title: Quantitative risk assessment in classification of drugs with identical API content.
abstract: When combating counterfeits it is equally important to recognize fakes and to avoid misclassification of genuine samples. This study presents a general approach to the problem using a newly-developed method called Data Driven Soft Independent Modeling of Class Analogy. The possibility to collect representative data for both training and validation is of great importance in classification modeling. When fakes are not available, we propose to compose the test set using the legitimate drug's analogs, manufactured by various producers. These analogs should have the identical API and a similar composition of excipients. The approach shows satisfactory results both in revealing counterfeits and in accounting for the future variability of the target class drugs. The presented case studies demonstrate that theoretically predicted misclassification errors can be successfully employed for the science-based risk assessment in drug identification.
authors.affiliation: Information and Methodological Center for Expertise, Stocktaking and Analysis of Circulation of Medical Products, Slavyanskay sq., 4-1, 109074 Moscow, Russia; N.N.Semenov Institute of Chemical Physics RAS, Kosygin 4, 119991 Moscow, Russia. Electronic address: rcs@chph.ras.ru.
authors.forename: O Ye
authors.initials: OY
authors.lastname: Rodionova
authors.affiliation: Information and Methodological Center for Expertise, Stocktaking and Analysis of Circulation of Medical Products, Slavyanskay sq., 4-1, 109074 Moscow, Russia; I.M. Sechenov First Moscow State Medical University, Trubetskaya str., 8. b.2, 119991 Moscow, Russia.
authors.forename: K S
authors.initials: KS
authors.lastname: Balyklova
authors.affiliation: Information and Methodological Center for Expertise, Stocktaking and Analysis of Circulation of Medical Products, Slavyanskay sq., 4-1, 109074 Moscow, Russia; Pirogov Russian National Research Medical University, Ostrovityanov str., 1, 117997 Moscow, Russia.
authors.forename: A V
authors.initials: AV
authors.lastname: Titova
authors.affiliation: N.N.Semenov Institute of Chemical Physics RAS, Kosygin 4, 119991 Moscow, Russia; Institute of Natural and Technical Systems RAS, Kurortny pr. 99/18, 354024 Sochi, Russia.
authors.forename: A L
authors.initials: AL
authors.lastname: Pomerantsev
mesh_terms: D058110:Counterfeit Drugs / Q000032:analysis / Q000737:chemistry; D005079:Excipients / Q000737:chemistry; D004364:Pharmaceutical Preparations / Q000032:analysis* / Q000737:chemistry*; D018570:Risk Assessment; D019265:Spectroscopy, Near-Infrared / Q000379:methods
chemical_list: D058110:Counterfeit Drugs; D005079:Excipients; D004364:Pharmaceutical Preparations
keywords: Counterfeiting; DD-SIMCA; Future variability; Misclassification errors; Near infrared spectrometry