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Record W3009851030

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

2020· article· en· W3009851030 on 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

Bibliographic record

VenueORBi (University of Liège) · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsnot available
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)Antiretroviral drugAntiretroviral treatmentQuality (philosophy)MedicineVirologyAntiretroviral therapyPhysicsViral load
DOInot available

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.681
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.314
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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