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A human monoclonal antibody panel (PD4, PD5, PD7, SC23 and SC29) was isolated from the B cells of convalescent patients and used to examine the S protein in SARS-CoV-2- infected cells. While all five antibodies bound conformational-specific epitopes within SARS-CoV-2 Spike (S) protein, only PD5, PD7, and SC23 were able to bind to the Receptor Binding Domain (RBD). Immunofluorescence microscopy was used to examine the S protein RBD in cells infected with the Singapore isolates SARS-CoV-2/0334 and SARS-CoV-2/1302. The RBD-binders exhibited a distinct cytoplasmic staining pattern that was primarily localised within the Golgi complex and was distinct from the diffuse cytoplasmic staining pattern exhibited by the non-RBD binders (PD4 and SC29). These data indicated that the S protein adopted a conformation in the Golgi complex that enabled the RBD recognition by the RBD-binders. The RBD-binders also recognised the uncleaved S protein indicating that S protein cleavage was not required for RBD recognition. Electron microscopy indicated high levels of cell-associated virus particles, and multiple cycle virus infection using RBD-binder staining provided evidence for direct cell-to-cell transmission for both isolates. Although similar levels of RBD-binder staining was demonstrated for each isolate, the SARS-CoV-2/1302 exhibited slower rates of cell-to-cell transmission. These data suggest that a conformational change in the S protein occurs during its transit through the Golgi complex that enables RBD recognition by the RBD-binders, and suggests that these antibodies can be used to monitor S protein RBD formation during the early stages of infection. ImportanceThe SARS CoV-2 spike (S) protein receptor binding domain (RBD) mediates the attachment of SARS CoV-2 to the host cell. This interaction plays an essential role in initiating virus infection and the S protein RBD is therefore a focus of therapeutic and vaccine interventions. However, new virus variants have emerged with altered biological properties in the RBD that can potentially negate these interventions. Therefore an improved understanding of the biological properties of the RBD in virus-infected cells may offer future therapeutic strategies to mitigate SARS CoV-2 infection. We used physiologically relevant antibodies that were isolated from the B cells of convalescent COVID19 patients to monitor the RBD in cells infected with SARS CoV-2 clinical isolates. These immunological reagents specifically recognise the correctly folded RBD and were used to monitor the appearance of the RBD in SARS CoV-2-infected cells and identified the site where the RDB first appears.
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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.
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SARS-CoV-2-neutralizing antibodies are promising therapeutics for COVID-19. However, little is known about the mechanisms of action of these antibodies or their effective dosing windows. We report the discovery and development of SC31, a potent SARS-CoV-2 neutralizing IgG1 antibody, originally isolated from a convalescent patient at day 27 after the onset of symptoms. Neutralization occurs via a binding epitope that maps within the ACE2 interface of the SARS-CoV-2 Spike protein, conserved across all common circulating SARS-CoV-2 mutants. In SARS-CoV-2 infected K18-human ACE2 transgenic mice, SC31 demonstrated potent survival benefit by dramatically reducing viral load concomitant with attenuated pro-inflammatory responses linked to severe systemic disease, such as IL-6. Comparison with a Fc-null LALA variant of SC31 demonstrated that optimal therapeutic efficacy of SC31 requires intact Fc-mediated effector functions that can further induce an IFN{gamma}-driven anti-viral immune response. Dose-dependent efficacy for SC31 was observed down to 5mg/kg when dosed before the activation of lung inflammatory responses. Importantly, despite Fc{gamma}R binding, no evidence of antibody dependent enhancement was observed with the Fc-competent SC31 even at sub-therapeutic doses. Therapeutic efficacy was confirmed in SARS-CoV-2-infected hamsters, where SC31 again significantly reduced viral load, decreased lung lesions and inhibited progression to severe disease manifestations. This study underlines the potential for significant COVID-19 patient benefit for the SC31 antibody that justifies rapid advancement to the clinic, as well as highlighting the importance of appropriate mechanistic and functional studies during development. One Sentence SummaryAnti-SARS-CoV-2 IgG1 antibody SC31 controls infection in vivo by blocking SP:ACE2 binding and triggering a Fc-mediated anti-viral response.
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The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) has led to the rapid initiation of urgently needed clinical trials of repurposed drug combinations and monotherapies. These regimens were primarily relying on mechanism-of-action based selection of drugs, many of which have yielded positive in vitro but largely negative clinical outcomes. To overcome this challenge, we report the use of IDentif.AI, a platform that rapidly optimizes infectious disease (ID) combination therapy design using artificial intelligence (AI). In this study, IDentif.AI was implemented on a 12-drug candidate therapy search set representing over 530,000 possible drug combinations. IDentif.AI demonstrated that the optimal combination therapy against SARS-CoV-2 was comprised of remdesivir, ritonavir, and lopinavir, which mediated a 6.5-fold improvement in efficacy over remdesivir alone. Additionally, IDentif.AI showed hydroxychloroquine and azithromycin to be relatively ineffective. The identification of a clinically actionable optimal drug combination was completed within two weeks, with a 3-order of magnitude reduction in the number of tests typically needed. IDentif.AI analysis was also able to independently confirm clinical trial outcomes to date without requiring any data from these trials. The robustness of the IDentif.AI platform suggests that it may be applicable towards rapid development of optimal drug regimens to address current and future outbreaks.
