Please cite this article in press as: Kharbach M, et al. Multivariate statistical process control in product quality review assessment — A case study. Ann Pharm Fr (2017), http://dx.doi.org/10.1016/j.pharma.2017.07.003 ARTICLE IN PRESS +Model PHARMA-517; No. of Pages 9 Annales Pharmaceutiques Françaises (2017) xxx, xxx—xxx Disponible en ligne sur ScienceDirect www.sciencedirect.com ORIGINAL ARTICLE Multivariate statistical process control in product quality review assessment — A case study Évaluation de la revue qualité produit par la maîtrise statistique du procédé multivariée — étude de cas M. Kharbach a,b , Y. Cherrah a , Y. Vander Heyden b , A. Bouklouze a,* a Pharmaceutical and toxicological analysis research team, laboratory of pharmacology and toxicology, faculty of medicine and pharmacy, university Mohammed V. Souissi, avenue Med Belarbi El Alaoui, BP 6203, 10000 Rabat, Morocco b Department of analytical chemistry and pharmaceutical technology, CePhaR, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium Received 29 November 2016; accepted 3 July 2017 KEYWORDS Annual product review; Statistical process control; Multivariate statistical process control; Principal component analysis; Hotelling’s T 2 Summary According to the Food and Drug Administration and the European Good Manufac- turing Practices (GMP) guidelines, Annual Product Review (APR) is a mandatory requirement in GMP. It consists of evaluating a large collection of qualitative or quantitative data in order to verify the consistency of an existing process. According to the Code of Federal Regulation Part 11 (21 CFR 211.180), all finished products should be reviewed annually for the quality standards to determine the need of any change in specification or manufacturing of drug prod- ucts. Conventional Statistical Process Control (SPC) evaluates the pharmaceutical production process by examining only the effect of a single factor at the time using a Shewhart’s chart. It neglects to take into account the interaction between the variables. In order to overcome this issue, Multivariate Statistical Process Control (MSPC) can be used. Our case study concerns an APR assessment, where 164 historical batches containing six active ingredients, manufactured in Morocco, were collected during one year. Each batch has been checked by assaying the six ∗ Corresponding author. E-mail addresses: a.bouklouze@um5s.net.ma, yvanvdh@vub.net.be (A. Bouklouze). http://dx.doi.org/10.1016/j.pharma.2017.07.003 0003-4509/© 2017 Académie Nationale de Pharmacie. Published by Elsevier Masson SAS. All rights reserved.