Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles.

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

RALDH1 Inhibition Shows Immunotherapeutic Efficacy in Hepatocellular Carcinoma.

Globally, hepatocellular carcinoma (HCC) is one of the most commonly diagnosed cancers and a leading cause of cancer-related death. We previously identified an immune evasion pathway whereby tumor cells produce retinoic acid (RA) to promote differentiation of intratumoral monocytes into protumor macrophages. Retinaldehyde dehydrogenase 1 (RALDH1), RALDH2, and RALDH3 are the three isozymes that catalyze RA biosynthesis. In this study, we have identified RALDH1 as the key driver of RA production in HCC and demonstrated the efficacy of RALDH1-selective inhibitors (Raldh1-INH) in suppressing RA production by HCC cells. Raldh1-INH restrained tumor growth in multiple mouse models of HCC by reducing the number and tumor-supporting functions of intratumoral macrophages as well as increasing T-cell infiltration and activation within tumors. Raldh1-INH also displayed favorable pharmacokinetic, pharmacodynamic, and toxicity profiles in mice thereby establishing them as promising new drug candidates for HCC immunotherapy.

Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds.

Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.

Discovering selective antiferroptotic inhibitors of the 15LOX/PEBP1 complex noninterfering with biosynthesis of lipid mediators.

Programmed ferroptotic death eliminates cells in all major organs and tissues with imbalanced redox metabolism due to overwhelming iron-catalyzed lipid peroxidation under insufficient control by thiols (Glutathione (GSH)). Ferroptosis has been associated with the pathogenesis of major chronic degenerative diseases and acute injuries of the brain, cardiovascular system, liver, kidneys, and other organs, and its manipulation offers a promising new strategy for anticancer therapy. This explains the high interest in designing new small-molecule-specific inhibitors against ferroptosis. Given the role of 15-lipoxygenase (15LOX) association with phosphatidylethanolamine (PE)-binding protein 1 (PEBP1) in initiating ferroptosis-specific peroxidation of polyunsaturated PE, we propose a strategy of discovering antiferroptotic agents as inhibitors of the 15LOX/PEBP1 catalytic complex rather than 15LOX alone. Here we designed, synthesized, and tested a customized library of 26 compounds using biochemical, molecular, and cell biology models along with redox lipidomic and computational analyses. We selected two lead compounds, FerroLOXIN-1 and 2, which effectively suppressed ferroptosis in vitro and in vivo without affecting the biosynthesis of pro-/anti-inflammatory lipid mediators in vivo. The effectiveness of these lead compounds is not due to radical scavenging or iron-chelation but results from their specific mechanisms of interaction with the 15LOX-2/PEBP1 complex, which either alters the binding pose of the substrate [eicosatetraenoyl-PE (ETE-PE)] in a nonproductive way or blocks the predominant oxygen channel thus preventing the catalysis of ETE-PE peroxidation. Our successful strategy may be adapted to the design of additional chemical libraries to reveal new ferroptosis-targeting therapeutic modalities.

Small molecule antiviral compound collection (SMACC): A comprehensive, highly curated database to support the discovery of broad-spectrum antiviral drug molecules.

Diseases caused by new viruses cost thousands if not millions of human lives and trillions of dollars. We have identified, collected, curated, and integrated all chemogenomics data from ChEMBL for 13 emerging viruses that hold the greatest potential threat to global human health. By identifying and solving several challenges related to data annotation accuracy, we developed a highly curated and thoroughly annotated database of compounds tested in both phenotypic and target-based assays for these viruses that we dubbed SMACC (Small Molecule Antiviral Compound Collection). The pilot version of the SMACC database contains over 32,500 entries for 13 viruses. By analyzing data in SMACC, we have identified ∼50 compounds with polyviral inhibition profile, mostly covering flavi- and coronaviruses. The SMACC database may serve as a reference for virologists and medicinal chemists working on the development of novel BSA agents in preparation for future viral outbreaks. SMACC is publicly available at https://smacc.mml.unc.edu.

Quantitative Bioactivity Signatures of Dietary Supplements and Natural Products.

Dietary supplements and natural products are often marketed as safe and effective alternatives to conventional drugs, but their safety and efficacy are not well regulated. To address the lack of scientific data in these areas, we assembled a collection of Dietary Supplements and Natural Products (DSNP), as well as Traditional Chinese Medicinal (TCM) plant extracts. These collections were then profiled in a series of in vitro high-throughput screening assays, including a liver cytochrome p450 enzyme panel, CAR/PXR signaling pathways, and P-glycoprotein (P-gp) transporter assay activities. This pipeline facilitated the interrogation of natural product-drug interaction (NaPDI) through prominent metabolizing pathways. In addition, we compared the activity profiles of the DSNP/TCM substances with those of an approved drug collection (the NCATS Pharmaceutical Collection or NPC). Many of the approved drugs have well-annotated mechanisms of action (MOAs), while the MOAs for most of the DSNP and TCM samples remain unknown. Based on the premise that compounds with similar activity profiles tend to share similar targets or MOA, we clustered the library activity profiles to identify overlap with the NPC to predict the MOAs of the DSNP/TCM substances. Our results suggest that many of these substances may have significant bioactivity and potential toxicity, and they provide a starting point for further research on their clinical relevance.

Application of temperature-responsive HIS-tag fluorophores to differential scanning fluorimetry screening of small molecule libraries.

Differential scanning fluorimetry is a rapid and economical biophysical technique used to monitor perturbations to protein structure during a thermal gradient, most often by detecting protein unfolding events through an environment-sensitive fluorophore. By employing an NTA-complexed fluorophore that is sensitive to nearby structural changes in histidine-tagged protein, a robust and sensitive differential scanning fluorimetry (DSF) assay is established with the specificity of an affinity tag-based system. We developed, optimized, and miniaturized this HIS-tag DSF assay (HIS-DSF) into a 1536-well high-throughput biophysical platform using the Borrelial high temperature requirement A protease (BbHtrA) as a proof of concept for the workflow. A production run of the BbHtrA HIS-DSF assay showed a tight negative control group distribution of Tm values with an average coefficient of variation of 0.51% and median coefficient of variation of compound Tm of 0.26%. The HIS-DSF platform will provide an additional assay platform for future drug discovery campaigns with applications in buffer screening and optimization, target engagement screening, and other biophysical assay efforts.

Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles.

Nucleic acid nanoparticles, or NANPs, rationally designed to communicate with the human immune system, can offer innovative therapeutic strategies to overcome the limitations of traditional nucleic acid therapies. Each set of NANPs is unique in their architectural parameters and physicochemical properties, which together with the type of delivery vehicles determine the kind and the magnitude of their immune response. Currently, there are no predictive tools that would reliably guide the design of NANPs to the desired immunological outcome, a step crucial for the success of personalized therapies. Through a systematic approach investigating physicochemical and immunological profiles of a comprehensive panel of various NANPs, the research team developes and experimentally validates a computational model based on the transformer architecture able to predict the immune activities of NANPs. It is anticipated that the freely accessible computational tool that is called an "artificial immune cell," or AI-cell, will aid in addressing the current critical public health challenges related to safety criteria of nucleic acid therapies in a timely manner and promote the development of novel biomedical tools.

Venglustat Inhibits Protein N-Terminal Methyltransferase 1 in a Substrate-Competitive Manner.

Venglustat is a known allosteric inhibitor for ceramide glycosyltransferase, investigated in diseases caused by lysosomal dysfunction. Here, we identified venglustat as a potent inhibitor (IC50 = 0.42 µM) of protein N-terminal methyltransferase 1 (NTMT1) by screening 58,130 compounds. Furthermore, venglustat exhibited selectivity for NTMT1 over 36 other methyltransferases. The crystal structure of NTMT1-venglustat and inhibition mechanism revealed that venglustat competitively binds at the peptide substrate site. Meanwhile, venglustat potently inhibited protein N-terminal methylation levels in cells (IC50 = 0.5 µM). Preliminary structure-activity relationships indicated that the quinuclidine and fluorophenyl parts of venglustat are important for NTMT1 inhibition. In summary, we confirmed that venglustat is a bona fide NTMT1 inhibitor, which would advance the study on the biological roles of NTMT1. Additionally, this is the first disclosure of NTMT1 as a new molecular target of venglustat, which would cast light on its mechanism of action to guide the clinical investigations.

Photostability and Antiproliferative Activity of Furan Analogues of Combretastatin A-4.

Cancer is one of the most serious health problems that usually require heavy medical treatment. It is important to ensure that no additional burden is placed on patients due to the modes of administration and/or poor quality of pharmaceuticals. In this regard, understanding, quantifying, and improving the photostability (resistance to UV light or sunlight) of drugs is among the important elements that can improve the patient's quality of life. In this work, the photochemical properties of a wide range of furanone analogues of combretastatin A-4 and their antiproliferative activity against A-431 epidermoid carcinoma cells were studied in a search for compounds with improved photostability and antiproliferative activity. It was found that the incorporation of an arylidene moiety led to a significant improvement in photostability, while the antiproliferative activity strongly depends on the nature of the aryl residue in the arylidene moiety. The high photostability of arylidenes was achieved due to the delocalization of the central double bond of the 1,3,5-hexatriene system, which limited the 6π-electrocyclization. The best results in terms of antiproliferative activity were obtained for thiophene arylidene (IC50 = 0.6 µM) and 3,4-diarylfuran (IC50 = 0.047 µM). The obtained results address the lack of data available now in scientific literature on the photodegradation of combretastatin A-4 analogues and should be taken into account in studies of the side effects of pharmaceuticals based on them.

Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents.

The COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for acute treatment of the disease. We investigate whether compounds that bind the human angiotensin-converting enzyme 2 (ACE2) protein can decrease SARS-CoV-2 replication without impacting ACE2's natural enzymatic function. Initial screening of a diversity library resulted in hit compounds active in an ACE2-binding assay, which showed little inhibition of ACE2 enzymatic activity (116 actives, success rate ∼4%), suggesting they were allosteric binders. Subsequent application of in silico techniques boosted success rates to ∼14% and resulted in 73 novel confirmed ACE2 binders with K d values as low as 6 nM. A subsequent SARS-CoV-2 assay revealed that five of these compounds inhibit the viral life cycle in human cells. Further effort is required to completely elucidate the antiviral mechanism of these ACE2-binders, but they present a valuable starting point for both the development of acute treatments for COVID-19 and research into the host-directed therapy.

Small Molecule Antiviral Compound Collection (SMACC): a database to support the discovery of broad-spectrum antiviral drug molecules.

Diseases caused by new viruses costs thousands if not millions of human lives and trillions of dollars in damage to the global economy. Despite the rapid development of vaccines for SARS-CoV-2, the lack of small molecule antiviral drugs that work against multiple viral families (broad-spectrum antivirals; BSAs) has left the entire world’s human population vulnerable to the infection between the beginning of the outbreak and the widespread availability of vaccines. Developing BSAs is an attractive, yet challenging, approach that could prevent the next, inevitable, viral outbreak from becoming a global catastrophe. To explore whether historical medicinal chemistry efforts suggest the possibility of discovering novel BSAs, we (i) identified, collected, curated, and integrated all chemical bioactivity data available in ChEMBL for molecules tested in respective assays for 13 emerging viruses that, based on published literature, hold the greatest potential threat to global human health; (ii) identified and solved the challenges related to data annotation accuracy including assay description ambiguity, missing cell or target information, and incorrect BioAssay Ontology (BAO) annotations; (iii) developed a highly curated and thoroughly annotated database of compounds tested in both phenotypic (21,392 entries) and target-based (11,123 entries) assays for these viruses; and (iv) identified a subset of compounds showing BSA activity. For the latter task, we eliminated inconclusive and annotated duplicative entries by checking the concordance between multiple assay results and identified eight compounds active against 3-4 viruses from the phenotypic data, 16 compounds active against two viruses from the target-based data, and 35 compounds active in at least one phenotypic and one target-based assay. The pilot version of our SMACC (Small Molecule Antiviral Compound Collection) database contains over 32,500 entries for 13 viruses. Our analysis indicates that previous research yielded very small number of BSA compounds. We posit that focused and coordinated efforts strategically targeting the discovery of such agents must be established and maintained going forward. The SMACC database publicly available at https://smacc.mml.unc.edu may serve as a reference for virologists and medicinal chemists working on the development of novel BSA agents in preparation for future viral outbreaks.

Allosteric binders of ACE2 are promising anti-SARS-CoV-2 agents.

The COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for an acute treatment for the disease. We investigate whether compounds that bind the human ACE2 protein can interrupt SARS-CoV-2 replication without damaging ACE2’s natural enzymatic function. Initial compounds were screened for binding to ACE2 but little interruption of ACE2 enzymatic activity. This set of compounds was extended by application of quantitative structure-activity analysis, which resulted in 512 virtual hits for further confirmatory screening. A subsequent SARS-CoV-2 replication assay revealed that five of these compounds inhibit SARS-CoV-2 replication in human cells. Further effort is required to completely determine the antiviral mechanism of these compounds, but they serve as a strong starting point for both development of acute treatments for COVID-19 and research into the mechanism of infection.

A platform of assays for the discovery of anti-Zika small-molecules with activity in a 3D-bioprinted outer-blood-retina model.

The global health emergency posed by the outbreak of Zika virus (ZIKV), an arthropod-borne flavivirus causing severe neonatal neurological conditions, has subsided, but there continues to be transmission of ZIKV in endemic regions. As such, there is still a medical need for discovering and developing therapeutical interventions against ZIKV. To identify small-molecule compounds that inhibit ZIKV disease and transmission, we screened multiple small-molecule collections, mostly derived from natural products, for their ability to inhibit wild-type ZIKV. As a primary high-throughput screen, we used a viral cytopathic effect (CPE) inhibition assay conducted in Vero cells that was optimized and miniaturized to a 1536-well format. Suitably active compounds identified from the primary screen were tested in a panel of orthogonal assays using recombinant Zika viruses, including a ZIKV Renilla luciferase reporter assay and a ZIKV mCherry reporter system. Compounds that were active in the wild-type ZIKV inhibition and ZIKV reporter assays were further evaluated for their inhibitory effects against other flaviviruses. Lastly, we demonstrated that wild-type ZIKV is able to infect a 3D-bioprinted outer-blood-retina barrier tissue model and disrupt its barrier function, as measured by electrical resistance. One of the identified compounds (3-Acetyl-13-deoxyphomenone, NCGC00380955) was able to prevent the pathological effects of the viral infection on this clinically relevant ZIKV infection model.

Cross-Platform Bayesian Optimization System for Autonomous Biological Assay Development.

Current high-throughput screening assay optimization is often a manual and time-consuming process, even when utilizing design-of-experiment approaches. A cross-platform, Cloud-based Bayesian optimization-based algorithm was developed as part of the National Center for Advancing Translational Sciences (NCATS) ASPIRE (A Specialized Platform for Innovative Research Exploration) Initiative to accelerate preclinical drug discovery. A cell-free assay for papain enzymatic activity was used as proof of concept for biological assay development and system operationalization. Compared with a brute-force approach that sequentially tested all 294 assay conditions to find the global optimum, the Bayesian optimization algorithm could find suitable conditions for optimal assay performance by testing 21 assay conditions on average, with up to 20 conditions being tested simultaneously, as confirmed by repeated simulation. The algorithm could achieve a sevenfold reduction in costs for lab supplies and high-throughput experimentation runtime, all while being controlled from a remote site through a secure connection. Based on this proof of concept, this technology is expected to be applied to more complex biological assays and automated chemistry reaction screening at NCATS, and should be transferable to other institutions.

A Genome-Edited ERα-HiBiT Fusion Reporter Cell Line for the Identification of ERα Modulators Via High-Throughput Screening and CETSA.

The estrogen receptor α (ERα) is a target of intense pharmacological intervention and toxicological biomonitoring. Current methods to directly quantify cellular levels of ERα involve antibody-based assays, which are labor-intensive and of limited throughput. In this study, we generated a post-translational reporter cell line, referred to as MCF7-ERα-HiBiT, by fusing a small pro-luminescent nanoluciferase (NLuc) tag (HiBiT) to the C-terminus of endogenous ERα in MCF7 cells. The tag allows the luminescent detection and quantification of endogenous ERα protein by addition of the complementary NLuc enzyme fragment. This MCF7-ERα-HiBiT cell line was optimized for quantitative high-throughput screening (qHTS) to identify compounds that reduce ERα levels. In addition, the same cell line was optimized for a qHTS cellular thermal shift assay to identify compounds that bind and thermally stabilize ERα. Here, we interrogated the MCF7-ERα-HiBiT assay against the NCATS Pharmacological Collection (NPC) of 2,678 approved drugs and identified compounds that potently reduce and thermally stabilize ERα. Our novel post-translational reporter cell line provides a unique opportunity for profiling large pharmacological and toxicological compound libraries for their effect on ERα levels as well as for assessing direct compound binding to the receptor, thus facilitating mechanistic studies by which compounds exert their biological effects on ERα.

Photoinduced Skeletal Rearrangement of Diarylethenes: Photorelease of Lewis Acid and Synthetic Applications.

The skeletal photorearrangement including 6π-electrocyclization induced by UV light of ortho-halogen-substituted diarylethenes has been studied. It has been found that the reaction pathways leading to bi- or tricyclic frameworks depend on the kind of halogen substituent and solvent. Photocyclization with halogen abstraction leads to bicyclic fused aromatics, while the tricyclic frameworks are formed due to the tandem 6π-electrocyclization/sigmatropic shift reaction. THF is preferred as the solvent in the former process and chloroform in the latter reaction. It was found for the first time that, owing to the ability of this series of diarylethenes to undergo skeletal photorearrangement with the release of the bromide cation, they can be used both as brominating agents and as Lewis acids for catalyzing electrophilic reactions.

Hybrid In Silico Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants.

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (∼16% of predicted hits) active compounds (efficacy > 30%, IC50 ≤ 15 µM). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further investigation resulted in the identification of allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown to inhibit the entry of pseudoparticles bearing spike protein of wild-type SARS-CoV-2, as well as South African B.1.351 and UK B.1.1.7 variants.

Deep learning identifies synergistic drug combinations for treating COVID-19.

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and single-agent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists.

A critical overview of computational approaches employed for COVID-19 drug discovery.

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.