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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

Curve fitting for parameter β1.
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Related In: Results  -  Collection


getmorefigures.php?uid=PMC3298374&req=5

f13-cin-11-2012-041: Curve fitting for parameter β1.

Mentions: The plot of nonlinear least square curve fitting for parameters β1 and k1 are given in Figures 13 and 14, respectively. It can be seen that β1 can be accurately estimated without much error. The true value is β1 = 0.349 and the mean of the estimates is β̂1 = 0.344. This is because the unperturbed tumor growth model for the first 15 days is a simple exponential curve that can be easily fitted. However, the error for estimating k1 is large due to the complicated dynamics when drug is applied, and nonlinear curve fitting may give inaccurate estimates because only approximate expression can be obtained for the tumor growth, as also observed by Magni, Simeoni and et al.54,55 The true value of k1 is 0.405, while the mean of the estimates is k̂1 = 0.616.


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)

Curve fitting for parameter β1.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f13-cin-11-2012-041: Curve fitting for parameter β1.
Mentions: The plot of nonlinear least square curve fitting for parameters β1 and k1 are given in Figures 13 and 14, respectively. It can be seen that β1 can be accurately estimated without much error. The true value is β1 = 0.349 and the mean of the estimates is β̂1 = 0.344. This is because the unperturbed tumor growth model for the first 15 days is a simple exponential curve that can be easily fitted. However, the error for estimating k1 is large due to the complicated dynamics when drug is applied, and nonlinear curve fitting may give inaccurate estimates because only approximate expression can be obtained for the tumor growth, as also observed by Magni, Simeoni and et al.54,55 The true value of k1 is 0.405, while the mean of the estimates is k̂1 = 0.616.

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