AIM: Chagas disease (ChD) is a neglected disease affecting approximately 7 million individuals in Latin America. Benznidazole (BZ) is the most commonly used treatment. Therefore, understanding the adverse effects of BZ is crucial for devising targeted monitoring and interventions to enhance patient management. METHODS: A retrospective cohort study of patients with ChD treated with BZ to identify and characterize BZ adverse drug reactions (ADRs). RESULTS: 518 patients were enrolled: 449 children (median age: 4yrs, range 1mo-17.75yrs) and 69 adults (median age: 25yrs, range 18-59). A 75% of pediatric patients received a median dose of BZ of 6.6 mg/kg/day (IQR25–75 = 5.7-7.3) for at least 60 days. Adult patients received a median BZ dose of 5.6 mg/kg/day (IQR25–75 = 5.2-6.1) for a median duration of 31 days (IQR25–75 = 30-60). Overall, 152/518 (29.34%) patients developed BZ-related ADRs, with an incidence of 116/449 (25.83%) in children and 36/69 (52.17%) in adults (OR = 0.32, CI95 = 0.19 to 0.54, p < 0.001). The study identified 240 ADRs, primarily mild to moderate, but severe ADRs occurred in 1.11% of children and 1.45% of adults. The skin was the most affected system in both groups. A 10.23% of patients abandoned treatment (53/518). Adults discontinued treatment more frequently than children (OR = 3.36 CI95 = 1.7 to 6.4, p < 0.001). CONCLUSION: Our study supports the safety of BZ for ChD in children and adults. Avoiding BZ treatment due to safety concerns does not seem to be supported by the evidence.

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.