Transcript Diapositive 1

ECOLE
NATIONALE
VETERINAIRE
TOULOUSE
Antibiotic dosage regimen based on
PK-PD concepts and the possible
minimization of resistance
PL Toutain
UMR 181 Physiopathologie et Toxicologie Expérimentales
INRA/ENVT
24th World Buiatric Congress. France. 15. October 2006
Nice buiatric 2006-1
Why to optimize dosage
regimen for antibiotics
1. To optimize efficacy
2. Reduce the emergence and
selection of resistance
Nice buiatric 2006-2
Dosage regimen and
antibioresistance
• The design of appropriate dosage regimens may be
the single most important contribution of clinical
pharmacology to the resistance problem
Schentag, Annal. Pharm. 1996
• Little attention has been focused on delineating the
correct drug dose to suppress the amplification of
less susceptible mutant bacterial sub-populations
Drusano et al (2005)
Nice buiatric 2006-3
Selecting a dosage regimen for a
particular animal or group of animals
Individual animal (or
herd) issues
Public health issues
Probability of “cure”
without side effects
Probability for avoiding
enriching a resistant
bacterial subpopulation
The prescribing veterinarian is the steward of a valuable resource and
must consider both individual health issues as well as public health ones
Possible conflict of interest between the two goals Nice buiatric 2006-4
Why to optimize dosage regimen
for antibiotics
1. To optimize efficacy
2. Reduce the emergence and selection of
resistance
–
Target pathogen: efficacy issue
–
Non target pathogen: human safety issue
•
Zoonotic bacteria (food borne pathogens)
•
Commensal flora (resistance gene reservoir)
Nice buiatric 2006-5
Biophases & antibiorésistance
AB: oral route
Proximal
G.I.T
1-F%
Distal
Gut flora
•Zoonotic (salmonella, campylobacter
•commensal ( enterococcus)
Environmental
exposure
Food chain
Blood
Man
Target biophase
Bug of vet interest
Résistance = lack of efficacy
Résistance = public health concern
Nice buiatric 2006-6
Biophases & antibiorésistance
G.I.T
Proximal
Gut flora
•Zoonotic (salmonella,
campylobacter
•commensal ( enterococcus)
Intestinal secretion
Bile
Systemic administration
Distal
Quinolones
Macrolides
Tétracyclines
Blood
Biophase
Bug of vet interest
Food chain
Environmental
exposure
Man
Résistance = public health concern
Résistance = lack of efficacy
Nice buiatric 2006-7
Public Health Concerns :
Human pathogenic bacteria spreading
from animal reservoirs
Current main concerns: Resistance
emerging to commonly used empiric
therapies for acute GI tract infections
 Salmonella
–Fluoroquinolone-resistance
–3rd gen. Cephalosporin-resistance
 Campylobacter
–Fluoroquinolone-resistance
–Macrolide-resistance
Nice buiatric 2006-8
Emergence of quinolone resistance in Salmonella
typhimurium DT104 in UK following licensing of
fluoroquinolones for use in food animals
Stöhr & Wegener, Drug resistance Updates, 2000, 3:207-209
Nice buiatric 2006-9
Dosage regimen and
resistance:
Epidemiological evidences
Nice buiatric 2006-10
Dosage regimen and prevention of
resistance
• Many factors (e.g.; broad vs. narrow
spectrum…) can contribute to the
development of bacteria resistance
• the most important risk factor is repeated
exposure to inappropriate antibiotic
concentrations (exposure)
 dosage regimen should minimize the
likelihood of exposing pathogens to sublethal
drug levels
Nice buiatric 2006-11
Drug factors influencing
resistance
• Regimen
– Route of administration, dose, interval
of administration, duration of treatment
Nice buiatric 2006-12
Effect on Penicillin resistance in
pneumococcus isolates (n=465) of duration of
b-lactam use, 6 months before swab collection
Nb of days of b- Odd ratios
lactam use
95% CI
1-7
8-14
>14
0.37- 2.02
0.73- 3.06
1.3 - 4.82
0.86
1.5
2.5
Nasrin et al. BMJ, 2002
Nice buiatric 2006-13
Effect of 14-day antibiotic dosing regimen on sensitivity
(MIC, µg/mL) to apramycin by E. coli recovered
AB dosing
regimen
Control (no AB)
Label
Rotation Similar AB
Rotation Dissimilar AB
Gradient 50, 100, 150
Pulse (3 days)
3
4.3
5.9
3.5
2.6
3.5
5.2
day post challenge
6
10
13
17
3.9 3.5
3.1
2.3
41.1 56
49
50
4.2 200 182
141
38.8 44
14
14.0
3.5 3.5 68.5 109.9
4.3 3.6
4.0
7.0
Mathew, 2003
31
2.6
6.6
7.6
3.8
2.8
3.7
Nice buiatric 2006-15
How to determine a
dosage regimen that is
both efficacious and that
minimizes the risk to
promote resistance
Nice buiatric 2006-16
How to find and confirm a dose
(dosage regimen)
• Dose titration
– Animal infectious model
• Clinical trial
• PK/PD
Nice buiatric 2006-17
The dose-titration
Nice buiatric 2006-18
The dose-titration:
experimental infectious model
• Severe
• not representative of the real world
– Prophylaxis vs. metaphylaxis vs.
curative
• power of the design generally low for
large species
• influence of the endpoints
Nice buiatric 2006-19
How to find and confirm a dose
(dosage regimen)
• Dose titration
– Animal infectious model
• Clinical trial
• PK/PD
Nice buiatric 2006-20
Bacteriological vs clinical
success:
the pollyanna phenomenon
Nice buiatric 2006-21
The Pollyanna phenomenon
If efficacy is measured by symptomatic
response, drugs with excellent antibacterial
activity will appear less efficacious than they
really are and drugs with poor antibacterial
activity will appear more efficacious than
they really are.
• The clinical efficacy does not always
indicate bacteriological efficacy making it
difficult to distinguish between
antimicrobials on clinical outcomes only
Nice buiatric 2006-22
The Pollyanna effect
Discrepency between clinical and bacteriological results
Otitis media
Antibiotic effect
100%
100%
89%
Efficacy (%)
80%
74%
60%
40%
27%
Clinical
success
20%
Placebo effect
0%
Bacteriological cure
Merchant et al. Pediatrics 1992
Nice buiatric 2006-23
The Pollyanna effect
Ceftiofur – oral
Response %
90
Mortality
Bacterial
shedding
60
30
0
0
0.5
2
16
64
Dose (mg/kg)
Yancey et al. 1990 Am. J. Vet.Res.
Nice buiatric 2006-24
EFFICACY OF ORAL PRADOFLOXACIN AND
AMOXYCILLIN/CLAVULANATE IN CANINE CYSTITIS
AND PROSTATITIS
Treatment
Number
of dogs
Clinical
Cure (%)
Reduction
of Total
Clinical
Score (%)
Bacteriological
cure
Pradofloxacin
85
89.3
96.8
85.3
Amoxicillin/
Clavulanate
77
83.9
93.4
48.0*
P=0.002
Data from Bayer Animal Health (VERAFLOX SYMPOSIUM)
Nice buiatric 2006-25
The Pollyanna phenomenon
• The clinical efficacy does not always
indicate bacteriological efficacy and a good
clinical efficacy is not enough to validate an
appropriate dosage regimen
Nice buiatric 2006-26
The role of antibiotics
is to eradicate the
causative organisms
from the site of
infection
Jacobs. Istambul, 2001
Nice buiatric 2006-27
How to find and confirm a dose
(dosage regimen)
• Dose titration
– Animal infectious model
• Clinical trial
• PK/PD
Nice buiatric 2006-28
The main goal of a PK/PD trial in
veterinary pharmacology
 To be an alternative to dosetitration studies to discover an
optimal dosage regimen
Nice buiatric 2006-29
What is PK/PD?
Nice buiatric 2006-30
Dose titration
Dose
Response
clinical
Black box
PK/PD
PK
Dose
PD
Body
pathogen
Response
Plasma
concentration
Nice buiatric 2006-31
PK/PD: in vitro
In vitro
Medium
concentratio
n
Test tube
Response
MIC
MIC is very variable from pathogen to pathogen and
should be acknowledged
The idea at the back of the PK/PD indices were to
develop surrogates able to predict clinical success by
scaling a PK variable by the MIC
Nice buiatric 2006-32
Dose titration
Dose
Response
clinical
Black box
PK/PD
PK
Dose
PD
Body
pathogen
Response
A plasma concentration
variable scaled by MIC
Nice buiatric 2006-33
Dose titration vs. PK/PD : the
explicative variable
Dose
effect
Effect
Effect
A PK/PD SURROGATE
AUC
DOSE
EXPOSURE
(external dose)
(internal dose)
AUC/MIC,
Exposure scaled by MIC
Nice buiatric 2006-34
PK/PD indices as indicator of
antibiotic efficacy
Nice buiatric 2006-35
The surrogates (predictors) of
antibiotic efficacy
AUC/MIC,
T>MIC,
Cmax/MIC
Nice buiatric 2006-36
PK/PD predictors of efficacy
• Cmax/MIC : aminoglycosides
• AUC/MIC : quinolones, tetracyclines, azithromycins,
• T>MIC : penicillins, cephalosporins, macrolides,
Cmax
Concentrations
Cmax/MIC
AUIC =
AUC
MIC
MIC
T>CMI
24h
Time
Nice buiatric 2006-37
Why these indices are
termed PK/PD
PK
AUIC #
AUC
CMI
=
Dose / Clearance
CMI50(90)
PD
 Dual dosage regimen adaptation
Nice buiatric 2006-38
Relationship between dose and
PK/PD predictors of efficacy
Breakpoint value
e.g. 125
PD
 AUC 
Clea ra nce(pe rhours ) 
  M IC
 M IC BP
Dose 
fu  F %
Bioavailability
PK
Free fraction
Nice buiatric 2006-39
Why plasma concentration
The site of infection
Update : 17 juillet 2015
Nice buiatric 2006-40
• Only the free (non-bound)
fraction (concentration) of the
drug can interact with bacterial
receptors
• Only the concentration of free
drug that is of concern for its
PK/PD relationship
Nice buiatric 2006-41
• MIC is a reasonable approximate
of the concentration of free drug
needed at the site of infection
Nice buiatric 2006-42
• Most infections of interest are
located extra-cellularly and direct
comparisons to total tissue
concentration with PD parameters
are meaningless
Cars, 1991
Nice buiatric 2006-43
Where are located the pathogens
ECF
Most bacteria of
clinical interest
- respiratory infection
- wound infection
- digestive tract inf.
Cell
(in phagocytic cell most often)
•
•
•
•
•
•
•
•
•
•
Legionnella spp
mycoplasma (some)
chlamydiae
Brucella
Cryptosporidiosis
Listeria monocytogene
Salmonella
Mycobacteria
Meningococci
Rhodococcus equi
Nice buiatric 2006-44
Barrier,
efflux
pump
Porous capillaries
Total concentration
(T>MIC, AUIC, Cmax/MIC)
Bound

F
Brain, retina, prostate
Tissular barrier
Surrogate marker
Interstitial fluid
Biophase for most bacteria
of veterinary therapeutical
interest
Mannhemia, Pasteurella
Haemophilus,
Streptococcus,
Staphylococcus, Coli,
Klebsiella
Bound

F
F
Biophase for facultative and
obligatory intracellular pathogens
Bound
Cell membrane
Plasma
Obligatory or
facultative
bacteria
Cytosol
(Listeria, Shigella)
Phagosome
(Chlamydiae)
F
Bound Cell
F
Phagolysosome
(S. aureus,
Brucella,
Salmonella)
Nice buiatric 2006-46
Tissue concentrations
According to EMEA
"unreliable information is
generated from assays of drug
concentrations in whole
tissues (e.g. homogenates)"
EMEA 2000
Nice buiatric 2006-47
Log10 CFU per Lung or Thigh
Relationship Between T>MIC and Efficacy for
Carbapenems (Red), Penicillins (Aqua) and
Cephalosporins (Yellow)
2
0
-2
-4
0
20
40
60
80
100
Time Above MIC (percent)
Nice buiatric 2006-49
Log10 CFU per lung at 24 h
Relationship between PK/PD parameters and efficacy for
cefotaxime against Klebsiella pneumoniae in a
pneumonia model
10
10
10
9
9
9
8
8
8
7
7
7
6
6
6
5
5
5
01
1
10 100 100010000
Peak MIC ratio
Craig CID, 1998
3
10
30 100 300 1000 3000
24 h AUC/MIC ratio
R² = 94%
0
20
40
60
80
100
Time above MIC (%)
Nice buiatric 2006-50
Efficacy index: clinical validation
Bacteriologic cure (%)
Bacteriological cure versus time above MIC in
otitis media (from Craig and Andes 1996)
100
S. pneumoniae
Penicillin
cephalosporins
50
0
0
50
100
H. influenzae
Penicillin
cephalosporins
Time above MIC (%)
• Free serum concentration need to exceed the MIC
of the pathogen for 40-50% of the dosing interval
to obtain bacteriological cure in 80% of patients
Nice buiatric 2006-51
PK/PD parameters: b-lactams
• Time above MIC is the important
parameter determining efficacy of the
b-lactams
• T>MIC required for static dose vary
from 25-40% of dosing interval for
penicillins and cephalosporins.
• Free drug levels of penicillins and
cephalosporins need to exceed the
MIC for 40-50% of the dosing interval
to produce maximum survival
Graig
Nice buiatric 2006-52
Betalactam
• Goal: to maximize the duration of
exposure over which free drug levels in
biophase exceed the MIC
• no further significant reduction in
bacteria count when concentration
exceed 4 MIC
Nice buiatric 2006-53
Efficacy index: clinical validation
Response rate (%)
Relationship between the maximal peak plasma level to
MIC ratio and the rate of clinical response in 236 patients
with Gram-negative bacterial infections treated with
aminoglycosides (gentamicin, tobramycin, amikacin)
100
80
60
2 4 6 8 10 12
Maximum peak/MIC ratio
Moor et al. 1984 J. Infect. Dis.
Nice buiatric 2006-56
Bacterial growth in serum containing
danofloxacin for incubation periods of 0.25 to 6h
Conc.
0
0.02
0.04
Log cfu/ml
1.E+09
0.06
0.08
1.E+06
0.12
0.16
1.E+03
0.20
0.24
1.E+00
0
1
2
3
4
5
6
0.28
0.32
Incubation time (h)
P. Lees
Nice buiatric 2006-59
Sigmoidal Emax relationship for bacterial count
vs ex vivo AUIC24h in goat 1 serum
Observed
Predicted
Log cfu/ml difference
1
Bacteriostatic AUIC24h = 18 h
0
-1
Bactericidal AUIC24h = 39 h
-2
-3
Elimination AUIC24h = 90 h
-4
-5
-6
-7
0
50
100
150
200
250
300
AUIC24h
P. Lees
Nice buiatric 2006-60
Ex vivo AUC24h/MIC (h) values for
danofloxacin and marbofloxacin in calf serum
Parameter
Bacteriostatic
Bactericidal
Elimination
Slope
Danofloxacin Marbofloxacin
15.9 ± 2.0
18.1 ± 1.9
33.5 ± 3.5
17.3 ± 4.2
37.3 ± 6.9
46.5 ± 6.8
119.6 ± 10.9
11.5 ± 3.3
Values are mean ± sem (n=6)
P. Lees
Nice buiatric 2006-61
PK/PD indices
Determination of breakpoint
values
To optimize efficacy
To minimize resistance
Update: 17/05/2004
Nice buiatric 2006-62
Effectiveness of PK/PD indices
as predictor for the
development of antimicrobial
resistance
Nice buiatric 2006-63
• There is evidence that the
likelihood for the selection of
bacteria with mutation conferring
resistance can be predicted on
basis of PK/PD relationship
Nice buiatric 2006-64
Impact of dosage regimen on the
emergence of resistance:
Experimental evidences
Nice buiatric 2006-65
AUIC and bacterial eradication
Nosocomial pneumonia treated with IV ciprofloxacin
AUIC was highly predictive of time to bacterial
eradication
• If AUIC >250 h/day :
% patients remaining
culture positive
• eradication of organism on day 1 of therapy
• good target for nosocomial pneumonia and compromised
host defense
100
AUIC < 125
50
AUIC 125-250
0
Schentag Symposium, 1999
AUIC > 250
4
8
12
Days after start of therapy
Nice buiatric 2006-66
Suboptimal antibiotic dosage as a risk
factor for selection of penicillinresistant Streptococcus pneumoniae :
in vitro kinetic model
• Odenholt et al. (2003) Antimicrobial
Agents and Chemotherapy, 47: 518-523
Nice buiatric 2006-67
Material and Methods
• Mixed culture of Stretococcus
pneumoniae containing ca. 90%
susceptible, 9% intermediate and 1%
resistant bacteria
• In vitro kinetic model
• Exposure to Penicillin : T>MIC varied from
– S = 46 to 100 %
– I = 6 to 100 %
– R = 0 to 48 %
Odenholt, 2003
Nice buiatric 2006-68
Selection by penicillin of resistant bacteria in
a mixed population of S.pneumoniae: control
log10 CFU/mL
A
10
10
9
9
8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1
0
0
0
Odenholt, 2003
3
6
9
12
Susceptible (90%)
Intermediate (9%)
Resistant (1%)
Total
24
Nice buiatric 2006-69
Selection by penicillin of resistant bacteria
in a mixed population of S.pneumoniae
10
B
9
8
log10 CFU/mL
7
S: T>MIC = 46%
6
I: T>MIC = 6%
5
R: t>MIC = 0%
4
Total
3
2
1
0
0
3
6
9
12
24
Odenholt, 2003
Nice buiatric 2006-70
Selection by penicillin of resistant bacteria
in a mixed population of S.pneumoniae
C
10
9
8
log10 CFU/mL
7
6
S: T>MIC = 75%
I: T>MIC = 38%
5
R: t>MIC = 0%
4
Total
3
2
1
0
Odenholt, 2003
0
3
6
9
12
24
Nice buiatric 2006-71
Selection by penicillin of resistant bacteria
in a mixed population of S.pneumoniae
10
D
9
8
log10 CFU/mL
7
6
S: T>MIC = 100%
I: T>MIC =54%
5
R: t>MIC = 0%
4
Total
3
2
1
0
0
3
6
9
12
24
Odenholt, 2003
Nice buiatric 2006-72
Selection by penicillin of resistant bacteria
in a mixed population of S.pneumoniae
E
10
9
8
log10 CFU/mL
7
S: T>MIC = 100%
6
I: T>MIC = 83%
R: t>MIC = 7%
5
Total
4
3
2
1
0
Odenholt, 2003
3
6
9
12
24
Nice buiatric 2006-73
Selection by penicillin of resistant bacteria
in a mixed population of S.pneumoniae
10
F
9
8
log10 CFU/mL
7
S: T>MIC = 100%
6
I: T>MIC = 100%
R: t>MIC = 48%
5
Total
4
3
2
1
Odenholt, 2003
0
3
6
9
12
24
Nice buiatric 2006-74
Optimisation of Meropenem minimum
concentration/MIC ratio to suppress in vitro
resistance of Pseudomonas aeruginosa
• Determined bactericidal activity of Meropenem and ability
to suppress P.aeruginosa resistance
• In vitro hollow fibre infection model (HFIM) inoculated with
dense inoculum (1x108 cfu/mL) and subjected to various
Meropenem exposures over 5 days
• Doses administered every 8h to achieve the same Cmax
but escalating unbound Cmin concentrations
Tam, V.H. et al (2005) Antimicrob.Agents Chemother. 49, 4920
Nice buiatric 2006-75
Optimisation of Meropenem minimum concentration/mic ratio to
suppress in vitro resistance of Pseudomonas aeruginosa
Placebo
Log 10 cfu/mL
Log 10 cfu/mL
12
8
4
8
6
4
2
Time (days)
0
0
1
2
3
T>MIC 100% & Cmin/MIC=6
10
Time (days)
0
0
5
4
T>MIC 84%
8
4
Time (days)
0
0
1
2
3
4
Log 10 cfu/mL
Log 10 cfu/mL
10
0
1
2
3
4
5
Log 10 cfu/mL
Log 10 cfu/mL
10
Time (days)
5
4
8
6
4
2
Time (days)
1
2
3
4
5
T>MIC 100% & Cmin/MIC=1.7+ tobramycin
T>MIC 100% & Cmin/MIC=1.7
0
3
T>MIC 100% & Cmin/MIC=10
10
0
4
2
0
5
8
1
10
8
6
4
2
0
Time (days)
0
1
2
3
4
5
Nice buiatric 2006-76
Optimisation of Meropenem minimum
concentration/MIC ratio to suppress in vitro
resistance of pseudomonas aeruginosa
results
• Resistance emerged when
– (a) T>MIC = 84%
– (b) T>MIC = 100% and Cmin/MIC = 1.7
• Resistance avoidance when
– (a) T>MIC = 100% and Cmin/MIC = 6.0
– or (b) T>MIC = 100% and Cmin/MIC = 1.7 plus
tobramycin
Nice buiatric 2006-77
Optimisation of Meropenem minimum
concentration/MIC ratio to suppress in vitro resistance
of Pseudomonas aeruginosa
conclusions
• Breakpoint to prevent resistance different of those selected for
clinical efficacy
• Because of the ceiling effect for T>MIC this variable may not be
satisfactory when the breakpoint exceeds 100%
• Cmin/MIC of Meropenem can be optimized to suppress the
emergence of non-plasmid-mediated P aeruginosa resistance
• Meropenem exposure necessary to avoid resistance may not be
achievable with conventional doses
• NOTE: The experimental conditions represent a very conservative
situation in a clinical setting (neutropenia and high bacterial burden)
Nice buiatric 2006-78
Surrogate indices and emergence of
resistance : Ceftizoxime in vivo
• In vivo murine study using mixed infection model related
mutation frequency to T>MIC (as percentage of dosing interval)
for ceftizoxime
• No resistance when T>MIC was <40% or = 100%
• Mutation frequency very low when T>MIC  87%
• Peak mutation frequency for T>MIC = 70%
For optimal efficacy the usual value quoted is T>MIC = 40-60%
Stearne et al (2002)
Nice buiatric 2006-79
Predictive value of PK/PD indices for
emergence of resistance:
time dependent antibiotic
• T>MIC should be 40-60% of the dosing
interval for clinical efficacy
BUT
• Plasma concentrations should be 3-4
times the MIC to optimally prevent
resistance
Nice buiatric 2006-80
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
Nice buiatric 2006-81
Impact of dosage regimen on the
emergence of resistance:
Experimental evidences for
quinolones
Nice buiatric 2006-82
AUIC (AUC/MIC) and bacterial resistance
• Ciprofloxacin AUIC predicts bacterial resistance in
nosocomial pneumoniae
Probability of
remaining susceptible
100
AUC/MIC > 101
75
50
AUC/MIC< 100
Resistance for
AUIC < 100
day
%
4
50
20
93
25
0
0
5
10
15
20
No.Days after start of therapy
AUIC < 100 = suboptimal
Schentag-Symposium 1999
Nice buiatric 2006-83
PK/PD and resistance development
• Data drawn from studies of five
different treatment regimens for
nosocomial pneumonia have
suggested that the probability of
selecting for resistant organisms
increase when AUIC < 100
(ciprofloxacin)
EMEA 2000
Nice buiatric 2006-84
Bacterial population responses to drug selective
pressure : examination of Garenoxacin’s effect on
Pseudomonas aeruginosa (1)
• Determined influence of Garenoxacin on ability to
suppressG P. aeruginosa resistance
• In vitro hollow fibre infection model inoculated with dense
inoculum (2.4 x 108 cfu/ml) and subject to various
Garenoxacin exposures of 2-3 days
• Doses administered once daily over 1h period to achieve
constant targetted Cmax at 1, 25 and 49h and AUC24 /MIC
ratios of 0, 10, 50, 75, 100 and 200h.
Tam, V.H. et al. J. Infect. Dis. 2005 192, 420
Nice buiatric 2006-85
Bacterial population responses to drug selective pressure : examination
of Garenoxacin’s effect on Pseudomonas aeruginosa
Control
AUC/MIC=10
AUC/MIC=48
AUC/MIC=89
AUC/MIC=108
AUC/MIC=201
Nice buiatric 2006-86
Bacterial population responses to drug selective pressure : examination
of garenoxacin’s effect on Pseudomonas aeruginosa (2)
• AUC24/MIC ratios used (10 to 200h) based on steady
state kinetics of unbound garenoxacin in humans
• MIC of resistant mutants at 48h 4-16x greater than wild
type
• Replacement of
– (a) majority of susceptible organisms by resistant mutants when
AUC/MIC = 10, 48 and 89h
– (b) all susceptible organisms by mutants when AUC/MIC = 108
and 137h
• No increase in resistant mutants when AUC/MIC = 201h
• Modelling data gave AUC24/MIC ratio of 190h to avoid
amplification of resistant sub-populations
–
The resistance suppression breakpoint
Nice buiatric 2006-87
In vitro pharmacodynamic evaluation of the
mutant selection window hypothesis using
four fluoroquinolones against Staph. aureus
•
In vitro model to simulate human pharmacokinetics of 4 fluoroquinolones
(monoexponential decline)
•
Inoculum of 108 cfu/ml
•
Cmax
•
Resulting AUC24/MIC values = 13 to 244h
•
Determination of MIC at 0 and 72h
•
Absence of WBCs
(a) = MIC
(b) >MIC <MPC (within MSW)
(c) >MPC
Firsov et al. (2003) Antimicrob.Agents Chemother. 47, 1604.
Nice buiatric 2006-88
The MPC hypothesis for 4 Quinolones
against S aureus
• As a test of the window idea Firsov and Zinner carried
out a pharmacodynamic study in which moxifloxacin
concentration was varied so that it was either above,
within, or below the selection window throughout
treatment using an in vitro model
• The dynamic model contained Staphylococcus aureus,
and at the times indicated by the arrows moxifloxacin
was added and samples were taken for analysis.
• Determination of MIC showed that resistant mutants
were enriched only when the moxifloxacin concentration
was inside the selection window for at least 20% of the
time.
Nice buiatric 2006-89
The MPC hypothesis for 4 Quinolones against S aureus
DRUGS
Cmax
AUC24/MIC
(h)
Change in
MIC
Gatifloxacin &
Ciprofloxacin
>MIC
15 to 16
Slight increase
Moxifloxacin &
Levofloxacin
MIC
13 to 17
None
All 4 drugs
>MIC
24 to 62*
Greatest
increase
All 4 drugs
>MIC
107 to 123
Small increase
All drugs
>> MIC
201 to 244**
None
*Concentrations within MSW over most of dose interval
**Concentrations >MPC over most of dose interval
Firsov et al (2003). Antimicrob. Agents Chemother. 47, 1604
Nice buiatric 2006-90
The MPC hypothesis for 4 Quinolones
against S aureus
CONCLUSIONS
• Resistant mutants selectivity enriched
when antibiotic concentrations fall
within MSW
• MIC72/MIC0 peak at AUC24/MIC of 43
• Only moxifloxacin may protect against
resistance at normal clinical doses
Nice buiatric 2006-91
Predictive value of PK/PD indices for
emergence of resistance: concentration
dependent antibiotic
• More clearly established than for time
dependent antibiotics
• For quinolones, the development of
resistance is mostly attributable to the
primary resistance pathway (mutation)
• Concepts of selection window and
AUIC are convergent
Nice buiatric 2006-92
Conditions for counter selective dosing to
avoid emergence of resistance
• The total organism burden substantially exceeds the
inverse of the mutational frequency to resistance
• There is a high probability of a resistant clone being
present at baseline
• The step size of change in MIC of the mutated population
is relatively small (<10-fold)
Appropriate dosing then able to suppress the
parent/sensitive population and also suffices to inhibit
the mutant sub-population
Drusano G.L. (2003) CID, 36 Suppl 1. 342-350
Nice buiatric 2006-93
Cmax/MIC and resistance
• Enoxacin
– Staphylococcus aureus, Klebsiella pneumoniae,
E.coli, P. aeruginosa
– Cmax = 3 MIC
• >99% reduction of initial inoculous
• regrowth at 24h unless Cmax/MIC >8
• if regrowth, MIC for the regrowing bacteria was 4-8 fold
that of parent strain
– Conclusion : there was selection of a resistant
subpopulation
• Cmax correlates with suppression of emergence of
resistance of organisms
Blaser et al. 1987 Antimicrob. Agent Chemother.
Nice buiatric 2006-94
PK/PD parameters vs. emergence
of resistance for fluoroquinolones
24-hr AUC/MIC
Resistance developed
P.aeruginosa Other GNB
<100 – monotherapy
80%
100%
>100 – monotherapy
33%
10%
Combinations
11%
0%
25%
12%
Thomas et al. AAC, 1998, 42:521
Nice buiatric 2006-95
AUIC > 250 h
• Bacterial killing is extremely fast with
eradication averaging 1.9 days
regardless the species of bacteria
Veterinary application: one shot
Nice buiatric 2006-96
What is the concentration needed to prevent
mutation and/or selection of bacteria with
reduced susceptibility?
• Beta-lactams:
– stay always above the 4xMIC
• Aminoglycosides:
– achieve a peak of 8x the MIC at least
• Fluoroquinolones:
– AUC/MIC > 200 and peak/MIC > 8
Nice buiatric 2006-97
Mutant Prevention
Concentration (MPC)
and the Selection Window (SW)
hypothesis
Nice buiatric 2006-98
Traditional explanation for
enrichment of mutants
Concentration
MIC
Selective Pressure
Time
Nice buiatric 2006-99
Traditional Explanation for
Enrichment of Mutants
• Placing MIC near the lower boundary of the selection
window contradicts traditional medical teaching in which
resistant mutants are thought to be selected primarily
when drug concentrations are below MIC
• This distinction is important because traditional dosing
recommendations to exceed MIC are likely to place drug
concentrations inside the selection window where they
will enrich resistant mutant subpopulations. While low
drug concentrations do not enrich resistant mutants, they
do allow pathogen population expansion; consequently,
low drug doses indirectly foster the generation of new
mutants that will be enriched by subsequent
antimicrobial challenge
Nice buiatric 2006-100
Blocking Growth of Single Mutants Forces Cells to Have a Double
Mutation to Overcome Drug
Without antibiotics
10-8
single mutant population
10-8
Wild pop
With antibiotics
10-8
single mutant population
Wild population
éradication
sensible
single mutant
Double mutant
Nice buiatric 2006-102
Mutants are not selected
at concentrations below MIC or
above the MPC
Nice buiatric 2006-103
Blocking Growth of Single Mutants Forces Cells to
Have a Double Mutation to Overcome Drug
attack by drug
frequency ~ 10-7
frequency ~ 10-7
wild type
double mutant
single mutant
frequency ~ 10-14
(number of bacteria during infection: < 1010)
Nice buiatric 2006-104
The selection window
• Selection of a resistant subpopulation
between selective concentrations (SC)
and mutant preventive concentrations
(MPC)
– fluoroquinolones and M. tuberculosis
– fluoroquinolones, chloramphenicol,
aminoglycosides, vancomycin and S.
aureus
– b-lactam antibiotics (cefotaxime and
amoxicillin) and E. coli
Nice buiatric 2006-105
Strategies for Restricting the
Development of Resistance
Nice buiatric 2006-106
Strategies for Restricting the
Development of Resistance
•
Three possible strategies for
restricting the development of
antimicrobial resistance.
1. To keep concentrations above the MPC
2. To narrow the selection window.
3. To use combination therapy in which
pharmacokinetic mismatch is avoided.
Nice buiatric 2006-107
Serum drug concentration
Strategies for Restricting the
Development of Resistance
1. Dose above
MPC
2. Narrow the
window
MPC
MPC~MIC
MIC
Time post-administration
Nice buiatric 2006-108
Closing the Window:
A goal for the developers of new antimicrobial compounds.
Nice buiatric 2006-109
What is the concentration needed to
prevent mutation and/or selection of
bacteria with reduced susceptibility?
• Beta-lactams: we do not know but most
likely stay always above the MIC…
• Aminoglycosides: achieve a peak of 8x the
MIC at least
• Fluoroquinolones: AUC/MIC > 100 h and
peak/MIC > 8
Nice buiatric 2006-110
Population approach to
determine a dosage regimen for
antibiotics
Nice buiatric 2006-111
Why a population approach
Development of resistance is a
collective phenomenon
Nice buiatric 2006-112
Population dosage regimen:
(The regulator point of view)
Population model (info)
To predict the single dosage regimen for most
animals in the population
Empirical antibiotherapy: The dose controlling 90% of the
overall population whatever the susceptibility of the bug,
the breed, age, sex etc
Nice buiatric 2006-113
Population dosage regimen:
flexible dosing regimens
Population model (info)
Covariates :
Sex, husbandry, breed...
To predict the best dosage regimen for a subgroup
of animals (breed, age, health status…)
•Targeted antibiotherapy: The dose controlling 90% of the overall
population when the susceptibility (MIC) of the bug is known
•Ethopharmacology/pharmacogenetics: doses may be tailored according
to genotypes or any other covariates
Nice buiatric 2006-114
Why Population PK/PD
• To take into account, explicitly ,variability
(and uncertainty) when selecting a
dosage regimen.
• Variability is not noise
Nice buiatric 2006-115
Not only the mean but also the
dispersion (variance) around
the mean are needed to predict
a population dosage regimen
for antibiotics
Nice buiatric 2006-116
The main goal of population
kinetics is to document sources
of variabilities
Nice buiatric 2006-117
Why a population approach
1)
The fact: Underexposure in only few animals within
a herd or a flock may lead to the establishment in
these animals of a less susceptible sub-population
of bugs that subsequently may transmit resistance
horizontally to other animals
2)
The risk factor: inter-animal variability (age, breed,
sex, health status….) that is not documented in
conventional preclinical studies.
Development of resistance is a
collective phenomenon
Nice buiatric 2006-118
Why a population approach
3) The solution: population PK/PD
investigations and Monte-Carlo
simulations
4) The ultimate Goal: An empirical
population dosage regimen
controlling a given quantile (e.g. 90%)
of a population and not an average
dosage regimen
Nice buiatric 2006-119
What is Monte Carlo simulations
•Roulette wheels, dice.. exhibit
random behavior and may be
viewed as a simple random
number generator
MCs is the term applied to
stochastic simulations that
incorporate random variability
into a model
Nice
Monte-Carlo
(Monaco)
Nice buiatric 2006-120
Monte Carlo simulation: applied to PK/PD models
Model: AUC/MIC
Generate random AUC and
MIC values
across the AUC & MIC
distributions that conforms
to their probabilities
PDF of AUC
PDF of MIC
Calculate a large number
of AUC/MIC ratios
PDF of AUC/MIC
Plot results in
a probability chart
% target attainment
(AUC:MIC, T>MIC)
Adapted from Dudley, Ambrose. Curr Opin Microbiol 2000;3:515−521
Nice buiatric 2006-122
AUC distribution for an hypothetical
antibiotic
AUC [0, 24 h] Distribution
16
Frequences (%)
14
12
10
8
6
4
2
0
3 4 5
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
AUC (mg.h.mL-1)
Nice buiatric 2006-123
PK Variability
1.6
Doxycycline
Concentrations mg/mL
1.4
n = 215
1.2
1
0.8
0.6
0.4
0.2
0
-5
0
5
10
15
20
25
30
Time (h)
Nice buiatric 2006-124
Pathogens %
MIC distribution
Pasteurella multocida
40
35
30
25
20
15
10
5
0
SUSCEPTIBLE
0.06250.125 0.25
0.5
1
2
4
MIC (m g/mL)
Nice buiatric 2006-125
Dosage regimen: application of
PK/PD concepts
The 2 sources of variability : PK and PD
PK: exposure
PD: MIC
AUC [0, 24 h] Distribution
MIC Distribution (simulation)
16
30
25
12
% de germes
Fréquences (%)
14
10
8
6
20
15
10
4
5
2
0
0
3 4 5
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
AUC (µg.h.mL-1)
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
1.1 1.2 1.3
CMI (µg/mL)
Distribution of PK/PD surrogates (AUC/MIC)
Monte-Carlo approach
Nice buiatric 2006-126
A working example to illustrate
what is Monte Carlo simulation
Nice buiatric 2006-127
Type of questions solved by Monte Carlo
investigations for the prudent use of
antibiotics
• What is the dose to be administrated to
guarantee that 90% of the cattle
population will actually achieve an
AUC/MIC of 80 (metaphylaxis) or 125
(curative treatment) for an empirical
(MIC unknown) or a targeted
antibiotherapy ( MIC determined)
Nice buiatric 2006-128
2 conditions for an optimal dosing regimen
1. Probability of “cure” = POC = 0.90
2. Time out of the MSW should be higher
than e.g.12h/day (50% of the dosing
interval) in 90% of cattle
Nice buiatric 2006-129
Solving the structural model to compute
the dose for my new quinolone
• With point estimates
– (mean, median, best-guess value…)
• With range estimates
– Typically calculate 2 scenarios: the best case &
the worst case (e.g. MIC90)
– Can show the range of outcomes
• By Monte Carlo Simulations
– Based on probability distribution
– Give the probability of outcomes
Nice buiatric 2006-130
Computation of the dose with point
estimates (mean clearance and F%, MIC90)
MIC90
Breakpoint value
(worst case scenario)
Mean value
 AUC 
Clearance(per hours) 
  MIC
 MIC  BP
Dose 
F%
Computation of an
“average dose”
Bioavailability
(Mean value)
Nice buiatric 2006-131
• An add-in design to help
Excel spreadsheet
modelers perform Monte
Carlo simulations
• Others features
– Search optimal solution (e.g.
dose) by finding the best
combination of decision
variables for the best possible
results
Nice buiatric 2006-132
Computation of the dose using Monte Carlo simulation
(Point estimates are replaced by distributions)
Log normal distribution: 9±2.07 mL/Kg/h
Observed distribution
Breakpoint value
Clearance  80  MIC
Dose 
F%
Dose to POC=0.9
Uniform distribution: 0.3-0.70
Nice buiatric 2006-133
Metaphylaxis:
dose to achieve a POC of 90% i.e. an AUC/MIC of 80
(empirical antibiotherapy)
Dose distribution
Nice buiatric 2006-134
Hypothetical antibiotic : selection of an empirical
(initial) dose for Pasteurella multocida
% of cattle above the breakpoint
x mg/kg
4x mg/kg
2x mg/kg
100%
90%
80%
60%
40%
20%
0%
0
24
48
72
96
120
144
168
192
AUC/MIC ratio (h)
Nice buiatric 2006-135
Sensitivity analysis
• Analyze the contribution of the different
variables to the final result (predicted dose)
• Allow to detect the most important drivers of
the model
Nice buiatric 2006-136
Sensitivity analysis
Metaphylaxis, empirical antibiotherapy
Contribution of
the MIC
distribution
Nice buiatric 2006-137
The second criteria to
determine the optimal dose:
the MSW & MPC
Nice buiatric 2006-138
Dosage regimen: implication for
drug resistance
• The presence of sublethal
concentrations of a drug exerts
selective pressure on population of
pathogens without eradicating it
• Under those circumstances, mutant
strains that possess a degree of drug
resistance are favored
 minimize the time that suboptimal drug
levels are present
Nice buiatric 2006-139
Kinetic disposition for an hypothetical antibiotic & for a
selected metaphylactic dose (3.8 mg/kg)
(monocompartmental model, oral route)
Log normal distribution: 9±2.07 mL/kg/h
F%
Uniform distribution: 0.3-0.70
Slope=Cl/Vc=0.09 per h (T1/2=7.7h)
concentrations (µg/mL)
concentrations
8
MPC
7
6
5
MIC
4
Série1
3
2
MSW
1
0
0
5
10
15
20
25
30
Time (min)
Nice buiatric 2006-140
Time>MPC for the POC 90% for metaphylaxis
(dose 3.8 mg/kg, empirical antibiotherapy)
Nice buiatric 2006-141
Sensitivity analysis
(dose
of 3.8mg/kg, curative treatment empirical antibiotherapy)
Clearance
Clearance (slope) is the only
influential factor of variability for
T>MPC not bioavailability as for
metaphylaxis
Nice buiatric 2006-142
Computation of the dose using Monte Carlo simulation
Targeted antibiotherapy
Nice buiatric 2006-143
Sensitivity analysis
(metaphylaxis, targeted antibiotherapy)
F%
Nice buiatric 2006-144
Computation of the dose (mg/kg):
metaphylaxis vs. curative treatment
Monte Carlo
curative
metaphylaxis
Efficacy
3.379
3.803
To guarantee
T>MPC in 90% of
pigs for 50% the
dosage interval
3.8
7.1
Nice buiatric 2006-145
Applications of a mathematical model to prevent in vivo
amplification of antibiotic-resistant bacterial population
during therapy.
• Granulocyte containing mouse thigh infection model based on
Pseudomonas aeruginosa (1x107 or 1x108* cfu/ml in 0.1 ml)
• Effect of escalating doses of levafloxacin on
amplification/suppression of susceptible and resistant populations
over 24h
• Mathematical modelling to predict effect of dose and therapy
duration on resistance emergence
• Prediction of drug dose selection to minimise resistance emergence
in clinical patients using Monte Carlo simulations.
* 108 organisms harbored 50-1,000 spontaneously drug resistant mutants
Jumbe et al. (2003) J. Clin. Invest. 112, 275
Nice buiatric 2006-146
Applications of a mathematical model to prevent in vivo
amplification of antibiotic-resistant bacterial population
during therapy.
• Maximal amplification of resistant mutants for
AUC24/MIC = 52h
• No amplification of resistant mutants for
AUC24/MIC = 157h
• 10,000 subject Monte Carlo simulation indicated
a target attainment rate of 61% for a 750 mg
dose of levofloxacin (and predicted attainment
rates of 25 and 62% for ciprofloxacin doses of
200 mg b.i.d. and 400 mg t.i.d.) for patients with
nosocomial pneumonia
Nice buiatric 2006-147
The weak link in MCs is Absence of a priori
knowledge on PK & PD distribution
• Population PK are needed to document
influence of different factors on drug
exposure
• Health vs. disease; age; sex; breed…
• PD: MIC distributions
• Truly representative of real world (prospective
rather than retrospective sampling)
• Possibility to use diameters distribution if the
calibration curve is properly defined
Nice buiatric 2006-148
Conclusion
Nice buiatric 2006-149
What is the contribution of the kineticist
to the prudent use of antibiotics
To assist the clinicians designing an
optimal dosage regimen
• To ensure that the selected antibiotic
reach the site of infection at an
appropriate effective concentration and
for an adequate duration to guarantee a
cure (clinical, bacteriological) and
without favoring antibioresistance
Nice buiatric 2006-150
• PK/PD cannot replace
confirmatory clinical trials of
efficacy but allow to arrive more
quickly to a better dosage
regimen recommendation
EMEA 2000
Nice buiatric 2006-151
CONCLUSIONS
• In vivo and in vitro studies in recent years have
addressed the question of dosage to avoid the
emergence of resistance
• “The approach is quite general and may be applied
for any new drug to determine the optimal doses that
minimise emergence of resistance” Jumbe et al
(2003)
• There is now a need to conduct similar studies with
veterinary pathogens and drugs used in veterinary
therapy
Nice buiatric 2006-152