Discovery of a Potent Dual Inhibitor of Aromatase and Aldosterone Synthase.

Estrogen deficiency derived from inhibition of estrogen biosynthesis is a typical condition of postmenopausal women and breast cancer (BCs) patients undergoing antihormone therapy. The ensuing increase in aldosterone levels is considered to be the major cause for cardiovascular diseases (CVDs) affecting these patients. Since estrogen biosynthesis is regulated by aromatase (CYP19A1), and aldosterone biosynthesis is modulated by aldosterone synthase (CYP11B2), a dual inhibitor would allow the treatment of BC while reducing the cardiovascular risks typical of these patients. Moreover, this strategy would help overcome some of the disadvantages often observed in single-target or combination therapies. Following an in-depth analysis of a library of synthesized benzylimidazole derivatives, compound X21 was found to be a potent and selective dual inhibitor of aromatase and aldosterone synthase, with IC50 values of 2.3 and 29 nM, respectively. Remarkably, the compound showed high selectivity with respect to 11ß-hydroxylase (CYP11B1), as well as CYP3A4 and CYP1A2. When tested in cells, X21 showed potent antiproliferative activity against BC cell lines, particularly against the ER+ MCF-7 cells (IC50 of 0.26 ± 0.03 µM at 72 h), and a remarkable pro-apoptotic effect. In addition, the compound significantly inhibited mTOR phosphorylation at its IC50 concentration, thereby negatively modulating the PI3K/Akt/mTOR axis, which represents an escape for the dependency from ER signaling in BC cells. The compound was further investigated for cytotoxicity on normal cells and potential cardiotoxicity against hERG and Nav1.5 ion channels, demonstrating a safe biological profile. Overall, these assays demonstrated that the compound is potent and safe, thus constituting an excellent candidate for further evaluation.

Design, synthesis, biological evaluation and crystal structure determination of dual modulators of carbonic anhydrases and estrogen receptors.

Multi-target compounds have become increasingly important for the development of safer and more effective drug candidates. In this work, we devised a combined ligand-based and structure-based multi-target repurposing strategy and applied it to a series of hexahydrocyclopenta[c]quinoline compounds synthesized previously. The in silico analyses identified human Carbonic Anhydrases (hCA) and Estrogen Receptors (ER) as top scoring candidates for dual modulation. hCA isoforms IX and XII, and ER subtypes ER⍺ and/or ERß are co-expressed in various cancer cell types, including breast and prostate cancer cells. ER⍺ is the primary target of anti-estrogen therapy in breast cancer, and the hCA IX isoform is a therapeutic target in triple-negative breast cancer. ER⍺-mediated transcriptional programs and hCA activity in cancer cells promote favorable microenvironments for cell proliferation. Interestingly, several lines of evidence indicate that the combined modulation of these two targets may provide significant therapeutic benefits. Moving from these first results, two additional hexahydrocyclopenta[c]quinoline derivatives bearing a sulfonamide zinc binding group (hCA) and a phenolic hydroxyl (ER) pharmacophoric group placed at the appropriate locations were designed and synthesized. Interestingly, these compounds were able to directly modulate the activities of both hCA and ER targets. In cell-based assays, they inhibited proliferation of breast and prostate cancer cells with micromolar potency and cell type-selective efficacy. The compounds inhibited hCA activity with nanomolar potency and isoform-selectivity. In transactivation assays, they reduced estrogen-driven ER activity with micro-molar potency. Finally, crystal structures of the synthesized ligands in complex with the two targets revealed that the compounds bind directly to the hCA active site, as well as to the ER ligand-binding domain, providing structural explanation to the observed activity and a rationale for optimization of their dual activity. To the best of our knowledge, this work describes the design, synthesis and biological characterization of the first dual modulators of hCA and ER, laying the ground for the structure-based optimization of their multi-target activity.

Structure-activity exploration of a small-molecule allosteric inhibitor of T790M/L858R double mutant EGFR.

EGFR is a protein kinase whose aberrant activity is frequently involved in the development of non-small lung cancer (NSCLC) drug resistant forms. The allosteric inhibition of this enzyme is currently one among the most attractive approaches to design and develop anticancer drugs. In a previous study, we reported the identification of a hit compound acting as type III allosteric inhibitor of the L858R/T790M double mutant EGFR. Herein, we report the design, synthesis and in vitro testing of a series of analogues of the previously identified hit with the aim of exploring the structure-activity relationships (SAR) around this scaffold. The performed analyses allowed us to identify two compounds 15 and 18 showing improved inhibition of double mutant EGFR with respect to the original hit, as well as interesting antiproliferative activity against H1975 NSCLC cancer cells expressing double mutant EGFR. The newly discovered compounds represent promising starting points for further hit-to-lead optimisation.

Identification of potential biological targets of oxindole scaffolds via in silico repositioning strategies.

Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the "repurposing" concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.

Chemoinformatics Analyses of Tau Ligands Reveal Key Molecular Requirements for the Identification of Potential Drug Candidates against Tauopathies.

Tau is a highly soluble protein mainly localized at a cytoplasmic level in the neuronal cells, which plays a crucial role in the regulation of microtubule dynamic stability. Recent studies have demonstrated that several factors, such as hyperphosphorylation or alterations of Tau metabolism, may contribute to the pathological accumulation of protein aggregates, which can result in neuronal death and the onset of a number of neurological disorders called Tauopathies. At present, there are no available therapeutic remedies able to reduce Tau aggregation, nor are there any structural clues or guidelines for the rational identification of compounds preventing the accumulation of protein aggregates. To help identify the structural properties required for anti-Tau aggregation activity, we performed extensive chemoinformatics analyses on a dataset of Tau ligands reported in ChEMBL. The performed analyses allowed us to identify a set of molecular properties that are in common between known active ligands. Moreover, extensive analyses of the fragment composition of reported ligands led to the identification of chemical moieties and fragment combinations prevalent in the more active compounds. Interestingly, many of these fragments were arranged in recurring frameworks, some of which were clearly present in compounds currently under clinical investigation. This work represents the first in-depth chemoinformatics study of the molecular properties, constituting fragments and similarity profiles, of known Tau aggregation inhibitors. The datasets of compounds employed for the analyses, the identified molecular fragments and their combinations are made publicly available as supplementary material.

LigAdvisor: a versatile and user-friendly web-platform for drug design.

Although several tools facilitating in silico drug design are available, their results are usually difficult to integrate with publicly available information or require further processing to be fully exploited. The rational design of multi-target ligands (polypharmacology) and the repositioning of known drugs towards unmet therapeutic needs (drug repurposing) have raised increasing attention in drug discovery, although they usually require careful planning of tailored drug design strategies. Computational tools and data-driven approaches can help to reveal novel valuable opportunities in these contexts, as they enable to efficiently mine publicly available chemical, biological, clinical, and disease-related data. Based on these premises, we developed LigAdvisor, a data-driven webserver which integrates information reported in DrugBank, Protein Data Bank, UniProt, Clinical Trials and Therapeutic Target Database into an intuitive platform, to facilitate drug discovery tasks as drug repurposing, polypharmacology, target fishing and profiling. As designed, LigAdvisor enables easy integration of similarity estimation results with clinical data, thereby allowing a more efficient exploitation of information in different drug discovery contexts. Users can also develop customizable drug design tasks on their own molecules, by means of ligand- and target-based search modes, and download their results. LigAdvisor is publicly available at https://ligadvisor.unimore.it/.

Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models.

The development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.

Drug Repurposing and Polypharmacology to Fight SARS-CoV-2 Through Inhibition of the Main Protease.

The outbreak of a new coronavirus (SARS-CoV-2), which is responsible for the COVID-19 disease and is spreading rapidly around the world, urgently requires effective therapeutic treatments. In this context, drug repurposing represents a valuable strategy, as it enables accelerating the identification of drug candidates with already known safety profiles, possibly aiding in the late stages of clinical evaluation. Moreover, therapeutic treatments based on drugs with beneficial multi-target activities (polypharmacology) may show an increased antiviral activity or help to counteract severe complications concurrently affecting COVID-19 patients. In this study, we present the results of a computational drug repurposing campaign that aimed at identifying potential inhibitors of the main protease (Mpro) of the SARS-CoV-2. The performed in silico screening allowed the identification of 22 candidates with putative SARS-CoV-2 Mpro inhibitory activity. Interestingly, some of the identified compounds have recently entered clinical trials for COVID-19 treatment, albeit not being assayed for their SARS-CoV-2 antiviral activity. Some candidates present a polypharmacology profile that may be beneficial for COVID-19 treatment and, to the best of our knowledge, have never been considered in clinical trials. For each repurposed compound, its therapeutic relevance and potential beneficial polypharmacological effects that may arise due to its original therapeutic indication are thoroughly discussed.

Structure-Activity Relationships of Hexahydrocyclopenta[c]quinoline Derivatives as Allosteric Inhibitors of CDK2 and EGFR.

Following the discovery of a type III allosteric modulator of cyclin-dependent kinase 2 (CDK2) characterized by a hexahydrocyclopenta[c]quinolone scaffold, three different series of its derivatives were synthesized and biologically evaluated. Docking of the synthesized compounds into the allosteric pocket of CDK2 allowed the elucidation of structure-activity relationships (SARs). Moreover, the compounds were tested on the wild-type epidermal growth factor receptor (EGFR) kinase domain (KD) and its clinically relevant T790M/L858R mutant form. Herein we describe the first SAR investigation of allosteric ligands that bind to the type III inhibitor pocket of CDK2 and EGFR-KD. Although the activity of the synthesized inhibitors needs to be improved, the obtained results provide clear-cut indications about pharmacophore requirements and selectivity determinants. Remarkably, this study led to the identification of a selective T790M/L858R EGFR allosteric inhibitor that is inactive toward both wild-type EGFR and CDK2. Finally, docking into the T790M/L858R EGFR-KD led us to hypothesize that the compounds bind to the double-mutant EGFR-KD by adopting a binding mode different from that in CDK2, thus rationalizing the observed selectivity profile.

Identification of small-molecule EGFR allosteric inhibitors by high-throughput docking.

AIM: The EGFR inhibitors represent the first-line treatment of non-small-cell lung cancer. However, the emergence of resistance urgently requires the development of new inhibitors targeting drug-resistant mutants. METHODOLOGY: A recently released structure of an EGFR kinase domain in complex with an allosteric inhibitor and a mutant protein model derived from it were used to set up a low-cost high-throughput docking protocol for the fast identification of EGFR allosteric inhibitors. CONCLUSION: The virtual screening of commercially available compounds led to the identification of interesting new hit compounds. The most promising hit was confirmed to be a new allosteric inhibitor of wild-type and T790M/L858R double mutant EGFR which was able to inhibit the growth of  non-small-cell lung cancer cell lines.

Exploration and Comparison of the Geometrical and Physicochemical Properties of an αC Allosteric Pocket in the Structural Kinome.

In this work, a comprehensive analysis of the local geometrical and physicochemical properties of a type III allosteric pocket located between the regulatory αC helix and the activation loop of protein kinases was made by comparing available crystal structures in the structural kinome. We first explored the structural kinome to outline the possible conformations of this site. Subsequently we characterized the positions of cocrystallized ligands of the structural kinome with respect to the structural variability of the allosteric site. Then, we searched for kinase structures with similar allosteric site conformation. The search returned 26 kinases with a DFG-in/αC-out conformation potentially prone to bind allosteric inhibitors, as well as different scaffolds that can be useful starting points for the design of new inhibitors. These promising allosteric pockets were probed by performing molecular docking of known active compounds taken from ChEMBL. Interestingly, none of the active compounds reported in ChEMBL had a purely allosteric binding mode, and none of the ATP-competitive ligands had chemical moieties extending into the allosteric pocket in more than two-thirds of the investigated kinases, indicating that the allosteric pocket is accessible but still largely unexplored by available inhibitors. Finally, we compared the physicochemical properties of the allosteric site in the structural kinome and discussed the peculiar and conserved features. These analyses may help the design of allosteric ligands tailored toward the intended kinase(s).

On the Integration of In Silico Drug Design Methods for Drug Repurposing.

Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug-target, target-disease, and ultimately drug-disease associations.