A translational modelling and simulation approach to exploit pre-clinical tuberculosis data

Transcription

1 A translational modelling and simulation approach to exploit pre-clinical tuberculosis data Wicha SG, Svensson R, Clewe O, Simonsson USH Pharmacometrics Research Group Dept. of Pharmaceutical Biosciences Uppsala University, Sweden

2 Global TB drug development pipeline 1

3 Model-informed Drug Discovery and Development (MID3) Target Authorization and Mechanistic Understanding Human PK and Dose Prediction Study Design Optimization Prediction from/of Early Clinical Responses Dose Selection and Label Recommendations Development of a model-based approach to exploit in vitro TB information for translational predictions along the drug development process Marshall et al. CPT Pharmacometrics Syst. Pharmacol

4 Rifampicin (RIF) treated log- and stationary phase cultures of M. tuberculosis (H37Rv) log In vitro rifampicin stationary time-kill curves In vitro data: Yanmin Hu & Anthony Coates St. Georges University London, UK Clewe et al. JAC

5 The Multistate Tuberculosis Pharmacometric model Culturable F Fast growing S Slow growing N Non growing Clewe et al. JAC Nonculturable (hidden) 4

6 Translational predictions to streamline MID3 in vitro Translational prediction in vitro Hollow-fiber system (HFS) animal Murine lung infection model clinical Early bactericidal activity (EBA) trial 5 5

7 Translational factors Drug-specific Pharmacokinetics murine human System-specific Mycobacterial growth properties System carrying capacity Isolate-specific Susceptibility of the mycobacterial isolate 6

8 Mycobacterial growth properties F-dominated culture S / N-dominated culture System-specific! Maximum growth Bmax Pre-incubation period of target system, e.g. Hollow-fiber: 4 days Animal: days Clinical: >150 days log10 Bacterial Number [/ml] Time [days] Bmax F S N CFU (F+S) 7

9 Consideration of mycobacterial isolate susceptibility Origin Origin MIC distribution Clinical reported MIC values for rifampicin Target MIC distribution Translational target Target MIC distribution 8

10 Postantibiotic effects (PAE) C RIF effect cmt Gumbo et al. AAC

11 Translational predictions to streamline MID3 in vitro Translational prediction in vitro Hollow-fiber system (HFS) animal Murine lung infection model clinical 10 Early bactericidal activity (EBA) trial 10

12 Prediction of Hollow fiber system (HFS) experiments Rifampicin regimen 600 mg 2100 mg 4200 mg Gumbo et al. AAC

13 Prediction of HFS experiments Pharmacokinetics Pharmacodynamic s RIF [mg/l] log10 Bacterial Number [/ml] No treatment mg RIF daily Time [days] O HFS observations F S N CFU (F+S) 12

14 Prediction of HFS experiments 2100 mg RIF twice weekly 4200 mg RIF once weekly Pharmacokinetics RIF [mg/l] log10 Bacterial Number [/ml] Pharmacodynamic s Time [days] O HFS observations F S N CFU (F+S) 13

15 Translational predictions to streamline MID3 in vitro Translational prediction in vitro Hollow-fiber system (HFS) animal Murine lung infection model clinical 14 Early bactericidal activity (EBA) trial 14

16 Prediction of PK/PD indices from animal studies Model-predicted PK/PD indices Murine lung infection model 4 weeks post infection mg/kg given 1-6 times in 144 h unbound murine plasma PK log 10 CFU/mL at day R 2 = e-02 1e+00 1e+02 1e+04 R 2 = R 2 = Observed PK/PD indices R 2 = R 2 = R 2 = 0.04 Jayaram et al. AAC e-02 1e+00 1e+02 1e+04 Cmax/MIC AUC24/MIC %T>MIC

17 Translational predictions to streamline MID3 in vitro Translational prediction in vitro Hollow-fiber system (HFS) animal Murine lung infection model clinical 16 Early bactericidal activity (EBA) trial 16

18 Pharmacokinetics General Pulmonary Distribution Model [1] Prediction of clinical EBA studies rifampicin Pharmacodynamics Multistate Tuberculosis Pharmacometric model [2] Plasma Target site [1] Clewe et al. Eur. J. Clin. Pharmacol., [2] Clewe et al. JAC

19 Prediction of clinical EBA studies rifampicin predicted mean EBA (p p 0.95 ) observed clinical mean EBA 18

20 Prediction of clinical EBA studies rifampicin predicted mean EBA (p p 0.95 ) observed clinical mean EBA values (p p 0.95 ) 19

21 Prediction of clinical EBA studies isoniazid predicted mean EBA (p p 0.95 ) observed clinical mean EBA 20

22 Importance of combining in vitro logand stationary phase data for predictions Rifampicin EBA day 0-2 Isoniazid EBA day

23 Conclusions and Perspectives The developed translational approach with efficacy estimates only from in vitro time-kill experiments predicted rifampicin effects observed across in vitro systems (hollow-fiber), in animal studies to determine PK/PD indices, in clinical early bactericidal activity studies Perspectives Evaluation of the approach with further TB drugs/combinations Extension of the translational framework to prediction of relapse 22

24 Acknowledgements Research conducted within the PreDiCT-TB consortia The research leading to these results has received funding from the Innovative Medicines Initiative Joint Undertaking ( under grant agreement n , resources of which are composed of financial contribution from the European Union s Seventh Framework Programme (FP7/ ) and EFPIA companies in kind contribution. 23