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Application of PK/PD modeling for optimization of linezolid therapy

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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Application of PK/PD modeling for optimization of linezolid therapy

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  1. Application of PK/PD modeling for optimization of linezolid therapy Julia Zayezdnaya Zack

  2. 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

  3. 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

  4. 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

  5. Drug (+) (-) Bacteria CFU/mL Pop 1 Pop2 Pop3 KD Replication IC50 IC50 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

  6. 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

  7. 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

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

  9. 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

  10. 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)

  11. Results: in silico predictions

  12. MIC 4 mg/L BID MIC 2 mg/L BID MIC 4 mg/L TID MIC 2 mg/L TID Results: in silico predictions

  13. Results: in silico predictions

  14. Results: in silico predictions

  15. 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

  16. 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)

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

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

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

  20. 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

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