Prevention of Emergence of Resistance: A Pharmacodynamic Solution G.L. Drusano, M.D. Professor and Director Division of Clinical Pharmacology Clinical Research Institute Albany Medical College & New York State Department of Health

Prevention of Emergence of Resistance: A Pharmacodynamic Solution

Resistance to antimicrobial agents often occur as a function of single point mutations Other mechanisms include spread of plasmids with multiple resistance determinants Horizontal transmission also confuses the issue Examples of a point mutation providing drug resistance are stable derepression of AMP C beta lactamases for 3 rd generation cephalosporins and target mutations or pump upregulation for fluoroquinolones

Prevention of Emergence of Resistance: A Pharmacodynamic Solution As these occur at a frequency of 1/10 8 or less frequently, infection site populations exceed this frequency, often by multiple logs Consequently, such total populations do not behave as a single, sensitive population, but as a mixture of two populations of differing drug susceptibility This raises an important question:

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Can a drug exposure be identified that will prevent the resistant subpopulation from taking over the total population?

The Team N. L. Jumbe, A. Louie, W. Liu,V. Tam, T. Fazili, R. Leary, C. Lowry, M.H. Miller and G. L. Drusano

S. pneumoniae outcome studies

P. aeruginosa outcome studies Rf in vitro Rf in vivo MIC (  g/mL) MBC (  g/mL) 2.35x x

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Clearly, Pseudomonas and Pneumococcus differ in their response Pneumococcus has no inoculum effect; Pseudomonas has a major inoculum effect The explanation probably rests in the mutational frequency to resistance Pseudomonas has a high frequency, while Pneumococcus has a frequency that is not measurable at the bacterial densities used in these experiments with this fluoroquinolone

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 [3] [4] [5] [6] [7],  =K and  = S,R [1] L = (1-(X S + X R )/POPMAX) [8] dC p = k cp C c - k pc C p dt [2]

K maxGS K maxGR K maxKS K maxKR H KS 6.26 H KR 2.37 C 50KS C 50KR K maxG -maximum growth rate (hr -1 ) in the presence of drug K maxK -maximum kill rate (hr -1 ) C 50K -drug concentration (  g/mL) to decrease kill rate by half H K -rate of concentration dependent kill Popmax -maximal population size Mean Parameter Estimates of the Model. Popmax = 3.6 x 10 10

Prevention of Emergence of Resistance: A Pharmacodynamic Solution 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

Prevention of Emergence of Resistance: A Pharmacodynamic Solution 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

Prevention of Emergence of Resistance: A Pharmacodynamic Solution

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

Prevention of Emergence of Resistance: A Pharmacodynamic Solution 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

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Now, for Pneumococcus We were unable to recover resistant mutants with levofloxacin as the selecting pressure in the mouse thigh model However, we then examined ciprofloxacin as the selecting agent Now, selecting mutants was straightforward

Study Design: Mouse Thigh Infection Model- Ciprofloxacin Studies [50mg/kg BID ~ AUC/MIC 100:1] Begin therapy Sacrifice, harvest, homogenize muscle -2 hr 0 hr 1. Microbial eradication 2. Selection of resistance Infect 24 hr BID + 2xMIC Cipro - Drug + 4xMIC Cipro + 3xMIC Levo

Drug #58 RC2 Cipro/ ±Reserpine 0.6/ /1.0 Levo/ ±Reserpine 0.6/ /0.6 Prevention of Emergence of Resistance: A Pharmacodynamic Solution

Strain 58, the RC2 and RC4 mutants were sequenced through Gyr A, Gyr B, Par C & Par E. The entire open reading frames were sequenced. No differences were seen between parent and the RC2 daughter strain. This, coupled with the decrement in ciprofloxacin MIC with reserpine exposure (3.5 mg/L  1.0 mg/L), implies RC2 is a pump mutant. For RC4, a mutation was found in parC (aa 79, ser  tyr) and this strain also decreased its MIC with addition of reserpine.

Prevention of Emergence of Resistance: A Pharmacodynamic Solution We have examined other new fluoroquinolones in this system or in our hollow fiber pharmacodynamic system All resemble levofloxacin and do not allow emergence of resistance for wild type isolates Why is ciprofloxacin different? Likely because it is the most hydrophilic drug and is most efficiently pumped by the PMRA pump

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Are there other factors that can alter the probability of emergence of resistance? The most likely is duration of therapy Fluoroquinolones induce an SOS response This resembles a “hypermutator phenotype” Therapy intensity and therapy duration should influence the probability of having the resistant population becoming ascendant

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Hollow fiber System allows simulation of human PK in vitro Useful for dose ranging and schedule dependency determinations Allows examination of different classes (beta lactams, fluroquinolones, etc.) The original hollow fiber system was used by Blaser & Zinner

Prevention of Emergence of Resistance: A Pharmacodynamic Solution A 10 day hollow fiber experiment was performed for MSSA and MRSA (CS) for 6 regimens The time to complete replacement of the population with resistant organisms was recorded CART was employed to look for a breakpoint in the exposure > 200/1 AUC/MIC ratio was identified

Prevention of Emergence of Resistance: A Pharmacodynamic Solution A stratified Kaplan-Meier analysis was performed with this breakpoint The breakpoint was significant (Mantel test p = ); Tarone-Ware and Breslow Gahan tests were also significant To prevent resistance, hit hard (> 200 AUC/MIC) and stop early (< 7 days)

Prevention of Emergence of Resistance: A Pharmacodynamic Solution The intensity of therapy and the duration of therapy have an impact upon the probability of emergence of resistance Short duration therapy trials should examine an endpoint of resistance frequency

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Tam et al ICAAC 2001

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Tam et al ICAAC 2001

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Tam et al ICAAC 2001

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Tam et al ICAAC 2001

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Tam et al ICAAC 2001

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Tam et al ICAAC 2001

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Tam et al ICAAC 2001

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)=2.4x10 8 Y 2 =X R, IC(2)= 30 [2] [3] [4] [5] [6],  =K and  = S,R [1] L = (1-X  /POPMAX) [7]

K maxGS K maxGR K maxKS K maxKR H KS 2.24 H KR 3.50 C 50KS C 50KR K maxG -maximum growth rate (hr -1 ) in the presence of drug K maxK -maximum kill rate (hr -1 ) C 50K -drug concentration (  g/mL) to decrease kill rate by half H K -rate of concentration dependent kill Popmax -maximal population size Mean Parameter Estimates of the Bacterial Growth/Kill Model. Popmax = 3.3 x 10 10

Prevention of Emergence of Resistance: A Pharmacodynamic Solution All regimens were simultaneously fit in a large population model The displayed graph is the predicted-observed plot for the drug concentrations after the Maximum A- posteriori Probability (MAP) Bayesian step

Prevention of Emergence of Resistance: A Pharmacodynamic Solution All regimens were simultaneously fit in a large population model The displayed graph is the predicted-observed plot for the total bacterial counts after the Maximum A- posteriori Probability (MAP) Bayesian step

Prevention of Emergence of Resistance: A Pharmacodynamic Solution All regimens were simultaneously fit in a large population model The displayed graph is the predicted-observed plot for the resistant bacterial counts after the Maximum A- posteriori Probability (MAP) Bayesian step

Prevention of Emergence of Resistance: A Pharmacodynamic Solution ‘Inverted-U’ Phenomenon –Resistant sub- populationis are initially amplified & then decline with increasing drug exposure Therapeutic Intensity Log10 CFU/mL Resistant Sub-Population

Prevention of Emergence of Resistance: A Pharmacodynamic Solution 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 187/1 Tam et al ICAAC 2001

Prevention of Emergence of Resistance: A Pharmacodynamic Solution P. aeruginosa - Prevention of Amplification of Resistant Subpopulation Prospective Validation

Prevention of Emergence of Resistance: A Pharmacodynamic Solution This was the same strain as employed in the mouse model, but a different fluoroquinolone The mouse model contained granulocytes, while the hollow fiber system does not The total drug target for the mouse model was 157 which is a free drug target of 110 The hollow fiber system target is 187 (1.7 fold  ) Craig found that targets increase by fold when granulocytes are removed These results are concordant with this finding

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Multiple Bacterial Populations Do Make a Difference! In Vitro pharmacodynamic model investigations frequently only examine the total bacterial population The presence of a small pre-existent population more resistant to the selecting drug pressure has major implications, particularly as the bacterial population size increases to (near) clinical infection size

Prevention of Emergence of Resistance: A Pharmacodynamic Solution P aeruginosa Log10 CFU/mL Daily AUC/MIC Breakpoint = 187

Prevention of Emergence of Resistance: A Pharmacodynamic Solution K. pneumoniae Log10 CFU/mL Daily AUC/MIC Breakpoint = 93

Prevention of Emergence of Resistance: A Pharmacodynamic Solution MSSA Log10 CFU/mL Daily AUC/MIC Breakpoint = 66

Prevention of Emergence of Resistance: A Pharmacodynamic Solution MRSA-CS Log10 CFU/mL Daily AUC/MIC Breakpoint = 143

Prevention of Emergence of Resistance: A Pharmacodynamic Solution MRSA-CR Log10 CFU/mL Daily AUC/MIC Breakpoint = 484

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Some drug exposures allow amplification of the resistant subpopulations Exposures can be identified that will prevent this amplification and, functionally suppress emergence of resistance