Translating diagnostics and drug delivery technologies to low-resource settings

Abstract

Diagnostics and drug delivery technologies engineered for low-resource settings aim to meet their technical design specifications using strategies that are compatible with limited equipment, infrastructure, and operator training. Despite many preclinical successes, very few of these devices have been translated to the clinic. Here, we identify factors that contribute to the clinical success of diagnostics and drug delivery systems for low-resource settings, including the need to engage key stakeholders at an early stage, and provide recommendations for the clinical translation of future medical technologies.

End-user feedback of rapid diagnostics in rural Kenya

“…We also aim to produce an HIV self-test that addresses the concerns described earlier. The absence of critical medical supplies, such as diagnostics for pervasive diseases like COVID-19 and HIV, is common in low-resource facilities, 23 and a deeper understanding of distribution methods is necessary to increase the availability of healthcare in underserved regions.…”

Digital microfluidics with distance-based detection – a new approach for nucleic acid diagnostics

“…Among many technologies that can be used for this application, nucleic acid amplification tests (NAATs) are particularly attractive because of their high analytical and clinical sensitivities. 1,2 The most popular format for POC NAATs are methods that rely on the polymerase chain reaction (PCR), which are advantageous because they are directly comparable to the gold-standard laboratory diagnostics that also rely on PCR. 3 A disadvantage of PCR is the inherent requirement for temperature cycling, which can be challenging to package into a miniaturized device footprint.…”

“…However, in practice, the requirement for the user to perform manual processing steps prior to use is a substantial limitation that makes many potential POC techniques impractical for use in the field. 2 Here we used digital microfluidics (DMF) to address this limitation, providing automated sample processing, and reducing the burden on the user. DMF has been used to automate PCR, [29][30][31][32] LAMP 33,34 and other NAAT techniques, 35,36 but has never before been combined with distance-based detection.…”

Multiparameter mobile blood analysis for complete blood count using contrast-enhanced defocusing imaging and machine vision

“…Moreover, it has been reported that 85% of the global population (65 billion) still lives in low- and middle-income countries (LMICs) that lack laboratory facilities and technicians but suffer from most global disease burdens. 25–27 Blood analysis has been recognized as a convenient and practical test for integrated surveillance of various epidemic diseases, including anemia, malaria, and neglected tropical diseases, which have caused high mortality and disability rate in LMICs. Hence, developing mHealth technology for delivering blood analysis globally is quite necessary and urgent.…”