BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems

We developed a detailed, whole-body physiologically based pharmacokinetic (PBPK) modeling tool for calculating the distribution of pharmaceutical agents in the various tissues and organs of a human or animal as a function of time. Ordinary differential equations (ODEs) represent the circulation of body fluids through organs and tissues at the macroscopic level, and the biological transport mechanisms and biotransformations within cells and their organelles at the molecular scale. Each major organ in the body is modeled as composed of one or more tissues. Tissues are made up of cells and fluid spaces. The model accounts for the circulation of arterial and venous blood as well as lymph. Since its development was fueled by the need to accurately predict the pharmacokinetic properties of imaging agents, BioDMET is more complex than most PBPK models. The anatomical details of the model are important for the imaging simulation endpoints. Model complexity has also been crucial for quickly adapting the tool to different problems without the need to generate a new model for every problem. When simpler models are preferred, the non-critical compartments can be dynamically collapsed to reduce unnecessary complexity. BioDMET has been used for imaging feasibility calculations in oncology, neurology, cardiology, and diabetes. For this purpose, the time concentration data generated by the model is inputted into a physics-based image simulator to establish imageability criteria. These are then used to define agent and physiology property ranges required for successful imaging. BioDMET has lately been adapted to aid the development of antimicrobial therapeutics. Given a range of built-in features and its inherent flexibility to customization, the model can be used to study a variety of pharmacokinetic and pharmacodynamic problems such as the effects of inter-individual differences and disease-states on drug pharmacokinetics and pharmacodynamics, dosing optimization, and inter-species scaling. While developing a tool to aid imaging agent and drug development, we aimed at accelerating the acceptance and broad use of PBPK modeling by providing a free mechanistic PBPK software that is user friendly, easy to adapt to a wide range of problems even by non-programmers, provided with ready-to-use parameterized models and benchmarking data collected from the peer-reviewed literature

Perspectives on Risk Perceptions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72341/1/j.1539-6924.1981.tb01409.x.pd

The role of evil in Old English narrative verse

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Quantitative analysis of chemotactic cell movement bias and target encounter: Experimental, statistical and modelling studies

A detailed mathematical description of cell movement paths in tissue is necessary for investigation of the role of cell motility in a variety of tissue processes, including angiogenesis, wound healing, and inflammatory and immune responses. Typically, cell paths are analyzed by extracting model parameters from experimental plots of mean-square displacement versus time. This has been a primary test of model validity. Although information about the fundamental dynamics of cell movement is obtained from this analysis of the first moment of cell paths, more detailed information is obtained from time series analysis (TSA) of the second moment, represented by the autocorrelation function (ACF) and the partial autocorrelation function (PACF). One can calculate the ACF and PACF expected from a particular model and compare this to the experimentally obtained ACF and PACF. The ACF and PACF together are therefore, in principle, useful as a criterion for cell motility model discrimination and validation. Such information is particularly crucial when details of individual cell paths are important in the dynamics and morphology of tissue processes. Experimental ACFs and PACFs obtained previously for fibroblasts are consistent with theoretical predictions derived from a continuous, Markovian process, in exhibiting exponential decay of the ACF with time interval, suggesting the utility of an Ornstein-Uhlenbeck (O-U) stochastic description of cell paths. As a result of our interest in phagocyte/target encounter, and in order to test the general validity of the O-U formulation, we extend the TSA to neutrophil, alveolar macrophage, and endothelial cell paths. We study the paths of neutrophils undergoing random motility in uniform concentrations of fNLLP (formyl-NorLeucyl-Leucyl-Phenylalanine), a known chemoattractant for these cells. Analysis of the cell displacements at uniform intervals in the time domain yields ACFs and PACFs consistent with those expected for a simple, continuous, Markov process, suggesting that one need only know the present state of a cell\u27s motility to predict its path. In addition, a modified O-U process is validated for simulations of chemotactic movement of these cell types as well. The application of TSA to cell paths is therefore a basis for a rigorous procedure for model discrimination and validation

Mathematical analysis of cell-target encounter rates in three dimensions. Effect of chemotaxis.

Efficient and rapid immune response upon challenge by an infectious agent is vital to host defense. The encounter of leukocytes (white blood cells of the immune system) with their targets is the first step in this response. Analysis of the kinetics of this process is essential not only to understanding dynamic behavior of the immune response, but also to elucidating the consequences of many leukocyte functional abnormalities. The motion of leukocytes in the presence of targets typically involves a directed, or chemotactic component. These immune cells orient the direction of their motion in the presence of gradients in chemical attractants generated by pathogens. Fisher and Lauffenburger (1987. Biophys. J. 51:705-716) developed a model for macrophage/bacterium encounter in two dimensions which includes chemotaxis, and applied it to the particular system of alveolar macrophages (phagocytic leukocytes on the lung surface). Their model showed that macrophage/target encounter is likely the rate-limiting step in clearance of bacteria from the lung surface (Fisher, E. S., D. A. Lauffenburger, and R. P. Daniele. 1988. Am. Rev. Resp. Dis. 137:1129-1134). We have extended this model to analyze the effects of cell motility properties and geometric parameters on cell-target encounter in three dimensions. The differential equation governing encounter time in three dimensions is essentially the same as that in two dimensions, except for changed probability values. Our results show that more highly directed motion is necessary in three dimensions to achieve substantially decreased encounter times than in two dimensions, because of the increased search dimensionality. These general results were applied to the particular system of neutrophils operating in three dimensions in response to a bacterial challenge in connective tissue. Our results provide a plausible rationalization for both the chemotactic and chemokinetic behavior observed in neutrophils. That is, these cells exhibit in vitro a greater chemotactic bias and a more dramatic variation of speed with attractant concentration than alveolar macrophages, and our results indicate that these behaviors can have a greater influence in three-dimensional connective tissue infection situations than in two-dimensional lung surface infection cases. In addition, we show that encounter apparently is not generally the rate-limiting step in this neutrophil response. These findings have important implications for correlating in vitro measured defects in cell motility and chemotaxis properties with in vivo functions of host defense against infection