An Engineering Approach to Optimize the Combinatorial Drug Treatment for Cancer and Infectious Diseases

Chih–Ming Ho

Thursday, 16 Sep 2010 at 5:30 pm – Great Hall, Memorial Union

Chih-Ming Ho is director of the Center for Cell Control, an NIH Nanomedicine Roadmap Center at the University of California, Los Angeles. His research offers an engineering approach to determining optimal drug cocktails for the treatment of cancer and infectious diseases. The feedback system control scheme requires only tens of iterations to identify the most effective combinatorial drugs from millions of possibilities. Ho, a member of the National Academy of Engineering and an Academician of Academia Sinica, is known for his work in micro/nano fluidics, bio-nano technologies and turbulence. He holds the Ben Rich-Lockheed Martin Professorship in the School of Engineering and Applied Science at UCLA and received his Ph.D. from The Johns Hopkins University. Electrical and Computer Engineering Distinguished Lecture Series.

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Abstract
A complex system is composed of a large number of interacting building blocks/elements which self organize, generating emerging properties that are usually not directly linked to those of the individual building elements. Cell is the most fundamental biological system and yet is a complex system.

In each living cell, the interactions among the bio molecules, proteins and nucleic acids intrinsically serve as the foundation of the extensive networks of signal and regulatory pathways. Emergent cellular functionalities are derived from the self-organization of these pathways, but can not be easily related to individual bio-molecular interactions. It becomes obvious that exploring and understanding the cell functions based on the bottom-up reductionist approach present significant challenges due to the sheer magnitude of pathway processes and pathway crosstalk. Furthermore, we frequently intend to direct cellular phenotypic and genotypic outcomes toward a desired state with a key example being the application of pharmacological agents to treat diseased cells in medicine. In other words, the drug application is an expedition to manipulate the cell fate by stimulating a far from understanding network.

Rather than laboriously mapping out the detailed cascade of signaling pathways from the bottom up, we take a top-down approach by employing a feedback system control (FSC) scheme to bypass the challenges associated with simultaneously considering multiple cellular regulatory pathways in cellular complex systems. In addition, we have harnessed these control schemes to rationally design combinatorial drug therapy modalities to direct the cellular system output with improved efficacy and low toxicity. This imposes another challenge which pertains to the large parameter space. For example, 6 drugs with 10 concentrations each would result in 1,000,000 potential search trials. With the feedback system optimization approach, we have demonstrated that only tens of searches instead of 1,000,000 cases are needed to identify the optimized drug cocktail. This work is supported by the NIH Nanomedicine Roadmap Program.

Cosponsored By:

• Electrical and Computer Engineering
• Information Infrastructure Institute
• Committee on Lectures (funded by Student Government)