Robust identification of molecularly targeted drug effect coefficient using H&lt;inf&gt;&amp;#x221E;&lt;/inf&gt; filter

Qian, Lijun; Li, Xiangfang; Dougherty, Edward R.

doi:10.1109/globalsip.2013.6736824

Cited by 1 publication

(1 citation statement)

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“…[15][16][17][18] Currently, drug efficacy improvement is a critical research need, and this implies the identification of a new or better therapeutic targets. In recent years, there has been an increasing usage of molecularly targeted agents (MTAs) [19][20][21][22][23] for cancer treatment. MTA design enables the interruption of vital components of crucial pathways in the system.…”

Section: Body Level Modelingmentioning

confidence: 99%

Integrating Multiscale Modeling with Drug Effects for Cancer Treatment

Oduola

Qian

et al. 2015

Cancer Inform

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In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system’s pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.

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