With the response comparison method, the tested curves are simultaneously fit into a constrained model, where the curves are forced to be parallel, and an independent model, where the curves are independently fitted. If an inappropriate curve fit model is selected, it could introduce bias into the parallelism metrics and may lead you to the wrong conclusion.Ĭalculating the relative potency of non-parallel curves is difficult due to the rare occurrence of curve fits that are perfectly parallel for assay data, especially for non-linear regressions. Therefore, choosing the correct curve fit model and applying a weighting factor, if necessary, that can accommodate these variations is the first important step to consider before parallelism analysis. Testing for parallelism Response comparison testīiological systems often do not behave as expected and generally add noise and variation to the data.
All of these methods can be used for linear and non-linear regression curves. This protocol is called Parallelism Test and is located in the SoftMax Pro Protocol Library in the Data Analysis subfolder. Furthermore, a parameter comparison method has also been developed using Fieller’s theorem. A protocol has been implemented with the F-test probability using the F-test 1,2 and the chi-squared probability with the chi-squared test 3.
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1 This application note explains both methods and outlines how to use them in SoftMax® Pro 7 Software to test for parallelism. Methods testing parallelism can be divided into two categories depending on how the parallelism hypothesis is tested: response comparison tests and parameter comparison tests. The relative potency is set to one for the reference curve (red circle) and the scaling factor used to transform the reference curve into the test curve (blue diamond) is the relative potency of the unknown agent. Parallel line model for linear regression.
However with non-linear regression curve fits, such as the 4-parameter and 5-parameter logistics, the sigmoidal dose-response curve has a variable slope over the entire concentration range (Figure 1).įigure 2. This methodology works well for linear regression curve fits where the slope is unchanged across the concentration range (Figure 2). 1 The relative potency is generally set to one for the reference curve (known agent) and the scaling factor used to transform the reference curve into the test curve (unknown agent) is the relative potency of the unknown agent. Two curves are defined to be parallel when one function is obtained from the other by a scaling factor either to the right or to the left on the x-axis, ƒ(x) = ƒ(rx), where x is the dose and r is the scaling factor, or relative potency. Parallel line analysis of dose response data sets with a constrained global 4-parameter curve fit. Testing for parallelism is a prerequisite to calculate the relative potency of a compound and plays an important role in many biological applications such as drug comparison, analyte confirmation, cross-reactivity, interfering substances, matrix compensation, concentration estimation, and inhibitory studies.įigure 1. Parallelism methods allow the user to establish if the biological response to two substances is similar or if two biological environments give similar dose-response curves to the same substances. PLA is commonly used to compare dose-response curves where there is no direct measurement of a product, but rather an effect is measured (Figure 1). High-Content Screening with AgileOptix Technologyīiological assays are frequently analyzed with the help of parallel line analysis (PLA).PROTEIN DETECTION, QUANTITATION, ANALYSIS.COVID-19 RESPONSE - We are committed to supporting our scientific community during this pandemic.