Biomedical subjects

A R Kansal

Publications and source records attributed to A R Kansal.

5 recordsLinked to original sources

PubMed2005

Application of predictive biosimulation within pharmaceutical clinical development: examples of significance for translational medicine and clinical trial design.

The challenge of accurately predicting human clinical outcome based on preclinical data has led to a high failure rate of compounds in human clinical trials. A series of methods are described by which biosimulation can address these challenges and guide the design and evaluation of experimental and clinical protocols. Early compound development often proceeds on the basis of preclinical data from animal models. The systematic evaluation possible in a simulation can assist in the critical step of translating the preclinical outcomes to human physiology. Later in the process, clinical trials definitively establish a therapy's beneficial effects, as well as any adverse side effects. Biosimulation allows for the optimal design of clinical trials to ensure that key issues are addressed effectively and efficiently, and in doing so, improves the success rate of the trials.

Animals↗

PubMed2001

Pattern of self-organization in tumour systems: complex growth dynamics in a novel brain tumour spheroid model.

We propose that a highly malignant brain tumour is an opportunistic, self-organizing and adaptive complex dynamic biosystem rather than an unorganized cell mass. To test the hypothesis of related key behaviour such as cell proliferation and invasion, we have developed a new in vitro assay capable of displaying several of the dynamic features of this multiparameter system in the same experimental setting. This assay investigates the development of multicellular U87MGmEGFR spheroids in a specific extracellular matrix gel over time. The results show that key features such as volumetric growth and cell invasion can be analysed in the same setting over 144 h without continuously supplementing additional nutrition. Moreover, tumour proliferation and invasion are closely correlated and both key features establish a distinct ratio over time to achieve maximum cell velocity and to maintain the system's temporo-spatial expansion dynamics. Single cell invasion follows a chain-like pattern leading to the new concept of a intrabranch homotype attraction. Since preliminary studies demonstrate that heterotype attraction can specifically direct and accelerate the emerging invasive network, we further introduce the concept of least resistance, most permission and highest attraction as an essential principle for tumour invasion. Together, these results support the hypothesis of a self-organizing adaptive biosystem.

Adaptation, Biological↗

PubMed2000

Emergence of a subpopulation in a computational model of tumor growth.

Malignant brain tumors consist of a number of distinct subclonal populations. Each of these subpopulations may be characterized by its own behaviors and properties. These subpopulations arise from the constant genetic and epigenetic alteration of existing cells in the rapidly growing tumor. However, since each single-cell mutation only leads to a small number of offspring initially, very few newly arisen subpopulations survive more than a short time. The present work quantifies "emergence", i.e. the likelihood of an isolated subpopulation surviving for an extended period of time. Only competition between clones is considered; there are no cooperative effects included. The probability that a subpopulation emerges under these conditions is found to be a sigmoidal function of the degree of change in cell division rates. This function has a non-zero value for mutations which confer no advantage in growth rate, which represents the emergence of a distinct subpopulation with an advantage that has yet to be selected for, such as hypoxia tolerance or treatment resistance. A logarithmic dependence on the size of the mutated population is also observed. A significant probability of emergence is observed for subpopulations with any growth advantage that comprise even 0.1% of the proliferative cells in a tumor. The impact of even two clonal populations within a tumor is shown to be sufficient such that a prognosis based on the assumption of a monoclonal tumor can be markedly inaccurate.

Brain Neoplasms↗

PubMed2000

Simulated brain tumor growth dynamics using a three-dimensional cellular automaton.

We have developed a novel and versatile three-dimensional cellular automaton model of brain tumor growth. We show that macroscopic tumor behavior can be realistically modeled using microscopic parameters. Using only four parameters, this model simulates Gompertzian growth for a tumor growing over nearly three orders of magnitude in radius. It also predicts the composition and dynamics of the tumor at selected time points in agreement with medical literature. We also demonstrate the flexibility of the model by showing the emergence, and eventual dominance, of a second tumor clone with a different genotype. The model incorporates several important and novel features, both in the rules governing the model and in the underlying structure of the model. Among these are a new definition of how to model proliferative and non-proliferative cells, an isotropic lattice, and an adaptive grid lattice.

Algorithms↗

PubMed2000

Cellular automaton of idealized brain tumor growth dynamics.

A novel cellular automaton model of proliferative brain tumor growth has been developed. This model is able to simulate Gompertzian tumor growth over nearly three orders of magnitude in radius using only four microscopic parameters. The predicted composition and growth rates are in agreement with a test case pooled from the available medical literature. The model incorporates several new features, improving previous models, and also allows ready extension to study other important properties of tumor growth, such as clonal competition.

Algorithms↗