Laboratory evaluation of twelve portable devices for medicine quality screening.


Journal

PLoS neglected tropical diseases
ISSN: 1935-2735
Titre abrégé: PLoS Negl Trop Dis
Pays: United States
ID NLM: 101291488

Informations de publication

Date de publication:
09 2021
Historique:
received: 17 12 2020
accepted: 02 04 2021
entrez: 30 9 2021
pubmed: 1 10 2021
medline: 15 12 2021
Statut: epublish

Résumé

Post-market surveillance is a key regulatory function to prevent substandard and falsified (SF) medicines from being consumed by patients. Field deployable technologies offer the potential for rapid objective screening for SF medicines. We evaluated twelve devices: three near infrared spectrometers (MicroPHAZIR RX, NIR-S-G1, Neospectra 2.5), two Raman spectrometers (Progeny, TruScan RM), one mid-infrared spectrometer (4500a), one disposable colorimetric assay (Paper Analytical Devices, PAD), one disposable immunoassay (Rapid Diagnostic Test, RDT), one portable liquid chromatograph (C-Vue), one microfluidic system (PharmaChk), one mass spectrometer (QDa), and one thin layer chromatography kit (GPHF-Minilab). Each device was tested with a series of field collected medicines (FCM) along with simulated medicines (SIM) formulated in a laboratory. The FCM and SIM ranged from samples with good quality active pharmaceutical ingredient (API) concentrations, reduced concentrations of API (80% and 50% of the API), no API, and the wrong API. All the devices had high sensitivities (91.5 to 100.0%) detecting medicines with no API or the wrong API. However, the sensitivities of each device towards samples with 50% and 80% API varied greatly, from 0% to 100%. The infrared and Raman spectrometers had variable sensitivities for detecting samples with 50% and 80% API (from 5.6% to 50.0%). The devices with the ability to quantitate API (C-Vue, PharmaChk, QDa) had sensitivities ranging from 91.7% to 100% to detect all poor quality samples. The specificity was lower for the quantitative C-Vue, PharmaChk, & QDa (50.0% to 91.7%) than for all the other devices in this study (95.5% to 100%). The twelve devices evaluated could detect medicines with the wrong or none of the APIs, consistent with falsified medicines, with high accuracy. However, API quantitation to detect formulations similar to those commonly found in substandards proved more difficult, requiring further technological innovation.

Sections du résumé

BACKGROUND
Post-market surveillance is a key regulatory function to prevent substandard and falsified (SF) medicines from being consumed by patients. Field deployable technologies offer the potential for rapid objective screening for SF medicines.
METHODS AND FINDINGS
We evaluated twelve devices: three near infrared spectrometers (MicroPHAZIR RX, NIR-S-G1, Neospectra 2.5), two Raman spectrometers (Progeny, TruScan RM), one mid-infrared spectrometer (4500a), one disposable colorimetric assay (Paper Analytical Devices, PAD), one disposable immunoassay (Rapid Diagnostic Test, RDT), one portable liquid chromatograph (C-Vue), one microfluidic system (PharmaChk), one mass spectrometer (QDa), and one thin layer chromatography kit (GPHF-Minilab). Each device was tested with a series of field collected medicines (FCM) along with simulated medicines (SIM) formulated in a laboratory. The FCM and SIM ranged from samples with good quality active pharmaceutical ingredient (API) concentrations, reduced concentrations of API (80% and 50% of the API), no API, and the wrong API. All the devices had high sensitivities (91.5 to 100.0%) detecting medicines with no API or the wrong API. However, the sensitivities of each device towards samples with 50% and 80% API varied greatly, from 0% to 100%. The infrared and Raman spectrometers had variable sensitivities for detecting samples with 50% and 80% API (from 5.6% to 50.0%). The devices with the ability to quantitate API (C-Vue, PharmaChk, QDa) had sensitivities ranging from 91.7% to 100% to detect all poor quality samples. The specificity was lower for the quantitative C-Vue, PharmaChk, & QDa (50.0% to 91.7%) than for all the other devices in this study (95.5% to 100%).
CONCLUSIONS
The twelve devices evaluated could detect medicines with the wrong or none of the APIs, consistent with falsified medicines, with high accuracy. However, API quantitation to detect formulations similar to those commonly found in substandards proved more difficult, requiring further technological innovation.

Identifiants

pubmed: 34591844
doi: 10.1371/journal.pntd.0009360
pii: PNTD-D-20-02187
pmc: PMC8483346
doi:

Substances chimiques

Counterfeit Drugs 0
Substandard Drugs 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0009360

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 202935/Z/16/Z
Pays : United Kingdom

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

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pubmed: 25897064
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pubmed: 26873200
Anal Chem. 2013 Jul 2;85(13):6453-60
pubmed: 23725012
BMJ Glob Health. 2018 Aug 29;3(4):e000725
pubmed: 30233826
Drug Saf. 2017 Sep;40(9):809-821
pubmed: 28528487
Am J Trop Med Hyg. 2017 May;96(5):1117-1123
pubmed: 28219992
Malar J. 2014 Mar 31;13:127
pubmed: 24678609
Am J Trop Med Hyg. 2015 Jun;92(6 Suppl):113-118
pubmed: 25897060
Trop Med Health. 2016 May 16;44:15
pubmed: 27433134
Global Health. 2018 Apr 25;14(1):43
pubmed: 29695278

Auteurs

Stephen C Zambrzycki (SC)

School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

Celine Caillet (C)

Lao-Oxford-Mahosot Hospital-Wellcome Trust-Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR.
Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom.
Infectious Diseases Data Observatory (IDDO) & WorldWide Antimalarial Resistance Network (WWARN), University of Oxford, Oxford, United Kingdom.

Serena Vickers (S)

Lao-Oxford-Mahosot Hospital-Wellcome Trust-Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR.
Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom.
Infectious Diseases Data Observatory (IDDO) & WorldWide Antimalarial Resistance Network (WWARN), University of Oxford, Oxford, United Kingdom.

Marcos Bouza (M)

School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

David V Donndelinger (DV)

School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

Laura C Geben (LC)

School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

Matthew C Bernier (MC)

School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

Paul N Newton (PN)

Lao-Oxford-Mahosot Hospital-Wellcome Trust-Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR.
Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom.
Infectious Diseases Data Observatory (IDDO) & WorldWide Antimalarial Resistance Network (WWARN), University of Oxford, Oxford, United Kingdom.

Facundo M Fernández (FM)

School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

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