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Getting the Dose Right The View from Academia

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. Getting the Dose Right The View from Academia.

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Getting the Dose Right The View from Academia

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  1. Getting the Dose RightThe 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 RightThe 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 RightThe 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

  4. Getting the Dose RightThe 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

  5. Getting the Dose RightThe 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

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

  7. Getting the Dose RightThe 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

  8. Getting the Dose RightThe 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

  9. 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-1 hr-1 L/hr Means 34.4 7.65 6.07 7.24 Medians 23.3 2.66 0.924 6.24 S.D. 33.5 9.59 12.0 4.36 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

  10. 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 1.374 3.952 11.596 – 1.347 (Age) 0.067 1.069 1.138 - 1.004 p = 0.001; McFadden’s 2 = 0.31

  11. 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 10000 subject Monte Carlo simulation Target Attainment Rate AUC/MIC Breakpoint Pseudomonas aeruginosaEnterobacter cloacae 87.0 72.4% 91.7%

  12. Getting the Dose RightThe View from Academia • What about resistance suppression?

  13. Mean Parameter Estimates of the Model. KmaxGS 0.117 KmaxGR 0.163 C50KS 123.5 C50KR 129.8 HKS 6.26 HKR 2.37 KmaxKS 94.01 KmaxKR 12.16 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

  14. Getting the Dose RightThe 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

  15. Getting the Dose RightThe 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

  16. Getting the Dose RightThe View from Academia

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

  18. Getting the Dose RightThe 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;112:275-285

  19. Getting the Dose RightThe 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

  20. Getting the Dose RightThe 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

  21. Getting the Dose RightThe 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;43:623-629. • 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;43:1549-1555.

  22. Getting the Dose RightThe View from Academia

  23. Getting the Dose RightThe View from Academia

  24. Getting the Dose RightThe 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

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