A PIM-1 Kinase Inhibitor Docking Optimization Study Based on Logistic Regression Models and Interaction Analysis.

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.

Improving the odds of success in antitumoral drug development using scoring approaches towards heterocyclic scaffolds.

One of the most commonly discussed topics in the field of drug discovery is the continuous search for anticancer therapies, in which small­molecule development plays an important role. Although a number of techniques have been established over the past decades, one of the main methods for drug discovery and development is still represented by rational, ligand­based drug design. However, the success rate of this method could be higher if not affected by cognitive bias, which renders many potential druggable scaffolds and structures overlooked. The present study aimed to counter this bias by presenting an objective overview of the most important heterocyclic structures in the development of anti­proliferative drugs. As such, the present study analyzed data for 91,438 compounds extracted from the Developmental Therapeutics Program (DTP) database provided by the National Cancer Institute. Growth inhibition data from these compounds tested on a panel of 60 cancer cell lines representing various tissue types (NCI­60 panel) was statistically interpreted using 6 generated scores assessing activity, selectivity, growth inhibition efficacy and potency of different structural scaffolds, Bemis­Murcko skeletons, chemical features and structures common among the analyzed compounds. Of the most commonly used rings, the most prominent anti­proliferative effects were produced by quinoline, tetrahydropyran, benzimidazole and pyrazole, while overall, the optimal results were produced by complex ring structures that originate from natural compounds. These results highlight the impact of certain ring structures on the anti­proliferative effects in drug design. In addition, considering that medicinal chemists usually focus their research on simpler scaffolds the majority of the time with no significant pay­off, the present study indicates several unused complex scaffolds that could be exploited when designing anticancer therapies for optimal results in the fight against cancer.

Comprehensive analysis of drugs to treat SARS­CoV­2 infection: Mechanistic insights into current COVID­19 therapies (Review).

The major impact produced by the severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) focused many researchers attention to find treatments that can suppress transmission or ameliorate the disease. Despite the very fast and large flow of scientific data on possible treatment solutions, none have yet demonstrated unequivocal clinical utility against coronavirus disease 2019 (COVID­19). This work represents an exhaustive and critical review of all available data on potential treatments for COVID­19, highlighting their mechanistic characteristics and the strategy development rationale. Drug repurposing, also known as drug repositioning, and target based methods are the most used strategies to advance therapeutic solutions into clinical practice. Current in silico, in vitro and in vivo evidence regarding proposed treatments are summarized providing strong support for future research efforts.

In Search of Outliers. Mining for Protein Kinase Inhibitors Based on Their Anti-Proliferative NCI-60 Cell Lines Profile.

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.

Computational Drug Repurposing Algorithm Targeting TRPA1 Calcium Channel as a Potential Therapeutic Solution for Multiple Sclerosis.

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 TRAIL.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), also known as Apo2L, has been investigated in the past decade for its promising anticancer activity due to its ability to selectively induce apoptosis in tumoral cells by binding to TRAIL receptors (TRAIL-R). Macromolecules such as agonistic monoclonal antibodies and recombinant TRAIL have not proven efficacious in clinical studies, therefore several small molecules acting as TRAIL-R agonists are emerging in the scientific literature. In this work we focus on systemizing these drug molecules described in the past years, in order to better understand and predict the requirements for a novel anti-tumoral therapy based on the TRAIL-R-induced apoptotic mechanism.

Application of molecular framework-based data-mining method in the search for beta-secretase 1 inhibitors through drug repurposing.

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 (AD) 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. A total of 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 characterized by four 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 seven 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 AD. Communicated by Ramaswamy H. Sarma.