Evidence map›Paper›PMID 26917250›Full record

ArticleMalaria journal2016

Assessment of the effectiveness of the CD3+ tool to detect counterfeit and substandard anti-malarials.

JaCinta S Batson, Daniel K Bempong, Patrick H Lukulay, Nicola Ranieri, R Duane Satzger, Leigh Verbois

Abstract read
In one paragraph

Article in Malaria journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

JaCinta S BatsonFood and Drug Administration, Forensic Chemistry Center, 6751 Steger Drive, Cincinnati, OH, 45237, USA. Jacinta.Batson@fda.hhs.gov.
Daniel K BempongUnited States Pharmacopeial Convention, 12601 Twinbrook Parkway, Rockville, MD, 20852, USA. DKB@usp.org.
Patrick H LukulayUnited States Pharmacopeial Convention, 12601 Twinbrook Parkway, Rockville, MD, 20852, USA. PHL@usp.org.
Nicola RanieriFood and Drug Administration, Forensic Chemistry Center, 6751 Steger Drive, Cincinnati, OH, 45237, USA. Nicola.Ranieri@fda.hhs.gov.
R Duane SatzgerFood and Drug Administration, Forensic Chemistry Center, 6751 Steger Drive, Cincinnati, OH, 45237, USA. RD.Satzger@fda.hhs.gov.
Leigh VerboisFood and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA. Leigh.Verbois@fda.hhs.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe US FDA recently developed CD3+, a counterfeit detection tool that is based on sample illumination at specific wavelengths of light and visual comparison of suspect sample and packaging materials to an authentic sample. To test performance of the CD3+ in field conditions, a study was conducted in Ghana which compared the CD3+ side-by-side with two existing medicine quality screening technologies-TruScan™ Portable Raman spectrometer and GPHF Minilab(®).

methodsA total of 84 anti-malarial test samples comprising artemether-lumefantrine tablets and artesunate-amodiaquine tablets were used. The technologies were evaluated for sensitivity in determining counterfeit/substandard (The term counterfeit or falsified is used in this article to refer to medicines that carry a false representation of identity or source or both. The term substandard is used to refer to medicines that do not meet the quality specifications given in the accepted pharmacopeia.) medicines, specificity in determining authentic products, and reliability of the results. Authentic samples obtained from manufacturers were used as reference standards. HPLC analysis data was used as the "gold standard" for decisions regarding a sample being authentic or substandard/counterfeit.

resultsCD3+ had a sensitivity of 1.00 in detecting counterfeit/substandard products compared to Minilab (0.79) and TruScan (0.79). CD3+ had a lower specificity (0.53) in determining authentic products compared to the specificities reached by Minilab (0.99) and TruScan (1.00). High sensitivity in this context means that the technology is effective in identifying substandard/counterfeit products whereas the low specificity means that the technique can sometimes mischaracterize good products as substandard/counterfeit. Examination of dosage units only (and not packaging) using CD3+ yielded improved specificity 0.64. When only assessment of sample identification was done, the TruScan provided sensitivity (1.00) and specificity (0.99); and the Minilab provided sensitivity (1.00) and specificity (1.00). All three technologies demonstrated 100 % reliability when used to analyse the same set of samples over 3 days by a single analyst and also when used to determine the same set of samples by three different analysts. Eight of the field samples were confirmed to be counterfeits with no active pharmaceutical ingredient content. All three technologies identified these samples as counterfeits.

conclusionsThe study revealed the relative effectiveness of the technologies as quality control tools. Using a combination of CD3+, with either the Minilab or TruScan, to screen for medicine quality will allow for complete examination of both the dosage units and the packaging to decide whether it is authentic or counterfeit.

Indexed as

AmodiaquineAntimalarialsArtemether, Lumefantrine Drug CombinationArtemisininsChromatography, Thin LayerCounterfeit DrugsDatabases, FactualDrug CombinationsEthanolaminesFluorenesQuality ControlReproducibility of ResultsSensitivity and SpecificitySpectrum Analysis, RamanTabletsAmodiaquineamodiaquine, artesunate drug combinationAntimalarialsArtemether, Lumefantrine Drug CombinationArtemisininsCounterfeit DrugsDrug CombinationsEthanolaminesFluorenesTablets

Identifiers

PMID26917250
PMCPMC4766612

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.