Children with primary immunodeficiency (PID) and secondary antibody deficiency (SAD) often require immunoglobulin replacement therapy due to low plasma immunoglobulin G (IgG) levels and recurrent infections. Existing pharmacokinetic models for immunoglobulin in primary immunodeficiency patients predominantly focus on adults, with limited attention to secondary antibody deficiencies and a lesser emphasis on paediatric populations. A population pharmacokinetic analysis was conducted using NONMEM® (7.5.1) on data from 64 patients, with a median age of 4.08 years (range: 0.06–16.8 years). A two-compartment model with first-order elimination, incorporating both additive and proportional residual error, adequately described the data. Inter-individual variability was modelled on clearance, volume of distribution, and baseline IgG levels, with allometric scaling to a 70 kg body weight applied a priori. The estimated clearance was 0.308 L−1 day−1 70 kg−1 (95% CI: 0.23–0.67), and the volume of distribution was 10.96 L−1 70 kg−1 (95% CI: 5.97–15.79). Patients with SAD exhibited a lower clearance rate of 54% compared to PID patients. Dosing simulations indicated that the recommended SAD dosing regimen maintained therapeutic IgG levels in the simulated population. However, only 44.8% to 51.9% of patients with PID achieved target IgG levels with the standard regimen. Administering a loading dose would improve the probability of maintaining therapeutic IgG levels during the 4-week dosing interval. This study provides insights into immunoglobulin pharmacokinetics in paediatric PID and SAD patients, guiding optimised dosing strategies.

Aim: nlmixr offers first-order conditional estimation with or without interaction (FOCE or FOCEi) and stochastic approximation estimation-maximisation (SAEM) to fit nonlinear mixed-effect models (NLMEM). We modelled metformin’s population pharmacokinetics with flip-flop characteristics within nlmixr framework and investigated SAEM and FOCEi’s performance with respect to bias, precision, and robustness. Method: Compartmental pharmacokinetic models were fitted. The final model was determined based on the lowest objective function value and visual inspection of goodness-of-fit plots. To examine flip-flop pharmacokinetics, k_a values of a typical concentration-time profile based on the final model were perturbed and changes in the steepness of the terminal elimination phase were evaluated. The bias and precision of parameter estimates were compared between SAEM and FOCEi using stochastic simulations and estimations. For robustness, parameters were re-estimated as the initial estimates were perturbed 100-times and resultant changes evaluated. Results: A one-compartment model with transit compartment for absorption best described the data. At low n, Stirling’s approximation of n! over-approximated plasma concentration unlike the log-gamma function. Flip-flop pharmacokinetics were evident as the steepness of the terminal elimination phase changed with k_a. Mean rRMSE for fixed-effect parameters was 0.932. When initial estimates were perturbed, FOCEi estimates of k_a and food effect on k_a appeared bimodal and were upward biased. Discussion: nlmixr is reliable for NLMEM even if flip-flop is present but caution should be exercised when using Stirling’s approximation for n! in the transit compartment model. SAEM was marginally superior to FOCEi in bias and precision, but SAEM was superior against initial estimate perturbations.