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Writing population analysis reports that fulfilling the specifications of submission readiness is a time‐consuming process and prone to human error. Thus, there is a need to streamline the creation of these reports through elements of standardization and automation. LaTeX is considered a suitable program package capable of creating long, modular, structured documents, and, because of its typographic quality, includes formula typesetting. The presented automation scripts together with easily adjustable LaTeX templates are designed to enable the reader to understand and reproduce a typical workflow from analysis to reporting. The focus of this tutorial is to use an example of a population pharmacokinetic analysis to show how to work with the proposed automated structures allowing even a reader new to the concept of LaTeX to automatize the reporting workflow and customize the templates for their specific needs. [ABSTRACT FROM AUTHOR]

Reliable detection and measurement of cell proliferation are essential in the preclinical assessment of carcinogenic risk of therapeutics. In this context, the assessment of mitogenic potential on mammary glands is crucial in the preclinical safety evaluation of novel insulins. The existing manual counting is time-consuming and subject to operator bias. To standardize the processes, make it faster, and resistant to errors, we developed a semiautomated image analysis system (CEPA software, which is open-source) for counting of proliferating cells in photomicrographs of mammary gland sections of rats labeled with Ki-67. We validated the software and met the predefined targets for specificity, accuracy, and reproducibility. In comparison to manual counting, the respective mean differences in absolute labeling indices (LIs) for CEPA software were 3.12% for user 1 and 3.05% for user 2. The respective regression analysis revealed a good correlation between the CEPA software user and manual counting. Moreover, the CEPA software showed enhanced reproducibility between independent users. The interuser variability is centered around 0 and the absolute difference was about 0.53% LI. Based on validation data, our software has superiority to the manual counting and is a valid and reliable tool for the routine analysis of cell proliferation in mammary glands from rats exposed to insulin analogs. [ABSTRACT FROM AUTHOR]

The toxicological profile of insulins is exclusively due to exaggerated pharmacology resulting in hypoglycemic findings. Insulin analogues displaying modifications and aimed at improving pharmacokinetics do not induce different toxicity. The main target is the brain displaying neuronal necrosis. Wallerian degeneration of nerves occurs rarely after severe hypoglycemia. These findings are of potential human relevance; nevertheless, these changes are induced in normoglycemic animals whereas diabetic patients suffer from hyperglycemia. Therefore, it is usually not difficult to achieve a therapeutic window for subsequent use in patients. Based upon this and in the absence of classical toxicity, there has been no scientific need for diabetic animal models. A greater challenge is the mitogenicity already inherent with regular insulin. Thus, the focus for preclinical safety evaluation of analogues is to demonstrate that modifications in regular insulin do not result in enhanced mitogenicity. The approaches used to assess the mitogenic potential of insulin analogues have changed over time driven by scientific progression and changes within the regulatory environment. Therefore, in vitro and in vivo evaluation of cell proliferation has become common practice, and to date there has been no evidence that the mitogenic potential of insulin analogues may be increased compared to regular insulin. [ABSTRACT FROM AUTHOR]

Background: The concept of Petri nets (PN) is widely used in systems biology and allows modeling of complex biochemical systems like metabolic systems, signal transduction pathways, and gene expression networks. In particular, PN allows the topological analysis based on structural properties, which is important and useful when quantitative (kinetic) data are incomplete or unknown. Knowing the kinetic parameters, the simulation of time evolution of such models can help to study the dynamic behavior of the underlying system. If the number of involved entities (molecules) is low, a stochastic simulation should be preferred against the classical deterministic approach of solving ordinary differential equations. The Stochastic Simulation Algorithm (SSA) is a common method for such simulations. The combination of the qualitative and semi-quantitative PN modeling and stochastic analysis techniques provides a valuable approach in the field of systems biology. Results: Here, we describe the implementation of stochastic analysis in a PN environment. We extended MONALISA - an open-source software for creation, visualization and analysis of PN - by several stochastic simulation methods. The simulation module offers four simulation modes, among them the stochastic mode with constant firing rates and Gillespie's algorithm as exact and approximate versions. The simulator is operated by a user-friendly graphical interface and accepts input data such as concentrations and reaction rate constants that are common parameters in the biological context. The key features of the simulation module are visualization of simulation, interactive plotting, export of results into a text file, mathematical expressions for describing simulation parameters, and up to 500 parallel simulations of the same parameter sets. To illustrate the method we discuss a model for insulin receptor recycling as case study. Conclusions: We present a software that combines the modeling power of Petri nets with stochastic simulation of dynamic processes in a user-friendly environment supported by an intuitive graphical interface. The program offers a valuable alternative to modeling, using ordinary differential equations, especially when simulating single-cell experiments with low molecule counts. The ability to use mathematical expressions provides an additional flexibility in describing the simulation parameters. The open-source distribution allows further extensions by third-party developers. The software is cross-platform and is licensed under the Artistic License 2.0. [ABSTRACT FROM AUTHOR]

Objective: Mathematical models of insulin absorption have been used to predict plasma insulin concentrations after administration, but few are specifically applicable to insulin glargine, which precipitates subcutaneously after injection., Materials and Methods: The formation and redissolution of subcutaneous depots of insulin glargine 100 U/mL (Gla-100) and insulin glargine 300 U/mL (Gla-300) are modeled. Surface-area-dependent redissolution is introduced to established diffusion and absorption pathways, and pharmacokinetic (PK) profiles are simulated and subsequently validated using experimental data from euglycemic glucose clamp studies. Simulations are used to predict the PK effect of adapting the timing of once-daily insulin injections and of switching from one insulin product to the other. -Results: Simulated PK profiles resemble those previously observed in clinical trials, with Gla-300 providing more gradual and prolonged release of Gla-300 vs. Gla-100, owing to a more compact depot. The predicted PK profile of Gla-300 shows less fluctuation in plasma insulin concentrations than that of Gla-100, and may be better suited to adapting the timing of daily injections to account for variation in daily activities. Simulating a switch from one insulin glargine product to the other results in temporary alteration of previous steady state, but this is regained within ~ 3 days., Conclusion: This study suggests that PK differences between Gla-300 and Gla-100 are a product of the more compact Gla-300 depot and its smaller surface area. The model employed also allowed estimation of insulin glargine concentrations when varying the time interval between injections as well as when switching from one insulin glargine product to the other.
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The insulin-dependent activation and recycling of the insulin receptor play an essential role in the regulation of the energy metabolism, leading to a special interest for pharmaceutical applications. Thus, the recycling of the insulin receptor has been intensively investigated, experimentally as well as theoretically. We developed a time-resolved, discrete model to describe stochastic dynamics and study the approximation of non-linear dynamics in the context of timed Petri nets. Additionally, using a graph-theoretical approach, we analyzed the structure of the regulatory system and demonstrated the close interrelation of structural network properties with the kinetic behavior. The transition invariants decomposed the model into overlapping subnetworks of various sizes, which represent basic functional modules. Moreover, we computed the quasi-steady states of these subnetworks and demonstrated that they are fundamental to understand the dynamic behavior of the system. The Petri net approach confirms the experimental results of insulin-stimulated degradation of the insulin receptor, which represents a common feature of insulin-resistant, hyperinsulinaemic states.