Nice buiatric 20061

1
Antibiotic dosage regimen based on PK-PD concepts
and the possible minimization of resistance
ECOLE NATIONALE VETERINAIRE T O U L O U S E

• PL Toutain
• UMR 181 Physiopathologie et Toxicologie
  Expérimentales
• INRA/ENVT

24th World Buiatric Congress. France. 15. October
2006
2
Why to optimize dosage regimen for antibiotics

• To optimize efficacy
• Reduce the emergence and selection of resistance

3
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)

4
Selecting a dosage regimen for a particular
animal or group of animals
Individual animal (or herd) issues Probability
of cure without side effects
Public health issues 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
5
Why to optimize dosage regimen for antibiotics

• To optimize efficacy
• 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)

6
Biophases antibiorésistance
G.I.T
Proximal
Distal

• Gut flora
• Zoonotic (salmonella, campylobacter
• commensal ( enterococcus)

1-F
F
Environmental exposure
Food chain
Man
Blood
Target biophase Bug of vet interest
Résistance public health concern
Résistance lack of efficacy
7
Biophases antibiorésistance
G.I.T
Proximal
Distal

• Gut flora
• Zoonotic (salmonella, campylobacter
• commensal ( enterococcus)

Intestinal secretion Bile
Quinolones Macrolides Tétracyclines
Food chain
Environmental exposure
Systemic administration
Man
Blood
Biophase Bug of vet interest
Résistance public health concern
Résistance lack of efficacy
8
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

9
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,
3207-209
10
Dosage regimen and resistanceEpidemiological
evidences
11
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

12
Drug factors influencing resistance

• Regimen
• Route of administration, dose, interval of
  administration, duration of treatment

13
Effect on Penicillin resistance in pneumococcus
isolates (n465) of duration of b-lactam use, 6
months before swab collection
Nb of days of b-lactam use 1-7 8-14 gt14
Odd ratios 0.86 1.5 2.5
95 CI 0.37- 2.02 0.73- 3.06 1.3 - 4.82
Nasrin et al. BMJ, 2002
14
Dosage regimen and antibiotic resistance

• Treatments were initiated 7 days post challenge
  (E. coli) and continued for 14 days

Treatment Control Label use Gradient
Pulse Rotation with dissimilar
antimicrobials Rotation with similar
antimicrobials
Dosing scheme No antibiotics Apramycin, 150g/ton
of feed for 14 days Apramycin, 50g/ton of feed
for 5 days, then 100g/ton for 5 days then
150g/ton for 4 days Apramycin, 150g/ton of feed
for 3 days, then 3 days without antibiotics,
sequence repeated throughout the 14-day
period Apramycin, 150g/ton of feed for 5 days,
then sulfamethazine formulated for 118g/kg BW in
drinking water for 5 days then carbadox, 50g/ton
of feed for 4 days Apramycin, 150g/ton of feed
for 5 days, then gentamicin 6.6 mg/L of drinking
water for 5 days then neomycin formulated for
22mg/kg BW in drinking water for 4 days
Mathew, 2003
15
Effect of 14-day antibiotic dosing regimen on
sensitivity (MIC, µg/mL) to apramycin by E. coli
recovered

• AB dosing day post challenge
• regimen 3 6 10 13 17 31
• Control (no AB) 4.3 3.9 3.5 3.1 2.3 2.6
• Label 5.9 41.1 56 49 50 6.6
• Rotation Similar AB 3.5 4.2 200 182 141 7.6
• Rotation Dissimilar AB 2.6 38.8 44 14 14.0 3.8
• Gradient 50, 100, 150 3.5 3.5 3.5 68.5 109.9 2.8
• Pulse (3 days) 5.2 4.3 3.6 4.0 7.0 3.7

Mathew, 2003
16
How to determine a dosage regimen that is both
efficacious and that minimizes the risk to
promote resistance
17
How to find and confirm a dose (dosage regimen)

• Dose titration
• Animal infectious model
• Clinical trial
• PK/PD

18
The dose-titration
19
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

20
How to find and confirm a dose (dosage regimen)

• Dose titration
• Animal infectious model
• Clinical trial
• PK/PD

21
Bacteriological vs clinical successthe
pollyanna phenomenon
22
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

23
The Pollyanna effect
Discrepency between clinical and bacteriological
results

• Otitis media

Antibiotic effect
Efficacy ()
Clinical success
Placebo effect
Bacteriological cure
Merchant et al. Pediatrics 1992
24
The Pollyanna effect

• Ceftiofur oral

90
Mortality
Bacterial
60
shedding
Response
30
0
0
0.5
2
16
64
Dose (mg/kg)
Yancey et al. 1990 Am. J. Vet.Res.
25
EFFICACY OF ORAL PRADOFLOXACIN AND
AMOXYCILLIN/CLAVULANATE IN CANINE CYSTITIS AND
PROSTATITIS
Data from Bayer Animal Health (VERAFLOX SYMPOSIUM)
26
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

27
The role of antibiotics is to eradicate the
causative organisms from the site of infection
Jacobs. Istambul, 2001
28
How to find and confirm a dose (dosage regimen)

• Dose titration
• Animal infectious model
• Clinical trial
• PK/PD

29
The main goal of a PK/PD trial in veterinary
pharmacology

• To be an alternative to dose-titration studies to
  discover an optimal dosage regimen

30
What is PK/PD?
31
Dose titration
Dose
Response clinical
Black box
PK/PD
PK
PD
Body
pathogen
Dose
Response
Plasma concentration
32
PK/PD in vitro
In vitro
Medium concentration
Response MIC
Test tube
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
33
Dose titration
Dose
Response clinical
Black box
PK/PD
PK
PD
Body
pathogen
Dose
Response
A plasma concentration variable scaled by MIC
34
Dose titration vs. PK/PD the explicative
variable
A PK/PD SURROGATE
Effect
Effect
effect
AUC/MIC,
Dose
AUC
EXPOSURE (internal dose)
DOSE (external dose)
Exposure scaled by MIC
35
PK/PD indices as indicator of antibiotic efficacy
36
The surrogates (predictors) of antibiotic efficacy
AUC/MIC, TgtMIC, Cmax/MIC
37
PK/PD predictors of efficacy

• Cmax/MIC aminoglycosides

• AUC/MIC quinolones, tetracyclines,
  azithromycins,

• TgtMIC penicillins, cephalosporins, macrolides,

Cmax
Cmax/MIC
AUC MIC
AUIC
Concentrations
MIC
Time
24h
TgtCMI
38
Why these indices are termed PK/PD
PK
AUC CMI
Dose / Clearance CMI50(90)

• AUIC

PD
? Dual dosage regimen adaptation
39
Relationship between dose and PK/PD predictors of
efficacy
Breakpoint value e.g. 125
PD
Bioavailability
PK
Free fraction
40
Why plasma concentrationThe site of infection
Update 22 novembre 2009
41

• 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

42

• MIC is a reasonable approximate of the
  concentration of free drug needed at the site of
  infection

43

• Most infections of interest are located
  extra-cellularly and direct comparisons to total
  tissue concentration with PD parameters are
  meaningless

Cars, 1991
44
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

45

• When there is no barrier to penetration, the
  level of free drug in serum is an adequate
  surrogate marker for biophase concentration

Cars, 1991
46
Barrier, efflux pump
Porous capillaries
Plasma
Interstitial fluid
Brain, retina, prostate
Biophase for most bacteria of veterinary
therapeutical interest
Surrogate marker (TgtMIC, AUIC, Cmax/MIC)
Tissular barrier
B
Bound ?? F
Mannhemia, Pasteurella Haemophilus,
Streptococcus, Staphylococcus, Coli, Klebsiella
Bound ?? F
B
lipophilicity
F
Efflux pump
Total concentration
Biophase for facultative and obligatory
intracellular pathogens
Bound
Cytosol (Listeria, Shigella)
B
Phagosome (Chlamydiae)
F
Cell
Cell membrane
Bound
B
F
B
B
Obligatory or facultative bacteria
Phagolysosome (S. aureus, Brucella, Salmonella)
47
Tissue concentrations

• According to EMEA
• "unreliable information is generated from assays
  of drug concentrations in whole tissues (e.g.
  homogenates)"

EMEA 2000
48
Magnitude of PK/PD parameter required for efficacy
Istambul, 2001
49
Relationship Between TgtMIC and Efficacy for
Carbapenems (Red), Penicillins (Aqua) and
Cephalosporins (Yellow)
50
Relationship between PK/PD parameters and
efficacy for cefotaxime against Klebsiella
pneumoniae in a pneumonia model
10
10
10
R² 94
9
9
9
8
8
8
Log10 CFU per lung at 24 h
7
7
7
6
6
6
5
5
5
3
10
30
100
300
1000
3000
01
1
10
100
1000
10000
100
60
80
20
40
0
24 h AUC/MIC ratio
Time above MIC ()
Peak MIC ratio
Craig CID, 1998
51
Efficacy index clinical validation
Bacteriological cure versus time above MIC in
otitis media (from Craig and Andes 1996)
100
S. pneumoniae Penicillin cephalosporins
50
Bacteriologic cure ()
H. influenzae Penicillin cephalosporins
0
0
100
50
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

52
PK/PD parameters ?-lactams

• Time above MIC is the important parameter
  determining efficacy of the ?-lactams
• TgtMIC 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
53
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

54
Comparison of relationships between 24-hr AUC/MIC
and efficacy against Pneumococci for
fluoroquinolones in animals and patients

• Patients with CAP and AECB
• 58 patients enrolled in a comparative trial of
  levofloxacin vs. gatifloxacin
• Free-drug 24-hr AUC/MIC lt 33.7, the probability
  of a microbiologic cure was 64
• Free-drug 24-hr AUC/MICgt33.7, the probability of
  a microbiologic cure was 100

Andes Craig Int. J. Antimicrob. Agents, 2002,
19 259
55
AUIC 125 h as a consensus descriptor of
antibiotic action

• Roughly speaking AUIC 125 h is equivalent to
  say that the mean concentration should be 5 times
  the MIC over 80 of the dosage interval (24h)

Schentag et al. 1990
56
Efficacy index clinical validation
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
Response rate ()
60
2
4
6
8
10
12
Maximum peak/MIC ratio
Moor et al. 1984 J. Infect. Dis.
57
Modern interests in pharmacodynamics

• Establish the PK/PD target required for effective
  antimicrobial therapy
• Identify which PK/PD parameter (TgtMIC, AUC/MIC,
  peak/MIC) best predicts in vivo antimicrobial
  activity
• Determine the magnitude of the PK/PD parameter
  required for in vivo efficacy (static effect, 1
  or 2 log kill)
• Define resistance for those situations where one
  cannot attain the target required for efficacy

58
Magnitude of PK/PD parameter required for
efficacy the case of quinolones for calf
59
Bacterial growth in serum containing danofloxacin
for incubation periods of 0.25 to 6h
Conc.
0
0.02
0.04
1.E09
0.06
0.08
1.E06
0.12
Log cfu/ml
0.16
1.E03
0.20
0.24
1.E00
0.28
0
1
2
3
4
5
6
0.32
Incubation time (h)

• P. Lees

60
Sigmoidal Emax relationship for bacterial count
vs ex vivo AUIC24h in goat 1 serum
Observed
Predicted
1
Bacteriostatic AUIC24h 18 h
0
-1
Bactericidal AUIC24h 39 h
-2
Log cfu/ml difference
-3
Elimination AUIC24h 90 h
-4
-5
-6
-7
0
50
100
150
200
250
300
AUIC24h

• P. Lees

61
Ex vivo AUC24h/MIC (h) values for danofloxacin
and marbofloxacin in calf serum

• Parameter Danofloxacin Marbofloxacin
• Bacteriostatic 15.9 2.0 37.3 6.9
• Bactericidal 18.1 1.9 46.5 6.8
• Elimination 33.5 3.5 119.6 10.9
• Slope 17.3 4.2 11.5 3.3
• Values are mean sem (n6)

P. Lees
62
PK/PD indicesDetermination of breakpoint values

• To optimize efficacy
• To minimize resistance

Update 17/05/2004
63
Effectiveness of PK/PD indices as predictor for
the development of antimicrobial resistance
64

• There is evidence that the likelihood for the
  selection of bacteria with mutation conferring
  resistance can be predicted on basis of PK/PD
  relationship

65
Impact of dosage regimen on the emergence of
resistanceExperimental evidences
66
AUIC and bacterial eradication

• Nosocomial pneumonia treated with IV
  ciprofloxacin
• AUIC was highly predictive of time to bacterial
  eradication
• If AUIC gt250 h/day
• eradication of organism on day 1 of therapy
• good target for nosocomial pneumonia and
  compromised host defense

100
AUIC lt 125
50
patients remaining culture positive
AUIC 125-250
AUIC gt 250
0
4
8
12
Days after start of therapy
Schentag Symposium, 1999
67
Suboptimal antibiotic dosage as a risk factor for
selection of penicillin-resistant Streptococcus
pneumoniae in vitro kinetic model

• Odenholt et al. (2003) Antimicrobial Agents and
  Chemotherapy, 47 518-523

68
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 TgtMIC varied from
• S 46 to 100
• I 6 to 100
• R 0 to 48

Odenholt, 2003
69
Selection by penicillin of resistant bacteria in
a mixed population of S.pneumoniae control
A
Odenholt, 2003
70
Selection by penicillin of resistant bacteria in
a mixed population of S.pneumoniae
B
Odenholt, 2003
71
Selection by penicillin of resistant bacteria in
a mixed population of S.pneumoniae
C
Odenholt, 2003
72
Selection by penicillin of resistant bacteria in
a mixed population of S.pneumoniae
D
Odenholt, 2003
73
Selection by penicillin of resistant bacteria in
a mixed population of S.pneumoniae
E
Odenholt, 2003
74
Selection by penicillin of resistant bacteria in
a mixed population of S.pneumoniae
F
Odenholt, 2003
75
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
76
Optimisation of Meropenem minimum
concentration/mic ratio to suppress in vitro
resistance of Pseudomonas aeruginosa
Placebo
TgtMIC 100 Cmin/MIC6
10
12
8
8
6
Log 10 cfu/mL
Log 10 cfu/mL
4
4
2
Time (days)
Time (days)
0
0
0
1
2
3
5
4
5
0
1
2
3
4
TgtMIC 84
TgtMIC 100 Cmin/MIC10
10
10
8
8
Log 10 cfu/mL
6
Log 10 cfu/mL
4
4
Time (days)
2
0
0
5
0
1
2
3
4
Time (days)
0
1
2
3
5
4
TgtMIC 100 Cmin/MIC1.7 tobramycin
TgtMIC 100 Cmin/MIC1.7
10
10
8
8
Log 10 cfu/mL
6
Log 10 cfu/mL
4
4
2
Time (days)
0
0
Time (days)
5
0
2
3
4
1
0
1
2
3
5
4
77
Optimisation of Meropenem minimum
concentration/MIC ratio to suppress in vitro
resistance of pseudomonas aeruginosaresults

• Resistance emerged when
• (a) TgtMIC 84
• (b) TgtMIC 100 and Cmin/MIC 1.7
• Resistance avoidance when
• (a) TgtMIC 100 and Cmin/MIC 6.0
• or (b) TgtMIC 100 and Cmin/MIC 1.7 plus
  tobramycin

78
Optimisation of Meropenem minimum
concentration/MIC ratio to suppress in vitro
resistance of Pseudomonas aeruginosaconclusions

• Breakpoint to prevent resistance different of
  those selected for clinical efficacy
• Because of the ceiling effect for TgtMIC 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)

79
Surrogate indices and emergence of resistance
Ceftizoxime in vivo

• In vivo murine study using mixed infection model
  related mutation frequency to TgtMIC (as
  percentage of dosing interval) for ceftizoxime
• No resistance when TgtMIC was lt40 or 100
• Mutation frequency very low when TgtMIC ? 87
• Peak mutation frequency for TgtMIC 70
• For optimal efficacy the usual value quoted is
  TgtMIC 40-60

Stearne et al (2002)
80
Predictive value of PK/PD indices for emergence
of resistance time dependent antibiotic

• TgtMIC 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

81
TgtMIC for 40-50 of the dosing intervalDaily
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
82
Impact of dosage regimen on the emergence of
resistanceExperimental evidences for
quinolones
83
AUIC (AUC/MIC) and bacterial resistance

• Ciprofloxacin AUIC predicts bacterial resistance
  in nosocomial pneumoniae

Resistance for AUIC lt 100 day 4 50 20 93
100
AUC/MIC gt 101
75
50
Probability of remaining susceptible
AUC/MIClt 100
25
0
0
5
10
15
20
No.Days after start of therapy
AUIC lt 100 suboptimal
Schentag-Symposium 1999
84
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 lt 100
  (ciprofloxacin)

EMEA 2000
85
Bacterial population responses to drug selective
pressure examination of Garenoxacins 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
86
Bacterial population responses to drug selective
pressure examination of Garenoxacins effect on
Pseudomonas aeruginosa
AUC/MIC10
Control
AUC/MIC48
AUC/MIC89
AUC/MIC108
AUC/MIC201
87
Bacterial population responses to drug selective
pressure examination of garenoxacins 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

88
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 (a) MIC
• (b) gtMIC ltMPC (within MSW)
• (c) gtMPC
• Resulting AUC24/MIC values 13 to 244h
• Determination of MIC at 0 and 72h
• Absence of WBCs

Firsov et al. (2003) Antimicrob.Agents Chemother.
47, 1604.
89
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.

90
Firsov et al (2003). Antimicrob. Agents
Chemother. 47, 1604
The MPC hypothesis for 4 Quinolones against S
aureus
Concentrations within MSW over most of dose
interval Concentrations gtMPC over most of dose
interval
91
The MPC hypothesis for 4 Quinolones against S
aureusCONCLUSIONS

• 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

92
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

93
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 (lt10-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
94
Cmax/MIC and resistance

• Enoxacin
• Staphylococcus aureus, Klebsiella pneumoniae,
  E.coli, P. aeruginosa
• Cmax 3 MIC
• gt99 reduction of initial inoculous
• regrowth at 24h unless Cmax/MIC gt8
• 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.
95
PK/PD parameters vs. emergence of resistance for
fluoroquinolones
Resistance developed
Thomas et al. AAC, 1998, 42521
96
AUIC gt 250 h

• Bacterial killing is extremely fast with
  eradication averaging 1.9 days regardless the
  species of bacteria

Veterinary application one shot
97
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 gt 200 and peak/MIC gt 8

98
Mutant Prevention Concentration (MPC)and the
Selection Window (SW) hypothesis
99
Traditional explanation for enrichment of mutants
Concentration
MIC
Selective Pressure
Time
100
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

101
The selection window hypothesis
Mutant prevention concentration (MPC) (to inhibit
growth of the least susceptible, single step
mutant)
MIC Selective concentration (SC) to block
wild-type bacteria
Mutant Selection window
Plasma concentrations
All bacteria inhibited
Growth of only the most resistant subpopulation
Growth of all bacteria
102
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
103
Mutants are not selected at concentrations below
MIC or above the MPC
104
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 lt 1010)
105
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

106
Strategies for Restricting the Development of
Resistance
107
Strategies for Restricting the Development of
Resistance

• Three possible strategies for restricting the
  development of antimicrobial resistance.
• To keep concentrations above the MPC
• To narrow the selection window.
• To use combination therapy in which
  pharmacokinetic mismatch is avoided.

108
Strategies for Restricting the Development of
Resistance

• Dose above MPC

• Narrow the window

MPC
Serum drug concentration
MPCMIC
MIC
Time post-administration
109
Closing the Window A goal for the developers of
new antimicrobial compounds.
110
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 gt 100 h and peak/MIC gt 8

111
Population approach to determine a dosage regimen
for antibiotics
112
Why a population approach
Development of resistance is a collective
phenomenon
113
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
114
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

115
Why Population PK/PD

• To take into account, explicitly ,variability
  (and uncertainty) when selecting a dosage
  regimen.
• Variability is not noise

116
Not only the mean but also the dispersion
(variance) around the mean are needed to predict
a population dosage regimen for antibiotics
117
The main goal of population kinetics is to
document sources of variabilities
118
Why a population approach

• 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
• 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
119
Why a population approach

• The solution population PK/PD investigations and
  Monte-Carlo simulations
• The ultimate Goal An empirical population
  dosage regimen controlling a given quantile (e.g.
  90) of a population and not an average dosage
  regimen

120
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
Monte-Carlo (Monaco)
Nice
121
Type of questions solved by Monte Carlo
investigations for the prudent use of antibiotics

• What is the dose of an antibiotic to be
  administrated to a group of cattle to guarantee
  that at least 90 of these cattle will achieve an
  AUC/MIC ratio (the selected PK/PD index) value of
  125 in the framework of an empirical
  antibiotherapy?
• The so-called target attainment rate (TAR)

122
Monte Carlo simulation applied to PK/PD models
Model AUC/MIC
PDF of AUC
Generate random AUC and MIC values across the
AUC MIC distributions that conforms to their
probabilities
PDF of MIC
Calculate a large number of AUC/MIC ratios
PDF of AUC/MIC
Plot results in a probability chart
target attainment (AUCMIC, TgtMIC)
Adapted from Dudley, Ambrose. Curr Opin Microbiol
20003515-521
123
AUC distribution for an hypothetical antibiotic
124
PK Variability
Doxycycline
n 215
125
MIC distributionPasteurella multocida
40
35
30
25
20
Pathogens
15
10
SUSCEPTIBLE
5
0
0.0625
0.125
0.25
0.5
1
2
4
MIC (
m
g/mL)
126
Dosage regimen application of PK/PD concepts
The 2 sources of variability PK and PD
PK exposure
PD MIC
Distribution of PK/PD surrogates
(AUC/MIC) Monte-Carlo approach
127
A working example to illustrate what is Monte
Carlo simulation
128
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)

129
2 conditions for an optimal dosing regimen

• Probability of cure POC 0.90
• Time out of the MSW should be higher than
  e.g.12h/day (50 of the dosing interval) in 90
  of cattle

130
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

131
Computation of the dose with point estimates
(mean clearance and F, MIC90)
MIC90 (worst case scenario)
Breakpoint value
Mean value
Bioavailability (Mean value)
Computation of an average dose
132

• 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

133
Computation of the dose using Monte Carlo
simulation(Point estimates are replaced by
distributions)
Log normal distribution 92.07 mL/Kg/h
Observed distribution
Breakpoint value
Dose to POC0.9
Uniform distribution 0.3-0.70
134
Metaphylaxis dose to achieve a POC of 90 i.e.
an AUC/MIC of 80(empirical antibiotherapy)
Dose distribution
135
Hypothetical antibiotic selection of an
empirical (initial) dose for Pasteurella multocida
x mg/kg
2x mg/kg
4x mg/kg
100
90
80
60
of cattle above the breakpoint
40
20
0
0
24
48
72
96
120
144
168
192
AUC/MIC ratio (h)
136
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

137
Sensitivity analysisMetaphylaxis, empirical
antibiotherapy
Contribution of the MIC distribution
138
The second criteria to determine the optimal
dose the MSW MPC
139
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

140
Kinetic disposition for an hypothetical
antibiotic for a selected metaphylactic dose
(3.8 mg/kg)(monocompartmental model, oral route)
Log normal distribution 92.07 mL/kg/h
F
Uniform distribution 0.3-0.70
SlopeCl/Vc0.09 per h (T1/27.7h)
MPC
MIC
concentrations
MSW
141
TimegtMPC for the POC 90 for metaphylaxis (dose
3.8 mg/kg, empirical antibiotherapy)
142
Sensitivity analysis (dose of 3.8mg/kg, curative
treatment empirical antibiotherapy)
Clearance
Clearance (slope) is the only influential factor
of variability for TgtMPC not bioavailability as
for metaphylaxis
143
Computation of the dose using Monte Carlo
simulationTargeted antibiotherapy
144
Sensitivity analysis (metaphylaxis, targeted
antibiotherapy)
F
145
Computation of the dose (mg/kg) metaphylaxis
vs. curative treatment
146
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
147
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

148
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

149
Conclusion
150
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

151

• PK/PD cannot replace confirmatory clinical trials
  of efficacy but allow to arrive more quickly to a
  better dosage regimen recommendation

EMEA 2000
152
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