The Residual sum of squares of these fits are compared based on an F- or Chi²-Test, respectively. At the same time all curves are fitted with no parameters shared among the different curves (unconstrained fit). Using a difference testing approach, the test and reference curves are fitted with shared parameters (constrained fit, global fit).
PLA can be done using different statistical approaches on the basis of these parameters to assess parallelism of the curves. 1 shows two typical dose response curves of a test (purple) and a reference substance (orange) both having comparable slopes and asymptotes thus considered parallel. Parallel-line analysis (PLA) is the statistical way to assess if curves are parallel, and if so, calculates the relative potencies of the substances.įig. The latter shall only be calculated if the underlying dose-response curves are parallel. Parallel-line assays are common in drug efficacy testing wherein one or more test substances are compared against a reference substance based on their relative potency. Here we describe how to apply PLA in BMG LABTECHs data analysis software MARS to inhibitor binding data to assess binding potencies of Prostaglandin D synthase inhibitors. Parallel-line analysis (PLA) is often applied in this context in order to prove parallelism of the underlying dose-response curves and subsequently estimate the relative potencies of substances compared to a reference substance. These types of bioassays, however, are also very common in other research areas. Finding compounds which lead to the same response as a reference substance but at a lower dose can be the key to lower the risk of side-effects and to lower manufacturing costs.
Screening for drug compounds with higher potency is of keen interest for many pharmaceutical drug development departments.
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