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A Systems Biology Approach in Therapeutic Response Study for Different Dosing Regimens-a Modeling Study of Drug Effects on Tumor Growth using Hybrid Systems.

Li X, Qian L, Bittner ML, Dougherty ER - Cancer Inform (2012)

Bottom Line: The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing.The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index.It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

ABSTRACT
Motivated by the frustration of translation of research advances in the molecular and cellular biology of cancer into treatment, this study calls for cross-disciplinary efforts and proposes a methodology of incorporating drug pharmacology information into drug therapeutic response modeling using a computational systems biology approach. The objectives are two fold. The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing. The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index. A dynamic hybrid systems model is proposed to study drug antitumor effect from the perspective of tumor growth dynamics, specifically the dosing and schedule of the periodic drug intake, and a drug's pharmacokinetics and pharmacodynamics information are linked together in the proposed model using a state-space approach. It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study.

No MeSH data available.


Related in: MedlinePlus

The tumor growth data from a typical run for the case of taking drug every day from day 16 to day 47.
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Related In: Results  -  Collection


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f12-cin-11-2012-041: The tumor growth data from a typical run for the case of taking drug every day from day 16 to day 47.

Mentions: Although the experimental data sets54,55 are not publicly available, the authors54,55 provided the parameter values such that the tumor growth model in their papers matches their experimental data very well. Hence, we perform numerical simulation using the model given in the study54,55 to produce synthetic data sets. Specifically, the PK data (drug plasma concentration) is generated by using the model of c(t) given by Equations (17)–(19) on page 138 of Magni et al54 and the corresponding parameter values given in Table 2 on page 140 of Magni et al.54 The tumor growth data during the entire treatment process is generated by firstly using the unperturbed model given by Magni et al54 for the first 15 days, then using the perturbed model given by Magni et al54 with the input from the PK data for 32 days (day 16 to day 47). Then the treatment is stopped from day 48 and on. In order to model the drug effect due to different drug plasma concentration, we also include the sigmoidal Emax model as given by Eq. (11) in our numerical simulation. The SIMULINK block diagrams for generating PK data and the entire treatment process are given in Figures 10 and 11, respectively. The generated synthetic data of a typical run is plotted in Figure 12 for the case of taking drug every day from day 16 to day 47.


A Systems Biology Approach in Therapeutic Response Study for Different Dosing Regimens-a Modeling Study of Drug Effects on Tumor Growth using Hybrid Systems.

Li X, Qian L, Bittner ML, Dougherty ER - Cancer Inform (2012)

The tumor growth data from a typical run for the case of taking drug every day from day 16 to day 47.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3298374&req=5

f12-cin-11-2012-041: The tumor growth data from a typical run for the case of taking drug every day from day 16 to day 47.
Mentions: Although the experimental data sets54,55 are not publicly available, the authors54,55 provided the parameter values such that the tumor growth model in their papers matches their experimental data very well. Hence, we perform numerical simulation using the model given in the study54,55 to produce synthetic data sets. Specifically, the PK data (drug plasma concentration) is generated by using the model of c(t) given by Equations (17)–(19) on page 138 of Magni et al54 and the corresponding parameter values given in Table 2 on page 140 of Magni et al.54 The tumor growth data during the entire treatment process is generated by firstly using the unperturbed model given by Magni et al54 for the first 15 days, then using the perturbed model given by Magni et al54 with the input from the PK data for 32 days (day 16 to day 47). Then the treatment is stopped from day 48 and on. In order to model the drug effect due to different drug plasma concentration, we also include the sigmoidal Emax model as given by Eq. (11) in our numerical simulation. The SIMULINK block diagrams for generating PK data and the entire treatment process are given in Figures 10 and 11, respectively. The generated synthetic data of a typical run is plotted in Figure 12 for the case of taking drug every day from day 16 to day 47.

Bottom Line: The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing.The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index.It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

ABSTRACT
Motivated by the frustration of translation of research advances in the molecular and cellular biology of cancer into treatment, this study calls for cross-disciplinary efforts and proposes a methodology of incorporating drug pharmacology information into drug therapeutic response modeling using a computational systems biology approach. The objectives are two fold. The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing. The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index. A dynamic hybrid systems model is proposed to study drug antitumor effect from the perspective of tumor growth dynamics, specifically the dosing and schedule of the periodic drug intake, and a drug's pharmacokinetics and pharmacodynamics information are linked together in the proposed model using a state-space approach. It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study.

No MeSH data available.


Related in: MedlinePlus