000205616 001__ 205616
000205616 005__ 20190225185206.0
000205616 0247_ $$2doi$$a10.1088/1478-3975/11/6/065003
000205616 022__ $$a1478-3967
000205616 02470 $$2ISI$$a000345896100004
000205616 037__ $$aARTICLE
000205616 245__ $$aDiscovery of a low order drug-cell response surface for applications in personalized medicine
000205616 260__ $$bIop Publishing Ltd$$c2014$$aBristol
000205616 269__ $$a2014
000205616 300__ $$a12
000205616 336__ $$aJournal Articles
000205616 520__ $$aThe cell is a complex system involving numerous components, which may often interact in a non-linear dynamic manner. Diseases at the cellular level are thus likely to involve multiple cellular constituents and pathways. As some drugs, or drug combinations, may act synergistically on these multiple pathways, they might be more effective than the respective single target agents. Optimizing a drug mixture for a given disease in a particular patient is particularly challenging due to both the difficulty in the selection of the drug mixture components to start out with, and the all-important doses of these drugs to be applied. For n concentrations of m drugs, in principle, n(m) combinations will have to be tested. As this may lead to a costly and time-consuming investigation for each individual patient, we have developed a Feedback System Control (FSC) technique which can rapidly select the optimal drug-dose combination from the often millions of possible combinations. By testing this FSC technique in a number of experimental systems representing different disease states, we found that the response of cells to multiple drugs is well described by a low order, rather smooth, drug-mixture-input/drug-effect-output multidimensional surface. The main consequences of this are that optimal drug combinations can be found in a surprisingly small number of tests, and that translation from in vitro to in vivo is simplified. This points to the possibility of personalized optimal drug mixtures in the near future. This unexpectedly simple input-output relationship may also lead to a simple solution for handling the issue of human diversity in cancer therapeutics.
000205616 6531_ $$abiological complex system
000205616 6531_ $$acombinatorial drug
000205616 6531_ $$afeedback system control (FSC)
000205616 6531_ $$apersonalized medicine
000205616 6531_ $$aprecision medicine
000205616 6531_ $$asynergetic and antagonistic interactions
000205616 700__ $$uShanghai Jiao Tong Univ, MedX Res Inst, Sch Biomed Engn, Shanghai 200030, Peoples R China$$aDing, Xianting
000205616 700__ $$uShanghai Jiao Tong Univ, MedX Res Inst, Sch Biomed Engn, Shanghai 200030, Peoples R China$$aLiu, Wenjia
000205616 700__ $$uEcole Polytech Fed Lausanne, Swiss Fed Inst Technol, Inst Chem Sci & Engn, CH-1015 Lausanne, Switzerland$$aWeiss, Andrea
000205616 700__ $$uShanghai Jiao Tong Univ, MedX Res Inst, Sch Biomed Engn, Shanghai 200030, Peoples R China$$aLi, Yiyang
000205616 700__ $$uUniv Calif Los Angeles, Dept Mech & Aerosp Engn, Los Angeles, CA 90095 USA$$aWong, Ieong
000205616 700__ $$uVrije Univ Amsterdam Med Ctr, Dept Med Oncol, Angiogenesis Lab, NL-1081 HV Amsterdam, Netherlands$$aGriffioen, Arjan W.
000205616 700__ $$aVan Den Bergh, Hubert
000205616 700__ $$aXu, Hongquan
000205616 700__ $$uEcole Polytech Fed Lausanne, Swiss Fed Inst Technol, Inst Chem Sci & Engn, CH-1015 Lausanne, Switzerland$$aNowak-Sliwinska, Patrycja
000205616 700__ $$uUniv Calif Los Angeles, Dept Mech & Aerosp Engn, Los Angeles, CA 90095 USA$$aHo, Chih-Ming
000205616 773__ $$j11$$tPhysical Biology$$k6$$q065003
000205616 909C0 $$0252512$$pGPM
000205616 909CO $$ooai:infoscience.tind.io:205616$$pSB$$particle
000205616 917Z8 $$x135992
000205616 937__ $$aEPFL-ARTICLE-205616
000205616 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000205616 980__ $$aARTICLE