Dalbavancin is indicated for the treatment of acute bacterial skin and skin structure infections caused by susceptible gram-positive microorganisms. This analysis represents the update of the population pharmacokinetics (popPK) modeling and target attainment simulations performed with data from the single-dose safety and efficacy study and an unrelated but substantial revision of the preclinical pharmacokinetic/pharmacodynamic target (fAUC/MIC, free area under concentration-time curve/minimum inhibitory concentration ratio). A 3-compartment distribution model with first-order elimination provided an appropriate fit, with typical dalbavancin clearance of 0.05 L/h and total volume of distribution of ß15 L. Impact of intrinsic factors was modest, although statistically significant (P < .05) relationships with total clearance were found for the following covariates: creatinine clearance, weight, and albumin -dose adjustment was only indicated for severe renal impairment. Under the new nonclinical target, simulations of the popPK model projected that >99% of subjects would achieve the nonclinical target at MIC values up to and including 2 mg/L.
Keywordsacute bacterial skin and skin structure infection, dalbavancin, long-acting intravenous antibiotic, pharmacokinetics, target attainment 1 Allergan plc,
A two-stage, numerical deconvolution approach was employed to develop level A in vitro-in vivo correlations using data for three formulations of an extended-release oral dosage form. The in vitro dissolution data for all formulations exhibited near-complete dissolution within the time frame of the test. The pharmacokinetic concentration-time profiles for 16 subjects in a cross-over study demonstrated notably limited bioavailability for the slowest formulation. These data were used as the basis for the IVIVC model development. Two models were identified that satisfied the nominal requirements for a conclusive internal predictability of the IVIVC, provided that all three formulations were used as internal datasets. These were a simple linear model with absorption cutoff and a piecewise-linear variable absorption scale model. A subsequent cross-validation of the models' robustness indicated that neither model predicted satisfactorily the pharmacokinetic characteristics of all formulations in a conclusive manner. The piecewise-linear variable absorption scale model provided the most accurate results, particularly with respect to the prediction of the slowest formulation's pharmacokinetic metrics. But this latter model also involved additional free parameters compared with the simple linear model with absorption cut-off. It is argued that more complex IVIVC models with extra parameterization require comprehensive validation to ascertain the accuracy and robustness of the model. In order to achieve this, it is necessary to ensure a complete suite of supporting datasets for internal and external validation, irrespective of the mathematical approach used subsequently to develop the IVIVC.
In nonlinear mixed effect (NLME) modeling, the intra-individual variability is a collection of errors due to assay sensitivity, dosing, sampling, as well as model misspecification. Utilizing stochastic differential equations (SDE) within the NLME framework allows the decoupling of the measurement errors from the model misspecification. This leads the SDE approach to be a novel tool for model refinement. Using Metformin clinical pharmacokinetic (PK) data, the process of model development through the use of SDEs in population PK modeling was done to study the dynamics of absorption rate. A base model was constructed and then refined by using the system noise terms of the SDEs to track model parameters and model misspecification. This provides the unique advantage of making no underlying assumptions about the structural model for the absorption process while quantifying insufficiencies in the current model. This article focuses on implementing the extended Kalman filter and unscented Kalman filter in an NLME framework for parameter estimation and model development, comparing the methodologies, and illustrating their challenges and utility. The Kalman filter algorithms were successfully implemented in NLME models using MATLAB with run time differences between the ODE and SDE methods comparable to the differences found by Kakhi for their stochastic deconvolution.
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