Xianting Ding's Biography

Xianting Ding, Professor, Shanghai Jiao Tong University, China

Dr. Xianting Ding is Associate professor at Department of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University. He received his Ph.D. degree from Department of Mechanical Engineering at University of California, Los Angeles (UCLA) in 2012. His research interests focus on optimization of combinatorial drugs for various life threatening diseases, such as cancer, HIV infection and TB infection. He also works extensively in drug-drug interactions for bio-complex systems, including tumor systems and Chinese herbal medicine.
Dr. Ding’s research interests also focused on understanding and controlling complex systems, particularly biological systems and chemical systems. Complex systems such like cells involve multiple layers of intracellular and extracellular signaling molecules. These molecules interact in a highly dynamic nonlinear way. Traditional drug development usually targets on a particular molecule, such as a piece of DNA or RNA, a protein, or an individual signaling receptor at cell membrane. Due to the intrinsic cellular complexity, this route has very limited yield of successful new drugs. Dr. Ding’s research focuses on an effective and rapid solution to identify optimal drug combinations. This new platform combines an engineering searching algorithm and bench experiments.

Feedback System Control (FSC): An Effective and Rapid Screening Platform for Combinatorial Drugs

Thursday, 18 September 2014 at 20:10

Add to Calendar ▼2014-09-18 20:10:002014-09-18 21:10:00Europe/LondonFeedback System Control (FSC): An Effective and Rapid Screening Platform for Combinatorial DrugsLab-on-a-Chip, Microfluidics and Microarray World Congress in San Diego, California, USASan Diego, California, USASELECTBIOenquiries@selectbiosciences.com

Drug combinations have been increasingly applied in clinical treatments towards various types of lethal diseases, including HIV, TB and cancers, due to the superior advantages of high efficacy, low toxicity and low occurrence of drug resistance. The death rate of HIV patients was dropped by 60% in two years after drug cocktails were introduced. While the drug combinations are in generally effective, optimizing drug combinations remains challenging. M drugs with N dose levels lead to NM total possibilities. For instance, 6 drugs with 10 dose levels ends up 1 million combinations, a prohibitive searching space for conventional trial-by-error type of drug optimization approaches. Furthermore, drug-drug interactions and drug-system interactions can be extremely complicated. Therefore, the information acquired from individual cellular molecules could hardly assess the accumulative response at the bio-system level. Herein, we introduce a Feedback System Control (FSC) approach, aiming to rapidly optimize drug combinations out of millions of possibilities. The FSC approach combines biological experimental tests and engineering feedback control algorithms, avoids the high-throughput examination on large dataset, optimizes a few combinations iteratively, bypasses the complicated intracellular molecular interactions, and is able to identify the optimal solution with only several rounds of experiments by testing less than 0.1% of the total searching space. The FSC platform technology has been successfully applied for optimizing drug combinations for 3 types of viral infections, 6 types of cancers, and other biological scenarios such as paradise control, stem cell maintenance and optimization of traditional Chinese medicine (TCM).

Add to Calendar ▼2014-09-18 00:00:002014-09-19 00:00:00Europe/LondonLab-on-a-Chip, Microfluidics and Microarray World CongressLab-on-a-Chip, Microfluidics and Microarray World Congress in San Diego, California, USASan Diego, California, USASELECTBIOenquiries@selectbiosciences.com

Corporate Sponsors