The Evaluation of Multiple Linear Regression-Based Limited Sampling Strategies for Mycophenolic Acid in Children with Nephrotic Syndrome

Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study

Mycophenolic acid (MPA) is commonly used to treat autoimmune diseases in children, and therapeutic drug monitoring is recommended to ensure adequate drug exposure. However, multiple blood sampling is required to calculate the area under the plasma concentration-time curve (AUC), causing patient discomfort and waste of human and financial resources. This study aims to use machine learning and deep learning algorithms to develop a prediction model of MPA exposure for pediatric autoimmune diseases with optimizing sampling frequency. Pediatric autoimmune patients' data were collected at Nanfang Hospital between June 2018 and June 2023. Univariate analysis was applied for feature selection. Ten algorithms, including Random Forest, XGBoost, LightGBM, Gradient Boosting Decision Tree, CatBoost, Artificial Neural Network, Grandient Boosting Machine, Transformer, Wide&Deep, and TabNet, were employed for modeling based on two, three, or four concentrations of MPA. A total of 614 MPA AUC0-12h samples from 209 patients were enrolled. Among the 10 models evaluated, the Wide&Deep model exhibited the best predictive performance. The predictive performance of the Wide&Deep model using four and three blood concentration points was similar (R 2 ≈ 1 for four points; R 2 = 0.95 for three points). No significant difference in accuracy within ±30% was observed between models utilizing three and four blood concentration points (p = 0.06). This study demonstrates that in the Wide&Deep model, MPA exposure can be accurately estimated with three sampling points in children with autoimmune diseases. This model could help reduce discomfort in pediatric patients without reducing the accuracy of MPA exposure estimates in clinical practice.

Therapeutic drug monitoring in childhood idiopathic nephrotic syndrome: a state of the art review

Immunosuppressants are commonly used as steroid-sparing agents in childhood idiopathic nephrotic syndrome (NS) to induce and sustain remissions. These drugs have narrow therapeutic indices with high inter- and intra-patient variability. Therapeutic drug monitoring (TDM) would therefore be essential to guide the prescription. Multiple factors in NS contribute to additional variability in drug concentrations, especially during relapses. In this article, we review the currently available evidence of TDM in NS and suggest a practical approach for clinicians' reference.

Limited sampling strategy to predict free mycophenolic acid area under the concentration-time curve in paediatric patients with nephrotic syndrome

In paediatric patients, there is no data on the recommended area under the concentration-time curve from 0 to 12 h (AUC_0-12 ) for free mycophenolic acid (fMPA), which is the active form of the drug, responsible for the pharmacological effect. We decided to establish the limited sampling strategy (LSS) for fMPA for its use in MPA therapeutic monitoring in children with nephrotic syndrome treated with mycophenolate mofetil (MMF). This study included 23 children (aged 11 ± 4 years) from whom eight blood samples were collected within 12 h after MMF administration. The fMPA was determined using the high-performance liquid chromatography with fluorescence detection method. LSSs were estimated with the use of R software and bootstrap procedure. The best model was chosen based on a number of profiles with AUC predicted within ± 20% of AUC_0-12 (good guess), r² , mean prediction error (%MPE) of ±10% and mean absolute error (%MAE) of less than 25%. The fMPA AUC_0-12 was 0.1669 ± 0.0697 μg h/mL and the free fraction was within 0.16%-0.81%. In total, there were 92 equations developed of which five fulfilled the acceptance criteria for %MPE, %MAE, good guess >80% and r²  > 0.900. These equations consisted of three time points: model 1 (C₁ , C₂ , C₆ ), model 2 (C₁ , C₃ , C₆ ), model 3 (C₁ , C₄ , C₆ ), model 5 (C₀ , C₁ , C₂ ), and model 6 (C₁ , C₂ , C₉ ). Although blood sampling up to 9 h after MMF dosing is impractical, it is crucial to include C₆ or C₉ in LSS to assess fMPA AUC_pred correctly. The most practical fMPA LSS, which fulfilled the acceptance criteria in the estimation group, was fMPA AUC_pred  = 0.040 + 2.220 × C₀  + 1.130 × C₁  + 1.742 × C₂ . Further studies should define the recommended fMPA AUC_0-12 value in children with nephrotic syndrome.

Effect of Meal Timings and Meal Content on the AUC0-12h of Mycophenolic Acid: A Simulation Study

Meal timings and content related to gallbladder emptying in the enterohepatic circulation are important for explaining the high variability in mycophenolic acid exposure. The limited sampling strategy (LSS) was established to estimate the area under the plasma concentration-time curve from time 0 to 12 hours (AUC_0-12h ) of mycophenolic acid in therapeutic drug monitoring. The aim of this study is to investigate the effect of meal timings and content on the AUC_0-12h of mycophenolic acid and to assess the influence of meals on LSS. A mycophenolic acid pharmacokinetic model with a mechanism-based enterohepatic circulation process was employed to perform simulations under various assumed meal scenarios. The simulations were compared to evaluate the effect of meal timings and meal content on mycophenolic acid AUC_0-12h . Monte Carlo simulations were performed using the meal parameter with the greatest impact on mycophenolic acid AUC_0-12h as a variable. The corresponding LSS equations were established, and the predictive performance was assessed. Both the meal timings and meal content affected the mycophenolic acid AUC_0-12h , and the postdose fasting period had the greatest impact. The predictive performance of the LSS is sensitive to the postdose fasting period. Therefore, meal timings may improve the estimation of mycophenolic acid AUC_0-12h and the efficacy of therapeutic drug monitoring.

Evaluation and Validation of the Limited Sampling Strategy of Polymyxin B in Patients with Multidrug-Resistant Gram-Negative Infection

Polymyxin B (PMB) is the final option for treating multidrug-resistant Gram-negative bacterial infections. The acceptable pharmacokinetic/pharmacodynamic target is an area under the concentration–time curve across 24 h at a steady state (AUC_ss,24h) of 50–100 mg·h/L. The limited sampling strategy (LSS) is useful for predicting AUC values. However, establishing an LSS is a time-consuming process requiring a relatively dense sampling of patients. Further, given the variability among different centers, the predictability of LSSs is frequently questioned when it is extrapolated to other clinical centers. Currently, limited data are available on a reliable PMB LSS for estimating AUC_ss,24h. This study assessed and validated the practicability of LSSs established in the literature based on data from our center to provide reliable and ready-made PMB LSSs for laboratories performing therapeutic drug monitoring (TDM) of PMB. The influence of infusion and sampling time errors on predictability was also explored to obtain the optimal time points for routine PMB TDM. Using multiple regression analysis, PMB LSSs were generated from a model group of 20 patients. A validation group (10 patients) was used to validate the established LSSs. PMB LSSs from two published studies were validated using a dataset of 30 patients from our center. A population pharmacokinetic model was established to simulate the individual plasma concentration profiles for each infusion and sampling time error regimen. Pharmacokinetic data obtained from the 30 patients were fitted to a two-compartment model. Infusion and sampling time errors observed in real-world clinical practice could considerably affect the predictability of PMB LSSs. Moreover, we identified specific LSSs to be superior in predicting PMB AUC_ss,24h based on different infusion times. We also discovered that sampling time error should be controlled within −10 to 15 min to obtain better predictability. The present study provides validated PMB LSSs that can more accurately predict PMB AUC_ss,24h in routine clinical practice, facilitating PMB TDM in other laboratories and pharmacokinetics/pharmacodynamics-based clinical studies in the future.

Sustained suppression of enterohepatic circulation of mycophenolic acid by antimicrobial-associated diarrhea in a kidney transplant recipient with Crohn's disease: A case report

Mycophenolic acid (MPA) undergoes enterohepatic circulation. A kidney transplant patient on mycophenolate mofetil was treated with tazobactam/piperacillin for pyelonephritis, and developed antimicrobial-associated diarrhea. Consequently, the MPA trough level decreased by approximately 90%. Furthermore, it took approximately a month for the MPA level to normalize even after diarrhea had resolved.

Two Innovative Approaches to Optimize Vancomycin Dosing Using Estimated AUC after First Dose: Validation Using Data Generated from Population PK Model Coupled with Monte-Carlo Simulation and Comparison with the First-Order PK Equation Approach

The revised consensus guidelines for optimizing vancomycin doses suggest that maintaining the area under the concentration-time curve to minimal inhibitory concentration ratio (AUC/MIC) of 400–600 mg·h/L is the target pharmacokinetic/pharmacodynamic (PK/PD) index for efficacy. AUC-guided dosing approach uses a first-order pharmacokinetics (PK) equation to estimate AUC using two samples obtained at steady state and one-compartment model, which can cause inaccurate AUC estimation and fail to achieve the effective PK/PD target early in therapy (days 1 and 2). To achieve an efficacy target from the third or fourth dose, two innovative approaches (Method 1 and Method 2) to estimate vancomycin AUC at steady state (AUCSS) using two-compartment model and three or four levels after the first dose are proposed. The feasibility of the proposed methods was evaluated and compared with another published dosing algorithm (Method 3), which uses two samples and a one-compartment approach. Monte Carlo simulation was performed using a well-established population PK model, and concentration-time profiles for virtual patients with various degrees of renal function were generated, with 1000 subjects per group. AUC extrapolated to infinity (AUC0–∞) after the first dose was estimated using the three methods, whereas reference AUC (AUCref) was calculated using the linear-trapezoidal method at steady state after repeated doses. The ratio of AUC0–∞: AUCref and % bias were selected as the indicators to evaluate the accuracy of three methods. Sensitivity analysis was performed to examine the influence of change in each sampling time on the estimated AUC0–∞ using the two proposed approaches. For simulated patients with various creatinine clearance, the mean of AUC0–∞: AUCref obtained from Method 1, Method 2 and Method 3 ranged between 0.98 to 1, 0.96 to 0.99, and 0.44 to 0.69, respectively. The mean bias observed with the three methods was −0.10% to −2.09%, −1.30% to −3.59% and −30.75% to −55.53%, respectively. The largest mean bias observed by changing sampling time while using Method 1 and Method 2 were −4.30% and −10.50%, respectively. Three user-friendly and easy-to-use excel calculators were built based on the two proposed methods. The results showed that our approaches ensured sufficient accuracy and achieved target PK/PD index early and were superior to the published methodologies. Our methodology has the potential to be used for vancomycin dose optimization and can be easily implemented in clinical practice.