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.

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.

Therapeutic candidates for the Zika virus identified by a high-throughput screen for Zika protease inhibitors.

When Zika virus emerged as a public health emergency there were no drugs or vaccines approved for its prevention or treatment. We used a high-throughput screen for Zika virus protease inhibitors to identify several inhibitors of Zika virus infection. We expressed the NS2B-NS3 Zika virus protease and conducted a biochemical screen for small-molecule inhibitors. A quantitative structure-activity relationship model was employed to virtually screen ∼138,000 compounds, which increased the identification of active compounds, while decreasing screening time and resources. Candidate inhibitors were validated in several viral infection assays. Small molecules with favorable clinical profiles, especially the five-lipoxygenase-activating protein inhibitor, MK-591, inhibited the Zika virus protease and infection in neural stem cells. Members of the tetracycline family of antibiotics were more potent inhibitors of Zika virus infection than the protease, suggesting they may have multiple mechanisms of action. The most potent tetracycline, methacycline, reduced the amount of Zika virus present in the brain and the severity of Zika virus-induced motor deficits in an immunocompetent mouse model. As Food and Drug Administration-approved drugs, the tetracyclines could be quickly translated to the clinic. The compounds identified through our screening paradigm have the potential to be used as prophylactics for patients traveling to endemic regions or for the treatment of the neurological complications of Zika virus infection.

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.

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.

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.

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.

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.

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 ß-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 ß-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/).

Rational Use of Heterogeneous Data in Quantitative Structure-Activity Relationship (QSAR) Modeling of Cyclooxygenase/Lipoxygenase Inhibitors.

Numerous studies have been published in recent years with acceptable quantitative structure-activity relationship (QSAR) modeling based on heterogeneous data. In many cases, the training sets for QSAR modeling were constructed from compounds tested by different biological assays, contradicting the opinion that QSAR modeling should be based on the data measured by a single protocol. We attempted to develop approaches that help to determine how heterogeneous data should be used for the creation of QSAR models on the basis of different sets of compounds tested by different experimental methods for the same target and the same endpoint. To this end, more than 100 QSAR models for the IC50 values of ligands interacting with cyclooxygenase 1,2 (COX) and seed lipoxygenase (LOX), obtained from ChEMBL database were created using the GUSAR software. The QSAR models were tested on the external set, including 26 new thiazolidinone derivatives, which were experimentally tested for COX-1,2/LOX inhibition. The IC50 values of the derivatives varied from 89 µM to 26 µM for LOX, from 200 µM to 0.018 µM for COX-1, and from 210 µM to 1 µM for COX-2. This study showed that the accuracy of the models is dependent on the distribution of IC50 values of low activity compounds in the training sets. In the most cases, QSAR models created based on the combined training sets had advantages in comparison with QSAR models, based on a single publication. We introduced a new method of combination of quantitative data from different experimental studies based on the data of reference compounds, which was called "scaling".

Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models.

Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here, we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with proteochemometrics, multitask deep learning, and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. The best models together with their modeling sets are provided through publicly available web services at https://predictor.ncats.io .

Prebiotic Syntheses Under Shock in the Water - Formamide - Potassium Bicarbonate - Sodium Hydroxide System.

Syntheses under shock in nitrogen bubbled samples of the water - formamide - bicarbonate - sodium hydroxide system at pH 8.63, 9.46 and 10.44 were performed in the stainless steel preservation capsules. The maximum temperature and pressure in the capsules reached 545 K and 12.5 GPa respectively. Using the LC-MS-MS analysis, the 21 synthesis products have been identified, including amines and polyamines, carboxamide, acetamide and urea derivatives, compounds containing aniline, pyrrolidine, pyrrole, imidazole, as well as alcohol groups. It was found that the Fischer-Tropsch-type syntheses with catalysis on the surface of the stainless steel of the conservation capsule associated with the adsorbed hydrogen cyanide reactions and transamidation processes play the main role in the shock syntheses. Formation reactions of all the above-mentioned compounds have been suggested. It was proposed that hydrogen cyanide, ammonia, isocyanic acid, aminonitrile, aminoacetonitrile, as well as adsorbed species H(a), CH(a), CH2(a), CHOH(a), NH2(a) and H2CNH(a) are especially important for the formation of the products. A reduction reaction of adsorbed bicarbonate with hydrogen to formaldehyde has been first postulated. In the studied system also classical reactions take place - Wöhler's synthesis of urea and Butlerov's synthesis of methenamine. It was suggest that material of meteorites may be an effective catalyst in the Fischer-Tropsch-type syntheses at falling of the iron-nickel meteorites in the water - formamide regions on the early Earth. It was concluded that life could have originated due to the impact of meteorites on alkaline water-formamide lakes located near volcanoes on the early Earth.

Mechanistic Aspects of Photoinduced Rearrangement of 2,3-Diarylcyclopentenone Bearing Benzene and Oxazole Moieties.

Photoinduced rearrangement of diarylethenes to naphthalenes or isoelectronic benzoannulated heterocycles is a novel reaction in preparative organic photochemistry. Recently it was shown that unsymmetrical diarylethenes containing benzene and oxazole derivatives efficiently undergo this transformation leading to amide derivatives of naphthalene. Mechanistic study of skeletal rearrangement for a typical representative of these compounds, namely 3-(5-methyl-2-phenyl-1,3-oxazol-4-yl)-2-phenylcyclopent-2-en-1-one, was performed by stationary and laser flash photolysis as well as density functional theory (DFT) calculations. The mechanism of the rearrangement was found to comprise several thermal stages. Both singlet and triplet states of the initial compound can be transformed to the reaction product, which results in the dependence of the quantum yield vs concentration of dissolved oxygen. Three reactive intermediates were registered in the laser flash photolysis experiment; the predicted structures were in accordance with DFT calculations of the electronic absorption spectra. In addition to the previously proposed mechanism of skeletal rearrangement based on a sigmatropic shift of hydrogen, two new parallel pathways based on formation of a carbanion/carbocation were determined.

Programmable Nucleic Acid Based Polygons with Controlled Neuroimmunomodulatory Properties for Predictive QSAR Modeling.

In the past few years, the study of therapeutic RNA nanotechnology has expanded tremendously to encompass a large group of interdisciplinary sciences. It is now evident that rationally designed programmable RNA nanostructures offer unique advantages in addressing contemporary therapeutic challenges such as distinguishing target cell types and ameliorating disease. However, to maximize the therapeutic benefit of these nanostructures, it is essential to understand the immunostimulatory aptitude of such tools and identify potential complications. This paper presents a set of 16 nanoparticle platforms that are highly configurable. These novel nucleic acid based polygonal platforms are programmed for controllable self-assembly from RNA and/or DNA strands via canonical Watson-Crick interactions. It is demonstrated that the immunostimulatory properties of these particular designs can be tuned to elicit the desired immune response or lack thereof. To advance the current understanding of the nanoparticle properties that contribute to the observed immunomodulatory activity and establish corresponding designing principles, quantitative structure-activity relationship modeling is conducted. The results demonstrate that molecular weight, together with melting temperature and half-life, strongly predicts the observed immunomodulatory activity. This framework provides the fundamental guidelines necessary for the development of a new library of nanoparticles with predictable immunomodulatory activity.

Photochemical Rearrangement of Diarylethenes: Reaction Efficiency and Substituent Effects.

In recent years, great synthetic potential of the photorearrangement of diarylethenes leading to naphthalene derivatives via a cascade process of photocyclization/[1,n]-H shift/cycloreversion has been demonstrated. In this work, first a multifaceted study of the influence of various factors on the efficiency of the photorearrangement of diarylethenes of furanone series containing benzene and oxazole derivatives as aryl residues has been carried out. The efficiency of this phototransformation (quantum yields) and the effect of methoxy substituents in the phenyl moiety have been studied. Despite the multistage process, the quantum yields of the photorearrangement are rather high (0.34-0.49). It has been found that the efficiency of photocyclization of diarylethenes increases with the introduction of electron-donating methoxy groups in the phenyl moiety. Using the DFT calculations, we have been able to estimate in the photoinduced isomer the distance between hydrogen atom and carbon atom to which it migrates in the result of the sigmatropic shift. For all studied diarylethenes, this value was 2.67-2.73 Å, which is less than the sum of van der Waals radii of carbon and hydrogen atoms (2.9 Å).

QSAR Modeling and Prediction of Drug-Drug Interactions.

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

General Photoinduced Sequential Electrocyclization/[1,9]-Sigmatropic Rearrangement/Ring-Opening Reaction of Diarylethenes.

A novel and efficient photochemical transformation of diarylethenes comprising a five-membered heterocyclic ring and phenyl moiety is described. This reaction provides a simple method for the preparation of functionalized naphthalene derivatives via photorearrangement reaction of diarylethenes, and the process is characterized by high efficiency that was determined by NMR monitoring. Some mechanistic aspects of this process have been also explored. It was found that the reaction includes tandem transformation of three basic processes: the photocyclization of the hexatriene system, [1,9]-sigmatropic rearrangement, and heterocyclic ring opening. Diarylethenes with different heterocycle moieties (thiophene, benzo[b]thiophene, furan, indole, imidazole, thiazole, oxazole, pyrazole) have been involved into this process, and the target naphthalenes with good yields have been obtained. The opportunity for use in the transformation of diarylethenes with different heterocyclic residues permits synthesis of naphthalenes with desired functional groups. The general character and high efficiency of the reaction promise that the transformation can be an effective synthetic route for the annulation of benzene rings to various aromatic systems, including heterocycles.