PK/PD APPROACH AND
ANTIMICROBIAL RESISTANCE
Pierre-Louis Toutain,
Ecole Nationale Vétérinaire
INRA & National veterinary School of Toulouse, France
7th International Conference on antimicrobial agents in veterinary medicine
Berlin September 16-19,2014
1
Introduction
• PK/PD concepts are now well established to
determine the dosing regimen for
antimicrobials (AM) in veterinary medicine.
– the previous presentation by Stefan
• The main goal is to optimize clinical efficacy,
but the risk of resistance development has
been generally ignored in these dose
optimizations
2
But of what resistance are we
speaking?
3
Prevent emergence of resistance:
but of what resistance?
Target pathogens
Zoonotics
Efficacy in animals
Efficacy in
man
Food chain
Animal health issue
Human
Individual issue
Commensal
Global ecological
problem
Environment
Public
health issue
4
Bacterial load exposed to antibiotics
during a treatment
Test
tube
1µg
Infected
Lungs
1 mg
Digestive
tract
Manure
waste
Several Kg
Several tons
Food chain
Soil, plant….
5
Duration of exposure of bacteria exposed
to antibiotics
Manure
Digestive
Infected
Test
Sludge
tract
Lungs
tube
waste
24h
Few days
Several weeks/months
Food chain
Soil, plant….
6
The priorities of a sustainable veterinary antimicrobial
therapy is related to public health issues, not to animal
health issues
A trade-off between these two objectives is
difficult or even impossible to achieve due to the
non selectivity of most veterinary antibiotics.
7
Outline of the presentation
• The classical integrated PK/P indices and the
question of resistance: the MSW
• The limits of the MSW
• The mechanism-based models
• Options to mitigate emergence and selection
of resistance
– Early/low dose treatment
– Duration of treatment
– Dug combinations
8
The mutant Selective Window
(MSW)
Currently the MSW is the only PK/PD index
that is use to mitigate the emergence of
resistance
9
Traditional hypothesis on emergence of
AMR
Concentration
MIC
Selective pressure for antibiotic concentration
lower than the MIC
Time
10
Current view for the emergence and selection of
resistance : situation II
No antibiotics & low inoculum size
Mutation rate10-8
105 CFU
Wild pop
No Mutant pop
With antibiotics
eradication
susceptible
résistant11
Current view for the emergence and selection of
resistance : situation II
No antibiotics & high inoculum
Mutation rate10-8
108 CFU
Wild pop
Mutant pop
5-10xMIC=MPC
With antibiotics
Mutation rate10-8
eradication
susceptible
Mutants population
12
The selection window hypothesis
Plasma concentrations
Mutant prevention concentration (MPC)
(to inhibit growth of the least susceptible, single step
mutant)
Mutant Selection
window
All bacteria
inhibited
Growth of only the most
resistant subpopulation
MIC
Selective concentration (SC)
to block wild-type bacteria
Growth of all
bacteria
Nice buiatric 2006-13
MIC & MPC for the main veterinary quinolones
for E. coli & S. aureus
14
The MSW
• The concept PK/PD of MSW was shown to be
useful for quinolones for which resistance
develops by mutational alterations of the drug
target, but the concept is less clear for others
mechanisms of resistance (e.g. plasmid
mediated resistance) and for other classes of
antibiotics even if the MIC/MPC ratio has been
proposed for a variety of veterinary antibiotics
(macrolides, cephalosporines, florfenicol)
15
Comparative MIC and MPC values for 285 M.
haemolytica strains collected from cattle
Ceftiofur
Enrofloxacine
Florfenicol
Tilmicosine
Tulathromycine
MIC50
0.016
0.016
MIC90
0.016
0.125
MPC50
1
0.25
MPC90
2
1
MPC/MIC
125
8
2
2
1
2
8
2
4
16
4
8
>32
8
4
≈8
4
Vet Microbiol 2012 Blondeau JM
16
MSW:
target pathogen vs. commensal flora
17
In the present study, we used fecal samples collected from these volunteers
during and after ciprofloxacin treatment to analyze the dynamics of the
emergence of resistance in E. coli over time in each volunteer.
18
Effect of ciprofloxacine on E coli
• During antibiotic exposure (on days 8 and 14), no E.
coli could be detected in most volunteers.
– This was explained by the high fecal concentrations of
ciprofloxacin, which was several thousand times greater
than both the MIC and the mutant-prevention
concentration against the dominant flora .
• Selection of resistance was unlikely during treatment.
– The appearance of QREC strains 4 weeks after the end of
ciprofloxacin therapy was observed
• explained by the pharmacokinetics of ciprofloxacin in stool,
because ciprofloxacin concentrations slowly decreased from day
14 to day 42, when they were undetectable, with ciprofloxacin
concentrations passing through the mutant selection window
between days 14 and 42, when emergence of resistance was
eventually detected in the fecal microbiota.
19
MSW: biophase vs Feces
QREC
From environment
Stools
100
10
Target pathogen
MPC
MIC
1
MSW
Time
MSW
20
What is the better option :
Collective vs. selective treatment
Treated
I have now my MSW
21
The limit MSW and of PK/PD
indices and of their breakpoint
values regarding the resistance
issues
22
The limit of MSW
• simulations with more advanced semimechanistic PK/PD models showed that the
classical PK/PD indices, including the MSW,
have several major limitations and there is a
need to go beyond these summary PK/PD
variables to efficiently combat resistance by
designing appropriate dosage regimens.
23
The three mains limits of classical
PK/PD and MSW indices
• They ignore information on the time-course of
the PK and PD.
– The U shaped curve of the MSW
• They rely on the MIC that is not a PD parameter
but a hybrid variable.
• They are established on 24 hours, a too short
period to study the adaptation of the bacteria to
antibiotic drug exposure and selection of
resistant bacterial subpopulations
24
T>MIC for 40-50% of the dosing interval:
Daily dosing vs. long-acting drug
Daily formulation
Long-acting drug/formulation
MIC
Both treatments ensure plasma concentrations above MIC for 50% of the
dosing interval (1 or 14 days) but they are not equivalent
25
MICs estimated with different inoculmum densities, relative
to that MIC at 2x105
Ciprofloxacin
Gentamicin
Linezolid
Oxacillin
Daptomycin
Vancomycin
26
What is a MIC?
• An hybrid variable
• Its reflects:
– The drug potency
– The drug efficacy
– the bacterial growth rate,
– the bacterial death rate
– and many other factors associated to its in vitro
measurement (inoculum size, selected milieu,
etc.).
27
What is a MIC?
An hybrid variable
From a mechanistic model point of view
AM Potency
AM efficacy
Rate of death
Defense mechanisms
Rate of growth
(supply shortage)28
Effect of resistance on Kkill
• The observed killing rate is a function of the
natural death rate(0) times a scalar given by the
Emax function
Target site alteration
Reduced Emax=Kkillmax/Kdeath
Drug efflux pumps and enzymatic drug
deactivation increase EC50
Can be surmounted by a higher dose
29
What is a MIC
• The MIC value is only a snaphot measure of
the net effect of the antibiotic under well
standardized conditions (18-24h, constant AM
concentration).
30
Clinical Pharmacokinetics 2005 44 201-210
31
Investigation of resistance require
more than 24h
32
Impact on the total population of
Staphylococcus aureus over time by two
regimens of garenoxacin (in vitro model)
The less intense regimen ceases to be effective after a delay of 5 days.
the residual population to be eradicated by the immune system
33
Impact on the less-susceptible population of S.
aureus over time by two regimens of
garenoxacin.
If therapy had been ended at day 4 or 5, little or no resistant
mutant amplification would have occurred
34
Limits of the classical PK/PD indices
to limit resistance
• Therefore, the classical PK/PD indices are not
well suited to understand and predict the
emergence of resistance.
• They are also unable to characterize the
effect of drug combinations that are one of
the best options to combat resistance
35
Mechanism-based model of
antimicrobials
36
The value of mechanism-based
models
• These models aim to give a better
understanding of the PK/PD relationship when
modeling the full time-course of bacterial
growth and killing.
37
A major review
38
The mechanism-based models:
4 submodels
• Models including equations to describe:
– The microorganisms growth: microorganisms submodel,
– the changing drug concentration: PK model
– The effect of AM drug: PD sub-model
• to describe the interaction between the two preceding
sub-models.
– They can also include a sub-model for the host
defenses.
39
Mechanism-based model of
antimicrobials
• Equation with:
– no replication inhibition
– Time-invariant susceptibility to drug
– Constant replication rate
40
The microorganism sub-models
• The microorganism sub-models can consider
simultaneously different bacterial
subpopulations with different levels of
susceptibility and they can differentiate
different mechanisms of resistance (alteration
of the mutation rate, adaptative resistance,
persisters)
41
PK/PD model for resistance and predicted
bacterial time-kill curves
B1, compartment with drug sensitive bacteria;
B2, compartment with less drug-sensitive bacteria;
42
PKPD model for resistance (persisters)
and predicted bacterial time-kill curves
B1, compartment with drug sensitive bacteria;
B3, compartment with non growing, drug-insensitive bacteria
43
44
The mechanism-based models
• The mechanism-based model can be used for
many purposes to test mechanistic
hypotheses, to predict untested doses and
complicated dosing regimens (PK mimicking in
vivo situations, drug combinations, duration of
treatment, etc).
45
Classical PK/PD indices vs. semimechanistic models
46
Classical PK/PD indices vs. semimechanistic models
• These semi-mechanistic models are able to
predict the classical PK/PD indices and their
breakpoint values.
47
Classical PK/PD indices vs. semimechanistic models
• However, they also predict that when the AM
half-life is short, the best predictor is always
T>MIC and when the half-life is long, the best
predictor is always AUC/MIC whatever the
antibiotic.
• For long-acting formulations AUC/MIC is likely
an universal PK/PD index
– This would greatly facilitate many tasks such as finding
an optimal dosage regimen and fixing sound clinical
breakpoints for susceptibility testing.
48
How to mitigate emergence of
resistance: practical aspects
49
How to combat resistance
• Early initiation of AM therapy
• Short duration
• Combination therapy with 2
antibiotics
50
The different uses of antibiotics in veterinary
medicine
Disease
health
Antibiotic consumption
Therapy
Metaphylaxis
(Control)
Pathogen load
Prophylaxis
(prevention)
Growth
promotion
Only a risk factor
High
Small
No
NA
51
marbofloxacin,
amoxicillin & cefquinome
52
Low dose and gut microbiota
• In addition this strategy of an early low-dose
treatment will be considered as the best in
terms of public health to minimize the
unselective impact of most AM used in
veterinary medicine on the gut microbiota.
53
Duration of therapy
54
Reducing exposure by reducing the
duration of therapy and prophylaxis
• With rare exceptions (e.g. bacteremia due to S.
aureus, endocarditis, osteomyelitis), there is no
evidence to support most of the traditional 10–
14-day courses of antibiotics, which are based
more on conventional wisdom than strong
evidence.
• Short-course therapy for urinary tract infection,
acute otitis media, tonsillopharyngitis, sinusitis
and pneumonia is slowly gaining support
(MASTIN study group, 2002; Lutters and Vogt,
2002).
55
The one-shot therapy
• The so-called one-shot therapy is the veterinary
option to minimize the treatment duration.
• Using PK/PD mechanistic models, it was shown
that the killing rate of concentration dependent
AM was dose-dependent and the goal of the oneshot high dose therapy is to kill as rapidly as
possible the target pathogens or at least a
sufficient fraction of the initial load to allow the
host natural defenses to eradicate the remnant
bacterial population.
56
Increasing exposure trough
combination therapy
57
What is the clinical value of AM
combination?
58
59
Conclusion (1)
1. The optimization of dosing regimens should
be accomplished by choosing the dose and
schedule that results that will achieve the
microbiological and clinical outcome desired
while simultaneously suppressing emergence
of resistance on both the target and
commensal flora.
2. Early treatment and short duration are
currently the two best options
60
Conclusions (2)
3. To combat resistance and the public health
impact, the best strategy is likely to be to
develop new antibiotics that are selective for
the target pathogens and not impacting the gut
microbiota (green antibiotics).
4. While waiting for this new generation of
veterinary antibiotics, we have to revisit the
current dosage regimens of antibiotics (dose,
dosing interval and treatment duration)
61
Conclusion (3)
5. This reevaluation should take into account the
different possible clinical conditions that are faced in
veterinary medicine (curative, metaphylaxia and
prophylaxia) and consider that a single regimen does
not fit all.
6. As veterinary medicine is resources-limited and
cannot test experimentally all possible situations and
hypotheses, veterinary pharmacologists should
explore more deeply the class of these so-called
mechanism-based models and their regulatory
acceptance should be rapidly considered.
•
62