X-ray diffraction of metastable structures from supercooled liquid hydrogen.

We report time resolved observations of the crystallization from liquid hydrogen, supercooled to temperatures below the melting point, using 11.2 keV X-ray diffraction from the Linac Coherent Light Source (LCLS). Changes to the metastable solid and liquid structure factors have been dynamically measured. This allows for a direct determination of the lowest energy crystal polymorphs, the stacking probabilities, as well as the liquid and solid densities and temperatures. Such measurements provide experimental evidence of an Arrhenius-like growth kinetics along the stacking direction during supercooling.

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.

Cystatin F attenuates neuroinflammation and demyelination following murine coronavirus infection of the central nervous system.

BACKGROUND: Cystatin F is a secreted lysosomal cysteine protease inhibitor that has been implicated in affecting the severity of demyelination and enhancing remyelination in pre-clinical models of immune-mediated demyelination. How cystatin F impacts neurologic disease severity following viral infection of the central nervous system (CNS) has not been well characterized and was the focus of this study. We used cystatin F null-mutant mice (Cst7-/-) with a well-established model of murine coronavirus-induced neurologic disease to evaluate the contributions of cystatin F in host defense, demyelination and remyelination. METHODS: Wildtype controls and Cst7-/- mice were intracranially (i.c.) infected with a sublethal dose of the neurotropic JHM strain of mouse hepatitis virus (JHMV), with disease progression and survival monitored daily. Viral plaque assays and qPCR were used to assess viral levels in CNS. Immune cell infiltration into the CNS and immune cell activation were determined by flow cytometry and 10X genomics chromium 3' single cell RNA sequencing (scRNA-seq). Spinal cord demyelination was determined by luxol fast blue (LFB) and Hematoxylin/Eosin (H&E) staining and axonal damage assessed by immunohistochemical staining for SMI-32. Remyelination was evaluated by electron microscopy (EM) and calculation of g-ratios. RESULTS: JHMV-infected Cst7-/- mice were able to control viral replication within the CNS, indicating that cystatin F is not essential for an effective Th1 anti-viral immune response. Infiltration of T cells into the spinal cords of JHMV-infected Cst7-/- mice was increased compared to infected controls, and this correlated with increased axonal damage and demyelination associated with impaired remyelination. Single-cell RNA-seq of CD45 + cells enriched from spinal cords of infected Cst7-/- and control mice revealed enhanced expression of transcripts encoding T cell chemoattractants, Cxcl9 and Cxcl10, combined with elevated expression of interferon-g (Ifng) and perforin (Prf1) transcripts in CD8 + T cells from Cst7-/- mice compared to controls. CONCLUSIONS: Cystatin F is not required for immune-mediated control of JHMV replication within the CNS. However, JHMV-infected Cst7-/- mice exhibited more severe clinical disease associated with increased demyelination and impaired remyelination. The increase in disease severity was associated with elevated expression of T cell chemoattractant chemokines, concurrent with increased neuroinflammation. These findings support the idea that cystatin F influences expression of proinflammatory gene expression impacting neuroinflammation, T cell activation and/or glia cell responses ultimately impacting neuroinflammation and neurologic disease.

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications.

Recent advances in machine learning (ML) have led to newer model architectures including transformers (large language models, LLMs) showing state of the art results in text generation and image analysis as well as few-shot learning (FSLC) models which offer predictive power with extremely small datasets. These new architectures may offer promise, yet the 'no-free lunch' theorem suggests that no single model algorithm can outperform at all possible tasks. Here, we explore the capabilities of classical (SVR), FSLC, and transformer models (MolBART) over a range of dataset tasks and show a 'goldilocks zone' for each model type, in which dataset size and feature distribution (i.e. dataset "diversity") determines the optimal algorithm strategy. When datasets are small ( < 50 molecules), FSLC tend to outperform both classical ML and transformers. When datasets are small-to-medium sized (50-240 molecules) and diverse, transformers outperform both classical models and few-shot learning. Finally, when datasets are of larger and of sufficient size, classical models then perform the best, suggesting that the optimal model to choose likely depends on the dataset available, its size and diversity. These findings may help to answer the perennial question of which ML algorithm is to be used when faced with a new dataset.

In Vitro Characterization and Rescue of VX Metabolism in Human Liver Microsomes.

Venomous agent X (VX) is an organophosphate acetylcholinesterase (AChE) inhibitor, and although it is one of the most toxic AChE inhibitors known, the extent of metabolism in humans is not currently well understood. The known metabolism in humans is limited to the metabolite identification from a single victim of the Osaka poisoning in 1994, which allowed for the identification of several metabolic products. VX has been reported to be metabolized in vitro by paraoxonase-1 and phosphotriesterase, although their binding constants are many orders of magnitude above the LD50, suggesting limited physiologic relevance. Using incubation with human liver microsomes (HLMs), we have now characterized the metabolism of VX and the formation of multiple metabolites as well as identified a Food and Drug Administration-approved drug [ethylenediaminetetraacetic acid (EDTA)] that enhances the metabolic rate. HLM incubation alone shows a pronounced increase in the metabolism of VX compared with buffer, suggesting that cytochrome P450-mediated metabolism of VX is occurring. We identified a biphasic decay with two distinct rates of metabolism. The enhancement of VX metabolism in multiple buffers was assessed to attempt to mitigate the effect of hydrolysis rates. The formation of VX metabolites was shown to be shifted with HLMs, suggesting a pathway enhancement over simple hydrolysis. Additionally, our investigation of hydrolysis rates in various common buffers used in biologic assays discovered dramatic differences in VX stability. The new human in vitro VX metabolic data reported points to a potential in vivo treatment strategy (EDTA) for rescue in individuals that are poisoned though enhancement of metabolism alongside existing treatments. SIGNIFICANCE STATEMENT: Venomous agent X (VX) is a potent acetylcholinesterase inhibitor and chemical weapon. To date, we do not possess a clear understanding of its metabolism in humans that would assist us in treating those exposed to it. This study now describes the human liver microsomal metabolism of VX and identifies ethylenediaminetetraacetic acid, which appears to enhance the rate of metabolism. This may provide a potential treatment option for human VX poisoning.

Stem cell secretome treatment improves whole-body metabolism, reduces adiposity, and promotes skeletal muscle function in aged mice.

Aging coincides with the progressive loss of muscle mass and strength, increased adiposity, and diminished physical function. Accordingly, interventions aimed at improving muscle, metabolic, and/or physical health are of interest to mitigate the adverse effects of aging. In this study, we tested a stem cell secretome product, which contains extracellular vesicles and growth, cytoskeletal remodeling, and immunomodulatory factors. We examined the effects of 4 weeks of 2×/week unilateral intramuscular secretome injections (quadriceps) in ambulatory aged male C57BL/6 mice (22-24 months) compared to saline-injected aged-matched controls. Secretome delivery substantially increased whole-body lean mass and decreased fat mass, corresponding to higher myofiber cross-sectional area and smaller adipocyte size, respectively. Secretome-treated mice also had greater whole-body physical function (grip strength and rotarod performance) and had higher energy expenditure and physical activity levels compared to control mice. Furthermore, secretome-treated mice had greater skeletal muscle Pax7+ cell abundance, capillary density, collagen IV turnover, reduced intramuscular lipids, and greater Akt and hormone sensitive lipase phosphorylation in adipose tissue. Finally, secretome treatment in vitro directly enhanced muscle cell growth and IL-6 production, and in adipocytes, it reduced lipid content and improved insulin sensitivity. Moreover, indirect treatment with secretome-treated myotube culture media also enhanced muscle cell growth and adipocyte size reduction. Together, these data suggest that intramuscular treatment with a stem cell secretome improves whole-body metabolism, physical function, and remodels skeletal muscle and adipose tissue in aged mice.

Sequential Contrastive and Deep Learning Models to Identify Selective Butyrylcholinesterase Inhibitors.

Butyrylcholinesterase (BChE) is a target of interest in late-stage Alzheimer's Disease (AD) where selective BChE inhibitors (BIs) may offer symptomatic treatment without the harsh side effects of acetylcholinesterase (AChE) inhibitors. In this study, we explore multiple machine learning strategies to identify BIs in silico, optimizing for precision over all other metrics. We compare state-of-the-art supervised contrastive learning (CL) with deep learning (DL) and Random Forest (RF) machine learning, across single and sequential modeling configurations, to identify the best models for BChE selectivity. We used these models to virtually screen a vendor library of 5 million compounds for BIs and tested 20 of these compounds in vitro. Seven of the 20 compounds displayed selectivity for BChE over AChE, reflecting a hit rate of 35% for our model predictions, suggesting a highly efficient strategy for modeling selective inhibition.

Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus.

Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 µM; M. abscessus IC90 59.1 µM) and tribromsalan (M. tuberculosis IC90 76.92 µM; M. abscessus IC90 147.4 µM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.

OCA-B promotes autoimmune demyelination through control of stem-like CD4 + T cells.

Stem-like T cell populations can selectively promote autoimmunity, but the activities that sustain these populations are incompletely understood. Here, we show that T cell-intrinsic loss of the transcription cofactor OCA-B protects mice from experimental autoimmune encephalomyelitis (EAE) while preserving responses to infection. In EAE models driven by antigen re-encounter, OCA-B deletion eliminates CNS infiltration, proinflammatory cytokine production and clinical disease. OCA-B-expressing CD4 + T cells within the CNS of mice with EAE display a memory phenotype and preferentially confer disease. In a relapsing-remitting EAE model, OCA-B T cell-deficiency specifically protects mice from relapse. During remission, OCA-B promotes the expression of Tcf7 , Slamf6 , and Sell in proliferating T cell populations. At relapse, OCA-B loss results in both the accumulation of an immunomodulatory CD4 + T cell population expressing Ccr9 and Bach2 , and the loss of effector gene expression from Th17 cells. These results identify OCA-B as a driver of pathogenic stem-like T cells.

Microglia influence immune responses and restrict neurologic disease in response to central nervous system infection by a neurotropic murine coronavirus.

Intracranial (i.c.) inoculation of susceptible mice with a glial-tropic strain of mouse hepatitis virus (JHMV), a murine coronavirus, results in an acute encephalomyelitis followed by viral persistence in white matter tracts accompanied by chronic neuroinflammation and demyelination. Microglia serve numerous functions including maintenance of the healthy central nervous system (CNS) and are among the first responders to injury or infection. More recently, studies have demonstrated that microglia aid in tailoring innate and adaptive immune responses following infection by neurotropic viruses including flaviviruses, herpesviruses, and picornaviruses. These findings have emphasized an important role for microglia in host defense against these viral pathogens. In addition, microglia are also critical in optimizing immune-mediated control of JHMV replication within the CNS while restricting the severity of demyelination and enhancing remyelination. This review will highlight our current understanding of the molecular and cellular mechanisms by which microglia aid in host defense, limit neurologic disease, and promote repair following CNS infection by a neurotropic murine coronavirus.

Synthesis and Evaluation of 9-Aminoacridines with SARS-CoV-2 Antiviral Activity.

There have been relatively few small molecules developed with direct activity against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Two existing antimalarial drugs, pyronaridine and quinacrine, display whole cell activity against SARS-CoV-2 in A549 + ACE2 cells (pretreatment, IC50 = 0.23 and 0.19 µM, respectively) with moderate cytotoxicity (CC50 = 11.53 and 9.24 µM, respectively). Moreover, pyronaridine displays in vitro activity against SARS-CoV-2 PLpro (IC50 = 1.8 µM). Given their existing antiviral activity, these compounds are strong candidates for repurposing against COVID-19 and prompt us to study the structure-activity relationship of the 9-aminoacridine scaffold against SARS-CoV-2 using traditional medicinal chemistry to identify promising new analogs. Our studies identified several novel analogs possessing potent in vitro activity in U2-OS ACE2 GFP 1-10 and 1-11 (IC50 < 1.0 µM) as well as moderate cytotoxicity (CC50 > 4.0 µM). Compounds such as 7g, 9c, and 7e were more active, demonstrating selectivity indices SI > 10, and 9c displayed the strongest activity (IC50 ≤ 0.42 µM, CC50 ≥ 4.41 µM, SI > 10) among them, indicating that it has potential as a new lead molecule in this series against COVID-19.

High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents.

Broad-spectrum anti-infective chemotherapy agents with activity against Trypanosomes, Leishmania, and Mycobacterium tuberculosis species were identified from a high-throughput phenotypic screening program of the 456 compounds belonging to the Ty-Box, an in-house industry database. Compound characterization using machine learning approaches enabled the identification and synthesis of 44 compounds with broad-spectrum antiparasitic activity and minimal toxicity against Trypanosoma brucei, Leishmania Infantum, and Trypanosoma cruzi. In vitro studies confirmed the predictive models identified in compound 40 which emerged as a new lead, featured by an innovative N-(5-pyrimidinyl)benzenesulfonamide scaffold and promising low micromolar activity against two parasites and low toxicity. Given the volume and complexity of data generated by the diverse high-throughput screening assays performed on the compounds of the Ty-Box library, the chemoinformatic and machine learning tools enabled the selection of compounds eligible for further evaluation of their biological and toxicological activities and aided in the decision-making process toward the design and optimization of the identified lead.

Time-resolved crystallography captures light-driven DNA repair.

Photolyase is an enzyme that uses light to catalyze DNA repair. To capture the reaction intermediates involved in the enzyme's catalytic cycle, we conducted a time-resolved crystallography experiment. We found that photolyase traps the excited state of the active cofactor, flavin adenine dinucleotide (FAD), in a highly bent geometry. This excited state performs electron transfer to damaged DNA, inducing repair. We show that the repair reaction, which involves the lysis of two covalent bonds, occurs through a single-bond intermediate. The transformation of the substrate into product crowds the active site and disrupts hydrogen bonds with the enzyme, resulting in stepwise product release, with the 3' thymine ejected first, followed by the 5' base.

1-Sulfonyl-3-amino-1H-1,2,4-triazoles as Yellow Fever Virus Inhibitors: Synthesis and Structure-Activity Relationship.

Yellow fever virus (YFV) transmitted by infected mosquitoes causes an acute viral disease for which there are no approved small-molecule therapeutics. Our recently developed machine learning models for YFV inhibitors led to the selection of a new pyrazolesulfonamide derivative RCB16003 with acceptable in vitro activity. We report that the N-phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine class, which was recently identified as active non-nucleoside reverse transcriptase inhibitors against HIV-1, can also be repositioned as inhibitors of yellow fever virus replication. As compared to other Flaviviridae or Togaviridae family viruses tested, both compounds RCB16003 and RCB16007 demonstrate selectivity for YFV over related viruses, with only RCB16007 showing some inhibition of the West Nile virus (EC50 7.9 µM, CC50 17 µM, SI 2.2). We also describe the absorption, distribution, metabolism, and excretion (ADME) in vitro and pharmacokinetics (PK) for RCB16007 in mice. This compound had previously been shown to not inhibit hERG, and we now describe that it has good metabolic stability in mouse and human liver microsomes, low levels of CYP inhibition, high protein binding, and no indication of efflux in Caco-2 cells. A single-dose oral PK study in mice has a T1/2 of 3.4 h and Cmax of 1190 ng/mL, suggesting good availability and stability. We now propose that the N-phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine class may be prioritized for in vivo efficacy testing against YFV.

COVID-19 and the impact on Alzheimer's disease pathology.

Coronavirus disease 2019 (COVID-19) has rapidly escalated into a global pandemic that primarily affects older and immunocompromised individuals due to underlying clinical conditions and suppressed immune responses. Furthermore, COVID-19 patients exhibit a spectrum of neurological symptoms, indicating that COVID-19 can affect the brain in a variety of manners. Many studies, past and recent, suggest a connection between viral infections and an increased risk of neurodegeneration, raising concerns about the neurological effects of COVID-19 and the possibility that it may contribute to Alzheimer's disease (AD) onset or worsen already existing AD pathology through inflammatory processes given that both COVID-19 and AD share pathological features and risk factors. This leads us to question whether COVID-19 is a risk factor for AD and how these two conditions might influence each other. Considering the extensive reach of the COVID-19 pandemic and the devastating impact of the ongoing AD pandemic, their combined effects could have significant public health consequences worldwide.

Modeling diffuse scattering with simple, physically interpretable models.

Diffuse scattering has long been proposed to probe protein dynamics relevant for biological function, and more recently, as a tool to aid structure determination. Despite recent advances in measuring and modeling this signal, the field has not been able to routinely use experimental diffuse scattering for either application. A persistent challenge has been to devise models that are sophisticated enough to robustly reproduce experimental diffuse features but remain readily interpretable from the standpoint of structural biology. This chapter presents eryx, a suite of computational tools to evaluate the primary models of disorder that have been used to analyze protein diffuse scattering. By facilitating comparative modeling, eryx aims to provide insights into the physical origins of this signal and help identify the sources of disorder that are critical for reproducing experimental features. This framework also lays the groundwork for the development of more advanced models that integrate different types of disorder without loss of interpretability.

In silico ADME/tox comes of age: twenty years later.

In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.

Human CYP2C19 Substrate and Inhibitor Characterization of Organophosphate Pesticides.

CYP2C19 is an important enzyme for organophosphate pesticide (OPP) metabolism. Because the OPPs can be both substrates and inhibitors of CYP2C19, we screened 45 OPPs for their ability to inhibit the activity of this enzyme and investigated the role of CYP2C19 in the metabolism of 22 of these molecules. We identified several nanomolar inhibitors of CYP2C19 as well as determined that thions, in general, are more potent inhibitors than oxons. We also determined that thions are readily metabolized by CYP2C19, although we saw no relationship between IC50 values and intrinsic clearance rates. This study may have implications for mitigating the risk of OPP poisoning.

Antiviral Evaluation of Dispirotripiperazines against Hepatitis B Virus.

Hepatitis B virus (HBV) is a hepatotropic DNA virus that replicates by reverse transcription. It chronically infects >296 million people worldwide, including ∼850,000 in the USA, and kills 820,000 annually worldwide. Current nucleos(t)ide analogue (NA) or pegylated interferon α therapies do not eradicate the virus and would benefit from a complementary antiviral drug. We performed a preliminary screen of 28 dispirotripiperazines against HBV, identifying 9 hits with EC50 of 0.7-25 µM. Compound 11826096 displays the most potent activity and represents a promising lead for future optimization. While the mechanism of action is unknown, preliminary assays limit possible targets to activities involved in RNA accumulation, translation, capsid assembly, and/or capsid stability. In addition, we built machine learning models to determine if they were able to predict the activity of this series of compounds. The novelty of these molecules indicated they were outside of the applicability domain of these models.

Comparing LD50/LC50 Machine Learning Models for Multiple Species.

The lethal dose or concentration which kills 50% of the animals (LD50 or LC50) is an important parameter for scientists to understand the toxicity of chemicals in different scenarios that can be used to make go-no-go decisions, and ultimately assist in the choice of the right personal protective equipment needed for containment. The LD50 assessment process has also required the use of many animals although modern methods have reduced the number of rats needed. Since a compound is usually considered highly toxic when the LD50 is lower than 25 mg/kg, such a classification provides potentially valuable safety information to synthetic chemists and other safety assessment scientists. The need for finding alternative approaches such as computational methods is important to ultimately reduce animal use for this testing further still. We now summarize our efforts to use public data for building in vivo LD50 or LC50 classification and regression machine learning models for various species (rat, mouse, fish and daphnia) and their 5-fold cross validation statistics with different machine learning algorithms as well as an external curated test set for mouse LD50. These datasets consist of different molecule classes, may cover different activity ranges, and also have a range of dataset sizes. The challenges of using such computational models are that their applicability domain will also need to be understood so that they can be used to make reliable predictions for novel molecules. These machine learning models will also need to be backed up with experimental validation. However, such models could also be used for efforts to bridge gaps in individual toxicity datasets. Making such models available also opens them up to potential misuse or dual use. We will summarize these efforts and propose that they could be used for scoring the millions of commercially available molecules, most of which likely do not have a known LD50 or for that matter any data in vitro or in vivo for toxicity.