Prevention of Emergence of Resistance: A Pharmacodynamic Solution

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

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 3rd 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 1/108 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 TeamN. 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 vitroRfin vivo MIC (g/mL) MBC (g/mL) 2.35x10-6 2.2x10-6 0.8 1.6

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

Bacteria (XT/R) Peripheral (thigh) Compartment (Cp) + kpc f(c) kcp Central Blood Compartment (Cc) dXS=KGS xXS x L - fKS(CcH) x XS dt [3] IP injection [4] dXR= KGR xXR x L- fKR(CcH) x XR dt [5] L = (1-(XS+XR)/POPMAX) ke KmaxCcH C H 50+CcH  , =K and = S,R [6] f(CcH)= dCc= kaCa+kpcCp-kcpCc-keCc dt [1] dCp = kcpCc - kpc Cp dt [2] Y1=XT=XS+XR Y2=XR [7] [8]

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

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

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

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

Prevention of Emergence of Resistance: A Pharmacodynamic Solution Drug #58RC2 Cipro/±Reserpine0.6/0.6 3.5/1.0 Levo/±Reserpine0.6/0.6 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 = 0.0007); 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

Bacteria (XT/R) Central Compartment (Cc) + Infusion SCl f(c) dXS=KGS xXS x L - fKS(CcH) x XS dt [2] dCc=Infusion-(SCl/V)xCc dt [1] [3] dXR= KGR xXR x L- fKR(CcH) x XR dt [4] L = (1-X/POPMAX) KmaxCcH C H 50+CcH  , =K and = S,R [5] f(CcH)= Y1=XT=XS+XR, IC(1)=2.4x108 Y2=XR , IC(2)= 30 [6] [7]

Mean Parameter Estimates of the Bacterial Growth/Kill Model. KmaxGS 0.745 KmaxGR 0.614 C50KS 16.94 C50KR 107.0 KmaxKS 27.85 KmaxKR 31.72 HKS 2.24 HKR 3.50 Popmax = 3.3 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

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 ResistantSub-Population • ‘Inverted-U’ Phenomenon • Resistant sub-populationis are initially amplified & then decline with increasing drug exposure Log10 CFU/mL Therapeutic Intensity

Prevention of Emergence of Resistance: A Pharmacodynamic SolutionP. 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 SolutionP. 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 1.5 -2.0 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 SolutionP aeruginosa Breakpoint = 187 Log10 CFU/mL Daily AUC/MIC

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

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

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

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

Prevention of Emergence of Resistance: A Pharmacodynamic Solution