Transcript Zack-ISAP-Post

Application of PK/PD modeling
for optimization of linezolid
therapy
Julia Zayezdnaya Zack
Background: MRSA & linezolid

Methicillin Resistant S.aureus (MRSA) is a
major nosocomial pathogen that has caused
severe morbidity and mortality
 Linezolid
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newer antibiotic: first drug of a new classoxazalidinone
activity against Gram-positive bacteria: used mainly
for MRSA and VRE infections and in patients with
hypersensitivity
MOA: binds to the bacterial 50S ribosome subunit
and inhibits the initiation of protein synthesis
Goal
To use a PD model based on kill-curves
and PK in humans to predict the impact
of differing dosage regimens on
timecourse of MRSA CFU
 To design and validate these predictions
using an in vitro PK/PD model

Methods: kill-curve experiments
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PD kill-curve experiments:
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fixed initial inoculum (~107)
constant drug concentrations: 0-10XMIC
sampling over 24 hours
were fit by a PD mixture model
PD mixture model:
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capacity limited replication
1st order elimination,
effect of LZD as a Hill-type model inhibiting
replication
Methods: PD model-Dynamics of
Bacterial Growth and Death
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Time course of total bacteria growth is a result of a mixture of
homogenous sub-populations (mixture model)
Model incorporates bacterial replication modelled as a capacity
limited function
1st order rate constant for death
Drug effect enhancing bacterial death or inhibiting replication
(-)
Replication
IC50
Drug
Bacteria
CFU/mL
Pop 1
Pop2
Pop3
(+)
KD
IC50
Methods: PD model-Dynamics of
Bacterial Growth and Death
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The differential equation, for each bacterial subpopulation, is as
follows:
d CFUi/dt = VGmax·CFUi/[CFUM + CFUTOT] – kd·CFUi
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CFUi , CFU/mL of the i th subpopulation
Vgmax, maximum velocity of growth (CFU/mL/hr)
CFUM, CFU/mL associated with half-maximal growth
CFUTOT, sum total of all subpopulations
kd, drug-free 1st-order death rate constant of the bacteria (hr-1)
all subpopulations were assumed to share a common VGmax,
CFUM, and kd
Methods: PD model-Dynamics of
Bacterial Growth and Death

Drug effect (E) was modelled as a Hill-type function that either
decreased bacterial replication or enhanced the 1st order death
rate constants, as follows:
E(t) = 1± [Emax·(C/MIC)H]/[SITMiH + (C/MIC)H]
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E(t) is multiplied by the replication term or the rate constant for
death
 Emax is the maximum drug effect
 C/MIC is ~ the inverse serum inhibitory titre (SIT-1)
 SITMi is the SIT at which E is 50% of the Emax, for the ith
subpopulation
 H is the Hill’s constant (reflects slope)
 SITMi and initial conditions were allowed to differ between
subpopulations
Results: kill-curve experiments
LZD vs. M R S A 0-10xM IC
12
10
GC
CFU10Log
8
0.5 x MIC
1 x MIC
2 x MIC
5 x MIC
10 x MIC
6
4
2
0
0
5
10
15
Hours
20
25
30
Methods: in silico simulations

Two clinical MRSA isolates each with
two sub-populations
MIC 2 mg/L: “sensitive” subpopulation SITM
of 0.4 X MIC and “resistant” subpopulation
SIT of 3X MIC
 MIC 4 mg/L: “sensitive” subpopulation SITM
of 0.6 X MIC and “resistant” subpopulation
SIT of 6 X MIC
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Methods: in silico simulations
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Use human PK model to predict concentration
profiles and the PD mixture model to predict
responses to different dosing regimens:
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600 mg PO q12h (BID)
900 mg PO at time 0, followed by 600mg PO q12h
(BIDDL)
600 mg PO q8h (TID)
1200 mg PO at time 0, followed by 600 mg PO q8h
(TIDDL)
Results: in silico predictions
9
Log10(CFU)
8
7
6
5
4
3
0 10 20 30 40 50 60 70 80 90 100
TIME (hr)
MIC_4_GC
MIC_4_BID
MIC_4_BIDDL
MIC_4_TID
MIC_4_TIDDL
MIC_2_GC
MIC_2_BID
MIC_2_BIDDL
MIC_2_TID
MIC_2_TIDDL
Results: in silico predictions
Log10(Difference from GC)
0
MIC 4 mg/L BID
MIC 2 mg/L BID
-1
MIC 4 mg/L TID
-2
-3
MIC 2 mg/L TID
-4
0 10 20 30 40 50 60 70 80 90 100
TIME (hr)
Results: in silico predictions
9
Log10(CFU)
8
7
6
5
4
3
0 10 20 30 40 50 60 70 80 90 100
TIME (hr)
MIC_2_GC
MIC_2_BID
MIC_2_BIDDL
MIC_2_TID
MIC_2_TIDDL
Results: in silico predictions
9
Log10(CFU)
8
7
6
5
4
0 10 20 30 40 50 60 70 80 90 100
TIME (hr)
MIC_4_GC
MIC_4_BID
MIC_4_BIDDL
MIC_4_TID
MIC_4_TIDDL
Methods: in vitro PK/PD model
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Bacterial strains: MRSA, MIC 2 and 4 mg/L
Drug: linezolid
In vitro PK/PD model: series of flasks with multiple
ports for delivery of the drug and media and for
removal of waste
Methods: in vitro PK/PD model
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What we are simulating:
normal volunteer PK parameters—
clearances, volumes, etc.
 dosing regimens:600 mg PO q12h (BID)
and 600 mg PO q8h (TID)
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Results: in vitro activity
9
GCs
8
7
6
BID MIC4
5
4
TID MIC2
3
TID MIC 4
BID MIC2
2
0
10
20
30
Time (hr)
40
50
60
Results: in vitro activity
Inoculum changes over 48 hrs for BID regimen
0
12h
24h
40h
48 h
-0.5
Change (Log10(CFU))
-1
-1.5
MIC 4 mg/L
-2
-2.5
-3
-3.5
MIC 2 mg/L
-4
-4.5
Time (hr)
Results: in vitro activity
Inoculum changes over 48 hrs
0
12h
24h
40h
48 h
-0.5
Change (Log10(CFU))
-1
-1.5
BID MIC 4 mg/L
-2
-2.5
TID MIC 4 mg/L
-3
-3.5
TID MIC 2 mg/L
-4
-4.5
Time (hr)
BID MIC 2 mg/L
Conclusions
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In silico and in vitro simulations: traditional
regimen is predicted to be ineffective against
MRSA with MIC 4 mg/L
Mutant selection phenomenon
Predictive value of in silico simulations:
despite deriving from very sparse kill-curve
experiments and extrapolating to 96 hrs
Challenges translating these results into
biological systems
Future work