A Comparative Study of Statistical Methods to Assess Dilutional Similarity - The four parameter logistic model is a better control method than the dilution effect statistic - BioPharm International

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A Comparative Study of Statistical Methods to Assess Dilutional Similarity
The four parameter logistic model is a better control method than the dilution effect statistic


BioPharm International


Using 95% confidence level, the assumption of parallelism will be rejected if F is larger than the critical point from an F distribution with 3 numerator degrees of freedom and (n–8) denominator degrees of freedom, where n is the total number of observations in the analysis.

COMPARISON OF THE METHODS


Figure 1. Total RNA Yield (mg) from Different Tissues (mg) The upper asymptotes do not match. Dilutional similarity rejected by F-test.
As part of the release of a drug product as a reference standard, a qualification study was undertaken to compare the potency between the new formulation with the original reference standard, STD. Figures 1– 5 are graphs comparing a test sample to a reference standard using a 4-parameter logistic model. These are independent tests of the formulations and not a sequence.


Figure 2. Yield of Total and mRNA from Different Tissues and Cells Divergence at higher concentrations. Dilutional similarity rejected by F-test.
The compounds were radio-tagged, depicting the measurement in counts/min. The starting point of each test is 200 mg/mL and then it is diluted in steps of 3-fold or 2-fold until the last measurement at 0.77 mg/mL. On all figures the x-axis is logarithmic and the y-axis is linear.

In order to compute the potency, the test sample has to be parallel to the reference standard. In Figure 1, the test sample and the reference standard do not have the same upper asymptote. The F-statistic was 3.67 with p-value = 0.0436 < 0.05 rejecting the dilutional similarity. However, the dilution effect computed was 5.95%, which did not reject the parallelism because it is ≤ 20%. In Figure 2 and Figure 3, the test of parallelism was rejected using F-statistics. The p-values were < 0.0001 and 0.0004, respectively. The dilution effects statistics computed were 16.89% and 19.31%, respectively, yielding an acceptance result for dilutional similarity, as it was ≤ 20%.

In Figure 4, the test of parallelism yielded a p-value of 0.992, which is ≥ 0.05 and the dilution effect measure computed was less than 20%. The two tests had comparable outcomes. In Figure 5, the two tests rejected the dilutional similarity.


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