1. The Endpoint from a Resistance Point of View A Symposium to Honor the Career of William A. Craig, M.D. George Drusano, M.D. Co-Director Ordway Research Institute

2. A Colleague Responsible for Much of What I am About to Show Dr. Arnold Louie

3. A Relevant Quote All Models Are Wrong; Some Models Are Useful Professor G.E.P. Box, a famous and very wise biostatistician

4. Pharmacodynamics Let us look at an example of “classical antibacterial pharmacodynamics”, where a survivorship endpoint was employed

5. Role of PK/PD – Survivorship Endpoint In this P. aeruginosa rat sepsis model, where a fluoroquinolone was employed for therapy (an AUC/MIC Ratio drug), several things are evident: 1. As MIC increases, survivorship decreases (same dose – isogenic mutants) 2. As Dose increases (same MIC) survivorship increases 3. Equal AUC/MIC ratios produce equivalent outcomes Drusano et al. Antimicrob Agents Chemother 1993;37:483-490

6. Pharmacodynamics One also needs to think about other desired endpoints: 1. clinical outcome/survivorship 2. microbiological outcome/cell kill 3. suppression of resistance 4. organism population eradication (TB) The amount of drug exposure to achieve the endpoints does differ Let us look at resistance suppression

7. Resistance Suppression and Outcome Optimization First, we will examine Pseudomonas aeruginosa in a hollow fiber infection model Then we will examine this same pathogen in a mouse thigh model of infection Then we will examine Staphylococcus aureus and: 1. examine the impact of therapy duration 2. examine the divergent effect on sensitive and resistant subpopulations when drug is withdrawn 3. examine the impact of granulocytes

8. Journal of Clinical Investigation 2003;112:275-285 & Nature Reviews Microbiology 2004;2:289-300 Resistance Suppression and Outcome Optimization

9. The use of the hollow fiber model for studying antimicrobial regimens was described by Blaser and Zinner and employed extensively by Dudley

10. Central Compartment (C c ) Infusion + Bacteria (X T/R ) f(c) dC c =Infusion-(SCl/V)xC c dt SCl dX S =K GS x X S x L - f KS (C c H  ) x X S dt dX R = K GR x X R x L- f KR (C c H  ) x X R dt K max   C c H  C H  50  +C c H  f   (C c H  )= Y 1 =X T =X S +X R, IC(1)= Initial Total Population Y 2 =X R, IC(2)= Initial Resistant Population [2] [3] [4] [5] [6],  =K and  = S,R [1] L = ( 1-(X R + X S )/POPMAX) [7]

11. Resistance Suppression and Outcome Optimization Tam V et al. Bacterial-population responses to drug selective pressure: Examination of garenoxacin’s effect on Pseudomonas aeruginosa. J Infect Dis 2005;192:420-428

12. P. aeruginosa - Prevention of Amplification of Resistant Subpopulation The amplification of the resistant sub-population is a function of the AUC/MIC ratio The response curve is an inverted “U”. The AUC/MIC ratio for resistant organism stasis is circa 185/1 Resistant organisms at baseline All other data points represent resistant organism counts at 48 hours of therapy 050100150200250 10 100 10 3 10 4 10 6 AUC 0-24 :MIC Ratio Resistant Mutants (CFU/mL) 10 7 10 5 Resistance Suppression and Outcome Optimization

13. Propspective Validation Study Tam V et al. Bacterial-population responses to drug selective pressure: Examination of garenoxacin’s effect on Pseudomonas aeruginosa. J Infect Dis 2005;192:420-428

14. Levofloxacin and Pseudomonas aeruginosa in a Mouse Thigh Infection Model Can a drug exposure be identified that will prevent the resistant subpopulation from taking over the total population? In contrast to the hollow fiber system, here we have an immune system

15. P. aeruginosa outcome studies Jumbe et al J Clin Invest 2003;112:275-285

16. Journal of Clinical Investigation 2003;112:275-285 & Nature Reviews Microbiology 2004;2:289-300 0 mg/kg 90 mg/kg 215 mg/kg600 mg/kg

17. Peripheral (thigh) Compartment (C p ) Central Blood Compartment (C c ) IP injection k cp k pc + Bacteria (X T/R ) f(c) dC c = k a C a +k pc Cp-k cp C c -k e C c dt keke dX S =K GS x X S x L - f KS (C c H  ) x X S dt dX R = K GR x X R x L- f KR (C c H  ) x X R dt K max   C c H  C H  50  +C c H  f   (C c H  )= Y 1 =X T =X S +X R Y 2 =X R [4] [5] [6] [7] [8],  =K and  = S,R [2] L = (1- (X R + X S ) /POPMAX) [9] dC p = k cp C c - k pc C p dt [3] dC a = -k a C a dt [1]

18. Jumbe et al J Clin Invest 2003;112:275-285 Drusano GL. Nat Rev Microbiol 2004;2:289-300

19. Resistance Suppression and Outcome Optimization Jumbe et al J Clin Invest 2003;112:275-285 Drusano GL. Nat Rev Microbiol 2004;2:289-300

20. Journal of Clinical Investigation 2003;112:275-285 & Nature Reviews Microbiology 2004;2:289-300

21. PK/PD We studied the quinolone garenoxacin against S. aureus We studied and modeled 7 regimens We performed a prospective validation with 4 hypotheses All were validated Resistance suppression requires more drug exposure than that for maximal rate of kill

22. PK/PD We performed an experiment where 4, 5 or 6 daily doses of drug (AUC/MIC ratio=100) were administered and the outcomes monitored out to day 13 We fit an expanded mathematical model to all the data simultaneously We included a natural death rate term for sensitive and resistant populations This allows us to look at relative biofitness in conjunction with the growth terms for the two populations

23. PK/PD

26. So, the longer therapy continues, the more amplification goes on of the resistant population with a suboptimal regimen To prevent resistance, shorter is better BUT, we also have to clear the infection, so therapy needs to be long enough to accomplish this end How much effect do we need? Enough to allow the immune system to do its job!

27. ParameterK max-growth POPMAXV max-kill * K m Unitsh -1 CFU/g h -1 CFU/g Mean0.6220.916x10 11 0.00535147660 SD0.1040.980x10 10 0.00400203928 ______________________________________________________________________ * Rate constant is multiplied by the granulocyte count

28. BRIDGING TO THE CLINIC Correlation with Pre-Clinical Models Do These Animal and Hollow Fiber Model Findings Correlate with Suppression of Resistance in the Clinic?

29. Taking the expectation demonstrates an overall target attainment of 62% and a predicted emergence of resistance rate of 38% PK-PD TARTGET ATTAINMENT Ciprofloxacin Against P. aeruginosa Use of Monte Carlo Simulation

30. Peloquin studied 200 mg IV Q 12 h of ciprofloxacin in nosocomial pneumonia - P aeruginosa resistance rate 70% (7/10 - pneumonia only) - 77% (10/13 - all respiratory tract) MCS (resistance suppression target) predicts emergence of resistance in 75% Fink et al studied ciprofloxacin in nosocomial pneumonia (400 mg IV Q 8 h) - P aeruginosa resistance rate 33% (12/36) MCS at this dose and schedule predicts suppression in 62% and emergence of resistance in 38% Peloquin et al Arch Int Med 1989;1492269-73 Fink et al AAC 1994;38:547-57 MONTE CARLO SIMUATION Is It Predictive?

31. WHAT ABOUT COMBINATION THERAPY FOR RESISTANCE SUPPRESSION? Sometimes, single agent therapy just can’t get the job done

32. Cefto-Levo Combo vs. P aeruginosa We wished to evaluate the combination of ceftobiprole plus levofloxacin West et al (Clin Ther 2003;25:485-506) demonstrated that levofloxacin in combination with a β-lactam suppressed Pseudomonas resistance in 17/17 instances in a HAP trial We employed a mouse thigh infection model to examine the cefto-levo combination for both cell kill and resistance suppression

33. Cefto-Levo Combo vs. P aeruginosa Methods The mouse thigh infection model as described by Craig was employed (inoculum 1.85 x 10 7 CFU) P aeruginosa ATCC 27853 was employed as the challenge organism (MIC 1.0 – Levo; MIC 2.0 mg/L – Cefto) Dose ranging studies were performed for each drug separately These were used to design the combination experiment using D-optimal design theory

34. Cefto-Levo Combo vs. P aeruginosa Methods Drug concentrations were measured by LC-MS/MS techniques Resistant organisms were determined by plating on agar infused with 2.5XMIC (Levo) or 3XMIC (Cefto) Combination cell kill was modeled employing the Universal Response Surface Approach of Greco et al The ability to suppress resistant subpopulations was evaluated by Monte Carlo Simulation (Lodise et al AAC 2007;51:2378-2387 for Ceftobiprole; Drusano et al JID 2004;189:1590-1597 for levofloxacin)

35. Cefto-Levo Combo vs. P aeruginosa Results Ceftobiprole Single Drug Therapy Levofloxacin Single drug Therapy

38. Ceftobiprole-Levofloxacin Combination Therapy Suppress Levofloxacin Resistance Suppress Ceftobiprole Resistance

39. Ceftobiprole-Levofloxacin Combination Therapy Suppress Ceftobiprole Resistance Suppress Levofloxacin Resistance

40. Ceftobiprole-Levofloxacin Combination Therapy The exposures that suppress resistance in both directions are Ceftobiprole T > MIC of 40% and Levofloxacin AUC/MIC of 6.7 Monte Carlo simulation demonstrates that doses of 750 mg daily of levofloxacin plus 500 mg of ceftobiprole as a 2 hour infusion every 8 hours achieve these targets 96.4% of the time at a minimum

41. Ceftobiprole-Levofloxacin Combination Therapy Ceftobiprole plus levofloxacin interact productively against Pseudomonas aeruginosa in the mouse thigh model Perhaps more importantly, resistance suppression is clearly demonstrable at modest drug exposures for both ceftobiprole and levofloxacin Human PK demonstrates that these resistance suppression exposures can be achieved greater that 96% of the time

42. Ceftobiprole-Levofloxacin Combination Therapy THIS MAKES NO SENSE For both levo and cefto, the Mex pumps cause efflux This means that there should be no cross protection – Right?????? BUT pumps are Michelis-Menten processes – perhaps the combination saturates the pump So, this is a promising combination, at least for wild-type strain

43. Anti-Infective PK/PD I have been trying to interest the anti-infective community in antimicrobial pharmacodynamics for almost a quarter of a century, certainly without notable success. WELL!

44. George

45. THANK YOU FOR YOUR ATTENTION