MzDOCK: A free ready-to-use GUI-based pipeline for molecular docking simulations.

Molecular docking is by far the most preferred approach in structure-based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI-based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co-crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK.

TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity.

Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at https://prometheus.farmacia.uniba.it/tisbe.

Pleiotropic Effects of Direct Oral Anticoagulants in Chronic Heart Failure and Atrial Fibrillation: Machine Learning Analysis.

Oral anticoagulant therapy (OAT) for managing atrial fibrillation (AF) encompasses vitamin K antagonists (VKAs, such as warfarin), which was the mainstay of anticoagulation therapy before 2010, and direct-acting oral anticoagulants (DOACs, namely dabigatran etexilate, rivaroxaban, apixaban, edoxaban), approved for the prevention of AF stroke over the last thirteen years. Due to the lower risk of major bleeding associated with DOACs, anticoagulant switching is a common practice in AF patients. Nevertheless, there are issues related to OAT switching that still need to be fully understood, especially for patients in whom AF and heart failure (HF) coexist. Herein, the effective impact of the therapeutic switching from warfarin to DOACs in HF patients with AF, in terms of cardiac remodeling, clinical status, endothelial function and inflammatory biomarkers, was assessed by a machine learning (ML) analysis of a clinical database, which ultimately shed light on the real positive and pleiotropic effects mediated by DOACs in addition to their anticoagulant activity.

Recent Advances in the Discovery of Novel Drugs on Natural Molecules.

Natural products (NPs) are always a promising source of novel drugs for tackling unsolved diseases [...].

Small Molecules for the Treatment of Long-COVID-Related Vascular Damage and Abnormal Blood Clotting: A Patent-Based Appraisal.

People affected by COVID-19 are exposed to, among others, abnormal clotting and endothelial dysfunction, which may result in deep vein thrombosis, cerebrovascular disorders, and ischemic and non-ischemic heart diseases, to mention a few. Treatments for COVID-19 include antiplatelet (e.g., aspirin, clopidogrel) and anticoagulant agents, but their impact on morbidity and mortality has not been proven. In addition, due to viremia-associated interconnected prothrombotic and proinflammatory events, anti-inflammatory drugs have also been investigated for their ability to mitigate against immune dysregulation due to the cytokine storm. By retrieving patent literature published in the last two years, small molecules patented for long-COVID-related blood clotting and hematological complications are herein examined, along with supporting evidence from preclinical and clinical studies. An overview of the main features and therapeutic potentials of small molecules is provided for the thromboxane receptor antagonist ramatroban, the pan-caspase inhibitor emricasan, and the sodium-hydrogen antiporter 1 (NHE-1) inhibitor rimeporide, as well as natural polyphenolic compounds.

From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.

Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84 µM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals.

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives.

INTRODUCTION: The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being. AREAS COVERED: This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies. EXPERT OPINION: The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.

Novel 6-alkyl-bridged 4-arylalkylpiperazin-1-yl derivatives of azepino[4,3-b]indol-1(2H)-one as potent BChE-selective inhibitors showing protective effects against neurodegenerative insults.

Due to the putative role of butyrylcholinesterase (BChE) in regulation of acetylcholine levels and functions in the late stages of the Alzheimer's disease (AD), the potential of selective inhibitors (BChEIs) has been envisaged as an alternative to administration of acetylcholinesterase inhibitors (AChEIs). Starting from our recent findings, herein the synthesis and in vitro evaluation of cholinesterase (ChE) inhibition of a novel series of some twenty 3,4,5,6-tetrahydroazepino[4,3-b]indol-1(2H)-one derivatives, bearing at the indole nitrogen diverse alkyl-bridged 4-arylalkylpiperazin-1-yl chains, are reported. The length of the spacers, as well as the type of arylalkyl group affected the enzyme inhibition potency and BChE/AChE selectivity. Two compounds, namely 14c (IC50 = 163 nM) and 14d (IC50 = 65 nM), bearing at the nitrogen atom in position 6 a n-pentyl- or n-heptyl-bridged 4-phenethylpiperazin-1-yl chains, respectively, proved to be highly potent mixed-type inhibitors of both equine and human BChE isoforms, showing more than two order magnitude of selectivity over AChE. The study of binding kinetics through surface plasmon resonance (SPR) highlighted differences in their BChE residence times (8 and 47 s for 14c and 14d, respectively). Moreover, 14c and 14d proved to hit other mechanisms known to trigger neurodegeneration underlying AD and other CNS disorders. Unlike 14c, compound 14d proved also capable of inhibiting by more than 60% the in vitro self-induced aggregation of neurotoxic amyloid-ß (Aß) peptide at 100 µM concentration. On the other hand, 14c was slightly better than 14d in counteracting, at 1 and 10 µM concentration, glutamate excitotoxicity, due to over-excitation of NMDA receptors, and hydrogen peroxide-induced oxidative stress assessed in neuroblastoma cell line SH-SY5Y. This paper is dedicated to Prof. Marcello Ferappi, former dean of the Faculty of Pharmacy of the University of Bari, in the occasion of his 90th birthday.

Novel Dual-Acting Hybrids Targeting Type-2 Cannabinoid Receptors and Cholinesterase Activity Show Neuroprotective Effects In Vitro and Amelioration of Cognitive Impairment In Vivo.

Alzheimer's disease (AD) is a neurodegenerative form of dementia characterized by the loss of synapses and a progressive decline in cognitive abilities. Among current treatments for AD, acetylcholinesterase (AChE) inhibitors have efficacy limited to symptom relief, with significant side effects and poor compliance. Pharmacological agents that modulate the activity of type-2 cannabinoid receptors (CB2R) of the endocannabinoid system by activating or blocking them have also been shown to be effective against neuroinflammation. Herein, we describe the design, synthesis, and pharmacological effects in vitro and in vivo of dual-acting compounds that inhibit AChE and butyrylcholinesterase (BChE) and target CB2R. Within the investigated series, compound 4g proved to be the most promising. It achieved IC50 values in the low micromolar to submicromolar range against both human cholinesterase isoforms while antagonizing CB2R with Ki of 31 nM. Interestingly, 4g showed neuroprotective effects on the SH-SY5Y cell line thanks to its ability to prevent oxidative stress-induced cell toxicity and reverse scopolamine-induced amnesia in the Y-maze forced alternation test in vivo.