CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair.

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.

CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair.

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.

Multiple ParA/MinD ATPases coordinate the positioning of disparate cargos in a bacterial cell.

In eukaryotes, linear motor proteins govern intracellular transport and organization. In bacteria, where linear motors involved in spatial regulation are absent, the ParA/MinD family of ATPases organize an array of genetic- and protein-based cellular cargos. The positioning of these cargos has been independently investigated to varying degrees in several bacterial species. However, it remains unclear how multiple ParA/MinD ATPases can coordinate the positioning of diverse cargos in the same cell. Here, we find that over a third of sequenced bacterial genomes encode multiple ParA/MinD ATPases. We identify an organism (Halothiobacillus neapolitanus) with seven ParA/MinD ATPases, demonstrate that five of these are each dedicated to the spatial regulation of a single cellular cargo, and define potential specificity determinants for each system. Furthermore, we show how these positioning reactions can influence each other, stressing the importance of understanding how organelle trafficking, chromosome segregation, and cell division are coordinated in bacterial cells. Together, our data show how multiple ParA/MinD ATPases coexist and function to position a diverse set of fundamental cargos in the same bacterial cell.

Imaging-Based Screening Identifies Modulators of the eIF3 Translation Initiation Factor Complex in Candida albicans.

Fungal pathogens like Candida albicans can cause devastating human disease. Treatment of candidemia is complicated by the high rate of resistance to common antifungal therapies. Additionally, there is host toxicity associated with many antifungal compounds due to the conservation between essential mammalian and fungal proteins. An attractive new approach for antimicrobial development is to target virulence factors: non-essential processes that are required for the organism to cause disease in human hosts. This approach expands the potential target space while reducing the selective pressure toward resistance, as these targets are not essential for viability. In C. albicans, a key virulence factor is the ability to transition to hyphal morphology. We developed a high-throughput image analysis pipeline to distinguish between yeast and filamentous growth in C. albicans at the single cell level. Based on this phenotypic assay, we screened the FDA drug repurposing library of 2,017 compounds for their ability to inhibit filamentation and identified 33 compounds that block the hyphal transition in C. albicans with IC50 values ranging from 0.2 to 150 µM. Multiple compounds showed a phenyl sulfone chemotype, prompting further analysis. Of these phenyl sulfones, NSC 697923 displayed the most efficacy, and by selecting for resistant mutants, we identified eIF3 as the target of NSC 697923 in C. albicans.

Imaging-based screening identifies modulators of the eIF3 translation initiation factor complex in Candida albicans.

Fungal pathogens like Candida albicans can cause devastating human disease. Treatment of candidemia is complicated by the high rate of resistance to common antifungal therapies. Additionally, there is host toxicity associated with many antifungal compounds due to the conservation between essential mammalian and fungal proteins. An attractive new approach for antimicrobial development is to target virulence factors: non-essential processes that are required for the organism to cause disease in human hosts. This approach expands the potential target space while reducing the selective pressure towards resistance, as these targets are not essential for viability. In C. albicans, a key virulence factor is the ability to transition to hyphal morphology. We developed a high-throughput image analysis pipeline to distinguish between yeast and filamentous growth in C. albicans at the single cell level. Based on this phenotypic assay, we screened the FDA drug repurposing library of 2,017 compounds for their ability to inhibit filamentation and identified 33 compounds that block the hyphal transition in C. albicans with IC 50 values ranging from 0.2 to 150 µM. Multiple compounds showed a phenyl vinyl sulfone chemotype, prompting further analysis. Of these phenyl vinyl sulfones, NSC 697923 displayed the most efficacy, and by selecting for resistant mutants, we identified eIF3 as the target of NSC 697923 in C. albicans .

Candida albicans selection for human commensalism results in substantial within-host diversity without decreasing fitness for invasive disease.

Candida albicans is a frequent colonizer of human mucosal surfaces as well as an opportunistic pathogen. C. albicans is remarkably versatile in its ability to colonize diverse host sites with differences in oxygen and nutrient availability, pH, immune responses, and resident microbes, among other cues. It is unclear how the genetic background of a commensal colonizing population can influence the shift to pathogenicity. Therefore, we examined 910 commensal isolates from 35 healthy donors to identify host niche-specific adaptations. We demonstrate that healthy people are reservoirs for genotypically and phenotypically diverse C. albicans strains. Using limited diversity exploitation, we identified a single nucleotide change in the uncharacterized ZMS1 transcription factor that was sufficient to drive hyper invasion into agar. We found that SC5314 was significantly different from the majority of both commensal and bloodstream isolates in its ability to induce host cell death. However, our commensal strains retained the capacity to cause disease in the Galleria model of systemic infection, including outcompeting the SC5314 reference strain during systemic competition assays. This study provides a global view of commensal strain variation and within-host strain diversity of C. albicans and suggests that selection for commensalism in humans does not result in a fitness cost for invasive disease.

A human liver organoid screening platform for DILI risk prediction.

BACKGROUND & AIMS: Drug-induced liver injury (DILI), both intrinsic and idiosyncratic, causes frequent morbidity, mortality, clinical trial failures and post-approval withdrawal. This suggests an unmet need for improved in vitro models for DILI risk prediction that can account for diverse host genetics and other clinical factors. In this study, we evaluated the utility of human liver organoids (HLOs) for high-throughput DILI risk prediction and in an organ-on-chip system. METHODS: HLOs were derived from three separate iPSC lines and benchmarked on two platforms for their ability to model in vitro liver function and identify hepatotoxic compounds using biochemical assays for albumin, ALT, AST, microscopy-based morphological profiling, and single-cell transcriptomics: i) HLOs dispersed in 384-well-formatted plates and exposed to a library of compounds; ii) HLOs adapted to a liver-on-chip system. RESULTS: Dispersed HLOs derived from the three iPSC lines had similar DILI predictive capacity as intact HLOs in a high-throughput screening format, allowing for measurable IC50 values of compound cytotoxicity. Distinct morphological differences were observed in cells treated with drugs exerting differing mechanisms of toxicity. On-chip HLOs significantly increased albumin production, CYP450 expression, and ALT/AST release when treated with known hepatoxic drugs compared to dispersed HLOs and primary human hepatocytes. On-chip HLOs were able to predict the synergistic hepatotoxicity of tenofovir-inarigivir and displayed steatosis and mitochondrial perturbation, via phenotypic and transcriptomic analysis, on exposure to fialuridine and acetaminophen, respectively. CONCLUSIONS: The high-throughput and liver-on-chip systems exhibit enhanced in vivo-like functions and demonstrate the potential utility of these platforms for DILI risk assessment. Tenofovir-inarigivr-associated hepatotoxicity was observed and correlates with the clinical manifestation of DILI observed in patients. IMPACT AND IMPLICATIONS: Idiosyncratic (spontaneous, patient-specific) drug-induced liver injury (DILI) is difficult to study due to the lack of liver models that function as human liver tissue and are adaptable for large-scale drug screening. Human liver organoids grown from patient stem cells respond to known DILI-causing drugs in both a high-throughput and on a physiological "chip" culture system. These platforms show promise for researchers in their use as predictive models for novel drugs before entering clinical trials and as a potential in vitro diagnostic tool. Our findings support further development of patient-derived liver organoid lines and their use in the context of DILI research.

The life and times of endogenous opioid peptides: Updated understanding of synthesis, spatiotemporal dynamics, and the clinical impact in alcohol use disorder.

The opioid G-protein coupled receptors (GPCRs) strongly modulate many of the central nervous system structures that contribute to neurological and psychiatric disorders including pain, major depressive disorder, and substance use disorders. To better treat these and related diseases, it is essential to understand the signaling of their endogenous ligands. In this review, we focus on what is known and unknown about the regulation of the over two dozen endogenous peptides with high affinity for one or more of the opioid receptors. We briefly describe which peptides are produced, with a particular focus on the recently proposed possible synthesis pathways for the endomorphins. Next, we describe examples of endogenous opioid peptide expression organization in several neural circuits and how they appear to be released from specific neural compartments that vary across brain regions. We discuss current knowledge regarding the strength of neural activity required to drive endogenous opioid peptide release, clues about how far peptides diffuse from release sites, and their extracellular lifetime after release. Finally, as a translational example, we discuss the mechanisms of action of naltrexone (NTX), which is used clinically to treat alcohol use disorder. NTX is a synthetic morphine analog that non-specifically antagonizes the action of most endogenous opioid peptides developed in the 1960s and FDA approved in the 1980s. We review recent studies clarifying the precise endogenous activity that NTX prevents. Together, the works described here highlight the challenges and opportunities the complex opioid system presents as a therapeutic target.

Predicting the Structural Impact of Human Alternative Splicing.

Protein structure prediction with neural networks is a powerful new method for linking protein sequence, structure, and function, but structures have generally been predicted for only a single isoform of each gene, neglecting splice variants. To investigate the structural implications of alternative splicing, we used AlphaFold2 to predict the structures of more than 11,000 human isoforms. We employed multiple metrics to identify splicing-induced structural alterations, including template matching score, secondary structure composition, surface charge distribution, radius of gyration, accessibility of post-translational modification sites, and structure-based function prediction. We identified examples of how alternative splicing induced clear changes in each of these properties. Structural similarity between isoforms largely correlated with degree of sequence identity, but we identified a subset of isoforms with low structural similarity despite high sequence similarity. Exon skipping and alternative last exons tended to increase the surface charge and radius of gyration. Splicing also buried or exposed numerous post-translational modification sites, most notably among the isoforms of BAX. Functional prediction nominated numerous functional differences among isoforms of the same gene, with loss of function compared to the reference predominating. Finally, we used single-cell RNA-seq data from the Tabula Sapiens to determine the cell types in which each structure is expressed. Our work represents an important resource for studying the structure and function of splice isoforms across the cell types of the human body.

Prioritizing Virtual Screening with Interpretable Interaction Fingerprints.

Machine learning-based drug discovery success depends on molecular representation. Yet traditional molecular fingerprints omit both the protein and pointers back to structural information that would enable better model interpretability. Therefore, we propose LUNA, a Python 3 toolkit that calculates and encodes protein-ligand interactions into new hashed fingerprints inspired by Extended Connectivity FingerPrint (ECFP): EIFP (Extended Interaction FingerPrint), FIFP (Functional Interaction FingerPrint), and Hybrid Interaction FingerPrint (HIFP). LUNA also provides visual strategies to make the fingerprints interpretable. We performed three major experiments exploring the fingerprints' use. First, we trained machine learning models to reproduce DOCK3.7 scores using 1 million docked Dopamine D4 complexes. We found that EIFP-4,096 performed (R2 = 0.61) superior to related molecular and interaction fingerprints. Second, we used LUNA to support interpretable machine learning models. Finally, we demonstrate that interaction fingerprints can accurately identify similarities across molecular complexes that other fingerprints overlook. Hence, we envision LUNA and its interface fingerprints as promising methods for machine learning-based virtual screening campaigns. LUNA is freely available at https://github.com/keiserlab/LUNA.

In Vitro Evaluation and Mitigation of Niclosamide's Liabilities as a COVID-19 Treatment.

Niclosamide, an FDA-approved oral anthelmintic drug, has broad biological activity including anticancer, antibacterial, and antiviral properties. Niclosamide has also been identified as a potent inhibitor of SARS-CoV-2 infection in vitro, generating interest in its use for the treatment or prevention of COVID-19. Unfortunately, there are several potential issues with using niclosamide for COVID-19, including low bioavailability, significant polypharmacology, high cellular toxicity, and unknown efficacy against emerging SARS-CoV-2 variants of concern. In this study, we used high-content imaging-based immunofluorescence assays in two different cell models to assess these limitations and evaluate the potential for using niclosamide as a COVID-19 antiviral. We show that despite promising preliminary reports, the antiviral efficacy of niclosamide overlaps with its cytotoxicity giving it a poor in vitro selectivity index for anti-SARS-CoV-2 inhibition. We also show that niclosamide has significantly variable potency against the different SARS-CoV-2 variants of concern and is most potent against variants with enhanced cell-to-cell spread including the B.1.1.7 (alpha) variant. Finally, we report the activity of 33 niclosamide analogs, several of which have reduced cytotoxicity and increased potency relative to niclosamide. A preliminary structure-activity relationship analysis reveals dependence on a protonophore for antiviral efficacy, which implicates nonspecific endolysosomal neutralization as a dominant mechanism of action. Further single-cell morphological profiling suggests niclosamide also inhibits viral entry and cell-to-cell spread by syncytia. Altogether, our results suggest that niclosamide is not an ideal candidate for the treatment of COVID-19, but that there is potential for developing improved analogs with higher clinical translational potential in the future.

In Vitro Evaluation and Mitigation of Niclosamide's Liabilities as a COVID-19 Treatment.

Niclosamide, an FDA-approved oral anthelmintic drug, has broad biological activity including anticancer, antibacterial, and antiviral properties. Niclosamide has also been identified as a potent inhibitor of SARS-CoV-2 infection in vitro , generating interest in its use for the treatment or prevention of COVID-19. Unfortunately, there are several potential issues with using niclosamide for COVID-19, including low bioavailability, significant polypharmacology, high cellular toxicity, and unknown efficacy against emerging SARS-CoV-2 variants of concern. In this study, we used high-content imaging-based immunofluorescence assays in two different cell models to assess these limitations and evaluate the potential for using niclosamide as a COVID-19 antiviral. We show that despite promising preliminary reports, the antiviral efficacy of niclosamide overlaps with its cytotoxicity giving it a poor in vitro selectivity index for anti-SARS-CoV-2 inhibition. We also show that niclosamide has significantly variable potency against the different SARS-CoV-2 variants of concern and is most potent against variants with enhanced cell-to-cell spread including B.1.1.7. Finally, we report the activity of 33 niclosamide analogs, several of which have reduced cytotoxicity and increased potency relative to niclosamide. A preliminary structure-activity relationship analysis reveals dependence on a protonophore for antiviral efficacy, which implicates nonspecific endolysosomal neutralization as a dominant mechanism of action. Further single-cell morphological profiling suggests niclosamide also inhibits viral entry and cell-to-cell spread by syncytia. Altogether, our results suggest that niclosamide is not an ideal candidate for the treatment of COVID-19, but that there is potential for developing improved analogs with higher clinical translational potential in the future. Importance: There is still an urgent need for effective anti-SARS-CoV-2 therapeutics due to waning vaccine efficacy, the emergence of variants of concern, and limited efficacy of existing antivirals. One potential therapeutic option is niclosamide, an FDA approved anthelmintic compound that has shown promising anti-SARS-CoV-2 activity in cell-based assays. Unfortunately, there are significant barriers for the clinical utility of niclosamide as a COVID-19 therapeutic. Our work emphasizes these limitations by showing that niclosamide has high cytotoxicity at antiviral concentrations, variable potency against variants of concern, and significant polypharmacology as a result of its activity as a nonspecific protonophore. Some of these clinical limitations can be mitigated, however, through structural modifications to the niclosamide scaffold, which we demonstrate through a preliminary structure activity relationship analysis. Overall, we show that niclosamide is not a suitable candidate for the treatment of COVID-19, but that structural analogs with improved drug properties may have higher clinical-translational potential.

Development of an automated screen for Kv7.2 potassium channels and discovery of a new agonist chemotype.

To identify pore domain ligands on Kv7.2 potassium ion channels, we compared wild-type (WT) and W236L mutant Kv7.2 channels in a series of assays with previously validated and novel agonist chemotypes. Positive controls were retigabine, flupirtine, and RL-81; i.e. Kv7.2 channel activators that significantly shift voltage-dependent activation to more negative potentials (ΔV50) at 5 µM. We identified 6 new compounds that exhibited differential enhancing activity between WT and W236L mutant channels. Whole cell patch-clamp electrophysiology studies were conducted to identify Kv7.2. Kv7.2/3, Kv7.4, and Kv7.5 selectivity. Our results validate the SyncroPatch platform and establish new structure activity relationships (SAR). Specifically, in addition to selective Kv7.2, Kv7.2/3, Kv7.4. and Kv7.5 agonists, we identified a novel chemotype, ZK-21, a 4-aminotetrahydroquinoline that is distinct from any of the previously described Kv7 channel modifiers. Using flexible receptor docking, ZK-21 was predicted to be stabilized by W236 and bind perpendicular to retigabine, burying the benzyl carbamate group into a tunnel reaching the core of the pore domain.

Structures of the σ2 receptor enable docking for bioactive ligand discovery.

The σ2 receptor has attracted intense interest in cancer imaging1, psychiatric disease2, neuropathic pain3-5 and other areas of biology6,7. Here we determined the crystal structure of this receptor in complex with the clinical candidate roluperidone2 and the tool compound PB288. These structures templated a large-scale docking screen of 490 million virtual molecules, of which 484 compounds were synthesized and tested. We identified 127 new chemotypes with affinities superior to 1 µM, 31 of which had affinities superior to 50 nM. The hit rate fell smoothly and monotonically with docking score. We optimized three hits for potency and selectivity, and achieved affinities that ranged from 3 to 48 nM, with up to 250-fold selectivity versus the σ1 receptor. Crystal structures of two ligands bound to the σ2 receptor confirmed the docked poses. To investigate the contribution of the σ2 receptor in pain, two potent σ2-selective ligands and one potent σ1/σ2 non-selective ligand were tested for efficacy in a mouse model of neuropathic pain. All three ligands showed time-dependent decreases in mechanical hypersensitivity in the spared nerve injury model9, suggesting that the σ2 receptor has a role in nociception. This study illustrates the opportunities for rapid discovery of in vivo probes through structure-based screens of ultra large libraries, enabling study of underexplored areas of biology.

Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets.

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-ß-thiophenylacrylamide (NP-BTA), an antifungal compound.

Morphological cell profiling of SARS-CoV-2 infection identifies drug repurposing candidates for COVID-19.

The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly identified and translated to clinical care. Traditional drug discovery methods have a >90% failure rate and can take 10 to 15 y from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious agents against SARS-CoV-2. From a library of 1,425 US Food and Drug Administration (FDA)-approved compounds and clinical candidates, we identified 17 hits that inhibited SARS-CoV-2 infection and analyzed their antiviral activity across multiple cell lines, including lymph node carcinoma of the prostate (LNCaP) cells and a physiologically relevant model of alveolar epithelial type 2 cells (iAEC2s). Additionally, we found that inhibitors of the Ras/Raf/MEK/ERK signaling pathway exacerbate SARS-CoV-2 infection in vitro. Notably, we discovered that lactoferrin, a glycoprotein found in secretory fluids including mammalian milk, inhibits SARS-CoV-2 infection in the nanomolar range in all cell models with multiple modes of action, including blockage of virus attachment to cellular heparan sulfate and enhancement of interferon responses. Given its safety profile, lactoferrin is a readily translatable therapeutic option for the management of COVID-19.

Drug-induced phospholipidosis confounds drug repurposing for SARS-CoV-2.

Repurposing drugs as treatments for COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has drawn much attention. Beginning with sigma receptor ligands and expanding to other drugs from screening in the field, we became concerned that phospholipidosis was a shared mechanism underlying the antiviral activity of many repurposed drugs. For all of the 23 cationic amphiphilic drugs we tested, including hydroxychloroquine, azithromycin, amiodarone, and four others already in clinical trials, phospholipidosis was monotonically correlated with antiviral efficacy. Conversely, drugs active against the same targets that did not induce phospholipidosis were not antiviral. Phospholipidosis depends on the physicochemical properties of drugs and does not reflect specific target-based activities-rather, it may be considered a toxic confound in early drug discovery. Early detection of phospholipidosis could eliminate these artifacts, enabling a focus on molecules with therapeutic potential.

Valproic acid-induced changes of 4D nuclear morphology in astrocyte cells.

Histone deacetylase inhibitors, such as valproic acid (VPA), have important clinical therapeutic and cellular reprogramming applications. They induce chromatin reorganization that is associated with altered cellular morphology. However, there is a lack of comprehensive characterization of VPA-induced changes of nuclear size and shape. Here, we quantify 3D nuclear morphology of primary human astrocyte cells treated with VPA over time (hence, 4D). We compared volumetric and surface-based representations and identified seven features that jointly discriminate between normal and treated cells with 85% accuracy on day 7. From day 3, treated nuclei were more elongated and flattened and then continued to morphologically diverge from controls over time, becoming larger and more irregular. On day 7, most of the size and shape descriptors demonstrated significant differences between treated and untreated cells, including a 24% increase in volume and 6% reduction in extent (shape regularity) for treated nuclei. Overall, we show that 4D morphometry can capture how chromatin reorganization modulates the size and shape of the nucleus over time. These nuclear structural alterations may serve as a biomarker for histone (de-)acetylation events and provide insights into mechanisms of astrocytes-to-neurons reprogramming.

Phospholipidosis is a shared mechanism underlying the in vitro antiviral activity of many repurposed drugs against SARS-CoV-2.

Repurposing drugs as treatments for COVID-19 has drawn much attention. A common strategy has been to screen for established drugs, typically developed for other indications, that are antiviral in cells or organisms. Intriguingly, most of the drugs that have emerged from these campaigns, though diverse in structure, share a common physical property: cationic amphiphilicity. Provoked by the similarity of these repurposed drugs to those inducing phospholipidosis, a well-known drug side effect, we investigated phospholipidosis as a mechanism for antiviral activity. We tested 23 cationic amphiphilic drugs-including those from phenotypic screens and others that we ourselves had found-for induction of phospholipidosis in cell culture. We found that most of the repurposed drugs, which included hydroxychloroquine, azithromycin, amiodarone, and four others that have already progressed to clinical trials, induced phospholipidosis in the same concentration range as their antiviral activity; indeed, there was a strong monotonic correlation between antiviral efficacy and the magnitude of the phospholipidosis. Conversely, drugs active against the same targets that did not induce phospholipidosis were not antiviral. Phospholipidosis depends on the gross physical properties of drugs, and does not reflect specific target-based activities, rather it may be considered a confound in early drug discovery. Understanding its role in infection, and detecting its effects rapidly, will allow the community to better distinguish between drugs and lead compounds that more directly impact COVID-19 from the large proportion of molecules that manifest this confounding effect, saving much time, effort and cost.