Rational design & engineering of cost-efficient Point-of-Care (POC) systems for rapid diagnostics of emergent and chronic degenerative diseases

Abstract

Patients require proper access to diagnostics to benefit from medicine and obtain proper treatment. However, diagnostics availability is one of the biggest challenges concerning global public healthcare. The recent Covid-19 pandemic has highlighted the consequences of the lack of fast and widely available diagnostics. This dissertation aims to propose a novel solution for addressing this complex healthcare challenge. We propose the engineering of cost-efficient Point-of-Care (POC) systems for the rapid diagnostics of emergent and chronic degenerative diseases. We chose SARS-CoV-2 as a model for emergent infectious diseasse due to the recent pandemic in winter 2019. In addition, cancer was selected as the representative of chronic degenerative diseases tdue to its incidence and mortality rate. To assess the relative importance of intervention strategies such as vaccination, social distancing, and rapid diagnostic, first we developed a population level surveillance model based on ordinary differential equations that simulate the effect of vaccine rate against Covid-19 spreading. The model revealed the benefits of rapid interventions such as fast vaccination campaigns and widespread diagnostics, and that in the absence of vaccines, rapid diagnostic followed by the quarantine of infected subjects. In alignment with this finding, we developed cost-effective and portable diagnostic methods to identify SARS-CoV-2 nucleic acids based on isothermal amplification strategies. First, we developed and characterized the performance of a point of-care (POC) do-it-yourself (DiY) device to identify SARS-CoV-2 RNA in less than 45 minutes. We also conducted a pilot study in Monterrey to evaluate the effectiveness of this DiY POC testing strategy based on a colorimetric LAMP & polyethylene-sulfonate membrane. We determined a sensitivity and specificity of 100% and 87%, respectively, highlighting the value of utilizing quick and accurate diagnostic responses. Furthermore, we engineered an Arduino-based detection system for the rapid diagnostics of SARS-CoV-2 in 5 saliva using a nucleic acid amplification strategy. We tested our Arduino-based detection system ability to discriminate between and positive saliva samples spiked with SARS-CoV 2 genetic material. We decided to further challenge the versatility of our system by testing its ability to discriminate between cancer and non-cancerous tissue spheroids. We were able to identify clusters based on the expression of selected genetic biomarkers by implementing our detection strategy. Overall, our solutions present with viable alternatives to alleviate the lack of accessible and cost-effective diagnostic platforms for infectious and chronic diseases. By implementing our systems and models we would increase early detection and offer easy to-implement population surveillance models to increase disease monitoring.