A Method for Evaluating Robustness of Limited Sampling Strategies—Exemplified by Serum Iohexol Clearance for Determination of Measured Glomerular Filtration Rate

Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning

INTRODUCTION

Valganciclovir, a prodrug of ganciclovir (GCV), is used to prevent cytomegalovirus infection after transplantation, with doses adjusted based on creatinine clearance (CrCL) to target GCV AUC0-24 h of 40-60 mg*h/L. This sometimes leads to overexposure or underexposure. This study aimed to train, test and validate machine learning (ML) algorithms for accurate GCV AUC0-24 h estimation in solid organ transplantation.

METHODS

We simulated patients for different dosing regimen (900 mg/24 h, 450 mg/24 h, 450 mg/48 h, 450 mg/72 h) using two literature population pharmacokinetic models, allocating 75% for training and 25% for testing. Simulations from two other literature models and real patients provided validation datasets. Three independent sets of ML algorithms were created for each regimen, incorporating CrCL and 2 or 3 concentrations. We evaluated their performance on testing and validation datasets and compared them with MAP-BE.

RESULTS

XGBoost using 3 concentrations generated the most accurate predictions. In testing dataset, they exhibited a relative bias of -0.02 to 1.5% and a relative RMSE of 2.6 to 8.5%. In the validation dataset, a relative bias of 1.5 to 5.8% and 8.9 to 16.5%, and a relative RMSE of 8.5 to 9.6% and 10.7% to 19.7% were observed depending on the model used. XGBoost algorithms outperformed or matched MAP-BE, showing enhanced generalization and robustness in their estimates. When applied to real patients' data, algorithms using 2 concentrations showed relative bias of 1.26% and relative RMSE of 12.68%.

CONCLUSIONS

XGBoost ML models accurately estimated GCV AUC0-24 h from limited samples and CrCL, providing a strategy for optimized therapeutic drug monitoring.

Leveraging machine learning in limited sampling strategies for efficient estimation of the area under the curve in pharmacokinetic analysis: a review

OBJECTIVE

Limited sampling strategies are widely employed in clinical practice to minimize the number of blood samples required for the accurate area under the curve calculations, as obtaining these samples can be costly and challenging. Traditionally, the maximum a posteriori Bayesian estimation has been the standard method for the area under the curve estimation based on limited samples. However, machine learning is emerging as a promising alternative for this purpose. Here, we review studies that utilize machine learning approaches to develop limited sampling strategies and compare the strengths and weaknesses of these machine learning methods.

METHODS

We searched the literature for studies that used machine learning to estimate the area under the curve using a limited sampling strategy approach.

RESULTS

We identified ten studies that developed machine learning models to estimate the area under the curve for six different drugs. Several of these models demonstrated good accuracy and precision in area under the curve estimation in reference to the traditional Bayesian approach, highlighting the potential of machine learning models in precision dosing.

CONCLUSIONS

Despite these promising early results, the development of machine learning for limited sampling strategies is still in its early stages. Further research might be needed to validate machine learning models with larger, high-quality clinical datasets to ensure their reliability and applicability in clinical settings.

Meropenem pharmacokinetic/pharmacodynamic target attainment and clinical response in ICU patients: A prospective observational study

BACKGROUND

Several studies report lack of meropenem pharmacokinetic/pharmacodynamic (PK/PD) target attainment (TA) and risk of therapeutic failure with intermittent bolus infusions in intensive care unit (ICU) patients. The aim of this study was to describe meropenem TA in an ICU population and the clinical response in the first 72 h after therapy initiation.

METHODS

A prospective observational study of ICU patients ≥18 years was conducted from 2014 to 2017. Patients with normal renal clearance (NRC) and augmented renal clearance (ARC) and patients on continuous renal replacement therapy (CRRT) were included. Meropenem was administered as intermittent bolus infusions, mainly at a dose of 1 g q6h. Peak, mid, and trough levels were sampled at 24, 48, and 72 h after therapy initiation. TA was defined as 100% T > 4× MIC or trough concentration above 4× MIC. Meropenem PK was estimated using traditional calculation methods and population pharmacokinetic modeling (P-metrics®). Clinical response was evaluated by change in C-reactive protein (CRP), Sequential Organ Failure Assessment (SOFA) score, leukocyte count, and defervescence.

RESULTS

Eighty-seven patients were included, with a median Simplified Acute Physiology (SAPS) II score 37 and 90 days mortality rate of 32%. Median TA was 100% for all groups except for the ARC group with 45.5%. Median CRP fell from 175 (interquartile range [IQR], 88-257) to 70 (IQR, 30-114) (p < .001) in the total population. A reduction in SOFA score was observed only in the non-CRRT groups (p < .001).

CONCLUSION

Intermittent meropenem bolus infusion q6h gives satisfactory TA in an ICU population with variable renal function and CRRT modality, except for ARC patients. No consistent relationship between TA and clinical endpoints were observed.