Post on 31-Mar-2015
Monte Carlo simulations and bioequivalence of antimicrobial drugs
NATIONALVETERINARYS C H O O L
T O U L O U S E
July 2005Didier Concordet
Why to revisit bioequivalence criteria for antibiotic products ?
At the 44th ICAAC, it was reported that BE does not predict therapeutic equivalence (neutropenic murine thigh infection model) for several different antibiotics and that current criteria for BE deserve attention
(abstracts A-1877,1878,1879)
Two main sources of variability
A given dose administered (or offered )to different animals does not lead to the same exposure in every animals
PK : Antibiotic exposure
PD : PathogenA same exposure to an antibiotic does not produce the same effect on different strains of a given pathogen
PK variability
0 10 20 30 40Time
Co
nc
en
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tio
ns
Exposure
PD variability
0%
10%
20%
30%
40%
50%
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90%
100%
0.125 0.25 0.5 1 2 4 8 16 32
MIC
% o
f M
IC
Co
nc
entr
ati
on
s µ
g/m
L
Time (h)
Link between PK and PD (PK/PD indice)
Time above MIC
MIC
T>MIC
0 5 10 15 20 25 30
Co
nc
entr
ati
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s µ
g/m
L
Time (h)
Link between PK and PD
MIC
Cmax
Cmax/MIC
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nc
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s µ
g/m
L
Time (h)
Link between PK and PD
MIC
AUIC (or AUC24h/MIC)
AUIC ≈ AUC/MIC
Schentag J and Tillotson, GS (1997). Chest. 112(6 suppl) :314S-319S
PK/PD indices
For a given MIC, an animal is assumed to be appropriately exposed as soon as:
AUIC≥ 60 to 125 h[T>CMI] ≥ 40 to 80%[Cmax/MIC] ≥ 10
These cut-off values are only indicative and should be selected based upon clinical considerations(bacteriological /clinical cure), to minimize the
likelihood of resistance etc.
0%
10%
20%
30%
40%
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90%
100%
0.125 0.25 0.5 1 2 4 8 16 32
MIC%
of
MIC
Monte-Carlo simulation
MIC distribution Exposure distribution
Here, percentage of appropriately exposed animals is the percentage of animals with [AUIC≥ 125]
ExposuresSelect randomly an animal in the target population i.e. draw its exposure from the exposure distribution
Draw randomly the MIC from the MIC distribution
AUIC=AUC24/MIC
Bioequivalence
Bioequivalence basic assumption :
Same effects
Same concentrations profile (i.e. AUC, Cmax and Tmax)
Practically
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Exposure
Average bioequivalence
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Average (Reference)
ExposureAverage exposure.
Average Bioequivalence
ExposuremRef. mTestmTest
1.25 mRef0.8 mRef
a priori equivalence range
Average BE does not guarantee the same distribution (in addition, here test and ref averages are different )
ExposuremRef.mTest
1.25 mRef0.8 mRef
Equivalence range
Monte Carlo simulation 1
0%
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100%
0.125 0.25 0.5 1 2 4 8 16 32
MIC
% o
f M
IC
Same distribution for Clearance,volume of distribution and KaReference Test
Average %F = 90%CV %F = 10%
Average %F = 90%CV %F = 30%
Monte Carlo simulation 1 (same averages, different variances)
0%
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0 50 100 150 200auic
% o
f a
nim
als
wit
h A
UIC
>a
uic 30% Reference
Test
00.10.20.30.40.50.60.70.80.9
1
0 50 100 150 200auic
% p
atie
nts
wit
h A
UIC
>au
ic
REF
GEN2
GEN1
Monte Carlo simulation 2Same MIC distribution as previouslyReference GEN 1
Average %F = 74%CV %F = 10%
Average %F =67%CV %F = 20%
35%
GEN 2Average %F =82%CV %F = 20%
Monte Carlo simulation 3
GEN 1
GEN 2
Same MIC distribution as previouslyGEN 1 GEN 2
Average %F = 90%CV %F = 10%
Average %F = 73.0%CV %F = 20%
0%
10%
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0 50 100 150 200auic
% o
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UIC
>a
uic
ex Vivo effect as a function of the PK/PD surrogate
Aliabadi FS, Lees P, AJVR, 62, 12, 2001.
Log cfu difference after 24 h of incubation vs AUIC
ex vivo effect vs AUIC
-7
-6
-5
-4
-3
-2
-1
0
1
0 50 100 150 200 250AUIC
Lo
g c
fu/m
L d
iffe
ren
ce
Link between AUIC and bacterial count (cfu)
Curve adapted from Aliabadi FS, Lees P, AJVR, 62, 12, 2001.
Hypothesis: same relationship between AUIC and cfu count in ex vivo and in vivo conditions
Monte Carlo simulation 3
GEN 1
GEN 2
Same MIC distribution as previouslyGeneric 1 Generic 2
Expected %F = 90%CV %F = 10%
Expected %F = 73.0%CV %F = 20%
0%
10%
20%
30%
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50%
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100%
0 50 100 150 200auic
% o
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wit
h A
UIC
>a
uic
0%
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100%
-6 -5 -4 -3 -2 -1 0 1Log cfu/ml difference
Per
cen
tag
e o
f an
imal
s
Bacteriostatic effect
Bactericidal effect
eradication
Efficacy expressed in terms of bacteriological action: the case of two generics
GEN 1GEN 2
Population bioequivalence may avoid these drawbacks
0%
10%20%
30%40%
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80%90%
100%
0.125 0.25 0.5 1 2 4
MIC
% o
f M
IC
Exposure =AUC24Select an animal at random in the target populationDraw its exposure from the exposure distribution
Draw a MIC from the MIC distribution
AUC24MIC
AUIC=AUC24/MIC
RefTest
Other bioequivalence definitions could be explored
%10RT AUICAUICP
PK /PD bioequivalence 1 : Two formulations R and T are bioequivalent when
AUIC(h)
Reference
Test
0.05
10%
%10RAUIC
less than 5%
Less demanding than pop BE
Other bioequivalence definitions could be explored
Reference
Test
Less demanding than pop BE
ExposuremRef.mTest
1.11 mRef0.9 mRef
Equivalence range
Average BE
Conclusions 1
Classical average BE (PK criteria) does not guarantee that a pioneer and a generic products are able to cover the same percentage of subjects as shown by PK/PD simulations
Conclusions 2
• Pop BE that guarantee that the PK exposure distributions of the pioneer a generic products do not differ more than an a priori selected value
Such bioequivalence depends on the current MIC distribution and should be re-evaluated with regard to MIC distribution drift
Several solutions to be explored
• PK/PD BE using actually a PK/PD criteria consisting to guarantee that
• the percentage of patients with an exposure less than the quantile 10% of the exposure of the pioneer is less than a selected percentage • a selected quantile (e.g. 10%) does not differs by more than an a priori value having a therapeutic meaning