Evaluation of portable devices for medicine quality screening: Lessons learnt, recommendations for implementation, and future priorities.
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ABSTRACT:
SUBMITTER: Caillet C
PROVIDER: S-EPMC8483386 | biostudies-literature | 2021 Sep
REPOSITORIES: biostudies-literature
Publications
Evaluation of portable devices for medicine quality screening: Lessons learnt, recommendations for implementation, and future priorities.
Caillet Céline C Vickers Serena S Vidhamaly Vayouly V Boutsamay Kem K Boupha Phonepasith P Zambrzycki Stephen S Luangasanatip Nantasit N Lubell Yoel Y Fernández Facundo M FM Newton Paul N PN
PLoS medicine 20210930 9
Céline Caillet and co-authors discuss a Collection on use of portable devices for the evaluation of medicine quality and legitimacy. ...[more]
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Project description:BackgroundPost-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 findingsWe 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%).ConclusionsThe 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.
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Project description:Background and purposeThe European SocieTy for Radiotherapy and Oncology Radiation Therapist Committee (ESTRO RTTC) published a guidance document and infographic providing recommendations to minimise risk of COVID-19 transmission in radiotherapy (RT) departments. The purpose of this study was to investigate the changes embedded in RT practice in the COVID-19 era and to recommend proactive measures to protect RT practice in future pandemics.Materials and methodsThe study was initiated by the ESTRO Radiation Oncology Safety and Quality Committee (ROSQC). A survey consisting of multiple choice, open ended and Likert scale questions was created to analyse the extent of changes embedded in RT practice in response to the COVID-19 pandemic under the four domains: patient care, RTT workflow, remote working and RT practice. This online survey was distributed globally in May 2020.Results229 respondents across 27 countries completed the survey. 60% of respondents reported continuing/commencing RT in COVID-19 patients. Routine testing of patients and RTTs was not common. Split teams' procedures, hot linacs and separate entrances were implemented by 50% of respondents. Remote working was implemented for RT team members where face to face patient contact was not essential. Lack of staff, connectivity issues and lack of confirmed positive cases in the department were the main reasons cited for not implementing recommended measures.ConclusionIt is suggested that RT departments have responded to the COVID-19 pandemic and implemented certain changes in RT practice. RT departments should act now to implement recommended proactive measures to protect patients and RTTs - frontline healthcare workers.
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Project description:BackgroundHemolytic uremic syndrome (HUS) is a rare condition which diagnosed with the triad of thrombocytopenia, microangiopathic hemolytic anemia, and acute renal injury. There is a high requirement for research to discover treatments. HUS registries can be used as an important information infrastructure. In this study, we identified and compared the different features of HUS registries to present a guide for the development and implementation of HUS registries.ResultsThe purposes of registries were classified as clinical (9 registries), research (7 registries), and epidemiological (5 registries), and only 3 registries pursued all three types of purposes. The data set included demographic data, medical and family history, para-clinical and diagnostic measures, treatment and pharmacological data, complications, and outcomes. The assessment strategies of data quality included monthly evaluation and data audit, the participation of physicians to collect data, editing and correcting data errors, increasing the rate of data completion, following guidelines and data quality training, using specific data quality indicators, and real-time evaluation of data at the time of data entry. 8 registries include atypical HUS patients, and 7 registries include all patients regardless of age. Only two registries focused on children. 4 registries apply prospective and 4 applied both prospective, and retrospective data collection. Finally, specialized hospitals were the main data source for these registries.ConclusionBased on the findings, we suggested a learning framework for developing and implementing an HUS registry. This framework includes lessons learned and suggestions for HUS registry purposes, minimum data set, data quality assurance, data collection methods, inclusion and exclusion criteria as well as data sources. This framework can help researchers develop HUS registries.
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