Single-drug therapies often fail in the treatment of complex diseases, such as diabetes, because they act on a specific target, and are often by-passed by the redundant mechanisms that confer robustness to biological systems, both in health and in disease. On the other hand, multi-drug therapies (MDTs) show greater efficacy, but efficient methodologies for their design are needed to handle the high number of combinatorial possibilities.
In this paper, we describe a methodology for MDT design based on the structured singular value, a tool from the robust control theory. The procedure can be applied to a mathematical model of the biological system under consideration, and it includes medical performance robustness as a design requirement. This aspect is of primary importance because robustness has been recognized as an intrinsic property of biological systems that maintain performance despite a variety of environmental conditions and the subject-to-subject variability of response. Furthermore, the development of a mathematical model always includes a certain level of uncertainty, due to the inevitable assumptions involved in its development, and the often scarce availability of experimental data.
The methodology is here applied to the insulin resistance condition, a primary impairment in the development of type 2 diabetes. Results from simulations confirm that the performance and robustness objectives are achieved under different input dynamics, and suggest potentially powerful therapeutic vector targets. Copyright © 2011 John Wiley & Sons, Ltd.