SummaryTransgene expression from the plant's plastid genome represents a promising strategy in molecular farming because of the plastid's potential to accumulate foreign proteins to high levels and the increased biosafety provided by the maternal mode of organelle inheritance.In this article, we explore the potential of transplastomic plants to produce human

Estimation methods for nonlinear mixed-effects modelling have considerably improved over the last decades. Nowadays, several algorithms implemented in different software are used. The present study aimed at comparing their performance for dose-response models. Eight scenarios were considered using a sigmoid E(max) model, with varying sigmoidicity and residual error models. One hundred simulated datasets for each scenario were generated. One hundred individuals with observations at four doses constituted the rich design and at two doses, the sparse design. Nine parametric approaches for maximum likelihood estimation were studied: first-order conditional estimation (FOCE) in NONMEM and R, LAPLACE in NONMEM and SAS, adaptive Gaussian quadrature (AGQ) in SAS, and stochastic approximation expectation maximization (SAEM) in NONMEM and MONOLIX (both SAEM approaches with default and modified settings). All approaches started first from initial estimates set to the true values and second, using altered values. Results were examined through relative root mean squared error (RRMSE) of the estimates. With true initial conditions, full completion rate was obtained with all approaches except FOCE in R. Runtimes were shortest with FOCE and LAPLACE and longest with AGQ. Under the rich design, all approaches performed well except FOCE in R. When starting from altered initial conditions, AGQ, and then FOCE in NONMEM, LAPLACE in SAS, and SAEM in NONMEM and MONOLIX with tuned settings, consistently displayed lower RRMSE than the other approaches. For standard dose-response models analyzed through mixed-effects models, differences were identified in the performance of estimation methods available in current software, giving material to modellers to identify suitable approaches based on an accuracy-versus-runtime trade-off.

Model‐based meta‐analysis was used to compare glycemic control, weight changes, and hypoglycemia risk across 24 antihyperglycemic drugs used to treat type 2 diabetes. Electronic searches identified 229 randomized controlled studies comprising 121,914 patients. To ensure fair and unbiased treatment comparisons, the analyses adjusted for important differences between studies, including duration of treatment, baseline glycated hemoglobin, and drug dosages. At the approved doses, glycemic control was typically greatest with glucagon‐like peptide 1 receptor agonists (GLP‐1RAs), and least with dipeptidyl peptidase‐4 (DPP‐4) inhibitors. Weight loss was highly variable across GLP‐1RAs but was similar across sodium‐glucose cotransporter 2 (SGLT2) inhibitors. Large weight increases were observed with sulfonylureas and thiazolidinediones. Hypoglycemia risk was highest with sulfonylureas, although gliclazide was notably lower. Hypoglycemia risk for DPP‐4 inhibitors, SGLT2 inhibitors, and thiazolidinediones was generally very low but increased slightly for both GLP‐1RAs and metformin. In summary, important differences between and within drug classes were identified.

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