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Model Definition
Estimation Steps

Short description:

Population pharmacokinetics of meropenem in adult patients

Format:	PharmML (0.6.1)
Related Publication:	Population pharmacokinetic analysis and dosing regimen optimization of meropenem in adult patients. Li C, Kuti JL, Nightingale CH, Nicolau DP Journal of clinical pharmacology, 10/2006, Volume 46, Issue 10, pages: 1171-1178 Affiliation: Center for Anti-Infective Research and Development, Hartford Hospital, Hartford, CT 06102, USA. Abstract: The objectives of this study were to develop a meropenem population pharmacokinetic model using patient data and use it to explore alternative dosage regimens that could optimize the currently used dosing regimen to achieve higher likelihood of pharmacodynamic exposure against pathogenic bacteria. We gathered concentration data from 79 patients (ages 18-93 years) who received meropenem 0.5, 1, or 2 g over 0.5- or 3-hour infusion every 8 hours. Meropenem population pharmacokinetic analysis was performed using the NONMEM program. A 2-compartment model fit the data best. Creatinine clearance, age, and body weight were the most significant covariates to affect meropenem pharmacokinetics. Monte Carlo simulation was applied to mimic the concentration-time profiles while 1 g meropenem was administrated via infusion over 0.5, 1, 2, and 3 hours. The 3-hour prolonged infusion improved the likelihood of obtaining both bacteriostatic and bactericidal exposures most notably at the current susceptibility breakpoints.
Contributors:	Lisa Ehmann

Context of model development:	Variability sources in PK and PD (CYP, Renal, Biomarkers);
Discrepancy between implemented model and original publication:	Covariate effect of creatinine clearance on CL was fixed to original value from publication since no information on the distribution of cretainine clearance was given in the original publication;
Long technical model description:	Two compartment model with linear elimination; ;
Model compliance with original publication:	No;
Model implementation requiring submitter’s additional knowledge:	No;
Modelling context description:	To understand PK and variability of meropenem in adult patients;
Modelling task in scope:	simulation;
Nature of research:	Clinical research & Therapeutic use;
Therapeutic/disease area:	Anti-infectives;

Validation Status:	Annotations are correct.
Certification Comment:	This model is not certified.

Mandatory file
- Executable_Meropenem_MDL.xml

Additional Files

Model owner: Lisa Ehmann
Submitted: Aug 30, 2016 12:46:45 PM
Last Modified: Aug 30, 2016 12:46:45 PM

Revisions

Version: 12
- Submitted on: Aug 30, 2016 12:46:45 PM
- Submitted by: Lisa Ehmann
- With comment: Model revised without commit message

Independent variable T

Function Definitions

combinedError2 (additive, proportional, f) = \sqrt{({proportional}^{2} + ({additive}^{2} \times f^{2}))}

Structural Model sm

Variable definitions

\frac{dCENTRAL}{dT} = ((\frac{(- Q \times CENTRAL)}{V1} + \frac{(Q \times PERIPHERAL)}{V2}) - \frac{(CL \times CENTRAL)}{V1})

\frac{dPERIPHERAL}{dT} = (\frac{(Q \times CENTRAL)}{V1} - \frac{(Q \times PERIPHERAL)}{V2})

CC = \frac{CENTRAL}{V1}

Initial conditions

CENTRAL = 0

PERIPHERAL = 0

Variability Model

Level	Type
DV	residualError
ID	parameterVariability

Covariate Model

Continuous covariate WT

logtWT = log (\frac{WT}{70})

Continuous covariate AGE

logtAGE = log (\frac{AGE}{35})

Continuous covariate CLCR

logtCLCR = log (\frac{CLCR}{83})

Parameter Model

Parameters

POP_CL

;

POP_V1

;

POP_Q

;

POP_V2

;

COV_CL_AGE

;

COV_CL_CLCR

;

COV_V1_WT

;

RUV_PROP

;

RUV_ADD

;

PPV_CL

;

PPV_V1

;

PPV_Q

;

PPV_V2

;

ETA_CL \sim N (0.0, PPV_CL)

— ID

ETA_V1 \sim N (0.0, PPV_V1)

— ID

ETA_Q \sim N (0.0, PPV_Q)

— ID

ETA_V2 \sim N (0.0, PPV_V2)

— ID

EPS_Y \sim N (0.0, 1.0)

— DV

log (CL) = (log (POP_CL) + ((log (\frac{AGE}{35}) \times COV_CL_AGE) + ((log (\frac{CLCR}{83}) \times COV_CL_CLCR) + ETA_CL)))

log (V1) = (log (POP_V1) + ((log (\frac{WT}{70}) \times COV_V1_WT) + ETA_V1))

log (Q) = (log (POP_Q) + ETA_Q)

log (V2) = (log (POP_V2) + ETA_V2)

Observation Model

Observation Y
Continuous / Residual Data

Parameters

Y = (CC + (combinedError2 (RUV_ADD, RUV_PROP, CC) \times EPS_Y))

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Fixed parameters

COV_CL_CLCR = 0.62

Initial estimates for non-fixed parameters

$POP_CL = 14.6$
$POP_V1 = 10.8$
$POP_Q = 18.6$
$POP_V2 = 12.6$
$COV_CL_AGE = - 0.34$
$COV_V1_WT = 0.99$
$RUV_PROP = 0.19$
$RUV_ADD = 0.47$
$PPV_CL = 0.118$
$PPV_V1 = 0.143$
$PPV_Q = 0.29$
$PPV_V2 = 0.102$

Estimation operations

1) Estimate the population parameters

Algorithm FOCEI

Step Dependencies

estimStep_1

DDMODEL00000213: Li_2006_PK_Meropenem_AdultPatients

Revisions

Function Definitions

Structural Model sm

Variability Model

Covariate Model

Parameter Model

Observation Model

Observation YContinuous / Residual Data

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Estimation operations

Step Dependencies

Observation Y
Continuous / Residual Data