Mechanism-based PKPD-models for Selection of …...Mechanism-based PKPD-models for Antibiotics •...
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Mechanism-based PKPD-models for Selection of Dosing Regimens for
Antibiotics
Lena Friberg Anders Kristoffersson and Elisabet Nielsen
Pharmacometrics Research Group Department of Pharmaceutical Biosciences
Uppsala University Sweden
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Selection of dosing regimens for antibiotics
Traditional way 1. Determine Type and Target magnitude of PK/PD index
– fAUC/MIC, fT>MIC or fCmax/MIC typically identified in mice (bacterial kill at 24h)
2. Find regimen that results in acceptable Probability of Target Attainment (PTA) – Simulations from a Population PK model, MIC (distribution) and the defined
Target magnitude
Assumptions: Same target independent of patient population Ex. Meropenem dosed according to 40% fT>MIC (Drusano et al. Clin Infect Dis, 2003)
Difficulties: Summary variables cannot handle complexities such as – Drug combinations – Resistance development
Evolving way
PKPD-modelling of data from in vitro time-kill experiments and in vivo data → Time-courses
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Mechanism-based PKPD-models for Antibiotics
• In vitro time-kill curve data
Static concentrations Dynamic concentrations
Ex. Model structure for gentamicin and colistin Mohamed et al., AAC 2012, Mohamed et al., JAC 2014
• Model structure includes – Natural bacterial growth – Drug effect – Resistance mechanism
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Prediction of PK/PD indices Simulate mouse study on meropenem
(Katsube et al., J Pharm Sci, 2008)
fCmax/MIC, fAUC/MIC and fT>MIC Log10 CFU/ml at 24h
3 x 4 dosing regimens (4 dosing intervals, 3 dose levels)
PK: t1/2 ~ 0.3 h
Model based on in vitro data
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• fT>MIC best PK/PD index as typically reported for carbapenems (and other β-lactams)
• Target of 40% fT>MIC recommended for meropenem (Drusano et al., Clin Infect Dis, 2003)
Simulation PK/PD indices - Meropenem Mouse PK
Mouse: t1/2 ~ 0.3 h (Katsube et al., J Pharm Sci, 2008)
fAUC/MIC fT>MIC fCmax/MIC
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Prediction of PK/PD indices Colistin in mice
Observed data in mice (Dudhani et al., AAC 2010)
3 log kill: 35
Predictions from same PK and a mechanism-based PKPD-model for colistin (Mohamed et al., JAC, 2014)
Khan et al., In manuscript
3 log kill: 12
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Nielsen et al., AAC 2011
Vanc
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Mox
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G
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Cef
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Pen
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PKPD-models based on in vitro data can predict
PK/PD-driver determined in vivo
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• 32% fT>MIC for 2-log kill is close to the commonly cited value of 40% (Drusano et al., Clin Infect Dis, 2003)
• fAUC/MIC is nearly as good predictor as fT>MIC
Simulation PK/PD indices - Meropenem Typical adult patient PK
fAUC/MIC fT>MIC fCmax/MIC
Typical: Adult, CrCL=83 ml/min 2-comp PK, t1/2,β ~ 1 h (Li et al., J Clin Pharmacol, 2006)
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• Best predictor moves towards fAUC/MIC for increased half-lives • fT>MIC indicates a higher target (exposure should be increased ) • fAUC/MIC indicates a lower target (exposure can be decreased)
Simulation PK/PD indices - Meropenem Different patient populations
Typical: Adult, CrCL=83 ml/min 2-comp PK, t1/2,β ~ 1 h (Li et al, J Clin Pharmacol 2006)
Renal dysfunction: Adult, CrCL=15 ml/min 2-comp PK, t1/2,β ~ 1.5 h (Li et al, J Clin Pharmacol 2006)
Preterm neonate: GA 31w 2-comp PK, t1/2,β ~ 1.5 h (van den Anker et al, AAC 2009)
fAUC/MIC fT>MIC fCmax/MIC
Selection of ’best’ PK/PD-index is sensitive to
PK in the population
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Probability of Target Attainment (PTA) Different dosing regimens of meropenem
• fT/MIC predicts higher PTA at a specific MIC level
2 mg, 1h inf q8h
2 mg, 3h inf q8h
6 mg / 24h cont. inf
Pro
babi
lity
of T
arge
t Atta
inm
ent
Typical CL Renal Dysfunction Augmented CL
Choice of PK/PD-driver and target will affect treatment
decisions for different MICs
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Value of continuous meropenem infusion in
different patient populations?
2 mg, 1h inf q8h
2 mg, 3h inf q8h
6 mg / 24h cont. inf
Typical CL Renal Dysfunction Augmented CL
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Conclusions
• Mechanism-based PKPD-models based on in vitro data can predict in vivo PKPD results
• Typically assumed to be one ´true´PK/PD index and target magnitude, but they are sensitive to – PK in the population – MIC value – Resistance development
– Design
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Potential uses of a mechanism-based PKPD-model based on in vitro data
• Improved designs of animal experiments – Ethical and financial benefits
• An understanding of the time-course of drug effects – Influence of resistance development – Predictions beyond experimental time?
• A range of dosing scenarios can be explored – Dosing regimens – Loading dose – Drug combinations
• Correlations between MIC and EC50 – Limited data needed to explore time-courses for new mutants
(Khan et al., Submitted)
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Thank you!