Multiple sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system (CNS) through neurodegeneration and demyelination, leading to physical/cognitive disability and neurological defects. A viable target for treating MS appears to be the Transient Receptor Potential Ankyrin 1 (TRPA1) calcium channel, whose inhibition has been shown to have beneficial effects on neuroglial cells and protect against demyelination. Using computational drug discovery and data mining methods, we performed an in silico screening study combining chemical graph mining, quantitative structure–activity relationship (QSAR) modeling, and molecular docking techniques in a global prediction model in order to identify repurposable drugs as potent TRPA1 antagonists that may serve as potential treatments for MS patients. After screening the DrugBank database with the combined generated algorithm, 903 repurposable structures were selected, with 97 displaying satisfactory inhibition probabilities and pharmacokinetics. Among the top 10 most probable inhibitors of TRPA1 with good blood brain barrier (BBB) permeability, desvenlafaxine, paliperidone, and febuxostat emerged as the most promising repurposable agents for treating MS. Molecular docking studies indicated that desvenlafaxine, paliperidone, and febuxostat are likely to induce allosteric TRPA1 channel inhibition. Future in vitro and in vivo studies are needed to confirm the biological activity of the selected hit molecules.

Targeting beta-secretase 1, also known as beta-amyloid precursor protein cleaving enzyme (BACE-1) for the inhibition of amyloid production has been intensely studied in the last decades in the search for stopping Alzheimer’s disease progression. The chances of finding a druggable BACE-1 inhibitor may be increased by drug repurposing, as this kind of molecules already fulfil certain requirements needed for further advancement. The study describes the development and application of a data mining method based on molecular frameworks and descriptor values of tested BACE-1 inhibitors, suitable for filtering large compound databases, in order to find molecules with high potency against this protease. 465 compounds extracted from the literature, tested against BACE-1, were analysed for finding molecular descriptor values and frameworks that ensure a high probability of strong inhibition. Resulting conclusions were used for filtering DrugBank database, containing ∼8700 approved and experimental drugs, obtaining 26 structures characterised by 4 major Bemis-Murcko frameworks: 2‐[3‐(2‐cyclohexylethyl)cyclohexyl]‐decahydronaphthalene, 3‐(2‐cyclohexylethyl)‐1,1'‐bi(cyclohexane), [5‐(cyclohexylmethyl)‐8‐cyclopentyloctyl]cyclohexane and (3‐cyclohexylcyclopentyl)cyclohexane. The compounds were further studied by molecular docking using the structure of the closed form of the enzyme, which revealed 7 compounds already involved in trials targeting BACE-1 inhibition, confirming the method’s specificity. The compounds that afforded the best binding energies were DB06925 (tyrosine-protein kinase inhibitor), DB12285 (Verubecestat) and DB08899 (Enzalutamide). Moreover, docking results indicated several other molecules with high in silico inhibitory potency that can be further studied for developing a potential treatment for Alzheimer’s disease.

Background: Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. Methods: This study introduces a machine learning-assisted drug repurposing framework integrating quantitative structure-activity relationship (QSAR) modeling, molecular fingerprints-based classification, molecular docking, and molecular dynamics (MD) simulations. Using this pipeline, we analyzed 4680 investigational and approved drugs from DrugBank database. Results: The machine learning models trained for drug repurposing showed satisfying performance and yielded the identification of saredutant, montelukast, and canertinib as potential AurB inhibitors. The candidates demonstrated strong binding energies, key molecular interactions with critical residues (e.g., Phe88, Glu161), and stable MD trajectories, particularly saredutant, a neurokinin-2 (NK2) antagonist. Conclusions: Beyond identifying potential AurB inhibitors, this study highlights an integrated methodology that can be applied to other challenging drug targets.

PIM-1 kinase is a serine-threonine phosphorylating enzyme with implications in multiple types of malignancies, including prostate, breast, and blood cancers. Developing better search methodologies for PIM-1 kinase inhibitors may be a good strategy to speed up the discovery of an oncological drug approved for targeting this specific kinase. Computer-aided screening methods are promising approaches for the discovery of novel therapeutics, although certain limitations should be addressed. A frequent omission that is encountered in molecular docking is the lack of proper implementation of scoring functions and algorithms on the post-docking results, which usually alters the outcome of the virtual screening. The current study suggests a method for post-processing docking results, expressed either as binding affinity or score, that considers different binding modes of known inhibitors to the studied targets while making use of in vitro data, where available. The docking protocol successfully discriminated between known PIM-1 kinase inhibitors and decoy molecules, although binding energies alone were not sufficient to ensure a successful prediction. Logistic regression models were trained to predict the probability of PIM-1 kinase inhibitory activity based on binding energies and the presence of interactions with identified key amino acid residues. The selected model showed 80.9% true positive and 81.4% true negative rates. The discussed approach can be further applied in large-scale molecular docking campaigns to increase hit discovery success rates.

Protein kinases play a pivotal role in signal transduction, protein synthesis, cell growth and proliferation. Their deregulation represents the basis of pathogenesis for numerous diseases such as cancer and pathologies with cardiovascular, nervous and inflammatory components. Protein kinases are an important target in the pharmaceutical industry, with 48 protein kinase inhibitors (PKI) already approved on the market as treatments for different afflictions including several types of cancer. The present work focuses on facilitating the identification of new PKIs with antitumoral potential through the use of data-mining and basic statistics. The National Cancer Institute (NCI) granted access to the results of numerous previously tested compounds on 60 tumoral cell lines (NCI-60 panel). Our approach involved analyzing the NCI database to identify compounds that presented similar growth inhibition (GI) profiles to that of existing PKIs, but different from approved oncologic drugs with other mechanisms of action, using descriptive statistics and statistical outliers. Starting from 34,000 compounds present in the database, we filtered 400 which displayed selective inhibition on certain cancer cell lines similar to that of several already-approved PKIs.

Starting from a typical case, we try to highlight the main critical elements, both in the diagnostic algorithm and in the therapeutic one. We hereby present a 54-year old patient, known with hepatic steatosis and renal microlithiasis, which undergoes an ultrasonography in ambulatory, on the background of unsystematised abdominal discomfort; the ultrasound reveals the presence of a tumoral mass in the right iliac fossa.