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www.biopharminternational.com October 2020 BioPharm International eBook 41 Regulatory Sourcebook Process Development the process as designed. CQA and CPP (including relevant process trends and quality of incoming materials or components, in-pro- cess material, and finished prod- uc t s) mu st b e cont rol le d a nd periodically rev iewed. The col- lection of these data will allow detec t ion of undesired process variability. I f supp or te d by appropr iate IT tools (8,9), the review may be e xe c ute d b e fore re lea si ng a ny batch; however, as this opportu - nity is not generally possible, cri- teria for frequency of the review must be established (e.g., produc- tion volumes, process criticality). Moreover, the frequency criteria must assure appropriate statisti- cal population: number of batches must be suitable for the applied statistical tools. Acceptance criteria must be established and appropri - ate investigations and corrective ac tions and preventive ac tions (CAPAs) must be generated in case of failure. According to FDA (3), "procedures should describe how t re nd i ng a nd c a lc u lat ions a re to b e p e r fo r me d a nd s ho u ld guard against overreaction to indi- vidual events as well as against fail- ure to detect unintended process variability." Su it able st at ist ic a l to ols for CPV are summarized in Table VII. P e r io d ic m e e t i n g s b e t w e e n the quality unit and production staff could help to meet intra- or inter-batch variations, sharing sta- tistical data, and coordinating cor- rective or preventive actions. After this stage, ways to improve or opt i m i ze t he process cou ld be suggested by a lter ing some aspects of the process or product, such as the operating conditions (ranges and set-points), process controls, components, or in-pro - cess material characteristics. In case of failure in accomplish- ing acceptance criteria, appropriate investigations must be started tak- ing into account the failed statisti- cal tool and concentrating efforts on single batches/small groups of batches (trends, shift, and single values out of warning limits, out - liers in histograms) or on entire population (capabilit y, not nor- mal or not centered histograms, anomalies in descriptive statistic, warning limits out of specifica- tion limits). Finally, Table VIII is a non-ex- haustive list of CPPs that should/ could be taken into account when assessing a statistical anomaly of several CQAs for a freeze- dried product. Before/when assessing any correlation of the CQA fail - ure w ith the CPP, the analy ti- ca l met hod a nd its va l idat ion data shou ld a lso be eva luated to confirm that any anomaly is not generated at laboratory level (e.g., non-robust met hod, lack of repeatabilit y). Moreover, the possible influence of the variabil- it y of incom ing mater ia ls a nd of relevant CMAs must be always t a k e n i n t o a c c o u n t f o r a n y C QA fa i lu re. It i s wor t h not- ing that the sterilizing filtration ste p h a s b e e n e xc lude d f rom this table, although the type of membra ne f ilter cou ld in t he - or y inf luence the assay due to adsor ption of the active ingre- dient, because it is assumed that the selection of the membrane fil- ter has been made during devel- opment st udies. Filling volume and capacity of the vial, which c o u l d i n f l u e n c e t h e a p p e a r- ance and cycle duration have not b e e n i nc lude d e it he r, for t he same reason, because it is assumed that these aspects have been opti- mized during the development studies. CONCLUSION T h i s work ha s i l lu st r ate d t he approaches and the tools available to implement the modern vision of PV for pharmaceutical finished products as described by the main reg ulator y author ities. Specif ic e xa mples have been g iven for ster i le l iqu id a nd f ree ze - d r ied products. Following the same approach, s ol id o r a l d o s a ge fo r m s w i l l be covered i n a not her a r t icle, "Enhancing Process Validation for Solid Oral Dosage Forms: Part 2" a nd genera l com ments on t he topic will be made (10). DISCLAIMER This article reflects the views of the authors and should not be con- strued to represent any pharma- ceutical company's or regulatory agency's views or policies. REFERENCES 1. L.X. Yu, Pharm. Res., 25 (4) 781–791 (2008). 2. ICH, Q8(R2) Pharmaceutical Development, step 4 version (2009). 3. FDA, Process Validation: General Principles and Practices Process (Rockville, MD, January 2011). 4. ICH, Q9 Quality Risk Management, step 4 version (2005). 5. FDA, Quality by Design (QbD): An Example Pharmaceutical Development Report for an Immediate Release (IR) Dosage Form (Rockville, MD, 2012) 6. European Commission, EudraLex Volume 4, Annex 15: Qualification and Validation (Brussels, March 2015). 7. EMA, Guideline on Process Validation for Finished Products—Information and Data to be Provided in Regulatory Submissions (London, UK, November 2016). 8. J.S. Oakland, Statistical Process Control (Elsevier Butterworth-Heinemann, Oxford, UK, 6th ed., 2008). 9. ISO 7870-1, Control Charts—Part 1: General Guidelines (2014). 10. F. Speroni, et al., "Enhancing Process Validation for Solid Oral Dosage Forms: Part 2," Pharmaceutical Technology Regulatory Sourcebook eBook (October 2020). BP