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Pharmacokinetic Modeling Concepts: Compartmental and Non-compartmental approach for Drug Designing

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Abstract

Pharmacokinetic modeling helps to estimate the ADME parameters of all the natural and synthetic drug substances in humans and animals. There are two types of approaches for predicting the kinetic processes inside the body i.e., model approach and model independent approach. Model approach is further divided into compartmental and physiological models and model independent approach consists of non-compartmental models. Compartmental modeling is the most widely used approach. It visually shows the rate processes of drug deposition and also predict the drug concentration time graph. Compartmental model consists of catenary and mammillary models. Mammillary models can either be one compartment, multi compartment or two compartments. The physiologically based pharmacokinetic models consists of compartments that correlates with various body tissues, connected by blood circulatory system. Another name for noncompartmental analysis is model-independent approach that means it does not require any compartment model.

Authors

1-Duaa Aslam Chaudhry
Undergraduate Students (Final year), Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan.

2-Urooj Fatima
Undergraduate Students (Final year), Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan.

3-Kainat Waqar
Undergraduate Students (Final year), Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan.

4-Mubashar Rehman
Assistant Professor, Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan.

Keywords

Pharmacokinetic, Models, Compartments, Parameters, Drug, Concentration

DOI Number

10.31703/gdddr.2016(I-I).02


Page Nos

9-18

Volume & Issue

I - I

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Published: Dec 2016

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