Optimized Dosing Regimens of Meropenem in Septic Children Receiving Extracorporeal Life Support

Objectives: To develop a population pharmacokinetic model of meropenem in children with sepsis receiving extracorporeal life support (ECLS) and optimize the dosage regimen based on investigating the probability of target attainment (PTA).Methods: The children with sepsis were prospectively enrolled in a pediatric intensive care unit from January 2018 to December 2019. The concentration-time data were fitted using nonlinear mixed effect model approach by NONMEM program. The stochastic simulation considering various scenarios based on proposed population pharmacokinetics model were conducted, and the PTAs were calculated to optimize the dosage regimens.Results: A total of 25 children with sepsis were enrolled, of whom13 received ECMO, 9 received CRRT, and 4 received ECMO combined with CRRT. 12 children received a two-step 3-h infusion and 13 children received 1-h infusion. Bodyweight and creatinine clearance had significant impacts on the PK parameters. ECMO intervention was not related to the PK properties. If 100%T > MIC was chosen as target, children receiving 40 mg/kg q8h over a 3 h-infusion only reached the PTA up to 77.4%. If bacteria with MIC 2 mg/L were to be treated with meropenem and the PTA target was 50%T > MIC, a dose of 40 mg/kg q8h for 1 h infusion would be necessary.Conclusions: The PK properties of meropenem in septic children receiving extracorporeal life support were best described. We recommended the opitimized dosing regimens for septic children receiving ECLS depending on the PTA of PK target 50%T > MIC and 100%T > MIC, for children with sepsis during ECLS with different body weight, estimated creatinine clearance (eCRCL) and MIC of bacteria.

Bayesian multi-response nonlinear mixed-effect model: application of two recent HIV infection biomarkers

Since the discovery of the human immunodeficiency virus (HIV) 35 years ago, the epidemic is still ongoing in France. To monitor the dynamics of HIV transmission and assess the impact of prevention campaigns, the main indicator is the incidence. One method to estimate the HIV incidence is based on biomarker values at diagnosis and their dynamics over time. Estimating the HIV incidence from biomarkers first requires modeling their dynamics since infection using external longitudinal data. The objective of the work presented here is to estimate the joint dynamics of two biomarkers from the PRIMO cohort. We thus jointly modeled the dynamics of two biomarkers (TM and V3) using a multi-response nonlinear mixed-effect model. The parameters were estimated using Bayesian Hamiltonian Monte Carlo inference. This procedure was first applied to the real data of the PRIMO cohort. In a simulation study, we then evaluated the performance of the Bayesian procedure for estimating the parameters of multi-response nonlinear mixed-effect models.

Efficient Precision Dosing Under Estimated Uncertainties via Koopman Expectations of Bayesian Posteriors with Pumas

Personalized precision dosing is about mathematically determining effective dosing strategies that optimize the probability of containing a patient’s outcome within a therapeutic window. However, the common Monte Carlo approach for generating patient statistics is computationally expensive because thousands of simulations need to be computed. In this manuscript we describe a new method which utilizes the Koopman operator to perform a direct computation of expected patient outcomes with respect to quantified uncertainties of Bayesian posteriors in a nonlinear mixed effect model framework. We detail how quantities such as the probability of being within the therapeutic window can be calculated with a choice of loss function on the Koopman expectation. We demonstrate a high performance parallelized implementation of this methodology in Pumas® and showcase the ability to accelerate the computation of these expectations by 50x-200x over Monte Carlo. We showcase how dosing can be optimized with respect to probabilistic statements respecting variable uncertainties using the Koopman operator. We end by demonstrating an end-to-end workflow, from estimating variable uncertainties via Bayesian estimation to optimizing a dose with respect to parametric uncertainty.

Fuzzy Evaluation of Pharmacokinetic Models

Population pharmacokinetic (PopPK) models allow researchers to predict and analyze drug behavior in a population of individuals and to quantify the different sources of variability among these individuals. In the development of PopPK models, the most frequently used method is the nonlinear mixed effect model (NLME). However, once the PopPK model has been developed, it is necessary to determine if the selected model is the best one of the developed models during the population pharmacokinetic study, and this sometimes becomes a multiple criteria decision making (MCDM) problem, and frequently, researchers use statistical evaluation criteria to choose the final PopPK model. The used evaluation criteria mentioned above entail big problems since the selection of the best model becomes susceptible to the human error mainly by misinterpretation of the results. To solve the previous problems, we introduce the development of a software robot that can automate the task of selecting the best PopPK model considering the knowledge of human expertise. The software robot is a fuzzy expert system that provides a method to systematically perform evaluations on a set of candidate PopPK models of commonly used statistical criteria. The presented results strengthen our hypothesis that the software robot can be successfully used to evaluate PopPK models ensuring the selection of the best PopPK model.