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 newer antibiotic: first drug of a new class-

oxazalidinone 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

PD kill-curve experiments: fixed initial inoculum (~107) constant drug concentrations: 0-10XMIC sampling over 24 hours were fit by a PD mixture model

PD mixture model: 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

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

BacteriaBacteriaCFU/mLCFU/mL

Pop 1Pop 1 Pop2Pop2 Pop3Pop3

KDReplication

IC50IC50

Drug(+)

(-)

Methods: PD model-Dynamics of Bacterial Growth and Death

The differential equation, for each bacterial subpopulation, is as follows:

d CFUi/dt = VGmax·CFUi/[CFUM + CFUTOT] – kd·CFUi

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]

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

Hours

0 5 10 15 20 25 30

CF

U1

0Lo

g

0

2

4

6

8

10

12

0.5 x MIC

GC

1 x MIC2 x MIC5 x MIC10 x MIC

Methods: in silico simulations

Two clinical MRSA isolates each with two sub-populationsMIC 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

Methods: in silico simulations

Use human PK model to predict concentration profiles and the PD mixture model to predict responses to different dosing regimens: 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

0 10 20 30 40 50 60 70 80 90 100TIME (hr)

3

4

5

6

7

8

9

Lo

g1

0(C

FU

)

MIC_2_TIDDLMIC_2_TIDMIC_2_BIDDLMIC_2_BIDMIC_2_GCMIC_4_TIDDLMIC_4_TIDMIC_4_BIDDLMIC_4_BIDMIC_4_GC

Results: in silico predictions

0 10 20 30 40 50 60 70 80 90 100TIME (hr)

-4

-3

-2

-1

0

Lo

g1

0(D

i ffe

ren

ce f r

om

GC

)

MIC 4 mg/L BID

MIC 2 mg/L BID

MIC 4 mg/L TID

MIC 2 mg/L TID

Results: in silico predictions

0 10 20 30 40 50 60 70 80 90 100TIME (hr)

3

4

5

6

7

8

9

Lo

g1

0(C

FU

)

MIC_2_TIDDLMIC_2_TIDMIC_2_BIDDLMIC_2_BIDMIC_2_GC

Results: in silico predictions

0 10 20 30 40 50 60 70 80 90 100TIME (hr)

4

5

6

7

8

9

Lo

g1

0(C

FU

)

MIC_4_TIDDLMIC_4_TIDMIC_4_BIDDLMIC_4_BIDMIC_4_GC

Methods: in vitro PK/PD model

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

What we are simulating:normal volunteer PK parameters—

clearances, volumes, etc.dosing regimens:600 mg PO q12h (BID)

and 600 mg PO q8h (TID)

2

3

4

5

6

7

8

9

0 10 20 30 40 50 60

Time (hr)

Results: in vitro activity

GCs

BID MIC4

TID MIC2

TID MIC 4BID MIC2

Results: in vitro activity

MIC 4 mg/L

MIC 2 mg/L

Inoculum changes over 48 hrs for BID regimen

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

12h 24h 40h 48 h

Time (hr)

Ch

ang

e (L

og

10(C

FU

))

Results: in vitro activity

BID MIC 4 mg/L

TID MIC 4 mg/L

TID MIC 2 mg/L

BID MIC 2 mg/L

Inoculum changes over 48 hrs

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

12h 24h 40h 48 h

Time (hr)

Ch

ang

e (L

og

10(C

FU

))

Conclusions

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