RESUMO
Despite human saliva representing a convenient and non-invasive clinical substrate for disease diagnosis and biomonitoring, its widespread utilization has been hampered by technical challenges. The non-Newtonian, heterogenous and highly viscous nature of clinical saliva samples complicate the development of automated fluid handling processes that are vital for accurate diagnoses. Furthermore, conventional saliva processing methods are either resource and/or time intensive precluding certain testing capabilities in low- and middle-income countries, with these challenges aggravated during a pandemic outbreak. The conventional approaches can also potentially alter analyte structure, reducing application opportunities in Point-of-Care diagnostics. To overcome these challenges, we introduce the SHEAR saliva collection device that preprocesses saliva for enhanced interfacing with downstream assays. We demonstrate the devices impact on reducing salivas viscosity, improving sample uniformity and, increasing diagnostic performance of COVID-19 Rapid Antigen Tests. Importantly, in addition to reporting technical advances and to address downstream implementation factors, we conducted a formal user experience study, which resulted in generally positive comments. Effective implementation of this device could be of support to realize the potential of saliva, particularly in large-scale and/or resource-limited settings for global and community health diagnostics.
RESUMO
ObjectivesWe aimed to harness IDentif.AI 2.0, a clinically actionable AI platform to rapidly pinpoint and prioritize optimal combination therapy regimens against COVID-19. MethodsA pool of starting candidate therapies was developed in collaboration with a community of infectious disease clinicians and included EIDD-1931 (metabolite of EIDD-2801), baricitinib, ebselen, selinexor, masitinib, nafamostat mesylate, telaprevir (VX-950), SN-38 (metabolite of irinotecan), imatinib mesylate, remdesivir, lopinavir, and ritonavir. Following the initial drug pool assessment, a focused, 6-drug pool was interrogated at 3 dosing levels per drug representing nearly 10,000 possible combination regimens. IDentif.AI 2.0 paired prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus (propagated, original strain, B.1.351 and B.1.617.2 variants) and Vero E6 assay with a quadratic optimization workflow. ResultsWithin 3 weeks, IDentif.AI 2.0 realized a list of combination regimens, ranked by efficacy, for clinical go/no-go regimen recommendations. IDentif.AI 2.0 revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived. ConclusionsIDentif.AI 2.0 rapidly revealed promising drug combinations for clinical translation. It pinpointed dose-dependent drug synergy behavior to play a role in trial design and realizing positive treatment outcomes. IDentif.AI 2.0 represents an actionable path towards rapidly optimizing combination therapy following pandemic emergence. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/21259321v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@f8a159org.highwire.dtl.DTLVardef@12908b7org.highwire.dtl.DTLVardef@fb6485org.highwire.dtl.DTLVardef@8493c3_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights- When novel pathogens emerge, the immediate strategy is to repurpose drugs. - Good drugs delivered together in suboptimal combinations and doses can yield low or no efficacy, leading to misperception that the drugs are ineffective. - IDentif.AI 2.0 does not use in silico modeling or pre-existing data. - IDentif.AI 2.0 pairs optimization with prospectively acquired experimental data using a SARS-CoV-2/Vero E6 assay. - IDentif.AI 2.0 pinpoints EIDD-1931 as a foundation for optimized anti-SARS-CoV-2 combination therapies.
