model code DCB=A(2)/V2 DCA=A(5)/V5 PD=EMAX*DCA/(EC5*(1+DCB/IC5)+DCA) DADT(1)= -KA*A(1) DADT(2)= KA*A(1)-K23*A(2)+ K32*A(3) -K2*A(2) DADT(3)= K23*A(2)-K32*A(3) DADT(4)= -K45*A(4) DADT(5)= K45*A(4)-K56*A(5)+ K65*A(6) -K5*A(5) DADT(6)= K56*A(5)-K65*A(6) DADT(7)= K*PD - K*A(7)