The Federal Court dismissed Shire’s application for an order prohibiting the Minister of Health from issuing a Notice of Compliance ( NOC) for Apotex’s extended release MAS product, holding that Shire had failed to show that Apotex’s product will infringe Shire’s patent. ( Shire) markets an extended release MAS product under the name ADDERALL XR® for use in the treatment of Attention Deficit Hyperactivity Disorder ( ADHD). Apotex has sought approval for a generic version of ADDERALL XR®. Nature of case: Prohibition application pursuant to section 6 of the Patented Medicines (Notice of Compliance) Regulations, SOR/93-133 (the Regulations) The participating drug checking services have access to adequate analytical tools to provide feedback to drug users and provide up-to-date information on NPS.Case: Shire Canada Inc v Apotex Inc, 2016 FC 382ĭrug: ADDERALL XR® (mixed amphetamine salts ( MAS)) The most common issues and errors are mainly unidentified compounds, presumably due to no up-to-date libraries, and/ or confusion between structural isomers, such as 3- and 4-chloroethcathinone, or structural analogs, such as MIPLA (N-methyl-N-isopropyl lysergamide) and LSD (D-lysergic acid diethylamide). The proficiency test scores range from 80 to 97.5% accuracy. Twenty blind substances, covering the most common categories of substances, were analyzed according to the existing protocols of the existing drug checking services, including several analytical methods such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography with diode array detector (LC-DAD). ![]() To evaluate challenges posed towards drug checking services, proficiency testing was set up to evaluate existing analytical techniques and investigate the capability to correctly identify circulating NPS. However, NPS cause a toxicological battle for the researchers, as factors such as the unpredictability and quick shift of the market complicate the detection. Next to that, it supports rapid identification of potential unwitting consumption. It combines chemical analysis of samples with direct engagement with people who use drugs (PWUD), giving the ability to increase preparedness and responsiveness towards NPS. The advantages of each of the illicit drug-detecting systems and their potential as forensic screening tools used in field scenarios are also discussed.ĭrug checking is a proven harm reduction strategy and provides real-time information on the market of new psychoactive substances (NPS). ![]() Finally, their performance in assigning the class identity of three classes of drugs of abuse, i.e., hallucinogenic (2C-x, DOx, and NBOMe) amphetamines, cannabinoids, and opioids, were compared based on confusion matrices and various classification parameters, such as balanced accuracy, sensitivity, and specificity. In order to account for the stochastic nature of these machine learning methods, both models were evaluated 10 times on a randomly distributed subset of the whole SWGDRUG IR Library, and the results were compared in detail. Then, several machine learning methods, i.e., Support Vector Machines (SVM), eXtreme Gradient Boosting (XGB), Random Forest, Gradient Boosting, and K-Nearest Neighbors (KNN), were used to assign the drug class membership. The results corroborated those of an exploratory analysis that was based on several dimensionality reduction methods, i.e., Principal Component Analysis (PCA), Independent Component Analysis (ICA), and autoencoders. A preliminary statistical analysis of selected spectra data extracted from the public SWGDRUG IR Library was first performed. We present a comparative study aiming to determine the most efficient multivariate model screening for the main drugs of abuse based on their ATR-FTIR spectra.
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