Does Sample Size, Sampling Strategy, or Handling of Concentrations Below the Lower Limit of Quantification Matter When Externally Evaluating Population Pharmacokinetic Models?

• Published in: European journal of drug metabolism and pharmacokinetics 2024-07, Vol.49 (4), p.419-436
• Main Authors: El Hassani, Mehdi, Liebchen, Uwe, Marsot, Amélie
• Format: Article
• Language: English
• Subjects: Anti-Bacterial Agents - administration & dosage; Anti-Bacterial Agents - pharmacokinetics; Biomedical and Life Sciences; Biomedicine; Computer Simulation; Drug Monitoring - methods; Human Physiology; Humans; Limit of Detection; Medical Biochemistry; Models, Biological; Original Research Article; Pharmaceutical Sciences/Technology; Pharmacology/Toxicology; Pharmacy; Sample Size; Tobramycin - administration & dosage; Tobramycin - pharmacokinetics; Vancomycin - administration & dosage; Vancomycin - pharmacokinetics
• ISSN: 0378-7966; 2107-0180; 2107-0180
• DOI: 10.1007/s13318-024-00897-1

Background and Objectives Precision dosing requires selecting the appropriate population pharmacokinetic model, which can be assessed through external evaluations (EEs). The lack of understanding of how different study design factors influence EE study outcomes makes it challenging to select the most suitable model for clinical use. This study aimed to evaluate the impact of sample size, sampling strategy, and handling of concentrations below the lower limit of quantification (BLQ) on the outcomes of EE for four population pharmacokinetic models using vancomycin and tobramycin as examples. Methods Three virtual patient populations undergoing vancomycin or tobramycin therapy were simulated with varying sample size and sampling scenarios. The three approaches used to handle BLQ data were to (1) discard them, (2) impute them as LLOQ/2, or (3) use a likelihood-based approach. EEs were performed with NONMEM and R. Results Sample size did not have an important impact on the EE results for a given scenario. Increasing the number of samples per patient did not improve predictive performance for two out of the three evaluated models. Evaluating a model developed with rich sampling did not result in better performance than those developed with regular therapeutic drug monitoring. A likelihood-based method to handle BLQ samples impacted the outcomes of the EE with lower bias for predicted troughs. Conclusions This study suggests that a large sample size may not be necessary for an EE study, and models selected based on TDM may be more generalizable. The study highlights the need for guidelines for EE of population pharmacokinetic models for clinical use.