2016 Europe-Nordic-US Symposium New Frontiers in ... · New Frontiers in Antibacterial Resistance Research Pharmacological Approaches to ... PK PD. Cell Kill and ... of garenoxacin’s

2016 Europe-Nordic-US Symposium

New Frontiers in Antibacterial

Resistance Research

Pharmacological Approaches to

Address AR

G.L. Drusano, M.D.

Professor and Director

Institute for Therapeutic Innovation

University of Florida

The vast bulk of the data presented here was supported by multiple R01’s from NIAID

Pharmacological Approaches to Address Antimicrobial Resistance

• What will we look at?

1. Impact of the intensity of drug exposure on bacterial cell kill and resistance emergence

2. Impact of duration of therapy on resistance emergence

3. Combination chemotherapy

Cell Kill and Resistance Emergence

Impact of Intensity of Drug Exposure

Cell Kill and Resistance Suppression in Pseudomonas aeruginosa


Jumbe et al J Clin Invest 2003;112:275-285

Bacterial burden at therapy initiation < inverse of the mutational frequency to resistance

Bacterial burden at therapy initiation > inverse of the mutational frequency to resistance

Peripheral (thigh)Compartment (Cp)

Central Blood Compartment (Cc)IP

injection

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+Cc

H f(Cc

H)=

Y1=XT=XS+XR

Y2=XR

[4]

[5]

[6]

[7]

[8]

, =K and = S,R

[2]

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

[9]dCp = kcpCc - kpc Cp

dt

[3]

dCa= -kaCa

dt[1]

PK

PD


Cell Kill and Resistance Suppression in Pseudomonas

aeruginosa

Journal of Clinical Investigation 2003;112:275-285 &Nature Reviews Microbiology 2004;2:289-300

Lines are NOT best-fit lines

They are prospective prediction

lines about which the data have

been scattered

Prospective Validation

Experiment

AUC/MIC = 52

AUC/MIC = 157

Resistance Suppression in Pseudomonas aeruginosa

The use of the hollow fiber model for studying antimicrobial regimens was described by Blaser and Zinner and employed extensively by Dudley


Tam V et al. Bacterial-population responses to drug selective pressure: Examination of garenoxacin’s effect on Pseudomonas aeruginosa. J Infect Dis 2005;192:420-428

● = Total Bacterial Burden; ● = Less-Susceptible Bacterial Burden

Prospective Validation Experiment

Predictions:1. AUC/MIC = 137 gives good

cell kill then fails due to resistance

2. AUC/MIC =200 gives good cell kill and suppresses resistance


• Why has it taken a while to get a handle on resistance suppression by dosing?

• We are used to looking for relationships that are monotonic – give more drug; obtain more exposure; drive better effect

• The functional form for resistance suppression is NON-MONOTONIC!


0 50 100 150 200 250

10

100

103

104

106

AUC0-24:MIC Ratio

Re

sist

an

t M

uta

nts

(C

FU

/mL)

107

105

Resistant organismsat baseline

Cell Kill –

Monotonic Function

Resistance Suppression –

Non-Monotonic Function

All other data points representresistant organism counts at48 hours of therapy

It’s easier to kill than suppress amplification of resistant clones

Taking the expectation demonstrates an overall target attainment of 62% and a predicted emergence of resistance rate of 38%

Patient Translation: Does the resistance-suppression target and Monte Carlo Simulation reflect clinical outcomes?


Is Monte Carlo Simulation Predictive?

Peloquin studied 200 mg IV Q 12 h of ciprofloxacin in nosocomial

pneumonia - P aeruginosa resistance rate 70% (7/10 - pneumonia only) -

77% (10/13 - pneumonia plus empyema [2] and bronchiectasis [1])

Monte Carlo simulation with a resistance suppression target

(AUC/MIC = 157) predicts suppression in 24.8%

Fink et al studied ciprofloxacin in nosocomial pneumonia at a dose of

400 mg IV Q 8 h - P aeruginosa resistance rate 33% (12/36)

Monte Carlo simulation at this dose predicts resistance in 38.2%

Peloquin et al Arch Int Med 1989;1492269-73 Fink et al AAC 1994;38:547-57


Address AR• What are the take-home messages?

1. Resistance suppression is non-monotonic i.e. follows an “inverted U”

2. Intermediate exposures actually amplify less-susceptible sub-populations


Address AR

• What are the take-home messages (cont’d)?

3. The size of the bacterial burden is critical – as it increases the probability of a primary mutant being present at baseline

4. Identifying a resistance suppression threshold of exposure is not enough – use Monte Carlo simulation to see how many in a population will attain the suppression threshold

Impact of Therapy Duration

Prospective Validation Experiment

Predictions:1. AUC/MIC = 100 suppresses for

5 days, then fails 2. AUC/MIC =280 suppresses for

at least 10 days

Total Bacterial Burden

Less-Susceptible Bacterial Burden


Address AR• What are the take-home messages?

1. The longer therapy goes, the harder it is to suppress amplification of less-susceptible populations

2. An inadequate regimen that is administered for too long a time may result in complete obliteration of the susceptible population –this population will never return at this point even when drug pressure is stopped


Address AR

Looking at Agents in Combination

Mono-Rx Pseudomonas aeruginosa

Cefepime vs P. aeruginosa

So, what’s

going on?

Why the

failure of

mono-Rx and

why the

success of

combo-Rx?

AAC 2012;

56:231-242

Impact of Baseline Bacterial Burden

• So, what is going on?

• We looked at the stability of cefepimeover time at different baseline inocula

• Inoculum and time-dependent hydrolysis was seen

• Hypothesis: β-lactamase mediated problem

Antimicrob Agents Chemother 2012;56:231-242

It is Probably the β-lactamase!

No resistance emergence!

Success of Combination TherapyIt Is the β-lactamase!

• As a protein synthesis inhibitor, we hypothesize that the aminoglycoside shuts down the expression of the ampC β-lactamase

AAC 2012; 56:231-242

Sometimes combination therapy has a salutary outcome and it is not just due to synergistic cell kill!

These results were recapitulated with qPCR as the readout


Address AR

• What are the take-home messages?

1. Again, the bacterial burden makes a difference! Think why VABP is so hard to treat

2. Sometimes, you simply can’t get there from here with monotherapy


Address AR

• What are the take-home messages (cont’d)?

3. Combination therapy is NOT simple and straightforward, but properly chosen can help ameliorate some of our problems


Address AR: Conclusions• We can have a significant impact on resistance

emergence if we pay attention to dosing

• The duration of therapy makes a difference

• This is a fine balance – long enough to cure the patient; short enough to suppress resistance

• Sometimes you just need combination therapy –Where? Serious infections with large bacterial burdens – e.g. VABP; especially non-fermentors

• Regulatory authorities need to think out of the box on this (Drs. Powell and Cavaleri of EMA get it)

Thank You for

Your Attention!

For those with any interest (at all) in this topic, it has recently been reviewed:1. Drusano GL, A Louie, A MacGowan, and W Hope. Suppression of Emergence of

Resistance in Pathogenic Bacteria: Keeping our Powder Dry-Part 1. Accepted.

Antimicrob Agents Chemother.

2. Drusano GL, W. Hope, A MacGowan, A Louie. Suppression of Emergence of

Resistance in Pathogenic Bacteria: Keeping our Powder Dry-Part 2.Accepted.

Antimicrob Agents Chemother.

Contact info for George Drusano: [email protected]; (407) 313 7060; (518) 281 7170 (mobile)

mailto:[email protected]

BACK UP SLIDES


Jumbe et al J Clin Invest 2003;112:275-285&Drusano GL. Nat Rev Microbiol 2004;2:289-300

Central Compartment (Cc)Infusion + Bacteria

(XT/R)

f(c)

dCc=Infusion-(SCl/V)xCc

dt

SCl

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 +Cc

H f(Cc

H)=

Y1=XT=XS+XR, IC(1)=1.01x108

Y2=XR , IC(2)= 58

[2]

[3]

[4]

[5]

[6]

, =K and = S,R

[1]

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

[7]

Combination Therapy for

Resistance Suppression

We have gone as long as 6 months; 1-2 months is standard for us in MTB studies



• The model system has 5 outputs

• There are 5 inhomogeneous differential equations

• Dimensionality is 28

• Drug interaction is quantitated through a variation of the Greco model

• Anyone wishing to go over the enabling equations can talk with me offline



Fully Susceptible Linezolid-S; Rif-R Rif-S; Linezolid-R


Resistance Suppression• The model system allows

Monte Carlo simulation to be performed

• This tells us what a fixed regimen will do in a large patient population

• Here circa half the patients will have the susceptible population wiped out with all 508/1000 patients left with only resistant isolates

Documents

2016 Europe-Nordic-US Symposium New Frontiers in ... · New Frontiers in Antibacterial Resistance Research Pharmacological Approaches to ... PK PD. Cell Kill and ... of garenoxacin’s