Gene Regulatory Networks Inference: Combining a Genetic Programming and<i>H</i><sub>∞</sub>Filtering Approach

Background Molecularly targeted agents (MTAs) are increasingly used for cancer treatment, the goal being to improve the efficacy and selectivity of cancer treatment by developing agents that block the growth of cancer cells by interfering with specific targeted molecules needed for carcinogenesis and tumor growth. This approach differs from traditional cytotoxic anticancer drugs. The lack of specificity of cytotoxic drugs allows a relatively straightforward approach in preclinical and clinical studies, where the optimal dose has usually been defined as the "maximum tolerated dose" (MTD). This toxicity-based dosing approach is founded on the assumption that the therapeutic anticancer effect and toxic effects of the drug increase in parallel as the dose is escalated. On the contrary, most MTAs are expected to be more selective and less toxic than cytotoxic drugs. Consequently, the maximum therapeutic effect may be achieved at a "biologically effective dose" (BED) well below the MTD. Hence, dosing study for MTAs should be different from cytotoxic drugs. Enhanced efforts to molecularly characterize the drug efficacy for MTAs in preclinical models will be valuable for successfully designing dosing regimens for clinical trials. Results A novel preclinical model combining experimental methods and theoretical analysis is proposed to investigate the mechanism of action and identify pharmacodynamic characteristics of the drug. Instead of fixed time point analysis of the drug exposure to drug effect, the time course of drug effect for different doses is quantitatively studied on cell line-based platforms using system identification, where tumor cells' responses to drugs through the use of fluorescent reporters are sampled over a time course. Results show that drug effect is time-varying and higher dosages induce faster and stronger responses as expected. However, the drug efficacy change along different dosages is not linear; on the contrary, there exist certain thresholds. This kind of preclinical study can provide valuable suggestions about dosing regimens for the in vivo experimental stage to increase productivity.

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“…Poor estimates of the noise covariance can result in filter divergence. An alternative would be using an H ∞ filter [50,51]. …”

Section: Methodsmentioning

confidence: 99%

Assessing the efficacy of molecularly targeted agents on cell line-based platforms by using system identification

Qian

Jiang

et al. 2012

BMC Genomics

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Gene Regulatory Networks Inference: Combining a Genetic Programming andH_∞Filtering Approach

Cited by 1 publication

References 40 publications

Assessing the efficacy of molecularly targeted agents on cell line-based platforms by using system identification

Assessing the efficacy of molecularly targeted agents on cell line-based platforms by using system identification

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Gene Regulatory Networks Inference: Combining a Genetic Programming andH∞Filtering Approach

Cited by 1 publication

References 40 publications

Assessing the efficacy of molecularly targeted agents on cell line-based platforms by using system identification

Assessing the efficacy of molecularly targeted agents on cell line-based platforms by using system identification

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Product

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Gene Regulatory Networks Inference: Combining a Genetic Programming andH_∞Filtering Approach