An integrative mechanistic model of thymocyte dynamics.

Background: The thymus plays a central role in shaping human immune function. A mechanistic, quantitative description of immune cell dynamics and thymic output under homeostatic conditions and various patho-physiological scenarios are of particular interest in drug development applications, e.g., in the identification of potential therapeutic targets and selection of lead drug candidates against infectious diseases. Methods: We here developed an integrative mathematical model of thymocyte dynamics in human. It incorporates mechanistic features of thymocyte homeostasis as well as spatial constraints of the thymus and considerations of age-dependent involution. All model parameter estimates were obtained based on published physiological data of thymocyte dynamics and thymus properties in mouse and human. We performed model sensitivity analyses to reveal potential therapeutic targets through an identification of processes critically affecting thymic function; we further explored differences in thymic function across healthy subjects, multiple sclerosis patients, and patients on fingolimod treatment. Results: We found thymic function to be most impacted by the egress, proliferation, differentiation and death rates of those thymocytes which are most differentiated. Model predictions also showed that the clinically observed decrease in relapse risk with age, in multiple sclerosis patients who would have discontinued fingolimod therapy, can be explained mechanistically by decreased thymic output with age. Moreover, we quantified the effects of fingolimod treatment duration on thymic output. Conclusions: In summary, the proposed model accurately describes, in mechanistic terms, thymic output as a function of age. It may be further used to perform predictive simulations of clinically relevant scenarios which combine specific patho-physiological conditions and pharmacological interventions of interest.

A tutorial for model-based evaluation and translation of cardiovascular safety in preclinical trials.

Assessment of drug-induced effects on the cardiovascular (CV) system remains a critical component of the drug discovery process enabling refinement of the therapeutic index. Predicting potential drug-related unintended CV effects in the preclinical stage is necessary for first-in-human dose selection and preclusion of adverse CV effects in the clinical stage. According to the current guidelines for small molecules, nonclinical CV safety assessment conducted via telemetry analyses should be included in the safety pharmacology core battery studies. However, the manual for quantitative evaluation of the CV safety signals in animals is available only for electrocardiogram parameters (i.e., QT interval assessment), not for hemodynamic parameters (i.e., heart rate, blood pressure, etc.). Various model-based approaches, including empirical pharmacokinetic-toxicodynamic analyses and systems pharmacology modeling could be used in the framework of telemetry data evaluation. In this tutorial, we provide a comprehensive workflow for the analysis of nonclinical CV safety on hemodynamic parameters with a sequential approach, highlight the challenges associated with the data, and propose respective solutions, complemented with a reproducible example. The work is aimed at helping researchers conduct model-based analyses of the CV safety in animals with subsequent translation of the effect to humans seamlessly and efficiently.

Modeling of Pharmacokinetic Profiles of Insulin Aspart and Biphasic Insulin Aspart 30/70.

This study includes modeling and simulation of insulin aspart pharmacokinetics (PK). The authors used PK data of biosimilar insulins-insulin aspart and biphasic insulin aspart 30/70-to develop a predictive population PK model for the insulins. The model was built via Monolix software, taking into account the weight-based dosing and the dose and body-weight effects on the parameters. The model-based simulations were performed using the R package mlxR for various administered doses and various ratios of insulin aspart forms for a better understanding of the insulin behavior. The optimal model was a 1-compartment model with a combination of zero- and first-order absorptions, with absorption lag for the soluble form of insulin aspart and first-order absorption for the insulin aspart protamine suspension. The assumption of identical behavior of 2 insulins at the distribution and elimination phases was made. The developed PK model was fitted successfully to the experimental data, and all fitted parameters displayed a moderate coefficient of variation. The PK model allows us to predict PK profiles for various doses and formulations of insulin aspart and can be used to improve the accuracy, safety, and ethics of novel clinical trials of insulin.