Advisor(s)

Tarek Mahfouz, PhD
Ohio Northern University
Pharmaceutical & Biomedical Sciences
t-mahfouz@onu.edu

Document Type

Video

Start Date

23-4-2021 9:00 AM

Description

The half-life of a drug is the time that it takes for the amount of drug in the body to be reduced by half. This important parameter indicates the length of time that a drug will persist in the body to produce pharmacological response. The half-life of a drug is affected by many variables including pKa, solubility, lipophilicity, molecular weight, and others. This research aims to identify which variables most affect the half-life of the non-steroidal anti-inflammatory drugs (NSAIDS) and to develop a linear regression model with strong predictive qualities. Such a model can be used to predict the half-life of new chemical structures which will facilitate the development of new, longer-lasting NSAIDs. A group of 19 NSAIDs were chosen to develop the regression model. The half-lives and the chemical and physical properties of the chosen NAIDs were gathered from the “Drugs” database (drugbank.com). Multiple linear regression analyses were utilized to determine the variables affecting NSAIDs half-live. Different permutations of variables were tested to identify the model that best fits the experimental data. Solubility (p=0.4279), number of enzymes that metabolize a given drug (p=0.3324), and hydrogen bond acceptor sites (p=0.16978) were identified as variables that strongly correlate to the half-life.

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Apr 23rd, 9:00 AM

Half-Life Regression Model

The half-life of a drug is the time that it takes for the amount of drug in the body to be reduced by half. This important parameter indicates the length of time that a drug will persist in the body to produce pharmacological response. The half-life of a drug is affected by many variables including pKa, solubility, lipophilicity, molecular weight, and others. This research aims to identify which variables most affect the half-life of the non-steroidal anti-inflammatory drugs (NSAIDS) and to develop a linear regression model with strong predictive qualities. Such a model can be used to predict the half-life of new chemical structures which will facilitate the development of new, longer-lasting NSAIDs. A group of 19 NSAIDs were chosen to develop the regression model. The half-lives and the chemical and physical properties of the chosen NAIDs were gathered from the “Drugs” database (drugbank.com). Multiple linear regression analyses were utilized to determine the variables affecting NSAIDs half-live. Different permutations of variables were tested to identify the model that best fits the experimental data. Solubility (p=0.4279), number of enzymes that metabolize a given drug (p=0.3324), and hydrogen bond acceptor sites (p=0.16978) were identified as variables that strongly correlate to the half-life.