Frontiers in Drug Design and Discovery

Volume 5


Atta-ur-Rahman, Mohammad Iqbal Choudhary

DOI: 10.2174/97816080520351100501
eISBN: 978-1-60805-203-5, 2010
ISBN: 978-1-60805-527-2
ISSN: 1574-0889 (Print)
ISSN: 2212-1064 (Online)

Indexed in: Scopus, EMBASE, EBSCO, Ulrich's Periodicals Directory.

Frontiers in Drug Design and Discovery is a book series devoted to publishing the latest and the most important advances in drug desig...[view complete introduction]
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Selecting an Appropriate Dose-Response Curve in Bioassay Development

- Pp. 67-96 (30)

Jason J.Z. Liao, Fenghai Duan, Yuan Meng and Qinjian Zhao


<p>Bioassay or biological assay is a type of in vitro experiment that is typically conducted to measure the effect or activity of a substance or stimulus in a biological system, for estimating the nature, constitution, or potency of a material (or of a response) [1]. Bioassays are essential in the development of new protein drugs and vaccines. The dose-response curve from a bioassay can be a linear or non-linear by nature. There are many different statistical models proposed in the literature. To quantify the dose-response curve of a bioassay, the commonly used non-linear dose-curve is a sigmoid-curve such as the symmetric four-parameter logistic (4PL) function or the asymmetric fiveparameter logistic (5PL) curve, which takes curve asymmetry into consideration to overcome some drawbacks of the four-parameter logistic function. These include, for example, biased estimates of important model parameters, poor characterization of pharmacological pathways and the mechanisms, and inaccurate inference of drug-receptor interactions.</p> <p>In this paper, we will focus on the pros and cons between a linear and a nonlinear curve fit for data collected from an analytical method, and the pros and cons using the symmetric 4PL and the asymmetric 5PL. In addition, we will describe a systematic way in selecting the linear dose range for a linear curve fit, and also discuss the design and other important issues such as selecting an appropriate variance function for a sigmoid-curve fit. Examples will be used to illustrate the key points.</p>

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