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 &

NYSDOH

Prevention of Emergence of Resistance: A Pharmacodynamic Solution

• Currently, the therapeutic armamentarium is amazingly limited for many Gram-negative pathogens

• Discovery programs do not promise any relief for at least 5 years

• We must learn to use available drugs more intelligently to preserve the susceptibility of the infecting flora to current agents



• Many organisms have resistance mechanisms that occur as a function of single point mutations

• Examples are stable derepression of type I beta lactamases for 3rd generation cephalosporins and target mutations or pump upregulation for fluoroquinolones

• As these occur at a frequency of 1/108 or greater, 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:


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


Let Us Compare and Contrast the Pharmacodynamics of Levofloxacin for Streptococcus pneumoniae and

Pseudomonas aeruginosa in a Mouse Thigh Infection Model

S. pneumoniae outcome studies

P. aeruginosa outcome studies

Rf in vitro Rfin vivo MIC (g/mL) MBC (g/mL)

2.35x10-6 2.2x10-6 0.8 1.6


• 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 Levofloxacin

Peripheral (thigh)Compartment (Cp)

Central Blood Compartment (Cc)

IPinjection

kcp kpc

+ Bacteria(XT/R)

f(c)

dCc= kaCa+kpcCp-kcpCc-keCc

dt

ke

dXS=KGS x XS x L - fKS(CcH ) x XS

dtdXR= KGR x XR x L- fKR(Cc

H ) x XR

dt

Kmax CcH

C H

50+CcH

f(CcH)=

Y1=XT=XS+XR

Y2=XR

[3]

[4]

[5]

[6]

[7]

, =K and = S,R

[1]

L = (1-(XS+XR)/POPMAX)

[8]

dCp = kcpCc - kpc Cp

dt

[2]

KmaxGS

0.117

KmaxGR

0.163

KmaxKS

94.01

KmaxKR

12.16

HKS

6.26

HKR

2.37

C50KS

123.5

C50KR

129.8

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

Mean Parameter Estimates of the Model.

Popmax = 3.6 x 1010


• 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


• 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



• We wished to evaluate the model prospectively

• Models, to be useful, need to predict the future

• We simulated a dose not previously studied that would encourage selection of resistance

• The study was carried out for 48, not 24 hours

• The model predicted the change in the resistant mutant population well


• 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


• We were able to determine how the overall (sensitive plus resistant) population responds to pressure from Levofloxacin

• 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


• 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 hr1. Microbial eradication

2. Selection of resistance

Infect

24 hr

BID

+ 2xMIC Cipro - Drug + 4xMIC Cipro + 3xMIC Levo

Drug #58 RC2

Cipro/±Reserpine 0.6/0.6 3.5/1.0

Levo/±Reserpine 0.6/0.6 0.6/0.6



• Strain 58, the RC2 and RC4 mutants were sequenced through Gyr A, Gyr B, Par C & Par E.

• The sequences examined were: GyrA (ORF 822 aa) aa 4-229; Gyr B (ORF 648 aa) aa 346-579; ParC (ORF 823 aa) aa 1-178; ParE (ORF 647 aa) aa 359-561. 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, sertyr) and this strain also decreased its MIC with addition of reserpine.

Begin therapy


-2 hr 0 hr

+ 2xMIC Cipro - Drug

Infect

24 hr

BID

+ 4xMIC Cipro

Begin therapy


-2 hr 0 hr

+ 3xMIC Levo - Drug

Infect

24 hr

BID

+ 3xMIC Cipro

#58-

RC

2#5

8-W

T

+ 3xMIC Levo

Study Design: Second Passage of First Stage Ciprofloxacin ResistantS. pneumoniae

Total Counts

Cipro Resistance

Levo Resistance* = no colonies detected in any

sample. Sample size 4 animals


• What next?• We are currently examining the RC2 mutant in the

mouse thigh model• In preliminary data, exposures to levofloxacin that

would kill the wild-type isolate did not kill the mutant, even though the MIC has not changed

• This finding has been recreated with another later generation fluoroquinolone in a hollow fiber model

• This implies that, counter to the output of Resistance 2000, sometimes newer drugs preserve the sensitivity of the flora better than older drugs


• 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


• 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


• 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)


CONCLUSIONS

• Probability of emergence of resistance is impacted upon by the intensity of therapy and by the duration of therapy

• Short duration therapy trials should examine an endpoint of resistance frequency

• As importantly, doses should be chosen to provide resistance counterselection exposures for a large fraction of the population. An example follows:


Target Attainment


Target Attainment


• While this example is for microbiological outcome, a similar analysis could (and should!) be performed for a prevention of resistance target

• Such a dose choice, coupled with short duration therapy will yield the highest probability of a good clinical and microbiological outcome and the lowest probability of the resistant subpopulation taking over the whole population

Prevention of Emergence of Resistance: A Pharmacodynamic Solution

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