1. 1 Motivation and philosophy for development of mechanistic PK/PD models in infectious diseases William A. Craig Symposium October 29 th 2008 University of Wisconsin, Madison Jürgen B Bulitta, Brian T Tsuji, Alan Forrest* (*Institute for Clinical Pharmacodynamics Ordway Research Institute) © 2008. Forrest A, Bulitta J, Tsuji BT; all rights reserved.

2. 2 Motivation for improved methods & models 1.Within strain heterogeneity in susceptibility, biologic fitness, virulence, etc 2.Time course of multiple responses 3.Effect of bacterial burden (inoculum effect) 4.Effect of protein binding on PD 5.Mechanistic description of drug combinations 6.Effects on biochemical pathways 7.Adaptive phenotypic resistance 8.Genotypic resistance 9.Role of gene expression on PD 10.Pathogen  host interaction (e.g. virulence, immune system, infection site)

3. 3 “The puzzle” Role of host defense Between patient variability in PK Shape of concentration time profile Drusano GL. Nat Rev Microbiol 2004 Host? Between patient variability in MIC Multiple bacterial subpopulations within one patient Time (h) Log 10 CFU mL -1 Bacterial killing rate constant vs. conc. 2007 © JB Bulitta, all rights reserved

4. 4 Meagher AK, Forrest A, et al. 2004, AAC 48:2061-8. Time (h) Concentration (mg/L) Log 10 CFU Saturable growth function & multiple sub-populations – 250 mg IR BID500 mg XR QD500 mg IR BID1000 mg XR QD Ciprofloxacin vs. E. Coli (MIC = 0.5 mg/L)

5. 5 Mechanistic PK/PD models in ID Bacterial cell Cell wall THF syn- thesis DNARNA Proteins Membrane Replication mis- reading Ceftazidime  PBP  autolysin Colistin interaction with cell membrane Tobramycin - dual drug effects Quinolone inhibiting DNA gyrase

6. 6 Ceftazidime vs. P. aeruginosa strain PAO1 Time (h) Log 10 (CFU/mL) A B C Curve fits from time-kill experiments Bulitta J et al. PAGANZ 2008. Data from: Henrichfreise B, et al. AAC 2007, 51:3642-9. Translation One-compartment in vitro PD model

7. 7 ‘External validation’ with literature data on cefta- zidime from 6 studies on P. aeruginosa ATCC 27853 ▲ Predicted Fitted Line of identity Predicted Log 10 (CFU/mL) Cappelletty et al. IV bolus at 0 h & CI at 10 mg/L IV bolus at 0 h & CI at 20 mg/L IV bolus at 0 h & CI at 5 mg/L IV bolus q8h for 48 h IV bolus q12h for 48 h McGrath et al. IV bolus at 0 and 8 h 64 256 4 16 0101 Ceftazidime concentration (mg/L) Tam et al. In silico predictions Fitted Log 10 (CFU/mL) IV bolus at 0h (Barclay 1995) Growth control (Barclay 1996) Time-kill 10 mg/L (Barclay 1996) Growth control (Cappelletty) Growth control (McGrath) Time-kill 100 mg/L (Shalit) Estimation dataset

8. 8 Signature profiles for ceftazidime vs. P. aeruginosa MIC = 2 mg/L With loading dose Dose partitioningDuration of infusion ≥66% f T>MIC

9. 9 Structural model for colistin vs. P. aeruginosa Intermediate population “Resistant” population k 2S k 2I k 2R Signal molecules Susceptible Population 1 st -order natural death k d Inhibition Imax Kill, IC 50 k deg kdkd kdkd t 1/2,I t 1/2,R Synthesis Growth half-life: t 1/2,S 1st-order, 2nd-order, or saturable process Synthesis of signal molecules Inhibitory effect 2 nd -order killing by colistin Synthesis Bulitta J et al. AAPS 2007 Poster W4553. Target site model

10. 10 Individual curve fits: Colistin vs. P. aeruginosa (MIC = 4 mg/L) Bulitta J et al. AAPS 2007 Poster W4553. Time (h) Log 10 (Colony forming units per mL) CFU 0 : 10 6 CFU/mLCFU 0 : 10 4 CFU/mL CFU 0 : 10 8 CFU/mLCFU 0 : 10 9 CFU/mL Colistin concentration (mg L -1 ) 0 2 4 8 16 32 64 128 256 No colony on plate plotted as 0. 2 x MIC 1 x MIC 0 & 0.5 x MIC

11. 11 PD front-loading for colistin & effect of immune system Free concentration (mg/L) PK of colistin Drug alone (without immune system) Drug and immune system Hypothetical effect of the immune system alone (without drug) Hypothetical effect of immune system 2008 © JB Bulitta, A Forrest, BT Tsuji, WJ Jusko, UB, all rights reserved

12. 12 Non-competitive action of two drugs on similar processes and anticipated responses Time Response Earp J et al. JPKPD 2004, 31:345-80.  Potentiation of effect Dashed lines: single drug Continuous line: Drug combination

13. Mechanistic PK/PD Models Co-model the time course of PK and PD Incorporate the major characteristics of the bug, drug(s) & host (& their interactions) Internal and external model qualification Improve translation between research platforms (TK, IVIM/HF, animal, human,…) A better framework for modelling multi- drug regimens 13

14. 14 Other Major Collaborators Many thanks to colleagues at ICPD / Ordway Research Institute including: George Drusano, Paul Ambrose and Sujata Bhavnani; Roger Nation, Jian Li, et al, at Monash University, in Melbourne Australia; and many other colleagues, fellows, and students!