Main Text: SARS-CoV-2, a novel coronavirus, emerged in December 2019 in Wuhan, China. Over the following months the resulting disease, subsequently named COVID-19, spread across the rest of the world and was declared a pandemic by the World Health Organization on March 11th, 2020. As of submission, there are over 2.2M reported infections worldwide and over 150,000 reported deaths, with an anticipated world wide death toll in the millions and an economic impact sufficient to cause a worldwide recession or depression 1, 2 . There are currently no vaccines available and the only available therapies are supportive or based largely on anecdotal work.
To rapidly identify and prioritize potential treatments of COVID-19, we adapted our deep learning-enabled drug discovery platform to evaluate a library of FDA-approved drugs, EMA-approved drugs or compounds in late stage clinical trials for modulation of the effect of SARS-CoV-2 on human cells. Through unbiased image analysis of cytological structures of perturbed cells (phenomics), profiles can be rapidly created for use as the basis of chemical suppressor screens 3, 4 . Such profiles can be generated to model many diseases across multiple therapeutic areas, including: inflammatory disease (cytokines), genetic diseases (siRNAs or CRISPR), or infectious disease with the live unmodified SARS-CoV-2 virus. This method can be deployed rapidly for high throughput phenotypic screening in any relevant cell types -including human -without development of disease-specific tools such as antibodies, reporters, or surrogate cell lines. This technique can detect traditional inhibitors of viral replication, but also novel or unexpected signals that could come about through disrupting the interactions of the virus with the host or modulating cytokine signalling.
To identify a human SARS-CoV-2 model suitable for high throughput screening, we infected monolayers of normal human renal cortical epithelial cells (HRCE), as well as normal human bronchial epithelial cells and Caco-2 cells, then fixed, stained, and imaged cells at 96 hours post infection as described previously ( Figure 1a and Supplemental Figure 1a ,b ) 4 . We also infected African green monkey kidney epithelial cells (Vero) as a control condition ( Supplemental Figure 1c ). To evaluate the specificity of the phenotypic profile to the impact of the virus we compared the effects of active SARS-CoV-2 to inactive ultraviolet-irradiated (UV-IR) and mock preparations of SARS-CoV-2 on cells. Images were processed using a proprietary deep learning neural network to generate high-dimensional featurizations of each image for the identification of distinct phenotypic profiles. Both HRCE and Vero cells demonstrated robust phenotypes compared to both mock and irradiated controls (Z-factors: HRCE active virus vs mock, 0.432; HRCE active virus vs irradiated, 0.438; Vero active virus vs mock, 0.803; Vero active virus vs irradiated, 0.827) ( Figure 1b ) . Notably, the phenomics assay achieved the aforementioned, highly separable assay window in HRCEs without significant changes in cell count at this time point compared to both mock and UV-IR controls. HRCEs were selected for further screening due to their disease relevance and robust disease-specific phenotype 5 . Chemical Suppressor screens were conducted by treating HRCE cells in six half-log doses with six replicates per dose for each compound approximately two hours after cell seeding. At 24 hours post seeding, cells were infected with SARS-CoV-2 at a MOI of 0.4 and incubated for 96 hours until fixation, staining and imaging. Following imaging, compounds were evaluated for their impact on the morphological profile of infection as defined by the difference between the active virus and the mock condition ('on-disease score') plus their overall impact on other elements of cell morphology not associated with the disease model ('off-disease score') ( Figure  2a, Supplemental Figure 2 ) . In vero cells, chloroquine, lopinavir, and remdesivir exhibited disease score suppression activity at IC50 values similar to what has been previously established for SARS-CoV-2 in cytopathic effect (CPE) and viral N-protein expression assays 6, 7 . A proprietary hit score algorithm based on the comparative on-disease vs off-disease score in dose-response was used to determine efficacy of compounds ( Figure 2b ) .
The vast majority of compounds currently reported as under evaluation in human clinical trials for COVID-19 showed no or weak on-disease efficacy ( Figure 2c ) 8 . However, remdesivir (as well as its parent nucleoside GS-441524) demonstrated strong repeatable efficacy in our model ( Figure 2d and Supplemental Figure 3 ). Of particular interest, chloroquine and hydroxychloroquine showed no benefit in the human cell model; however, they showed weak benefit in Vero cells, albeit with very high off-disease activity ( Figure 2d and Supplemental Figure 4 ).
Next we report on three compounds not currently in trials for COVID-19 which demonstrated moderate to strong effects in our model. The two clinical-stage assets were added to our library based on in silico or in vitro analyses suggesting their potential utility. Aloxistatin (E64d) is an irreversible cysteine protease inhibitor initially developed for muscular dystrophy and advanced through Phase 3 in Japan 9 ( Figure 2e ). Recent studies have confirmed that the cysteine protease cathepsin L is required for SARS-CoV-2 viral entry, and aloxistatin treatment reduced cellular entry of SARS-CoV-2 pseudovirions by 92.3% 10 .
Silmitasertib (CX-4945) is a selective inhibitor of protein kinase casein kinase 2 (CK2) currently in clinical development in oncology 11 ( Figure 2f ). CK2 was recently suggested as a therapeutic target for SARS-CoV-2 based on large scale interactome data and hypothesized to promote an antiviral cellular state by reducing turnover of stress granules 12 . Alternatively, results from SARS-CoV-1 suggest that CK2 inhibition may prevent nucleocapsid shuttling from the nucleus to the cytoplasm 13 .
Almitrine is a respiratory stimulant used in acute respiratory failure such as chronic obstructive pulmonary disease (COPD) that is available in Europe, but not currently available in the US 14 ( Figure 2g ). Recent structure-based computational studies hypothesized that almitrine binds to the SARS-CoV-2 main viral proteinase (Mpro, or 3CLpro), an important viral target given the necessity of this protein for viral replication [15] [16] [17] . Our results demonstrate for the first time activity of almitrine in human cells infected with SARS-CoV-2, suggesting almitrine may represent a novel SARS-CoV-2 protease-targeting antiviral.
Further, we have identified chemical classes of compounds which, across the class, display weak but consistent rescue, and are reasonably well-tolerated in humans, offering the potential to be elements of combination therapies.
The most significantly enriched class of molecules identified were beta-adrenergic receptor antagonists with 9 of 32 molecules demonstrating above threshold activity ( Supplemental Figure 5a , P < 0.0001). Among the most active molecules in the class were carvedilol and celiprolol; both are molecules with combined beta and alpha adrenergic blockade, properties that are thought to enhance the effectiveness of these drugs in patients with combined heart failure and COPD 18 . An association between beta adrenergic blocker use and SARS-COV-2 has not been previously demonstrated, but given the elevated risk of mortality among patients with preexisting heart disease 19 , it is intriguing to consider whether subclasses may offer enhanced benefit for these patients.
A second mechanistic class that demonstrated enrichment was PI3K/mTOR. Sirolimus and its derivative compounds, and two of three PI3K inhibitors demonstrated above threshold activity ( Supplemental Figure 5b , P < 0.0001). Therapeutic benefit of mTOR modulating agents in SARS-CoV-2 was hypothesized based on human protein-protein interactome results suggesting enriched interactions of viral proteins with human targets of mTORC1 12 . Similarly, a systems computational pharmacology approach predicted sirolimus as a putative therapeutic based on SARS-CoV-2 protein network mapping 20 . Notably, inhibition of mTOR with sirolimus reduced MERS-CoV infection by ~60% in cell-based studies 21 . Furthermore, diabetes represents a second major risk factor for severe SARS-CoV-2, and given dysregulation of mTOR signaling in the diabetic state, it is appealing to consider these results may reveal a mechanistic basis for adverse outcomes in these patients 19 .
A third mechanistic class that demonstrated significant enrichment among actives in the screen was vitamin D receptor agonists ( Supplemental Figure 5c , P < 0.0001). While the role of vitamin D in immunity is well established 22, 23 and vitamin D supplementation is subject of an ongoing investigational study 24 , limited evidence supports a beneficial role for vitamin D in prevention or resolution of SARS-CoV-2 infection. A large meta-analysis of acute respiratory tract infection found that supplementation was beneficial overall, but most protective in patients with baseline vitamin D deficiency 25 .
Lastly, several notable mechanistic classes demonstrated lack of any activity or were significantly enriched among molecules that trended toward amplifying the disease phenotype. ACE inhibitors and angiotensin receptor blockers (ARBs) have been the subject of interest given their widespread use and reported viral entry into the cell via the ACE2 receptor 26 . These classes showed no significant activity. However, beta-adrenergic receptor agonists such as albuterol and formoterol commonly administered as bronchodilators led to a statistically significant amplification of the SARS-CoV-2 phenotypes ( Supplemental Figure 5d , P <0.0001) and may warrant caution in patients with COVID-19.
Together, these data show for the first time a high-dimensional analysis of more than 1,660 approved drugs in a human cellular model of SARS-CoV-2 infection. Although this in vitro screen represents data from only a single human cell type, HRCE, this method is likely broadly applicable to other primary human cell models. These data may be useful as the basis for prioritizing further work given constrained resources and serve to generate hypotheses for other researchers in this rapidly progressing field. In our case, this data will serve as the basis for ongoing efforts to identify a cocktail of additive or synergistic, orally available, rapidly manufacturable, and safe FDA-approved drugs with robust activity for further study in animals or humans as a stop-gap to a vaccine, and more broadly utilizable than remdesivir. Finally, from signing an agreement with our BSL3 partner facility to submitting these data required only 4 weeks. With some optimization, this type of work could potentially be conducted in under 3 weeks and across much larger chemical libraries, suggesting that this broad approach-which is able to identify in vitro signals of efficacy across a wide variety of mechanisms -could be a robust tool for rapid therapeutic discovery in future pandemics.