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 (~10 7 ) constant drug concentrations: 0-10XMIC sampling over 24 hours were fit by a PD mixture model PD mixture model: capacity limited replication 1 st 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 BacteriaCFU/mL Pop 1 Pop 1 Pop2 Pop2 Pop3 Pop3 KDKD Replication IC 50 Drug (+) (-)

Methods: PD model-Dynamics of Bacterial Growth and Death The differential equation, for each bacterial subpopulation, is as follows: d CFU i /dt = VG max ·CFUi/[CFU M + CFU TOT ] – kd·CFU i CFU i, CFU/mL of the i th subpopulation Vgmax, maximum velocity of growth (CFU/mL/hr) CFU M, CFU/mL associated with half-maximal growth CFU TOT, sum total of all subpopulations kd, drug-free 1 st -order death rate constant of the bacteria (hr -1 ) all subpopulations were assumed to share a common VGmax, CFU M, 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 ]/[SIT Mi H + (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 ) SIT Mi is the SIT at which E is 50% of the Emax, for the ith subpopulation H is the Hill’s constant (reflects slope) SIT Mi and initial conditions were allowed to differ between subpopulations

Results: kill-curve experiments 0.5 x MIC GC 1 x MIC 2 x MIC 5 x MIC 10 x MIC

Methods: in silico simulations Two clinical MRSA isolates each with two sub-populations MIC 2 mg/L: “sensitive” subpopulation SIT M of 0.4 X MIC and “resistant” subpopulation SIT of 3X MIC MIC 4 mg/L: “sensitive” subpopulation SIT M 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

MIC 4 mg/L BID MIC 2 mg/L BID MIC 4 mg/L TID MIC 2 mg/L TID

Results: in silico predictions

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)

Results: in vitro activity GCs BID MIC4 TID MIC2 TID MIC 4 BID MIC2

Results: in vitro activity MIC 4 mg/L MIC 2 mg/L

Results: in vitro activity BID MIC 4 mg/L TID MIC 4 mg/L TID MIC 2 mg/L BID MIC 2 mg/L

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