Skip to content
2000
Volume 13, Issue 15
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

In recent years, biomedical research and drug design became one of the fastest growing branches of scientific and industrial development. The tremendous scale (number of projects and volume of information to process) promoted by trillions of dollars invested in these fields called for completely new approaches to obtain, analyze, and apply information to clinical practice, pharmacological research and the medical industry. This issue was put together as a result of our long-standing interest in the various aspects of research and drug design. It is dedicated to the use of new mathematical models in various fields of medical research. Our previous Current Pharmaceutical Design issue (2004) addressed the concept of multi-functional drug targets in diverse model systems [1]. This issue, accordingly, continues our inquiry into various types of models used in research and the subsequent creation of novel agents and improved therapies. This issue aims to give the reader an inside view into the concepts of intelligent research design and biomedical information processing. In Part-1 of this topic, we began with articles on the use of mathematics in basic science research to uncover the processes underlining the most complex events that are so crucial to understand in order to successfully conduct medical research, design drugs and simply practice medicine nowadays. Then, we began discussing the prediction capabilities of using mathematical models in biomedical research and in medicine, such as the prediction of drug delivery efficiency or patient treatment outcome. In Part-2, Jadhav, Eggleton and Konstantopoulos [2] address the application of mathematical modeling of cell adhesion in shear flow in order to target drug delivery for the treatment of inflammation and cancer metastasis. They conclude that these multiscale mathematical models can be employed to predict optimal drug carrier-cell binding through isolated parameter studies and engineering optimization schemes, which will be essential for developing effective drug carriers for delivery of therapeutic agents to afflicted sites of the host. Then, Dvorchik and his co-authors [3] analyze prognostic models in hepatocellular carcinoma (HCC) and the statistical methodologies behind them. These methods are evolving at a very fast pace and are extremely promising. The symbiosis of microarrays analysis, genotyping techniques and statistical modeling presents a powerful tool to further advance our knowledge of cancer development and progression. In the next section, two reviews [4, 5] delve into the use of mathematical models in the analysis of treatment results and potential optimization of outcomes. First, Qazi, DuMez and Uckun [4] describe the use of a parametric lognormal model to calculate and compare survival statistics in the clinical treatment of advanced/metastatic pancreatic, breast and colon cancers. Then Apolloni, Bassis, Gaito and Malchiodi [5] propose a new statistical framework, called Algorithmic Inference, for overcoming crucial difficulties usually met when computing confidence intervals about medical treatment or pollution effectiveness and abandoning general simplifying hypotheses such as errors’ Gaussian distribution. In the final review [6], Tsibulsky and Norman give insight into the mathematical modeling of behaviors of the animal models used in biomedical research. We would like to thank all the authors for their contributions and hope that this issue will stimulate new communication and collaborations. References [1] Current Pharmaceutical Design, Volume 10, Number 15, June 2004. [2] Jadhav S, Eggleton CD, Konstantopoulos, K. Mathematical Modeling of Cell Adhesion in Shear Flow: Application to Targeted Drug Delivery in Inflammation and Cancer Metastasis. Curr Pharm Des 2007; 13(15): 1511-1526. [3] Dvorchik I, Demetris AJ, Geller DA, Carr BI, Fontes P, Finkelstein, SD, Cappella NK, Marsh JW.Prognostic Models in Hepatocellular Carcinoma (HCC) and Statistical Methodologies Behind Them. Curr Pharm Des 2007; 13(15): 1527-1532. [4] Qazi S, DuMez D, Uckun FM. Meta analysis of advanced cancer survival data using lognormal parametric fitting: A statistical method to identify effective treatment protocols. Curr Pharm Des 2007; 13(15): 1533-1544. [5] Apolloni B, Bassis B, Gaito S, Malchiodi D. Appreciation of medical treatments through confidence intervals. Curr Pharm Des 2007; 13(15): 1545-1570. [6] Tsibulsky VL, Norman AB. Mathematical models of behavior of an individual animal. Curr Pharm Des 2007; 13(15): 1571- 1595.

Loading

Article metrics loading...

/content/journals/cpd/10.2174/138161207780765855
2007-05-01
2025-05-19
Loading full text...

Full text loading...

/content/journals/cpd/10.2174/138161207780765855
Loading

  • Article Type:
    Research Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test