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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

Sigmoidal Emax model (m = 4), and approximation by our PD model.
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Related In: Results  -  Collection


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f3-cin-11-2012-041: Sigmoidal Emax model (m = 4), and approximation by our PD model.

Mentions: The PK model provides the concentration time course resulting from the administered dose and the continuous description of concentration will serve as input function for the PD model, which relates the concentration to the observed effect. Generally, the magnitude of pharmacological effect increases monotonically with increased dose, eventually reaching a plateau level where further increases in dose have little additional effect.13 The classic and most commonly used concentration-effect model is the Hill equation,64 also called the sigmoidal Emax model65 or logistic model.66 The relationship between the concentration of the drug candidate and its effect is most often nonlinear. In some cases, the curve even looks like a “roller coaster”, which is referred to as the “double Hill Model”.67 One common method is to replace certain slowly changing variables by their piecewise linear approximation. In this study, we use hybrid systems to approximate the sigmoidal Emax PD model (see Fig. 3). The Emax model has the general form:(11)E=EmaxCmEC50m+Cm,where Emax is the maximum effect, C is the concentration, EC50 is the concentration necessary to produce 50% of Emax, and m represents a sigmoidity factor or steepness of the curve.


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)

Sigmoidal Emax model (m = 4), and approximation by our PD model.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3-cin-11-2012-041: Sigmoidal Emax model (m = 4), and approximation by our PD model.
Mentions: The PK model provides the concentration time course resulting from the administered dose and the continuous description of concentration will serve as input function for the PD model, which relates the concentration to the observed effect. Generally, the magnitude of pharmacological effect increases monotonically with increased dose, eventually reaching a plateau level where further increases in dose have little additional effect.13 The classic and most commonly used concentration-effect model is the Hill equation,64 also called the sigmoidal Emax model65 or logistic model.66 The relationship between the concentration of the drug candidate and its effect is most often nonlinear. In some cases, the curve even looks like a “roller coaster”, which is referred to as the “double Hill Model”.67 One common method is to replace certain slowly changing variables by their piecewise linear approximation. In this study, we use hybrid systems to approximate the sigmoidal Emax PD model (see Fig. 3). The Emax model has the general form:(11)E=EmaxCmEC50m+Cm,where Emax is the maximum effect, C is the concentration, EC50 is the concentration necessary to produce 50% of Emax, and m represents a sigmoidity factor or steepness of the curve.

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