Fractional dynamics pharmacokinetics–pharmacodynamic models

While an increasing number of fractional order integrals and differential equations applications have been reported in the physics, signal processing, engineering and bioengineering literatures, little attention has been paid to this class of models in the pharmacokinetics–pharmacodynamic (PKPD) literature. One of the reasons is computational: while the analytical solution of fractional differential equations is available in special cases, it this turns out that even the simplest PKPD models that can be constructed using fractional calculus do not allow an analytical solution. In this paper, we first introduce new families of PKPD models incorporating fractional order integrals and differential equations, and, second, exemplify and investigate their qualitative behavior. The families represent extensions of frequently used PK link and PD direct and indirect action models, using the tools of fractional calculus. In addition the PD models can be a function of a variable, the active drug, which can smoothly transition from concentration to exposure, to hyper-exposure, according to a fractional integral transformation. To investigate the behavior of the models we propose, we implement numerical algorithms for fractional integration and for the numerical solution of a system of fractional differential equations. For simplicity, in our investigation we concentrate on the pharmacodynamic side of the models, assuming standard (integer order) pharmacokinetics

Mathematical Modelling as a Proof of Concept for MPNs as a Human Inflammation Model for Cancer Development

<p><b>Left:</b> Typical development in stem cells (top panel A) and mature cells (bottom panel B). Healthy hematopoietic cells (full blue curves) dominate in the early phase where the number of malignant cells (stipulated red curves) are few. The total number of cells is also shown (dotted green curves). When a stem cell mutates without repairing mechanisms, a slowly increasing exponential growth starts. At a certain stage, the malignant cells become dominant, and the healthy hematopoietic cells begin to show a visible decline. Finally, the composition between the cell types results in a takeover by the malignant cells, leading to an exponential decline in hematopoietic cells and ultimately their extinction. The development is driven by an approximately exponential increase in the MPN stem cells, and the development is closely followed by the mature MPN cells. <b>Right:</b> B)The corresponding allele burden (7%, 33% and 67% corresponding to ET, PV, and PMF, respectively) defined as the ratio of MPN mature cells to the total number of mature cells.</p

Shortened β-cell lifespan leads to β-cell deficit in a rodent model of type 2 diabetes

Since the fundamental defect in both type 1 and type 2 diabetes is β-cell failure, there is increasing interest in the capacity, if any, for β-cell regeneration. Insights into typical β-cell age and lifespan during normal development and how these are influenced in diabetes is desirable to realistically establish the prospects for β-cell regeneration as means to reverse the deficit in β-cell mass in diabetes. We assessed the mean β-cell age and lifespan by the classical McKendrick-von Foester equation that describes the age-based heterogeneity of β-cells in terms of the time-varying β-cell formation and loss estimated by a β-cell turnover model. This modeling approach was applied to evaluate β-cell lifespan in a rodent model of type 2 diabetes in comparison with nondiabetic controls. When rats were 10 mo old, mean β-cell lifespan was 1 mo vs. 6 mo in rats with type 2 diabetes vs. controls. A shortened β-cell lifespan in a rat model of type 2 diabetes results in a decrease in mean β-cell age and thus contributes to decreased β-cell mass

Compartmental models: theory and practice using the SAAM II software system.

Understanding in vivo the functioning of metabolic systems at the whole-body or regional level requires one to make some assumptions on how the system works and to describe them mathematically, that is, to postulate a model of the system. Models of systems can have different characteristics depending on the properties of the system and the database available for their study; they can be deterministic or stochastic, dynamic or static, with lumped or distributed parameters. Metabolic systems are dynamic systems and we focus here on the most widely used class of dynamic (differential equation) models: compartmental models. This is a class of models for which the governing law is conservation of mass. It is a very attractive class to users because it formalizes physical intuition in a simple and reasonable way. Compartmental models are lumped parameter models, in that the events in the system are described by a finite number of changing variables, and are thus described by ordinary differential equations. While stochastic compartment models can also be defined, we discuss here the deterministic versions--those that can work with exact relationships between model variables. These are the models most widely used in discussions of endocrinology and metabolism. In this chapter, we will discuss the theory of compartmental models, and then discuss how the SAAM II software system, a system designed specifically to aid in the development and testing of multicompartmental models, can be used