1. Getting the Dose Right The View from Academia
GL Drusano, M.D.
Co-Director
Ordway Research Institute &
Research Physician
NY State Department of Health
Director
Division of Clinical Pharmacology
Albany Medical College

2. Getting the Dose Right The View from Academia
What are the determinants of “getting the dose right”?
The first question that needs to be addressed is “What outcome is desired”?
Multiple outcomes are reasonable -Good clinical outcome -Good microbiological outcome -Suppression of resistance -Minimization of concentration-related toxicities

3. Getting the Dose Right The View from Academia
Dose choice becomes an issue in the Phase-I time frame (actually earlier, but the proper data becomes available at this time)
Because of this, the choice of outcome is somewhat limited – Clinical outcome cannot, at this time, be an outcome measure
The most common measure, then, is microbiological outcome determined from in vitro or animal models

5. Getting the Dose Right The View from Academia
Once a target exposure is chosen, what other pieces of information are required? -Population PK: this quantifies the variability in how the drug dose is handled by a population -MIC distribution for the target pathogens: this quantifies how the MIC will vary -Protein binding: in general, only free drug is microbiologically active

6. Getting the Dose Right The View from Academia
Now we can evaluate how well a specific drug dose will attain the desired target
This evaluation will be done employing Monte Carlo simulation

7. P aeruginosa
Expectation = 65.6%
E cloacae
Expectation = 88.3%

8. Getting the Dose Right The View from Academia
What are the questions now? -Was the target correct? -Is the target attainment rate adequate?
The target [1 log10 (CFU/g) decline] is reasonably conservative, but one must be careful about the infection site
The answer to the second question depends on the patient and the consequences of being wrong: -G-penic patient vs uncomplicated SSTI -Mother vs Mother-In-Law

9. Getting the Dose Right The View from Academia
What now?
Now it is important to recapitulate the analysis in real patients in the Phase I/II environment
Why?
The PK is determined in a target population
Correlation with preclinical animal data provides near certainty of “no surprises” in Phase III
“real world” organisms are seen and can be gauged against the original MIC distributions
Monte Carlo simulations can be re-calculated with “real” data

10. Population mean pharmacokinetic parameter values derived from 58 Patients
with Nosocomial Pneumonia Receiving Drug X as a 1.5 Hour Constant Rate,
Intravenous Infusion Vol Kcp Kpc CL
Units L hr hr L/hr
Means
Medians
S.D
Vol = Volume of the central compartment; Kcp and Kpc are first order ntercompartmental
transfer rate constants connecting the central and peripheral compartments; CL = Total clearance of Drug X

11. Final model for microbiological outcome for patients with
nosocomial pneumonia receiving Drug X daily
Final Model for Microbiological Outcome
Constant Parameter Odds Ratio 95% Confidence Interval for Odds Ratio
(AUC/MIC > 87)
-2.197
– 1.347
(Age)
p = 0.001; McFadden’s 2 = 0.31

14. Target attainment rates (AUC/MIC ratio of 87) for a dose of Drug X for distributions of Pseudomonas aeruginosa (n = 404) and Enterobacter cloacae (n = 297) isolates employing a subject Monte Carlo simulation
Target Attainment Rate
AUC/MIC Breakpoint Pseudomonas aeruginosa Enterobacter cloacae
% %

15. Getting the Dose Right The View from Academia
What about resistance suppression?

17. Mean Parameter Estimates of the Model.
KmaxGS
0.117
KmaxGR
0.163
KmaxKS
94.01
KmaxKR
12.16
HKS
6.26
HKR
2.37
C50KS
123.5
C50KR
129.8
Popmax = 3.6 x 1010
KmaxG -maximum growth rate (hr-1) in the presence of drug
KmaxK -maximum kill rate (hr-1)
C50K -drug concentration (g/mL) to decrease kill rate by half
HK -rate of concentration dependent kill
Popmax -maximal population size

18. Getting the Dose Right The View from Academia
All regimens were simultaneously fit in a large population model
The displayed graph is the predicted-observed plot for the total population after the Maximum A-posteriori Probability (MAP) Bayesian step

19. Getting the Dose Right The View from Academia
All regimens were simultaneously fit in a large population model
The displayed graph is the predicted-observed plot for the resistant population after the Maximum A-posteriori Probability (MAP) Bayesian step

20. Getting the Dose Right The View from Academia

21. Journal of Clinical Investigation 2003;112:275-285

22. Getting the Dose Right The View from Academia
In this experiment, a dose was selected to generate an exposure that would prevent emergence of resistance
As this was at the limit of detection, the measured population sometimes had “less than assay detectable” for the colony count
These were plotted at the detection limit
Journal of Clinical Investigation 2003;112:

23. Getting the Dose Right The View from Academia
We were able to determine how the overall (sensitive plus resistant) population responds to pressure from this fluoroquinolone
More importantly, we were able to model the resistant subpopulation and choose a dose based on simulation to suppress the resistant mutants
The prospective validation demonstrated that the doses chosen to encourage and suppress the resistant mutants did, indeed, work

24. Getting the Dose Right The View from Academia
What about having a high probability of attaining a good outcome while not encumbering the patient with a high probability of a concentration-related toxicity?
For this we will examine aminoglycosides, where we have concentration-effect and concentration-toxicity probability relationships

25. Getting the Dose Right The View from Academia
Efficacy Probability Function
Kashuba AD, AN Nafziger, GL Drusano and JS Bertino, Jr. Optimizing aminoglycoside therapy for nosocomial pneumonia caused by Gram-negative bacteria. AAC 1999;43:
Toxicity Probability Function
Rybak MJ, BJ Abate, SL Kang, MJ Ruffing, SA Lerner and GL Drusano. Prospective evaluation of the effect of an aminoglycoside dosing regimen on rates of observed nephrotoxicity and ototoxicity. AAC 1999;43:

26. Getting the Dose Right The View from Academia

27. Getting the Dose Right The View from Academia

29. Getting the Dose Right The View from Academia-Conclusions
Why go through all this mathematical *!$# ? -Because, at the end of the therapy is a patient, who would appreciate getting better without toxicity -Also, because choosing the dose correctly allows trials with anti-infectives to have the lowest Phase III failure rate of ANY therapeutic area