ProTox 3.0: a webserver for the prediction of toxicity of chemicals.

Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in computational methods, the in silico methods can offer significant benefits to both regulatory needs and requirements for risk assessments and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox 3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance. ProTox envisages itself as a complete, freely available computational platform for in silico toxicity prediction for toxicologists, regulatory agencies, computational chemists, and medicinal chemists. The ProTox 3.0 webserver is free and open to all users, and there is no login requirement and can be accessed via https://tox.charite.de. The web server takes a 2D chemical structure as input and reports the toxicological profile of the compound for each endpoint with a confidence score and overall toxicity radar plot and network plot.

Withdrawn 2.0-update on withdrawn drugs with pharmacovigilance data.

One challenge in the development of novel drugs is their interaction with potential off-targets, which can cause unintended side-effects, that can lead to the subsequent withdrawal of approved drugs. At the same time, these off-targets may also present a chance for the repositioning of withdrawn drugs for new indications, which are potentially rare or more severe than the original indication and where certain adverse reactions may be avoidable or tolerable. To enable further insights into this topic, we updated our database Withdrawn by adding pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS), as well as mechanism of action and human disease pathway prediction features for drugs that are or were temporarily withdrawn or discontinued in at least one country. As withdrawal data are still spread over dozens of national websites, we are continuously updating our lists of discontinued or withdrawn drugs and related (off-)targets. Furthermore, new systematic entry points for browsing the data, such as an ATC tree, were added, increasing the accessibility of the database in a user-friendly way. Withdrawn 2.0 is publicly available without the need for registration or login at https://bioinformatics.charite.de/withdrawn_3/index.php.

SuperNatural 3.0-a database of natural products and natural product-based derivatives.

Natural products (NPs) are single chemical compounds, substances or mixtures produced by a living organism - found in nature. Evolutionarily, NPs have been used as healing agents since thousands of years and still today continue to be the most important source of new potential therapeutic preparations. Natural products have played a key role in modern drug discovery for several diseases. Furthermore, following consumers' increasing demand for natural food ingredients, many efforts have been made to discover natural low-calorie sweeteners in recent years. SuperNatural 3.0 is a freely available database of natural products and derivatives. The updated version contains 449 058 natural compounds along with their structural and physicochemical information. Additionally, information on pathways, mechanism of action, toxicity, vendor information if available, drug-like chemical space prediction for several diseases as antiviral, antibacterial, antimalarial, anticancer, and target specific cells like the central nervous system (CNS) are also provided for the natural compounds. The updated version of the database also provides a valuable pool of natural compounds in which potential highly sweet compounds are expected to be found. The possible taste profile of the natural compounds was predicted using our published VirtualTaste models. The SuperNatural 3.0 database is freely available via http://bioinf-applied.charite.de/supernatural_3, without any login or registration.

SuperPred 3.0: drug classification and target prediction-a machine learning approach.

Since the last published update in 2014, the SuperPred webserver has been continuously developed to offer state-of-the-art models for drug classification according to ATC classes and target prediction. For the first time, a thoroughly filtered ATC dataset, that is suitable for accurate predictions, is provided along with detailed information on the achieved predictions. This aims to overcome the challenges in comparing different published prediction methods, since performance can vary greatly depending on the training dataset used. Additionally, both ATC and target prediction have been reworked and are now based on machine learning models instead of overall structural similarity, stressing the importance of functional groups for the mechanism of action of small molecule substances. Additionally, the dataset for the target prediction has been extensively filtered and is no longer only based on confirmed binders but also includes non-binding substances to reduce false positives. Using these methods, accuracy for the ATC prediction could be increased by almost 5% to 80.5% compared to the previous version, and additionally the scoring function now offers values which are easily assessable at first glance. SuperPred 3.0 is publicly available without the need for registration at: https://prediction.charite.de/index.php.

Dupilumab Use Is Associated With Protection From Coronavirus Disease 2019 Mortality: A Retrospective Analysis.

We previously found that type 2 immunity promotes coronavirus disease 2019 (COVID-19) pathogenesis in a mouse model. To test relevance to human disease, we used electronic health record databases and determined that patients on dupilumab (anti-interleukin [IL]-4R monoclonal antibody that blocks IL-13 and IL-4 signaling) at the time of COVID-19 infection had lower mortality.

Comparison of the risk of hospital admission, need for ventilation, sepsis, pneumonitis and death among the recent monkeypox outbreak and historical outbreaks.

BACKGROUND: The course of monkeypox can be severe. Our aim was to retrospectively compare the risk of hospital admission, the need for ventilation, sepsis, pneumonitis and death between the recent outbreak and historical outbreaks. MATERIALS AND METHODS: Cases of monkeypox were retrieved from the TriNetX database and assigned to either cohort I (recent outbreak between May 1st and September 16th, 2022) and cohort II (historical outbreaks before May 1st, 2022). After matching for age distribution, statistical analysis was performed. RESULTS: Of 640 patients with monkeypox 81 subjects per cohort remained after matching (mean age±standard deviation = 36.1±18.3 years). Within 56 days after diagnosis 10 patients per cohort were hospitalized (12.4%) and/or developed sepsis (12.4%). The risk of ventilation and pneumonitis were significantly lower among cohort I compared with cohort II (0 vs. 10 cases; risk difference = 12.4%; p = 0.001; Log-Rank test). No cases of death were recorded. CONCLUSION: Even though monkeypox provides a risk of severe courses, the infection is self-limiting in most cases. Unlike past outbreaks, the risk of ventilation and pneumonitis may be relatively low among recent outbreaks.

VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds.

Taste is one of the crucial organoleptic properties involved in the perception of food by humans. Taste of a chemical compound present in food stimulates us to take in food and avoid poisons. Bitter taste of drugs presents compliance problems and early flagging of potential bitterness of a drug candidate may help with its further development. Similarly, the taste of chemicals present in food is important for evaluation of food quality in the industry. In this work, we have implemented machine learning models to predict three different taste endpoints-sweet, bitter and sour. The VirtualTaste models achieved an overall accuracy of 90% and an AUC of 0.98 in 10-fold cross-validation and in an independent test set. The web server takes a two-dimensional chemical structure as input and reports the chemical's taste profile for three tastes-using molecular fingerprints along with confidence scores, including information on similar compounds with known activity from the training set and an overall radar chart. Additionally, insights into 25 bitter receptors are also provided via target prediction for the predicted bitter compounds. VirtualTaste, to the best of our knowledge, is the first freely available web-based platform for the prediction of three different tastes of compounds. It is accessible via http://virtualtaste.charite.de/VirtualTaste/without any login requirements and is free to use.

PROMISCUOUS 2.0: a resource for drug-repositioning.

The development of new drugs for diseases is a time-consuming, costly and risky process. In recent years, many drugs could be approved for other indications. This repurposing process allows to effectively reduce development costs, time and, ultimately, save patients' lives. During the ongoing COVID-19 pandemic, drug repositioning has gained widespread attention as a fast opportunity to find potential treatments against the newly emerging disease. In order to expand this field to researchers with varying levels of experience, we made an effort to open it to all users (meaning novices as well as experts in cheminformatics) by significantly improving the entry-level user experience. The browsing functionality can be used as a global entry point to collect further information with regards to small molecules (∼1 million), side-effects (∼110 000) or drug-target interactions (∼3 million). The drug-repositioning tab for small molecules will also suggest possible drug-repositioning opportunities to the user by using structural similarity measurements for small molecules using two different approaches. Additionally, using information from the Promiscuous 2.0 Database, lists of candidate drugs for given indications were precomputed, including a section dedicated to potential treatments for COVID-19. All the information is interconnected by a dynamic network-based visualization to identify new indications for available compounds. Promiscuous 2.0 is unique in its functionality and is publicly available at http://bioinformatics.charite.de/promiscuous2.

Voronoia 4-ever.

We present an updated version of the Voronoia service that enables fully automated analysis of the atomic packing density of macromolecules. Voronoia combines previous efforts to analyse 3D protein and RNA structures into a single service, combined with state-of-the-art online visualization. Voronoia uses the Voronoi cell method to calculate the free space between neighbouring atoms to estimate van der Waals interactions. Compared to other methods that derive van der Waals interactions by calculating solvent-free surfaces, it explicitly considers volume or packing defects. Large internal voids refer either to water molecules or ions unresolved by X-ray crystallography or cryo-EM, cryptic ligand binding pockets, or parts of a structural model that require further refinement. Voronoia is, therefore mainly used for functional analyses of 3D structures and quality assessments of structural models. Voronoia 4-ever updates the database of precomputed packing densities of PDB entries, allows uploading multiple structures, adds new filter options and facilitates direct access to the results through intuitive display with the NGL viewer. Voronoia is available at: htttp://proteinformatics.org/voronoia.

Comparison of five-year survival rates among patients with oral squamous cell carcinoma with and without association with syphilis: a retrospective case-control study.

BACKGROUND: Syphilis is an infectious disease that is at least discussed to be premalignant. This potential, combined with its general pathological impact, raises the question if syphilis increases mortality in oral cancer patients. The aim of the study was to assess if the five-year survival rates among patients suffering from oral squamous cell carcinoma (OSCC) with (cohort I) and without association with syphilis (cohort II) differ. METHODS: Retrospective clinical data of patients diagnosed with OSCC (International Classification of Diseases [ICD]-10 codes C01-06) within the past 20 years from the access date September 25, 2021 were retrieved from the TriNetX network (TriNetX, Cambridge, Massachusetts, USA) to gain initial cohort 0. Subjects also diagnosed with syphilis (ICD-10 codes A51-53) were assigned to cohort I. Cohort II was comprised of the remaining individuals of cohort 0 by creating a group with the same number of patients as cohort I, and by matching for age and gender. Subsequently, Kaplan-Meier analysis and Cox proportional hazards regression were performed, and risk, odds and hazard ratios were calculated. RESULTS: Of a total of 73,736 patients in cohort 0, 199 individuals were each assigned to cohort I and II. During the five-year period after tumor diagnosis, 39 and 30 patients in cohort I and II died. The five-year survival probabilities did not significantly differ between the cohorts (I vs. II = 74.19% vs. 75.01%; p = 0.52; Log-Rank test), nor the risk of dying (I vs. II = 19.6% vs. 15.08%; risk difference = 4.52%; p = 0.23). The calculated risk, odds and hazard ratios were 1.3 (95% confidence interval [CI] = 0.84; 2.00), 1.37 (95% CI = 0.81; 2.31) and 1.17 (95% CI = 0.73; 1.88), respectively. CONCLUSIONS: The obtained results indicate that the survival rate of individuals with OSCC might not be negatively influenced if syphilis is present/associated. However, the results need to be interpreted cautiously due to limitations related to the retrospective approach, especially as data on the tumor staging were not accessible. TRIAL REGISTRATION: Due to the retrospective nature of the study, no registration was necessary.

Man vs. machine: comparison of pharmacogenetic expert counselling with a clinical medication support system in a study with 200 genotyped patients.

BACKGROUND: Medication problems such as strong side effects or inefficacy occur frequently. At our university hospital, a consultation group of specialists takes care of patients suffering from medication problems. Nevertheless, the counselling of poly-treated patients is complex, as it requires the consideration of a large network of interactions between drugs and their targets, their metabolizing enzymes, and their transporters, etc. PURPOSE: This study aims to check whether a score-based decision-support system (1) reduces the time and effort and (2) suggests solutions at the same quality level. PATIENTS AND METHODS: A total of 200 multimorbid, poly-treated patients with medication problems were included. All patients were considered twice: manually, as clinically established, and using the Drug-PIN decision-support system. Besides diagnoses, lab data (kidney, liver), phenotype (age, gender, BMI, habits), and genotype (genetic variants with actionable clinical evidence I or IIa) were considered, to eliminate potentially inappropriate medications and to select individually favourable drugs from existing medication classes. The algorithm is connected to automatically updated knowledge resources to provide reproducible up-to-date decision support. RESULTS: The average turnaround time for manual poly-therapy counselling per patient ranges from 3 to 6 working hours, while it can be reduced to ten minutes using Drug-PIN. At the same time, the results of the novel computerized approach coincide with the manual approach at a level of > 90%. The holistic medication score can be used to find favourable drugs within a class of drugs and also to judge the severity of medication problems, to identify critical cases early and automatically. CONCLUSION: With the computerized version of this approach, it became possible to score all combinations of all alternative drugs from each class of drugs administered ("personalized medication landscape ") and to identify critical patients even before problems are reported ("medication alert"). Careful comparison of manual and score-based results shows that the incomplete manual consideration of genetic specialties and pharmacokinetic conflicts is responsible for most of the (minor) deviations between the two approaches. The meaning of the reduction of working time for experts by about 2 orders of magnitude should not be underestimated, as it enables practical application of personalized medicine in clinical routine.

Non-psychotropic cannabinoids as inhibitors of TET1 protein.

Non-psychotropic cannabinoids (e.g., cannabidiol, cannabinol and cannabigerol) are contained in numerous alimentary and medicinal products. Therefore, predicting and studying their possible side effects, such as changes in DNA methylation, is an important task for assessing the safety of these products. Interference with TET enzymes by chelating ferrous ions can contribute to the altered methylation pattern. All tested cannabinoids displayed a strong affinity for Fe(II) ions. Cannabidiol and cannabinol exhibited potent inhibitory activities (IC50 = 4.8 and 6.27 µM, respectively) towards the TET1 protein, whereas cannabigerol had no effect on the enzyme activity. An in silico molecular docking study revealed marked binding potential within the catalytic cavity for CBD/CBN, but some affinity was also found for CBG, thus the total lack of activity remains unexplained. These results imply that cannabinoids could affect the activity of the TET1 protein not only due to their affinity for Fe(II) but also due to other types of interactions (e.g., hydrophobic interactions and hydrogen bonding).

SuperCYPsPred-a web server for the prediction of cytochrome activity.

Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug-drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs-published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http://insilico-cyp.charite.de/SuperCYPsPred/). The web server does not require log in or registration and is free to use.

Oestrogen-mediated upregulation of the Mas receptor contributes to sex differences in acute lung injury and lung vascular barrier regulation.

Epidemiological data from the SARS-CoV-2 outbreak suggest sex differences in mortality and vulnerability; however, sex-dependent incidence of acute respiratory distress syndrome (ARDS) remains controversial and the sex-dependent mechanisms of endothelial barrier regulation are unknown. In premenopausal women, increased signalling of angiotensin (Ang)(1-7) via the Mas receptor has been linked to lower cardiovascular risk. Since stimulation of the Ang(1-7)/Mas axis protects the endothelial barrier in acute lung injury (ALI), we hypothesised that increased Ang(1-7)/Mas signalling may protect females over males in ALI/ARDS.Clinical data were collected from Charité inpatients (Berlin) and sex differences in ALI were assessed in wild-type (WT) and Mas-receptor deficient (Mas-/- ) mice. Endothelial permeability was assessed as weight change in isolated lungs and as transendothelial electrical resistance (TEER) in vitroIn 734 090 Charité inpatients (2005-2016), ARDS had a higher incidence in men as compared to women. In murine ALI, male WT mice had more lung oedema, protein leaks and histological evidence of injury than female WT mice. Lung weight change in response to platelet-activating factor (PAF) was more pronounced in male WT and female Mas-/- mice than in female WT mice, whereas Mas-receptor expression was higher in female WT lungs. Ovariectomy attenuated protection in female WT mice and reduced Mas-receptor expression. Oestrogen increased Mas-receptor expression and attenuated endothelial leakage in response to thrombin in vitro This effect was alleviated by Mas-receptor blockade.Improved lung endothelial barrier function protects female mice from ALI-induced lung oedema. This effect is partially mediated via enhanced Ang(1-7)/Mas signalling as a result of oestrogen-dependent Mas expression.

L-leucine stimulation of glucose uptake and utilization involves modulation of glucose - lipid metabolic switch and improved bioenergetic homeostasis in isolated rat psoas muscle ex vivo.

The antidiabetic effect of l-leucine has been attributed to its modulatory effect on glucose uptake and lipid metabolism in muscles. However, there is a dearth on its effect on glucose metabolism in muscles. Thus, the present study investigated the effect of l-leucine - stimulated glucose uptake on glucose metabolism, dysregulated lipid metabolic pathways, redox and bioenergetic homeostasis, and proteolysis in isolated psoas muscle from Sprague Dawley male rats. Isolated psoas muscles were incubated with l-leucine (30-240 µg/mL) in the presence of 11.1 mMol glucose at 37 ˚C for 2 h. Muscles incubated in only glucose served as the control, while muscles not incubated in l-leucine and/or glucose served as the normal control. Metformin (6.04 mM) was used as the standard antidiabetic drug. Incubation with l-leucine caused a significant increase in muscle glucose uptake, with an elevation of glutathione levels, superoxide dismutase, catalase, E-NTPDase and 5'nucleotidase activities. It also led to the depletion of malondialdehyde and nitric oxide levels, ATPase, chymotrypsin, acetylcholinesterase, glycogen phosphorylase, glucose-6-phosphatase, fructose-1,6-bisphosphatase and lipase activities. There was an alteration in lipid metabolites, with concomitant activation of glycerolipid metabolism, fatty acid metabolism, and fatty acid elongation in mitochondria in the glucose-incubated muscle (negative control). Incubation with l-leucine reversed these alterations, and concomitantly deactivated the pathways. These results indicate that l-leucine-enhanced muscle glucose uptake involves improved redox and bioenergetic homeostasis, with concomitant suppressed proteolytic, glycogenolytic and gluconeogenetic activities, while modulating glucose - lipid metabolic switch.

Oxidative testicular injury: effect of L-leucine on redox, cholinergic and purinergic dysfunctions, and dysregulated metabolic pathways.

The antioxidant and anti-proinflammatory activities of L-leucine were investigated on oxidative testicular injury, ex vivo. In vitro analysis revealed L-leucine to be a potent scavenger of free radicals, while inhibiting acetylcholinesterase activity. Oxidative injury was induced in testicular tissues using FeSO4. Treatment with L-leucine led to depletion of oxidative-induced elevated levels of NO, MDA, and myeloperoxidase activity, with concomitant elevation of reduced glutathione and non-protein thiol levels, SOD and catalase activities. L-leucine caused a significant (p < 0.05) alteration of oxidative-elevated acetylcholinesterase and chymotrypsin activities, while concomitantly elevating the activities of ATPase, ENTPDase and 5'-nucleotidase. L-leucine conferred a protective effect against oxidative induced DNA damage. Molecular docking revealed molecular interactions with COX-2, IL-1 beta and iNOS. Treatment with L-leucine led to restoration of oxidative depleted ascorbic acid-2-sulfate, with concomitant depletion of the oxidative induced metabolites: D-4-Hydroxy-2-oxoglutarate, L-cystine, adenosine triphosphate, maleylacetoacetic acid, cholesteryl ester, and 6-Hydroxy flavin adenine dinucleotide. Treatment with L-leucine reactivated glycolysis while concomitantly deactivating oxidative-induced citrate cycle and increasing the impact-fold of purine metabolism pathway. L-leucine was predicted not to be an inhibitor of CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4, with a predicted LD50 value of 5000 mg/Kg and toxicity class of 5. Additionally, L-leucine showed little or no in vitro cytotoxicity in mammalian cells. These results suggest the therapeutic potentials of L-leucine on oxidative testicular injury, as evident by its ability to attenuate oxidative stress and proinflammation, while stalling cholinergic dysfunction and modulating nucleotide hyrolysis; as well as modulate oxidative dysregulated metabolites and their pathways.

Allosteric Modulation of the CB1 Cannabinoid Receptor by Cannabidiol-A Molecular Modeling Study of the N-Terminal Domain and the Allosteric-Orthosteric Coupling.

The CB1 cannabinoid receptor (CB1R) contains one of the longest N termini among class A G protein-coupled receptors. Mutagenesis studies suggest that the allosteric binding site of cannabidiol (CBD) involves residues from the N terminal domain. In order to study the allosteric binding of CBD to CB1R we modeled the whole N-terminus of this receptor using the replica exchange molecular dynamics with solute tempering (REST2) approach. Then, the obtained structures of CB1R with the N terminus were used for ligand docking. A natural cannabinoid receptor agonist, Δ9-THC, was docked to the orthosteric site and a negative allosteric modulator, CBD, to the allosteric site positioned between extracellular ends of helices TM1 and TM2. The molecular dynamics simulations were then performed for CB1R with ligands: (i) CBD together with THC, and (ii) THC-only. Analyses of the differences in the residue-residue interaction patterns between those two cases allowed us to elucidate the allosteric network responsible for the modulation of the CB1R by CBD. In addition, we identified the changes in the orthosteric binding mode of Δ9-THC, as well as the changes in its binding energy, caused by the CBD allosteric binding. We have also found that the presence of a complete N-terminal domain is essential for a stable binding of CBD in the allosteric site of CB1R as well as for the allosteric-orthosteric coupling mechanism.

Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study.

BACKGROUND: The aim of this study was to evaluate the possibility of breath testing as a method of cancer detection in patients with oral squamous cell carcinoma (OSCC). METHODS: Breath analysis was performed in 35 OSCC patients prior to surgery. In 22 patients, a subsequent breath test was carried out after surgery. Fifty healthy subjects were evaluated in the control group. Breath sampling was standardized regarding location and patient preparation. All analyses were performed using gas chromatography coupled with ion mobility spectrometry and machine learning. RESULTS: Differences in imaging as well as in pre- and postoperative findings of OSCC patients and healthy participants were observed. Specific volatile organic compound signatures were found in OSCC patients. Samples from patients and healthy individuals could be correctly assigned using machine learning with an average accuracy of 86-90%. CONCLUSIONS: Breath analysis to determine OSCC in patients is promising, and the identification of patterns and the implementation of machine learning require further assessment and optimization. Larger prospective studies are required to use the full potential of machine learning to identify disease signatures in breath volatiles.

Evidence for treatment with estradiol for women with SARS-CoV-2 infection.

BACKGROUND: Given that an individual's age and gender are strongly predictive of coronavirus disease 2019 (COVID-19) outcomes, do such factors imply anything about preferable therapeutic options? METHODS: An analysis of electronic health records for a large (68,466-case), international COVID-19 cohort, in 5-year age strata, revealed age-dependent sex differences. In particular, we surveyed the effects of systemic hormone administration in women. The primary outcome for estradiol therapy was death. Odds ratios (ORs) and Kaplan-Meier survival curves were analyzed for 37,086 COVID-19 women in two age groups: pre- (15-49 years) and peri-/post-menopausal (> 50 years). RESULTS: The incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is higher in women than men (by about + 15%) and, in contrast, the fatality rate is higher in men (about + 50%). Interestingly, the relationships between these quantities are linked to age: pre-adolescent girls and boys had the same risk of infection and fatality rate, while adult premenopausal women had a significantly higher risk of infection than men in the same 5-year age stratum (about 16,000 vs. 12,000 cases). This ratio changed again in peri- and postmenopausal women, with infection susceptibility converging with men. While fatality rates increased continuously with age for both sexes, at 50 years, there was a steeper increase for men. Thus far, these types of intricacies have been largely neglected. Because the hormone 17ß-estradiol influences expression of the human angiotensin-converting enzyme 2 (ACE2) protein, which plays a role in SARS-CoV-2 cellular entry, propensity score matching was performed for the women's sub-cohort, comparing users vs. non-users of estradiol. This retrospective study of hormone therapy in female COVID-19 patients shows that the fatality risk for women > 50 years receiving estradiol therapy (user group) is reduced by more than 50%; the OR was 0.33, 95% CI [0.18, 0.62] and the hazard ratio (HR) was 0.29, 95% CI [0.11,0.76]. For younger, pre-menopausal women (15-49 years), the risk of COVID-19 fatality is the same irrespective of estradiol treatment, probably because of higher endogenous estradiol levels. CONCLUSIONS: As of this writing, still no effective drug treatment is available for COVID-19; since estradiol shows such a strong improvement regarding fatality in COVID-19, we suggest prospective studies on the potentially more broadly protective roles of this naturally occurring hormone.

Statin and rottlerin small-molecule inhibitors restrict colon cancer progression and metastasis via MACC1.

MACC1 (Metastasis Associated in Colon Cancer 1) is a key driver and prognostic biomarker for cancer progression and metastasis in a large variety of solid tumor types, particularly colorectal cancer (CRC). However, no MACC1 inhibitors have been identified yet. Therefore, we aimed to target MACC1 expression using a luciferase reporter-based high-throughput screening with the ChemBioNet library of more than 30,000 compounds. The small molecules lovastatin and rottlerin emerged as the most potent MACC1 transcriptional inhibitors. They remarkably inhibited MACC1 promoter activity and expression, resulting in reduced cell motility. Lovastatin impaired the binding of the transcription factors c-Jun and Sp1 to the MACC1 promoter, thereby inhibiting MACC1 transcription. Most importantly, in CRC-xenografted mice, lovastatin and rottlerin restricted MACC1 expression and liver metastasis. This is-to the best of our knowledge-the first identification of inhibitors restricting cancer progression and metastasis via the novel target MACC1. This drug repositioning might be of therapeutic value for CRC patients.