Transcript Document

13th ISAP Pharmacokinetics/pharmacodynamics
Educational Workshop
Part 3 PK/PD modelling of Anti infective Agents
In vitro MIC based PK/PD
Alasdair MacGowan
Bristol Centre for Antimicrobial Research & Evaluation
University of Bristol & North Bristol NHS Trust
Southmead Hospital
Bristol, UK
Why are MIC based studies important > MICs are the most widely used and accepted measure
of antibacterial potency
> large databases of information on new and old
(comparator) agents
> web ideal environment to store distribution data c.f.
(range; MIC50; MIC90)
> standard methodology (CLSI; EUCAST; ISO/CEN
reference method)
> recognised by regulatory authorities, device manufacturers,
professional bodies
> basis of breakpoint setting
gold standard for disc susceptibility testing and automated
methods in clinical laboratories
> pK/pD models based on MIC data are predictive of human
outcomes
In vitro MIC based PK/PD encompasses -
1.
persistent antibiotic effects (PAE, CERT)
2.
time kill curves
3.
interaction studies (chequer boards; time kill curves)
[pK concentrations or MIC multiples]
4.
in vitro pharmacokinetic models (step reductions
in concentration; dilutional; dialysis; hollow fibre etc)
5.
animal models
OBJECTIVES OF STUDYING pK/pD IN IN VITRO MODELS
•
tests of efficacy
descriptive studies
determination of dominant pK/pD parameter
determination of the magnitude of the dominant
pK/pD parameter
•
(emergence of resistance)
•
pK/pD
TESTS OF EFFICACY
•
human dosing or other
•
activity of agent vs target pathogen
•
activity of agent and comparator(s) vs target pathogen
•
activity of agent v target pathogen with various MIC
values of the agent
•
activity of agent vs target pathogen with various
mechanisms of resistance to the agent
•
activity of agent vs different bacterial species of
target pathogen
•
activity of various agent formulations against the target pathogen
Activity against target pathogens
viable count (logcfu/mL)
Antibacterial effect of moxifloxacin 400mg od simulated serum
concentrations against respiratory pathogens with typical MICs
8
7
6
5
4
3
2
0
6
12
18
time (h)
S pneumoniae MIC 0.08mg/L
H influenzae MIC 0.06mg/L
M catarrhalis MIC 0.1mg/L
24
Activity against target pathogens
a) JI (MRSA) pre treatment
9
log cfu/mL
8
7
6
5
4
3
2
0
4
8
12
16
20
time (h)
growth control
minocycline
minocycline +rifampicin
24
Activity against target pathogens
b) J2 (MRSA) post treatment
9
log cfu/mL
8
7
6
5
4
3
2
0
4
8
12
time (h)
growth control
16
20
minocycline
minocycline+rifampicin
24
Activity against defined resistances/comparisons
d) E.coli 33212
9
log CFU/ml
8
7
6
5
4
3
2
0
12
24
36
48
time (h)
ceftriaxone
MIC 1.5mg/L
ertapenem
MIC 0.02mg/L
pip/taz
MIC 8mg/L
Activity against defined resistances/comparisons/MIC
drives outcome
d) K.pneumoniae 6673
9
log CFU/ml
8
7
6
5
4
3
2
0
12
24
36
48
time (h)
ceftriaxone
MIC 40mg/L
ertapenem
MIC 0.12mg/L
pip/taz
MIC 28mg/L
Activity against defined resistance mechanisms/MIC
does not drive outcome
Effect of resistance mechanisms on the antibacterial effect of
gemifloxacin with strains of S.pneumoniae with the same MIC
(0.06mg/L)
9
8
log cfu/ml
7
6
5
4
3
2
0
12
24
36
48
60
72
time (hr)
MIC 0.06mg/L no known resistance mechanism (SMH21810)
MIC 0.06 mg/L 1st step par C mutation (SMH21813)
MIC 0.06mg/L efflux (SMH21850)
Activity of ceftazidime - 2g 8hly iv simulation with or
without AM 112 against E coli with CTX M1
viable count (logcfu/ml)
9
8
7
6
5
4
3
detection
limit
2
1
0
0
CAZ alone
8
16
time (hrs)
CAZ+AM112 24hly
Bowker et al, 2003, ICAAC, A-1158
24
CAZ +AM112 8hly
CONCLUSIONS ON DESCRIPTIVE TESTS OF EFFICACY -
easy, limited, some useful information,
shows MIC does not tell all
DETERMINATION OF DOMINANT pK/pD PARAMETER

dosing regimen employed
(differentiation between AUC/MIC; Cmax/MIC;
T > MIC)

end point chosen

analytic tools used

susceptibilities of target strains

effects of aggregation of data
(i.e. species or mechanisms)
PRODUCING VARIABILITY IN pK/pD PARAMETERS
dose escalation
dose fractionation
MIC differences
ranges in pD/pK parameters
(AUC/MIC; Cm/MIC; T > MIC)
GEMIFLOXACIN DOSE FRACTIONATION PLUS
MIC RANGE 0.016 - 0.24mg/L
Spearman rank
Correlation (95% CI)
AUC/MIC v Cm/MIC
AUC/MIC v T > MIC
Cm/MIC v T > MIC
0.77 (0.42 - 0.92)
0.87 (0.60 - 0.96)
0.42 (0.14 - 0.77)
END POINTS measures of antibacterial effect (ABE)
measures in time (X axis)
time to kill 90, 99, 99.9 etc
time to maximum kill
measures in viable count (Y axis)
log kill at 12, 24, 36 etc h
~
log kill after dose ( )
maximum kill
“integrated” measures (X and Y)
slope of kill curve
areas around the kill curve
AREA MEASURES
control
IE
log cfu/ml
test
AAC
AUBC
time
AAC: area above the curve
AUBC: area under the bacterial (kill) curve AUBKC
IE: intensity of effect (area between curves, (log C - log T) xt)
AUBKC (test) /AUBKC (control) ratio
EFFECT OF CHOSEN ANTIBACTERIAL EFFECT
MEASURE ON DOMINANT pK/pD PARAMETER
gemifloxacin
160mg x 4 over 48 hr
320mg x 2 over 48 hr
640mg x 1 over 48 hr
five strains of S pneumoniae
(MIC 0.016; 0.06; 0.1; 0.16; 0.24 mg/L)
pD parameters:
AUC/MIC (72 - 1219)
Cmax/MIC (3 - 131)
T > MIC (38 - 100%)
MacGowan et al, 2003, AAC, 45, 2916
using Emax model
AUC/MIC related to AUBKC48 best
Cmax/MIC and T>MIC less well
weighted least squares regression analysis
AUC/MIC and T>MIC predictors of AUBKC48
Cmax/MIC not predictive of AUBKC48
Cox proportional hazards regression
- ABE: time to kill 99.9% inoculum
univariate analysis AUC/MIC, Cm/MIC and T > MIC related
to T99.9
multivariate model
Cmax/MIC related to T99.9
Cm/MIC
<5
5 - 9.9
10 - 29.9
>30
relative risk
1.0
7.7
11.6
20.7
95% CI
2.2 - 27.2
4.3 - 31.8
2.3 - 68.3
COX PROPORTIONAL HAZARDS REGRESSION
ANALYSIS FOR T99.9 AND REGROWTH
81 experiments with gemifloxacin and moxifloxacin against
S. pneumoniae
In 59/81 experiment T99.9 achieved
In 24/59 regrowth occurred
AUC/MIC, Cm/MIC and T > MIC related to both measures
T99.9 related to Cm/MIC especially > 5
Regrowth related to T > MIC
AUC/MIC did not predict T99.9 or regrowth
MacGowan et al, 2003, JAC, 47S1, 50
THEREFORE - with fluoroquinolones
T99.9
slope 
Cm/MIC
log cfu/ml
Regrowth
IE
 T > MIC
AUBKC  AUC/MIC
log change in viable count at
24hr (cfu/ml)
Dose response curve moxifloxacin against E coli using human pharmacokinetics
2
AUC/MIC stasis = 46
1
0
AUC/MIC 99.9% kill = 84
-1
AUC/MIC 95% max effect =118
-2
AUC/MIC max effect = 390
-3
-4
-5
-6
0
1
2
3
log dose (mg)
400mg
Magnitude of the pD index effect of inoculum moxifloxacin
vs E.coli
Figure 1 Relationship of AUC/MIC to change in viable count
at 24h for an intial inoculum of 10 6 and 108 cfu/ml
log change in viable
count at 24hrs
2
1
0
-1
-2
-3
-4
-5
-6
-7
1
2
3
AUC/MIC
106 cfu/ml inoculum
108 cfu/ml inoculum
Magnitude of the pD index : effect of inoculum
vancomycin vs UKEMRSA 15/16
antibacterial
effect
log drop at
24h
static
initial inoculum
106 CFU/ml
108 CFU/ml
dose
free drug
dose
free drug
g/d
AUC/MIC
g/d
AUC/MIC
0.05
17
-
-1 log10
0.1
33
0.67
213
-2 log10
0.4
127
0.9
294
-3 log10
0.95
303
>4
>1274
-4 log10
>4
>1274
>4
>1274
Effect response relationships for moxifloxacin versus E.coli
(aerobic & anaerobic), B.fragilis, GPAC and C.perfringens
isolated
B.fragilis
GPAC
minimum
effect
+1.4 ± 0.5 +1.3 ± 0.3
(log
cfu/ml)
maximu
m effect
-3.2 ± 0.2 -2.5 ± 0.1
(log
cfu/ml)
AUC/MIC for
static
7.6
5.6
effect
40%
10.5
7.3
effect
(8-13.8) (5.6 –9.4)
90%
effect
25.7
17.4
99%
effect
81.0
42.7
2
r
0.90
0.94
C.perfringens
E.coli
aerobic anaerobic
+2.2 ± 0.4
+1.4 ± 0.5
+1.5 ± 0.2
-3.7 ± 0.1
-4.4 ± 0.3
4.5 ± 0.2
7.6
26
41
8.6
(7.5-9.8)
34
(26-44)
54
(37-79)
16.2
59
96
31.6
0.93
105
0.86
148
0.98
Relationship between dalbavancin AUC24/MIC and
antibacterial effect at 24h, 120h and 240h
log change in viable
count
3
2
1
0
-1
-2
-3
-4
-5
1
2
3
4
log AUC/MIC
log change in count at 24h
log change in count at 120h
log change in count at 240h
Relationship between free drug AUC24/MIC and
antibacterial effect at 24h, 120h and 240h – dalbavancin
vs S.aureus described using an Emax model
minimum response
(no effect; log CFU/ml)
maximum response
(log CFU/ml)
EC50 AUC/mIC
(95% CI)
AUC/MIC for static to
-1 log drop
-2 log to –3 log drop
> -4 log drop
24h
+2.3 ± 0.3
time
120h
+2.0 ± 0.3
240h
+2.0 ± 0.3
-2.9 ± 0.2
-4.7 ± 0.3
-4.7 ± 0.3
46
(26-80)
131
(82-208)
218
(123-386)
36-81
>214
-
55-105
195-389
1170
100-182
331-676
1910
Some problems with MIC based pK/pD modelling
defining the correct pD index target (static, -1 log,
-2 log drop @ 24h,? 48h? 5d end point)
prediction in chronic infection (bone; abscess formation)
variability in the pD target size
Problems in paradise (2)
Variability in the pD target size
Daptomycin vs S.aureus
reference
inoculum
total
static dose
AUC
MIC
static
effect
Louie et al, 2001
Dandekar et al,
2003
Safdaver et al,
2004
105
106
7.0mg/kg
~ 10.0mg/kg
272
120
106
106-7
NS
20.8mg/kg
360
388
106-7
106-7
106-7
28.6mg/kg
22.5mg/kg
21.9mg/kg
537
420
409
av
358
237-480
120-537
95% CI
range
Conclusions
MIC based pK/pD in in vitro models
1.
Simple descriptive effects of simulated doses
2.
Many of potential antibacterial effect end points
3.
Allow study of emergence of resistance)
4.
Allow identification of dominant pD index and size
for effect
5.
Multitude of technical and analytic factors affect
validity of outcomes