To ensure consistency and adherence, a procedure should be established and analysts should be trained to perform such experiments.
Because this method provides relative product cleanability, it is important that each experiment be conducted in a consistent
manner. When performing cleaning evaluations to compare new products to the validated worst case, an additional check can
be incorporated to ensure that each evaluation is conducted in a consistent manner. This is achieved by comparing the data
for a control molecule (e.g., a worst-case product) to the established data set or the "gold standard" generated for the control
during the characterization study. The same statistical method, the TOST, can be used to fulfill this requirement. For example,
an analyst may need to perform an experiment to determine the cleanability of new product N relative to the validated product
W. The cleanability of validated product W has been pre-established by prior characterization work. To ensure that the analyst
performed the experiment adequately, a comparability test using the TOST can be used to compare the equivalency between data
generated by an analyst for product W to the established data set. The equivalency between the two data sets would demonstrate
that the experiment was indeed adequate and reliable.
The two-one-sided t-test (TOST) is a statistical method well accepted by the FDA and industry for evaluating the comparability between two groups
of data. In the case of a scale-down cleaning evaluation, this statistical approach has been applied to determine the relative
cleanability of two products. The TOST compares two group means and their confidence intervals by comparing them to a predefined
equivalence limit. The predefined equivalence limit should be established by evaluating the variability involved with such
experimental evaluations. To incorporate an additional check for analyst consistency, TOST can be applied to ensure that the
data obtained from different analysts for a particular product (control molecule) are equivalent.
The authors thank Ed Walls and Erwin Freund (Process Development, Amgen, Inc.) for reviewing this work and providing their
Cylia Chen is a senior associate scientist, Nitin Rathore is a senior scientist, and Wenchang Ji is a principal scientist, all in drug product and device development, and Abe Germansderfer is a principal quality engineer, corporate quality, all at Amgen, Inc., Thousand Oaks, CA, 805.313.6393, firstname.lastname@example.org
1. US Food and Drug Administration. Guidance for industry. Current good manufacturing practice for finished pharmaceuticals.
Rockville, MD; 2004. Available from:
2. Sanchez JAM. Equipment cleaning validation within a multi-product manufacturing facility. BioPharm Int. 2006;19(20):38–49.
3. Mollah AH, White EK. Risk-based cleaning validation in biopharmaceutical API manufacturing. BioPharm Int. 2005;18(11):34–40.
4. Sharnez R, Lathia J, Kahlenberg D, Prabhu S. In situ monitoring of soil dissolution dynamics: A rapid and simple method
for determining worst-case soils for cleaning validation. PDA J Pharm Sci Technol. 2004;58:203–14.
5. Le Blanc DA. Validated cleaning technologies for pharmaceutical manufacturing. CRC Press LLC;2000.
6. Rathore N, Qi W, Ji W. Cleaning characterization of protein drug products using UV-vis spectroscopy. Biotechnol Prog.
7. Rathore N, et al. Bench scale characterization of cleaning process design space for biopharmaceuticals. BioPharm Int.
8. Chambers D, et al. Analytical method equivalency: an acceptable analytical practice. Pharm Technol. 2005:9:64–80.
9. US Food and Drug Administration. Guidance for industry. Statistical approaches to establishing bioequivalence. Rockville,
10. US FDA. Guidance for industry. Bioavailability and bioequivalence studies for orally administered drug Products—general
considerations. Rockville, MD;2003.
10. National Institute of Standards and Technology. NIST/SEMATECH e-Handbook of Statistical Methods. Available from:
11. SAS Institute Inc. JMP statistical discovery from SAS. Release 6. 2006.