Extrapolation of Drug Clearance in Children ≤ 2 Years of Age from Empirical Models Using Data from Children (> 2 Years) and Adults

Physiologically-Based Pharmacokinetic Modeling and Dosing Optimization of Cefotaxime in Preterm and Term Neonates

BACKGROUND

Cefotaxime is commonly used in treating bacterial infections in neonates. To characterize the pharmacokinetic process in neonates and evaluate different recommended dosing schedules of cefotaxime, a physiologically-based pharmacokinetic (PBPK) model of cefotaxime was established in adults and scaled to neonates.

METHODS

A whole-body PBPK model was built in PK-SIM® software. Three elimination pathways are composed of enzymatic metabolism in the liver, passive filtration through glomerulus, and active tubular secretion mediated by renal transporters. The ontogeny information was applied to account for age-related changes in cefotaxime pharmacokinetics. The established models were verified with realistic clinical data in adults and pediatric populations. Simulations in neonates were conducted and 100 % of the dosing interval where the unbound concentration in plasma was above the minimum inhibitory concentration (fT>MIC) was selected as the target index for dosing regimen evaluation.

RESULTS

The developed PBPK models successfully described the pharmacokinetic process of cefotaxime in adults and were scaled to the pediatric population. Good verification results were achieved in both adults' and neonates' PBPK models, indicating a good predictive performance. The optimal dosage regimen of cefotaxime was proposed according to the postnatal age (PNA) and gestational age (GA) of neonates. For preterm neonates (GA < 36 weeks), dosages of 25 mg/kg every 8 h in PNA 0-6 days and 25 mg/kg every 6 h in PNA 7-28 days were suggested. For term neonates (GA ≥ 36 weeks), dosages of 33 mg/kg every 8 h in PNA 0-6 days and 33 mg/kg every 6 h in PNA 7-28 days were recommended.

CONCLUSIONS

Our study may provide useful experience in practicing PBPK model-informed precision dosing in the pediatric population.

External validation and updating of the infusion rate individualization of soybean oil-based intravenous lipid emulsion: A descriptive cohort study

BACKGROUND

Safe and efficient provision of intravenous lipid emulsion (ILE) requires a strategy to individualize infusion rates. Estimating the maximum acceptable infusion rate (MaxInfRate) of soybean oil-based ILE (SO-ILE) in individuals by using a triglyceride (TG) kinetic model was reported to be feasible. In this study, we aimed to externally validate and, if needed, update the MaxInfRate estimation.

METHODS

The maximum TG concentration (TGmax) in patients receiving SO-ILE at MaxInfRate was evaluated to determine if it met the definition of being <400 mg/dl for 90th percentile of patients. The TG kinetic model was evaluated through prediction performance checks and was subsequently updated using the data set of both the previous model development and present validation studies.

RESULTS

Out of 83 patients, 74 had TGmax <400 mg/dl, corresponding to a probability of 89.2% (95% CI, 81.9%-95.2%), and the 90th percentile of TGmax was 400 mg/dl (95% CI, 328-490 mg/dl), closely aligned with the theoretical values. However, the individual TGmax values were biased by the infusion rate because the covariate effects were overestimated in the TG kinetic model, requiring a minor revision. The updated MaxInfRate with the combined data set showed unbiased and more accurate predictions.

CONCLUSION

The MaxInfRate was validated in external inpatients and updated with all available data. MaxInfRate estimation for individuals could be an option for the safe and efficient provision of SO-ILE.

How to select the initial dose for a pediatric study?

In typical clinical development programs, a new drug is first developed for the adult use. Drugs are often approved for adult use or in the process of obtaining approval in adults in the target indication before pediatric development is initiated. In designing the first pediatric clinical trial, one of the challenges is to select the initial dose to be tested. The ICH E11 R1 guidance advises that chronologic age alone may not always be the most appropriate categorical determinant to define developmental subgroups in pediatric studies. In this manuscript, the approaches to utilize available data in adults related to those factors beyond age to inform the starting dose selection in pediatric drug development are discussed. Practical considerations and approaches are provided for informing pediatric starting dose. Additional considerations to use pre-clinical information are provided in the case when adult information is limited or not available.

A Simple Method for the Prediction of Therapeutic Proteins (Monoclonal and Polyclonal Antibodies and Non-Antibody Proteins) for First-in-Pediatric Dose Selection: Application of Salisbury Rule

In order to conduct a pediatric clinical trial, it is important to optimize pediatric dose as accurately as possible. In this study, a simple weight-based method known as ‘Salisbury Rule’ was used to predict pediatric dose for therapeutic proteins and was then compared with the observed pediatric dose. The observed dose was obtained mainly from the FDA package insert and if dosing information was not available from the FDA package insert then the observed dose was based on the dose given to an age group in a particular study. It was noted that the recommended doses of most of the therapeutic proteins were extrapolated to pediatrics from adult dose based on per kilogram (kg) body weight basis. Since it is widely believed that pediatric dose should be selected based on the pediatric clearance (CL), a CL based pediatric dose was projected from the following equation: Dose in children = Adult dose × (Observed CL in children/Observed adult CL). In this study, this dose was also considered observed pediatric dose for comparison. A ±30% prediction error (predicted vs. observed) was considered acceptable. There were 21 monoclonal antibodies, 5 polyclonal antibodies in children ≥ 2 years of age, 4 polyclonal antibodies in preterm and term neonates, and 11 therapeutic proteins (non-antibodies) in the study. In children < 30 kg body weight, the predicted doses were within 0.5−1.5-fold prediction error for 87% (monoclonal antibody), 100% (polyclonal antibody), and 92% (non-antibodies) observations. In children > 30 kg body weight, the predicted doses were within 0.5−1.5-fold prediction error for 96% (monoclonal antibody), 100% (polyclonal antibody), and 100% (non-antibodies) observations. The Salisbury Rule mimics more to CL-based dose rather than per kg body weight-based extrapolated dose from adults. The Salisbury Rule for the pediatric dose prediction can be used to select first-in-children dose in pediatric clinical trials and may be in clinical settings.

Model-Informed Pediatric Drug Development: Application of Pharmacometrics to Define the Right Dose for Children

One of the biggest challenges in pediatric drug development is defining a safe and effective dose in pediatric populations, which span across a wide age and development range from neonates to adolescents. Model-informed drug development approaches are particularly suited to address knowledge gaps including data leveraging to increase the success of pediatric studies. Considering the often limited number of patients available for study and logistic difficulties to collect the necessary data in pediatric populations, the application of pharmacometrics and modeling and simulation techniques can improve clinical trial efficiency, increase the probability of regulatory success, and optimize therapeutic individualization in support of dedicated trials. This review describes the state of pediatric model-informed drug development to define the right dose for children and provides suggestions for future development.

Methods Used for Pediatric Dose Selection in Drug Development Programs Submitted to the US FDA 2012-2020

Dosing is a critical aspect of drug development in pediatrics that has led to trial failures and the inability to label the drug for pediatric use by the US Food and Drug Administration. Developing a structured approach for pediatric dose selection requires knowledge of the current approaches and their success or failure. This study describes the current experience with pediatric dosing methods from 2012 to 2020 and had 2 primary objectives: (1) to identify how the initial pediatric dose was selected and (2) to identify the pivotal dosing strategy used to identify the initially selected dose for safety and efficacy for pediatric clinical trials. Through September 2020, a total of 275 pediatric drug development programs were characterized for initial and pivotal dosing strategies. The success rate for labeling for pediatric use was 76.4%. The most common initial dosing strategy was previous experience with the product, followed by allometric scaling and exposure matching with adults. The most common pivotal dosing strategy was titration to target response in 33% of programs, with the second and third most common being pharmacokinetic/pharmacodynamic studies (30%) and exposure matching (20%), respectively. Additionally, about one-half of pediatric programs incorporated model-informed drug development. The emergence of titration to target response may signal a shift toward precision medicine in pediatric patients. Future work in pediatric drug dose selection should move toward the development of a structured pediatric dose selection approach.

A GFR-Based Method to Predict the Effect of Renal Impairment on the Exposure or Clearance of Renally Excreted Drugs: A Comparative Study Between a Simple GFR Method and a Physiologically Based Pharmacokinetic Model

Objective

The objective of this study was to compare the predictive performances of a glomerular filtration rate (GFR) model with a physiologically based pharmacokinetic (PBPK) model to predict total or renal clearance or area under the curve of renally excreted drugs in subjects with varying degrees of renal impairment.

Methods

From the literature, 11 studies were randomly selected in which total or renal clearance or area under the curve of drugs in subjects with different degrees of renal impairment were predicted by PBPK models. In these published studies, drugs were given to subjects intravenously or orally. The PBPK model was generally a whole-body model whereas the GFR model was as follows: Predicted total clearance (CL_T) = CL_T in healthy subjects × (GFR in RI/GFR in H), Predicted AUC = AUC in healthy subjects × (GFR in H/GFR in RI), where H is the healthy subjects and RI is renal impairment. The predicted clearance or area under the curve values using PBPK and GFR models were compared with the observed (experimental pharmacokinetic) values. The acceptable prediction error was within the 0.5- to 2-fold or 0.5- to 1.5-fold prediction error.

Results

There were 33 drugs with a total number of 101 observations (area under the curve, total and renal clearance in subjects with mild, moderate, and severe renal impairment). From PBPK and GFR models, out of 101 observations, 94 (93.1%) and 96 (95.0%) observations were within the 0.5- to 2-fold prediction error, respectively.

Conclusions

This study indicates that the predictive power of a simple GFR model is similar to a PBPK model for the prediction of clearance or area under the curve in subjects with renal impairment. The GFR method is simple, robust, and reliable and can replace complex empirical PBPK models.

First dose in neonates: pharmacokinetic bridging study from juvenile mice to neonates for drugs metabolized by CYP3A

First dose prediction is challenging in neonates. Our objective in this proof-of-concept study was to perform a pharmacokinetic (PK) bridging study from juvenile mice to neonates for drugs metabolized by CYP3A. We selected midazolam and clindamycin as model drugs. We developed juvenile mice population PK models using NONMEM. The PK parameters of these two drugs in juvenile mice were used to bridge PK parameters in neonates using different correction methods. The bridging results were evaluated by the fold-error of 0.5- to 1.5-fold. Simple allometry with and without a correction factor for maximum lifespan potential could be used for a bridging of clearance (CL) and volume of distribution (V_d), respectively, from juvenile mice to neonates. Simulation results demonstrated that for midazolam, 100% of clinical studies for which both the predictive CL and V_d were within 0.5- to 1.5-fold of the observed. For clindamycin, 75% and 100% of clinical studies for which the predictive CL and V_d were within 0.5- to 1.5-fold of the observed. A PK bridging of drugs metabolized by CYP3A is feasible from juvenile mice to neonates. It could be a complement to the ADE and PBPK models to support the first dose in neonates.