Post on 17-Sep-2018
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
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
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
Resistance Suppression in Pseudomonas aeruginosa
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
Cell Kill and Resistance Suppression in Pseudomonas aeruginosa
• 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!
Cell Kill and Resistance Suppression in Pseudomonas aeruginosa
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?
Resistance Suppression in Pseudomonas aeruginosa
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
Pharmacological Approaches to
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
Pharmacological Approaches to
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
Pharmacological Approaches to
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
Pharmacological Approaches to
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
Pharmacological Approaches to
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
Pharmacological Approaches to
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
Pharmacological Approaches to
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: gdrusano@ufl.edu; (407) 313 7060; (518) 281 7170 (mobile)
BACK UP SLIDES
Cell Kill and Resistance Suppression in Pseudomonas aeruginosa
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
Combination Therapy for
Resistance Suppression
• 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
Combination Therapy for
Resistance Suppression
Fully Susceptible Linezolid-S; Rif-R Rif-S; Linezolid-R
Combination Therapy for
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