A Stocastic Semi-Empirical Model for First Order Decay Pharmacokinetics

I.C., Okoro

A Stocastic Semi-Empirical Model for First Order Decay Pharmacokinetics

I.C., Okoro *

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A Stocastic Semi-Empirical Model for First Order Decay Pharmacokinetics

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Abstract

A first order stochastic semi-empirical model for pharmacokinetics is presented and the real response of drug concentration to vital pharmacokinetics parameters studied. By invoking Gaussian kinetics and the residual drug concentration eliminated, the probability densities and the response of concentration profiles are theoretically simulated, using empirical data based on our experience. The drug is administered for 3 days at regular time intervals of 3hr and 6hr, respectively, by refreshing the drug halflife. Results show that the amount of drug residue decreases with increasing dose, but increases with increase in ingestion time interval for corresponding dose. It is also shown that the real drug concentration increases to a threshold and decreases marginally for subsequent dose. However it is difficult to predict the response of drug concentration with changes in ingestion time interval. We recommend that for higher drug concentration the half-life be increased. Our simulation results qualitatively agree with those documented in the literatures.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

I.C., Okoro. 2013. "A Stocastic Semi-Empirical Model for First Order Decay Pharmacokinetics". Global Journal of Science Frontier Research - A: Physics & Space Science GJSFR-A Volume 13 (GJSFR Volume 13 Issue A6).

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GJSFR Volume 13 Issue A6

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords

drug half-life first order pharmacokinetics gaussian kinetics real drug concentration residue stochastic dynamics

Classification

GJSFR-A Classification FOR Code: 029999

Submission ReceivedJune 25, 2013
Peer Review Double Blind
Handling Editor
Accepted July 24, 2013
Published August 4, 2013

Version of record

v1.2

Issue date

August 24, 2013

Language

English

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Article Matrices

Total Score: 146

Country: Nigeria

Subject: Global Journal of Science Frontier Research

Authors: I.C., Okoro (PhD/Dr. count: 0)

View Count (all-time): 234

Total Views (Real + Logic): 2005

Total Downloads (simulated): 96

Publish Date: 2013 08, Sun

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Total Views: 2K

Total Downloads: 96

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