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.

Water/organic liquid interface properties with amine, carboxyl, thiol, and methyl terminal groups as seen from MD simulations

Molecular dynamics simulations were performed to study structural and dynamic properties of polar butanamine/water/butanamine, pentanoic acid/water/pentanoic acid, butanethiol/water/butanethiol, and nonpolar pentane/water/pentane systems. The mass density profiles along the interface normal to the organic liquid/water system, the difference in the local structure of H2 O molecules in bulk and in the vicinity of interface, as well as the diffusion behavior of water molecules at the interface with above-mentioned organic liquids have been investigated. Our MD simulation has shown that the diffusion of water molecules across the water/organic liquid interface is influenced by the hydrogen bondsn HB between water molecules and the terminal groups of organic liquids. It was found that the loss of the hydrogen bondsn HB in the nonpolar organic liquid leads to a decrease in the value of the normal component of the diffusion coefficientD z , while the tangential diffusion coefficients, bothD x andD y , increase.

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.

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.

Electrically driven kinklike distorting waves in microsized liquid crystals

Electrically driven kinklike distortion regimes in a microsized liquid crystal channel have been investigated both experimentally and analytically. Kinklike distortion waves were excited by the interaction between the electric field E and the gradient ∇n[over ̂] of the director field in a homogeneously aligned liquid crystal (HALC) channel. Having obtained the evolution of the normalized light intensity, which was recorded by the high-speed camera, the process of excitation and evolution of the traveling wave in the HALC channel was visualized for the first time. It was shown, based on a nonlinear extension of the classical Ericksen-Leslie theory, that in the case when the electric field E≫E_{th}, the flow of liquid crystal material completely stops and a new mechanism for converting the electric field arises in the form of the electrically driven distorting traveling kinklike wave, which can be excited in the LC channel, composed of 4-n-pentyl-4^{'}-cyanobiphenyl molecules.

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.

Skeletal photoinduced rearrangement of diarylethenes: ethene bridge effects

A skeletal photorearrangement involving UV-induced 6π-electrocyclization of diarylethenes with various ethene bridges has been studied. It has been found that deprotonation is the predominant step among the three possible alternative reaction pathways (radical abstraction, deprotonation, or sigmatropic shift) following 6π-electrocyclization, and incorporation of an electronegative carbonyl group into the geminal position to the phenyl residue results in a reduction in the reaction time and an increase in the yield of the desired product. The significant increase in the reaction time in less polar solvents (toluene, TCM) also indicates a large contribution of the deprotonation step to the skeletal photorearrangement of diarylethenes. Performing the reaction in toluene in the presence of tertiary amines leads to a reduction in the reaction time and an increase in the yield of the desired product. The best results were achieved when the reaction was carried out in toluene in the presence of DIPEA. The experimental results are in good agreement with the DFT calculations.

[The significance of the glymphatic pathway in the relationship between the sleep-wake cycle and neurodegenerative diseases]

Selective and progressive death of neurons is a characteristic feature of the process of neurodegeneration and leads to corresponding neuronal dysfunctions. Neurodegenerative diseases represent a heterogeneous group of clinically distinct disorders with similar molecular mechanisms of pathogenesis. They are based on the processes of abnormal aggregation of proteins, the formation of fibrillary insoluble structures and their deposition in the form of histopathological inclusions in the tissues of the nervous system. Disturbance of homeostatic functions that regulate neuronal ion and energy metabolism, biosynthesis and degradation of proteins and nucleotides, chronic hypoxia and the penetration of toxic and inflammatory substances into the brain from the bloodstream not only cause metabolic changes associated with age and disorders in the sleep-wake cycle, but also contribute to the development of neurodegenerative diseases. In animal studies, clearance pathways have been identified in which solutes and specific tracers are excreted perivascular into the meningeal lymphatics. The glymphatic pathway promotes the removal of metabolites, including Aβ amyloid and tau protein, from the parenchymal extracellular space of the brain. The glymphatic system is discussed to be more efficient during natural sleep, and fluid dynamics through this pathway exhibit daily fluctuations and are under circadian control. This review systematizes the key aspects and the data of recent research on the role of the glymphatic pathway and astroglial AQP-4 as its main determinant in maintaining homeostatic fluid circulation in the brain in normal and pathological conditions, in particular in relation to the regulatory role of the sleep-wake cycle and in development of neurodegeneration.

[The significance of glymphatic pathway in the relationship between the sleep-wake cycle and neurodegenerative diseases]

Selective and progressive death of neurons is a characteristic feature of the process of neurodegeneration and leads to corresponding neuronal dysfunctions. Neurodegenerative diseases represent a heterogeneous group of clinically distinct disorders with similar molecular mechanisms of pathogenesis. They are based on the processes of abnormal aggregation of proteins, the formation of fibrillary insoluble structures and their deposition in the form of histopathological inclusions in the tissues of the nervous system. Disturbance of homeostatic functions that regulate neuronal ion and energy metabolism, biosynthesis and degradation of proteins and nucleotides, chronic hypoxia and the penetration of toxic and inflammatory substances into the brain from the bloodstream not only cause metabolic changes associated with age and disorders in the sleep-wake cycle, but also contribute to the development of neurodegenerative diseases. In animal studies, clearance pathways have been identified in which solutes and specific tracers are excreted perivascular into the meningeal lymphatics. The glymphatic pathway promotes the removal of metabolites, including Aβ amyloid and tau protein, from the parenchymal extracellular space of the brain. The glymphatic system is discussed to be more efficient during natural sleep, and fluid dynamics through this pathway exhibit daily fluctuations and are under circadian control. This review systematizes the key aspects and scientific data of recent studies on the role of the glymphatic pathway and astroglial AQP-4 as its main determinant in maintaining homeostatic fluid circulation in the brain in normal and pathological conditions, in particular in relation to the regulatory role of the sleep-wake cycle and in development of neurodegeneration.

[Changes in sleep patterns and the doom-scrolling (doom-surfing) phenomenon as modifiable risk factors for anxiety due to continuous stress of the COVID-19 pandemic]

OBJECTIVE

To evaluate the modifiable daily behavior patterns associated with increased anxiety indicators in the general population in response to the COVID-19 pandemic.

MATERIAL AND METHODS

The study examined the characteristics of the Russian population (n=7777) of the international multicenter project COMET-G. In particular, variables were targeted to describe deviations in the behavior of adults during the period of application of measures of social isolation in connection with the pandemic, and revealing a relationship with the total score on the Spielberger State Anxiety Scale (STAI-S). Among these variables, experts selected those that could potentially be subject to change in the short term, that is, act as manageable or modifiable risk factors for the development of anxiety. The selected variables were analyzed in a statistical PLS-model to identify indicators that make the most significant contribution to the increase in the total anxiety score.

RESULTS

Our statistical model explained 48.4% of the variability in the STAI-S anxiety total scores related to changes in daily life habits. In particular, doom-scrolling/doom-surfing about the spread of the virus and the COVID-19 pandemic, changes in sleep patterns and usual daily life activities due to social isolation measures presented as factors significantly contributing to the increase of state anxiety.

CONCLUSION

Given the manageable or modifiable risk factors that we have identified, public awareness and therapeutic recommendations, pointing to the need to (I) control the amount of time spent in the internet and monitor their internet-based content consumption, (II) regulate sleep-wake patterns, (III) maintain daily habits and household activities, may reduce the likelihood of developing anxiety disorders in the context of the impact of a global chronic stress due to the COVID-19 pandemic and associated social isolation measures.

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 T_m values with an average coefficient of variation of 0.51% and median coefficient of variation of compound T_m 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.

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.

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.

Charge Fluctuations on a Flat Interface between Dielectric and Electrolyte or Dense Plasma

An interface between a dielectric and a medium containing charge carriers with moderate mobility is considered. In equilibrium, stochastic fluxes of positive and negative particles toward the surface have equal average current density j₀, and we suppose that the surface absorbs all falling charges. All over the surface, this results in the emergence of oppositely charged spots of various sizes D fluctuating and interacting with each other. Fourier expansion reduces this collection of interacting spots to the ensemble of independently fluctuating charge density waves. An exact solution of the Poisson equation for a single wave on a flat surface was obtained and provided strict proof that a fluctuating electric field is quite strong just above each charge spot but diminishes exponentially with the distance from the plane. The lifetime τ of a charge spot is inversely proportional to the density j₀ of the stochastic current while proportional to j0τ fluctuation's amplitudes independent of j₀. The fluctuation's parameter dependence on the charge spot's size D can vary according to the conducting medium properties.

Small Molecule Antiviral Compound Collection (SMACC): a database to support the discovery of broad-spectrum antiviral drug molecules.. [PMID: 35860225 PMCID: PMC9298133 DOI: 10.1101/2022.07.09.499397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

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 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.

Allosteric binders of ACE2 are promising anti-SARS-CoV-2 agents.. [PMID: 35313579 PMCID: PMC8936107 DOI: 10.1101/2022.03.15.484484]

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.

Abstract Figure

TOC Graphic: Overall study design.

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.

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.

[Study of the effectiveness of neuroprotective therapy in restoring motor function in patients during the acute period of ischemic stroke]

OBJECTIVE

The purpose of the work was to study the effect of neuroprotective therapy with Cellex on the features of recovery of movement disorders in patients in the acute period of ischemic stroke.

MATERIAL AND METHODS

A single-center randomized study was conducted to assess the efficacy and safety of the use of neuroprotective therapy with Cellex in patients in the acute period of ischemic stroke. The study included 60 patients with a stroke duration of no more than 3 days in the middle cerebral artery area and vertebrobasillar area with moderate and severe central hemiparesis. Patients of the study group received 1 ml of Cellex. subcutaneously 1 time per day for 10 days. All patients received drug therapy and rehabilitation measures within the framework of the standard of care for patients with ischemic stroke.

RESULTS

Against the background of the therapy, by the end of the study on days 14-21, the study and control groups showed a significant improvement according to clinical scales: NIHSS, mRS, RMI. The patients of the study group showed a more pronounced recovery of motor function, relative to the comparison group, according to motor scales: «A-D» FMA (54 [53; 62] and 42 [34; 51], p=0.03), «E-F» FMA (29 [28; 33] and 25 [18; 27], p=0.03), ARAT (47 [48; 57], 32 [24; 48], p=0.046). Among the patients of the study group, by the end of the study, the severity of mild stroke was 67%, relative to the comparison group 11% (χ²_1df=6.48; p=0.01). The use of neuroprotective therapy in the form of Cellex had a positive effect on both the prognostic score and the long-term assessment according to the SSS scale, due to the regression of motor disorders of the upper and lower extremities.

CONCLUSION

The study has demonstrated the effectiveness of the use of neuroprotective therapy in the treatment of movement disorders in patients in the acute period of ischemic stroke. Therapy with Cellex helped to reduce the severity of stroke, and had a positive effect on the prognosis of the disease.

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.

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.

[Motor rehabilitation of patients in the acute period of stroke using virtual reality technology]

OBJECTIVE

To study an effect of adjuvant rehabilitation using implicit virtual reality on the dynamics of the motor function of the lower extremities in patients in the acute period of ischemic stroke.

MATERIALS AND METHODS

The study was carried out to assess the effectiveness and safety of rehabilitation using virtual reality in 60 patients with lower central paresis in the acute period of ischemic stroke, lasting from 3 to 5 days. Patients of the study group additionally received rehabilitation using the hardware-software complex ReviVR, which allows to stimulate the patient's plantar surface by means of pneumo cuffs synchronously with the step of his animated body. Animation of movement was demonstrated to the patient using virtual reality glasses. The duration of the classes was 10 days, 20-25 minutes each. The total duration of rehabilitation measures in the study and comparison groups was 3-4 hours.

RESULTS

A significant regression on NIHSS (3 [-4; -1] and -1 [-2; 0], p<0.001) and a progress on RMI (3 [1; 3] and 2 [0; 2], p<0.001, respectively), between the study group and the control group were found. Changes on FMA-LE section (E-F) occurred on day 10, between the study and comparison groups (9 [5; 16] and 4 [0; 7], respectively, p=0.04). The improvement in FMA-LE out of synergy, in the standing position, indicated an increased readiness of the patient to form an independent walk.

CONCLUSION

The study has shown that the use of virtual reality rehabilitation increases the effectiveness of motor rehabilitation in patients with lower central paresis in the acute period of stroke.

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.

Photocyclization of Diarylethenes: The Effect of Electron and Proton Acceptors as Additives

The effect of electron and proton acceptors on the photocyclization of diarylethenes has been studied. Without any additives, the deprotonation reaction is predominant, although other processes, including the sigmatropic shift, are not excluded. A deuterium exchange experiment has shown that a strong base (DABCO) facilitates the deprotonation reaction, thereby limiting the sigmatropic shift. In the presence of an oxidizing agent or additional sources of radicals (O₂, I₂, TEMPO), the processes of deprotonation and rearrangement (H-shift) are practically not observed, and the reaction proceeds along a radical pathway with the formation of phenanthrene or its heterocyclic analogue.

Discovery of TMPRSS2 Inhibitors from Virtual Screening as a Potential Treatment of COVID-19

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has prompted researchers to pivot their efforts to finding antiviral compounds and vaccines. In this study, we focused on the human host cell transmembrane protease serine 2 (TMPRSS2), which plays an important role in the viral life cycle by cleaving the spike protein to initiate membrane fusion. TMPRSS2 is an attractive target and has received attention for the development of drugs against SARS and Middle East respiratory syndrome. Starting with comparative structural modeling and a binding model analysis, we developed an efficient pharmacophore-based approach and applied a large-scale in silico database screening for small-molecule inhibitors against TMPRSS2. The hits were evaluated in the TMPRSS2 biochemical assay and the SARS-CoV-2 pseudotyped particle entry assay. A number of novel inhibitors were identified, providing starting points for the further development of drug candidates for the treatment of coronavirus disease 2019.

[Sleep disorders in synucleinopathy]

The article highlights the current state of the problem of sleep disorders in various neurodegenerative diseases. The clinical picture and diagnosis of these disorders are described in detail. Separately, the emphasis is made on the mechanisms underlying development of these disorders and their features in various forms of synucleinopathies. The mediator and physiological changes that underlie sleep disorders in various nosological units of synucleinopathies are discussed in detail. The current attitude to certain sleep disorders as predictors of neurodegenerative diseases is evaluated. The role of the glymphatic system in the development of these disorders is considered. Also, modern therapeutic strategies for sleep disorders in neurodegenerative diseases are discussed.

Small Molecule Inhibitors of Activation-Induced Deaminase Decrease Class Switch Recombination in B Cells

Activation-induced deaminase (AID) not only mutates DNA within the immunoglobulin loci to generate antibody diversity, but it also promotes development of B cell lymphomas. To tame this mutagen, we performed a quantitative high-throughput screen of over 90 000 compounds to see if AID activity could be mitigated. The enzymatic activity was assessed in biochemical assays to detect cytosine deamination and in cellular assays to measure class switch recombination. Three compounds showed promise via inhibition of switching in a transformed B cell line and in murine splenic B cells. These compounds have similar chemical structures, which suggests a shared mechanism of action. Importantly, the inhibitors blocked AID, but not a related cytosine DNA deaminase, APOBEC3B. We further determined that AID was continually expressed for several days after B cell activation to induce switching. This first report of small molecules that inhibit AID can be used to gain regulatory control over base editors.

CATMoS: Collaborative Acute Toxicity Modeling Suite

BACKGROUND

Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals.

OBJECTIVES

The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg).

METHODS

An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches.

RESULTS

The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results.

DISCUSSION

CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

A target-agnostic screen identifies approved drugs to stabilize the endoplasmic reticulum-resident proteome

Endoplasmic reticulum (ER) dysregulation is associated with pathologies including neurodegenerative, muscular, and diabetic conditions. Depletion of ER calcium can lead to the loss of resident proteins in a process termed exodosis. To identify compounds that attenuate the redistribution of ER proteins under pathological conditions, we performed a quantitative high-throughput screen using the Gaussia luciferase (GLuc)-secreted ER calcium modulated protein (SERCaMP) assay, which monitors secretion of ER-resident proteins triggered by calcium depletion. We identify several clinically used drugs, including bromocriptine, and further characterize them using assays to measure effects on ER calcium, ER stress, and ER exodosis. Bromocriptine elicits protective effects in cell-based models of exodosis as well as in vivo models of stroke and diabetes. Bromocriptine analogs with reduced dopamine receptor activity retain similar efficacy in stabilizing the ER proteome, indicating a non-canonical mechanism of action. This study describes a strategic approach to identify small-molecule drugs capable of improving ER proteostasis in human disease conditions.

Electrically driven nematic flow in microfluidic capillary with radial temperature gradient

An electrically driven fluid pumping principle and a mechanism of kinklike distortion of the director field n[over ̂] in the microsized nematic volume has been described. It is shown that the interactions, on the one hand, between the electric field E and the gradient of the director's field ∇n[over ̂], and, on the other hand, between the ∇n[over ̂] and the temperature gradient ∇T arising in a homogeneously aligned liquid crystal microfluidic channel, confined between two infinitely long horizontal coaxial cylinders, may excite the kinklike distortion wave spreading along normal to both cylindrical boundaries. Calculations show that the resemblance to the kinklike distortion wave depends on the value of radially applied electric field E and the curvature of these boundaries. Calculations also show that there exists a range of parameter values (voltage and curvature of the inner cylinder) producing a nonstandard pumping regime with maximum flow near the hot cylinder in the horizontal direction.

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2

Antiviral drug development for coronavirus disease 2019 (COVID-19) is occurring at an unprecedented pace, yet there are still limited therapeutic options for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2, thus generating better antiviral efficacy. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Sixteen synergistic and eight antagonistic combinations were identified; among 16 synergistic cases, combinations of the US Food and Drug Administration (FDA)-approved drug nitazoxanide with remdesivir, amodiaquine, or umifenovir were most notable, all exhibiting significant synergy against SARS-CoV-2 in a cell model. However, the combination of remdesivir and lysosomotropic drugs, such as hydroxychloroquine, demonstrated strong antagonism. Overall, these results highlight the utility of drug repurposing and preclinical testing of drug combinations for discovering potential therapies to treat COVID-19.

Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods

Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple single- and multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications.

Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the "Big Data" Era

The rise of novel artificial intelligence (AI) methods necessitates their benchmarking against classical machine learning for a typical drug-discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by the human ether-à-go-go-related gene (hERG), leads to a prolonged QT interval of the cardiac action potential and is a significant safety pharmacology target for the development of new medicines. Several computational approaches have been employed to develop prediction models for the assessment of hERG liabilities of small molecules including recent work using deep learning methods. Here, we perform a comprehensive comparison of hERG effect prediction models based on classical approaches (random forests and gradient boosting) and modern AI methods [deep neural networks (DNNs) and recurrent neural networks (RNNs)]. The training set (∼9000 compounds) was compiled by integrating the hERG bioactivity data from the ChEMBL database with experimental data generated from an in-house, high-throughput thallium flux assay. We utilized different molecular descriptors including the latent descriptors, which are real-value continuous vectors derived from chemical autoencoders trained on a large chemical space (>1.5 million compounds). The models were prospectively validated on ∼840 in-house compounds screened in the same thallium flux assay. The best results were obtained with the XGBoost method and RDKit descriptors. The comparison of models based only on latent descriptors revealed that the DNNs performed significantly better than the classical methods. The RNNs that operate on SMILES provided the highest model sensitivity. The best models were merged into a consensus model that offered superior performance compared to reference models from academic and commercial domains. Furthermore, we shed light on the potential of AI methods to exploit the big data in chemistry and generate novel chemical representations useful in predictive modeling and tailoring a new chemical space.

SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules

Small, colloidally aggregating molecules (SCAMs) are the most common source of false positives in high-throughput screening (HTS) campaigns. Although SCAMs can be experimentally detected and suppressed by the addition of detergent in the assay buffer, detergent sensitivity is not routinely monitored in HTS. Computational methods are thus needed to flag potential SCAMs during HTS triage. In this study, we have developed and rigorously validated quantitative structure-interference relationship (QSIR) models of detergent-sensitive aggregation in several HTS campaigns under various assay conditions and screening concentrations. In particular, we have modeled detergent-sensitive aggregation in an AmpC β-lactamase assay, the preferred HTS counter-screen for aggregation, as well as in another assay that measures cruzain inhibition. Our models increase the accuracy of aggregation prediction by ∼53% in the β-lactamase assay and by ∼46% in the cruzain assay compared to previously published methods. We also discuss the importance of both assay conditions and screening concentrations in the development of QSIR models for various interference mechanisms besides aggregation. The models developed in this study are publicly available for fast prediction within the SCAM detective web application (https://scamdetective.mml.unc.edu/).

Electrically driven nematic flow in microfluidic devices containing a temperature gradient

Fluid pumping principle has been developed utilizing the interaction, on the one hand, between the electric field E and the gradient ∇n[over ̂] of the director's field, and, on the other hand, between the ∇n[over ̂] and the temperature ∇T gradient arising in a homogeneously aligned liquid crystal (HALC) microfluidic channel. Calculations, based upon the nonlinear extension of the classical Ericksen-Leslie theory, with accounting the entropy balance equation, show that due to the coupling among the ∇T, ∇n,[over ̂] and E in the HALC microfluidic channel the horizontal flow v=v_{x}i[over ̂]=ui[over ̂] may be excited. The direction and magnitude of v is influenced both by the heat flux q across the microfluidic channel and the strength of the electric field E. The results of calculations showed that the dependence of the maximum value of the equilibrium velocity distribution |u_{max}(E/E_{th})| across the LC channel versus electric field E/E_{th} is characterized by maximum value at E/E_{th}=2.0. In the case when the electric field E≫E_{th}, the horizontal flow of the LC material completely stops and a novel mechanism of converting of the electric field in the form of the kinklike wave reorientation of the director field n[over ̂] can be excited in the LC channel.

Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro

COVID-19 is undoubtedly the most impactful viral disease of the current century, afflicting millions worldwide. As yet, there is not an approved vaccine, as well as limited options from existing drugs for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro . Overall, we identified 16 synergistic and 8 antagonistic combinations, 4 of which were both synergistic and antagonistic in a dose-dependent manner. Among the 16 synergistic cases, combinations of nitazoxanide with three other compounds (remdesivir, amodiaquine and umifenovir) were the most notable, all exhibiting significant synergy against SARS-CoV-2. The combination of nitazoxanide, an FDA-approved drug, and remdesivir, FDA emergency use authorization for the treatment of COVID-19, demonstrate a strong synergistic interaction. Notably, the combination of remdesivir and hydroxychloroquine demonstrated strong antagonism. Overall, our results emphasize the importance of both drug repurposing and preclinical testing of drug combinations for potential therapeutic use against SARS-CoV-2 infections.

An OpenData portal to share COVID-19 drug repurposing data in real time

The National Center for Advancing Translational Sciences (NCATS) has developed an online open science data portal for its COVID-19 drug repurposing campaign - named OpenData - with the goal of making data across a range of SARS-CoV-2 related assays available in real-time. The assays developed cover a wide spectrum of the SARS-CoV-2 life cycle, including both viral and human (host) targets. In total, over 10,000 compounds are being tested in full concentration-response ranges from across multiple annotated small molecule libraries, including approved drug, repurposing candidates and experimental therapeutics designed to modulate a wide range of cellular targets. The goal is to support research scientists, clinical investigators and public health officials through open data sharing and analysis tools to expedite the development of SARS-CoV-2 interventions, and to prioritize promising compounds and repurposed drugs for further development in treating COVID-19.

Nonmechanical principle for producing a flow in a homogeneously aligned microfluidic nematic channel

Nonmechanical fluid pumping principle has been developed utilizing the interactions of both the director [Formula: see text] and velocity v fields and temperature T redistribution across a two-dimensional homogeneously-aligned nematic (HAN) microfluidic channel under the influence both of a heat flux [Formula: see text] and the surface electric field E₀, originating from the surface charge density [Formula: see text]. The heat flux [Formula: see text] is caused by the laser beam pulse focused on the channel's boundary, whereas the normally directed electric field is due to electric double layers, that is naturally created within the liquid crystal near a charged surface. Calculations, based upon the nonlinear extension of the classical Ericksen-Leslie theory, with accounting the entropy balance equation, show that due to the coupling between the [Formula: see text] and [Formula: see text], in the HAN microfluidic channel the vortical flow [Formula: see text] may be excited. The direction and magnitude of [Formula: see text] is influenced by [Formula: see text] and E₀, as well as by the thickness of the HAN microfluidic channel.

Predicting liver cytosol stability of small molecules

Over the last few decades, chemists have become skilled at designing compounds that avoid cytochrome P (CYP) 450 mediated metabolism. Typical screening assays are performed in liver microsomal fractions and it is possible to overlook the contribution of cytosolic enzymes until much later in the drug discovery process. Few data exist on cytosolic enzyme-mediated metabolism and no reliable tools are available to chemists to help design away from such liabilities. In this study, we screened 1450 compounds for liver cytosol-mediated metabolic stability and extracted transformation rules that might help medicinal chemists in optimizing compounds with these liabilities. In vitro half-life data were collected by performing in-house experiments in mouse (CD-1 male) and human (mixed gender) cytosol fractions. Matched molecular pairs analysis was performed in conjunction with qualitative-structure activity relationship modeling to identify chemical structure transformations affecting cytosolic stability. The transformation rules were prospectively validated on the test set. In addition, selected rules were validated on a diverse chemical library and the resulting pairs were experimentally tested to confirm whether the identified transformations could be generalized. The validation results, comprising nearly 250 library compounds and corresponding half-life data, are made publicly available. The datasets were also used to generate in silico classification models, based on different molecular descriptors and machine learning methods, to predict cytosol-mediated liabilities. To the best of our knowledge, this is the first systematic in silico effort to address cytosolic enzyme-mediated liabilities.

Endonuclease FEN1 Coregulates ERα Activity and Provides a Novel Drug Interface in Tamoxifen-Resistant Breast Cancer

Estrogen receptor α (ERα) is a key transcriptional regulator in the majority of breast cancers. ERα-positive patients are frequently treated with tamoxifen, but resistance is common. In this study, we refined a previously identified 111-gene outcome prediction-classifier, revealing FEN1 as the strongest determining factor in ERα-positive patient prognostication. FEN1 levels were predictive of outcome in tamoxifen-treated patients, and FEN1 played a causal role in ERα-driven cell growth. FEN1 impacted the transcriptional activity of ERα by facilitating coactivator recruitment to the ERα transcriptional complex. FEN1 blockade induced proteasome-mediated degradation of activated ERα, resulting in loss of ERα-driven gene expression and eradicated tumor cell proliferation. Finally, a high-throughput 465,195 compound screen identified a novel FEN1 inhibitor, which effectively blocked ERα function and inhibited proliferation of tamoxifen-resistant cell lines as well as ex vivo-cultured ERα-positive breast tumors. Collectively, these results provide therapeutic proof of principle for FEN1 blockade in tamoxifen-resistant breast cancer. SIGNIFICANCE: These findings show that pharmacologic inhibition of FEN1, which is predictive of outcome in tamoxifen-treated patients, effectively blocks ERα function and inhibits proliferation of tamoxifen-resistant tumor cells.

Evaluating kratom alkaloids using PHASE

Kratom is a botanical substance that is marketed and promoted in the US for pharmaceutical opioid indications despite having no US Food and Drug Administration approved uses. Kratom contains over forty alkaloids including two partial agonists at the mu opioid receptor, mitragynine and 7-hydroxymitragynine, that have been subjected to the FDA's scientific and medical evaluation. However, pharmacological and toxicological data for the remaining alkaloids are limited. Therefore, we applied the Public Health Assessment via Structural Evaluation (PHASE) protocol to generate in silico binding profiles for 25 kratom alkaloids to facilitate the risk evaluation of kratom. PHASE demonstrates that kratom alkaloids share structural features with controlled opioids, indicates that several alkaloids bind to the opioid, adrenergic, and serotonin receptors, and suggests that mitragynine and 7-hydroxymitragynine are the strongest binders at the mu opioid receptor. Subsequently, the in silico binding profiles of a subset of the alkaloids were experimentally verified at the opioid, adrenergic, and serotonin receptors using radioligand binding assays. The verified binding profiles demonstrate the ability of PHASE to identify potential safety signals and provide a tool for prioritizing experimental evaluation of high-risk compounds.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

Photochemical rearrangement of diarylethenes: synthesis of functionalized phenanthrenes

A novel protocol for the synthesis of functionalized phenanthrenes through photocyclization of diarylethenes (DAE) under UV irradiation is proposed. The reaction proceeds through 6π-electrocyclization with the formation of a cyclic (closed) intermediate that undergoes a rearrangement affording unsymmetrical phenanthrenes in good yields. However, in contrast to benzene derivatives, the photocyclization of naphthalene diarylethenes proceeds more slowly, which is confirmed by DFT calculations. The transformation was performed on a 1 mmol scale. The scalability showed that the diarylethenes bearing oxazole, thiazole, pyrazole and imidazole as aryl moieties are more prone to photorearrangement and can be used in preparative organic synthesis.