Comparing drug release profiles is a critical step in pharmaceutical development, especially when assessing bioequivalence between a generic and a brand-name product. The f2 similarity factor is the industry standard for this comparison.
Enter values separated by commas (e.g., 10, 25, 50...)
Ensure the number of time points matches the reference.
Understanding the f2 Similarity Factor
The f2 similarity factor is a logarithmic transformation of the sum-squared error of differences between the test and reference products over all time points. It provides a single value that represents the closeness of two dissolution profiles.
The Mathematical Formula
The calculation follows the FDA-approved equation:
Where:
- n is the number of time points.
- Rt is the cumulative percentage of the reference drug dissolved at time t.
- Tt is the cumulative percentage of the test drug dissolved at time t.
Interpreting Your Results
According to regulatory guidelines (FDA and EMA), the interpretation of the f2 value is as follows:
- f2 > 50: The profiles are considered similar. An f2 value of 50 corresponds to an average difference of 10% at all time points.
- f2 = 100: The profiles are identical.
- f2 < 50: The profiles are considered dissimilar.
Regulatory Requirements for Valid f2 Calculation
To ensure the f2 value is statistically valid for regulatory submission, several conditions must usually be met:
- At least three time points (excluding zero) are required.
- The time points for both reference and test must be the same.
- Only one measurement should be considered after 85% dissolution of both products.
- The relative standard deviation (RSD) should not be more than 20% at the early time points and not more than 10% at later time points.
Why Use Our f2 Dissolution Calculator?
Manual calculation of the logarithmic sum-squared error can be prone to human error. Our tool provides an instant, accurate calculation based on the standard pharmaceutical formulas. Whether you are working on formulation development, scale-up post-approval changes (SUPAC), or stability testing, this tool simplifies your data analysis workflow.