Pharmacophore modeling, virtual screening, and in vitro testing reveal haloperidol, eprazinone, and fenbutrazate as neurokinin receptors ligands

Discovery of a Potential Molluscicide Based on Protein PcRoo in Gill Cilia of Pomacea canaliculata

In modern pesticide discovery, target-based drug design is an attractive and cost-effective approach. Previous studies found that protein rootletin (PcRoo) is a target protein of arecoline, when interacted with Pomacea canaliculata. In this study, we modeled the target protein through threading, and the binding energy between arecoline and protein PcRoo was calculated as -5.02 kcal/mol by molecular docking. Furthermore, two target compounds, baclofen and acedoben, with molluscicidal activity in theory were obtained by virtual screening in database DrugBank. The in vivo bioassay showed that baclofen could induce typical poisoning symptoms on P. canaliculata, which were characterized by weakness of foot muscles and loss of gill cilia, and the LC50 value was 16.2437 mg/L (72 h). Additionally, after 15 mg/L baclofen treatment, the oxygen consumption rate, ammonia excretion rate, and oxygen nitrogen ratio of P. canaliculata declined. Furthermore, the treatment of baclofen also decreased the gene expression level of PcRoo. These trends were the same as the changes after 5 mg/L arecoline treatment. The pharmacophore characteristics were further analyzed, and the results showed that the chemical structures of baclofen and arecoline were correlated in molluscicidal activity. These findings indicate that baclofen has the potential to be used as a molluscicide in agricultural production, and other new molluscicides may be obtained by virtual screening based on protein PcRoo.

In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis

We have performed theoretical calculations with 70 drugs that have been considered in 231 clinical trials as possible candidates to repurpose drugs for schizophrenia based on their interactions with the dopaminergic system. A hypothesis of shared pharmacophore features was formulated to support our calculations. To do so, we have used the crystal structure of the D2-like dopamine receptor in complex with risperidone, eticlopride, and nemonapride. Linagliptin, citalopram, flunarizine, sildenafil, minocycline, and duloxetine were the drugs that best fit with our model. Molecular docking calculations, molecular dynamics outcomes, blood-brain barrier penetration, and human intestinal absorption were studied and compared with the results. From the six drugs selected in the shared pharmacophore features input, flunarizine showed the best docking score with D2, D3, and D4 dopamine receptors and had high stability during molecular dynamics simulations. Flunarizine is a frequently used medication to treat migraines and vertigo. However, its antipsychotic properties have been previously hypothesized, particularly because of its possible ability to block the D2 dopamine receptors.

Ligand-based discovery of small molecules suppressing cancer cell proliferation via autophagic flux inhibition

Autophagy is a conserved self-degradation system closely related to cancer progression. Small molecule inhibitors of autophagy have proven to be efficient tools in cancer therapy and are in high demand. Here we report the discovery of two compounds (LZ02/01) capable of suppressing cancer cell proliferation via inhibiting autophagy flux and promoting apoptosis. Potential autophagy inhibitors were selected based on the pharmacophore model derived from the structures of known autophagy inhibitors. LZ02/01-mediated autophagy flux disruption and apoptosis promotion in breast and hepatocellular carcinoma cells (MCF-7 and Hep3B) were examined using a combination of molecular methods in vitro and in vivo. The synergistic tumor-suppressing effects of LZ02 and chloroquine were validated by adopting a xenograft mice model of human breast cancer. Two potential inhibitors (LZ02/01) targeting an autophagy pathway were discovered from the Enamine database. In both MCF-7 and Hep3B cells, LZ02 and LZ01 had the effect of causing the co-occurrence of autophagic flux inhibition and apoptosis induction, robustly suppressing the growth, proliferation, and cell cycle progression. Further tests revealed that FoxO3a and its downstream target genes regulating autophagy, apoptosis, and cell cycle progression were activated and overexpressed, suggesting such effects of LZ02/01 on autophagy and apoptosis were associated with the activation and overexpression of FoxO3a. In addition, LZ02/01-mediated apoptosis is not independent; it was verified to be promoted by autophagic flux inhibition. Meanwhile, synergistic effects on tumor growth reduction were detected in the xenograft mice model of human breast cancer simultaneously treated with LZ02 and chloroquine. Our findings suggest that LZ01 and LZ02 are potent in suppressing cancer cell proliferation and tumor growth through autophagic flux inhibition and apoptosis promotion. The synergistic anti-cancer effects of LZ02 with chloroquine may provide a rational basis for prospective cancer therapy. KEY MESSAGES: A ligand-based pharmacophore model of high quality is constructed to query hits and two novel scaffold lead compounds LZ01/02 were identified by high-throughput virtual screening. LZ01/02 works to inhibit autophagic flux by attenuating lysosome function. LZ01/02 induces apoptosis through autophagic flux inhibition and apoptosis is the main mechanism to inhibit MCF-7 and Hep3B cancer cell proliferation. The synergistic antitumor growth effects of LZ02 and chloroquine are verified in human xenograft model.

Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures

Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch.

Pharmacophore Modeling and in Silico/in Vitro Screening for Human Cytochrome P450 11B1 and Cytochrome P450 11B2 Inhibitors

Cortisol synthase (CYP11B1) is the main enzyme for the endogenous synthesis of cortisol and its inhibition is a potential way for the treatment of diseases associated with increased cortisol levels, such as Cushing's syndrome, metabolic diseases, and delayed wound healing. Aldosterone synthase (CYP11B2) is the key enzyme for aldosterone biosynthesis and its inhibition is a promising approach for the treatment of congestive heart failure, cardiac fibrosis, and certain forms of hypertension. Both CYP11B1 and CYP11B2 are structurally very similar and expressed in the adrenal cortex. To facilitate the identification of novel inhibitors of these enzymes, ligand-based pharmacophore models of CYP11B1 and CYP11B2 inhibition were developed. A virtual screening of the SPECS database was performed with our pharmacophore queries. Biological evaluation of the selected hits lead to the discovery of three potent novel inhibitors of both CYP11B1 and CYP11B2 in the submicromolar range (compounds 8–10), one selective CYP11B1 inhibitor (Compound 11, IC₅₀ = 2.5 μM), and one selective CYP11B2 inhibitor (compound 12, IC₅₀ = 1.1 μM), respectively. The overall success rate of this prospective virtual screening experiment is 20.8% indicating good predictive power of the pharmacophore models.

Development of pharmacophore models for small molecules targeting RNA: Application to the RNA repeat expansion in myotonic dystrophy type 1

RNA is an important drug target, but current approaches to identify bioactive small molecules have been engineered primarily for protein targets. Moreover, the identification of small molecules that bind a specific RNA target with sufficient potency remains a challenge. Computer-aided drug design (CADD) and, in particular, ligand-based drug design provide a myriad of tools to identify rapidly new chemical entities for modulating a target based on previous knowledge of active compounds without relying on a ligand complex. Herein we describe pharmacophore virtual screening based on previously reported active molecules that target the toxic RNA that causes myotonic dystrophy type 1 (DM1). DM1-associated defects are caused by sequestration of muscleblind-like 1 protein (MBNL1), an alternative splicing regulator, by expanded CUG repeats (r(CUG)^exp). Several small molecules have been found to disrupt the MBNL1-r(CUG)^exp complex, ameliorating DM1 defects. Our pharmacophore model identified a number of potential lead compounds from which we selected 11 compounds to evaluate. Of the 11 compounds, several improved DM1 defects both in vitro and in cells.

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling

Biological effects of small molecules in an organism result from favorable interactions between the molecules and their target proteins. These interactions depend on chemical functionalities, bonds, and their 3D-orientations towards each other. These 3D-arrangements of chemical functionalities that make a small molecule active towards its target can be described by pharmacophore models. In these models, chemical functionalities are represented as so-called features. Commonly, they are obtained either from a set of active compounds or directly from the observed protein-ligand interactions as present in X-ray crystal structures, NMR structures, or docking poses. In this review, we explain the basics of pharmacophore modeling including dataset generation, 3D-representations and conformational analysis of small molecules, pharmacophore model construction, model validation, and its benefits to virtual screening and other applications.