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Near infrared spectroscopy as a screening technique for the quality control of antiretroviral drugs for HIV treatment in Swiss prisons

2020· article· en· W3009851030 sur OpenAlexaboutno aff
Riccardo Deidda, Lamyae El Morabit, Charlotte De Bleye, Pierre‐Yves Sacré, Julien Boccard, Philippe Hubert, Eric Ziémons, Nathalie Vernaz, Elena Tobolkina, Jean‐Luc Veuthey, Serge Rudaz

Notice bibliographique

RevueORBi (University of Liège) · 2020
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueSpectroscopy Techniques in Biomedical and Chemical Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHuman immunodeficiency virus (HIV)Antiretroviral drugAntiretroviral treatmentQuality (philosophy)MedicineVirologyAntiretroviral therapyPhysicsViral load
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

1 Introduction Human immunodeficiency virus (HIV) infection remains one of the major public health challenges over the world. In 2018, according to the Joint United Nations Program on HIV/AIDS, nearly 37.9 million people are living with HIV [1]. Antiretroviral therapy has shown a great effectiveness in reducing mortality and morbidity related to AIDS and has thus allowed AIDS to evolve from a deadly disease to a chronic one [2]. However, most of the antiretroviral drugs are still under patent protection, and therefore their price is a major barrier to their access in low- and middle-income countries. In this context, the “Doha Declaration” was adopted in 2001 allowing these countries to produce certain patented drugs, by giving them contractual licenses. These “unapproved generic drugs” present the same active principal ingredients (APIs), galenic form and dosage, but can differ in used excipients or additives [3]. In Switzerland, people living in prison (PLP) are often not covered by compulsory insurance and their access to treatment is therefore limited. In this context, Swiss Buyer’s clubs have been created with the aim of importing “unapproved generic drugs” via recognized suppliers based in low- and middle-income countries. Consequently, quality control tests have to be performed in order to guarantee the quality and safety of these pharmaceutical products [2, 3, 4]. Separation techniques, such as liquid chromatography (LC) and capillary electrophoresis (CE), remain the gold standard to determine the API content in pharmaceutical formulations quantitatively. However, they provide only limited information about other components of the sample, such as excipients and additives. Furthermore, as a sample preparation is required before analysis, their use implies the sacrifice of at least one sample, that is undesirable for expensive samples, or when a limited number of tablets is available. Therefore, near infrared spectroscopy (NIR) can offer relevant advantages allowing fast direct analysis of the samples without prior preparation [5]. The goal of this project is the evaluation of NIR spectroscopy as a screening tool to confirm the identity of tablets coming from different selected manufacturers. 2 Material and methods Drug samples were obtained from the Medical Direction Geneva University Hospitals. Handheld NIR-S-G1 (Tellspec, Canada) was used to perform NIR analyses. The wavelength range was from 900 to 1700 nm (11111 – 5882 cm-1). Matlab R2018a software (The MathWorks, Massachusetts) and PLS toolbox® (version 8.6.2, Eigenvector Research, Washington) were used for data treatment and computation. 3 Results and discussion Six patented anti-HIV drugs and their respective generic formulations have been selected for this study and analysed by NIR spectroscopy: Truvada® (emtricitabine, enofovir disoproxil), Descovy® (emtricitabine, tenofovir alafenamide), Atripla® (emtricitabine, tenofovir disoproxil, efevirenz), Isentress® (raltegravir), Tivicay® (dolutegravir), Triumeq® (dolutregavir, abacavir, lamivudine). Some of them present one or more API(s) in common. When building the data set, inter- and intra- batch variabilities were taken into consideration by selecting different batches. Ten tablets were selected from each batch and one spectrum was acquired on each sample. Before modeling, various types of preprocessing were tested in order to better exploit the spectral information. Patented drugs often showed relevant spectral differences from their generic formulations. Since NIR spectroscopy allows obtaining information about both chemical and physical properties of samples, small differences in the formulations permitted to easily differentiate between the two. Data-driven soft independent modelling of class analogy (DD-SIMCA) models were chosen as one-class classification technique and a model was built for each patented and generic drug. Based on a calibration set, this chemometric tool allows the evaluation of a critical distance, which has been used to define the acceptance area limits for future identifications (α = 0.05). In fact, all the spectra falling within this area can be associated to the modelled class and then to a specific pharmaceutical drug. 4 Conclusion NIR spectroscopy shows great potential as screening technique for the quality control of antiretroviral drugs for HIV treatment in Swiss prisons. In fact, a proper chemometric model could be used to assess the identity and then the conformity of drugs before performing further tests, if required. 5 References [1] Global HIV & AIDS statistics – 2019 fact sheet. [cited 2019 Nov 27]. Available from: https://www.unaids.org/en/resources/fact-sheet [2] WHO, UNAIDS, UNDP. Using TRIPS flexibilities to improve access to HIV treatment, 2011. [cited 2019 Nov 27] Available from: http://files.unaids.org/en/media/unaids/contentassets/documents/unaidspublication/2011/JC2049_PolicyBrief_TRIPS_en.pdf [3] WTO Ministerial conferences – Doha 4th Ministerial – TRIPS declaration. [cited 2019 Nov 2019] Available from: https://www.wto.org/english/thewto_e/minist_e/min01_e/min01_e.htm [4] Vernaz, N., Calmy, A., Hurst, S., Jackson, Y., Negro, F., Perrier, A., Wolf, H. A buyers’ club to improve access to hepatitis C treatment for vulnerable populations. Swiss Med Wkly. 2018 [5] Deidda, R., Sacré, P.-Y., Clavaud, M., Coïc, L., Avohou, H., Hubert, Ph., Ziemons, E. Vibrational spectroscopy in analysis of pharmaceuticals: Critical review of innovative portable and handheld NIR and Raman spectrophotometers. Trends Anal. Chem. 114, 251 – 259, 2019.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,681
Score d'incertitude au seuil0,379

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,314
Écart entre enseignants0,294 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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