Deciphering Imidazoline Off-targets by Fishing in the Class A of GPCR field.

Based on the finding that a central antihypertensive agent with high affinity for I1-type imidazoline receptors - rilmenidine, shows cytotoxic effects on cultured cancer cell lines, it has been suggested that imidazoline receptors agonists might have a therapeutic potential in the cancer therapy. Nevertheless, potential rilmenidine side effects caused by activation of α-adrenoceptors, or other associated receptors and enzymes, might hinder its therapeutic benefits. Considering that human α-adrenoceptors belong to the rhodopsin-like class A of G-protein-coupled receptors (GPCRs) it is reasonable to assume that imidazolines might have the affinity for other receptors from the same class. Therefore, to investigate possible off-target effects of imidazoline ligands we have prepared a reverse docking protocol on class A GPCRs, using imidazoline ligands and their decoys. To verify our in silico results, three ligands with high scores and three ligands with low scores were tested for antagonistic activity on α2 - adrenoceptors.

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches.

BACKGROUND: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. RESULTS: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. CONCLUSION: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

The Forty-Sixth Euro Congress on Drug Synthesis and Analysis: Snapshot †.

The 46th EuroCongress on Drug Synthesis and Analysis (ECDSA-2017) was arranged within the celebration of the 65th Anniversary of the Faculty of Pharmacy at Comenius University in Bratislava, Slovakia from 5-8 September 2017 to get together specialists in medicinal chemistry, organic synthesis, pharmaceutical analysis, screening of bioactive compounds, pharmacology and drug formulations; promote the exchange of scientific results, methods and ideas; and encourage cooperation between researchers from all over the world. The topic of the conference, "Drug Synthesis and Analysis," meant that the symposium welcomed all pharmacists and/or researchers (chemists, analysts, biologists) and students interested in scientific work dealing with investigations of biologically active compounds as potential drugs. The authors of this manuscript were plenary speakers and other participants of the symposium and members of their research teams. The following summary highlights the major points/topics of the meeting.

3D-QSAR, Virtual Screening, Docking and Design of Dual PI3K/mTOR Inhibitors with Enhanced Antiproliferative Activity.

AIM AND OBJECTIVE: Altered activity of PI3K/mTOR signaling pathway is one of the most common aberrations found in various forms of neoplastic lesions. Dual inhibition of PI3K and mTOR represents a reasonably attractive concept in potential cancer treatment. The main aim of this work was to design novel PI3K/mTOR inhibitors with enhanced antiproliferative activity. MATERIALS AND METHODS: 3D-QSAR pharmacophore modeling studies were performed on two groups comprised of 37 and 48 dual PI3K/mTOR inhibitors. Obtained 3D-pharmacophores were used in design of new dual PI3K/mTOR inhibitors. Based on the in silico ADMET data, structure-based virtual screening and docking studies, the most promising novel candidates were selected. RESULTS: Four reliable PLS models with good statistical parameters (q2 = 0.72, r2 pred = 0.93; q2 = 0.81, r2 pred= 0.88 for 3D-QSAR (mTOR) models and q2 = 0.79, r2pred = 0.93; q2 = 0.79, r2 pred = 0.94 for 3D-QSAR (PI3K) models) were obtained and new highly selective and potent dual PI3K/mTOR inhibitors were designed. Further in silico ADMET profiling of the designed compounds selected the most promising novel PI3K/mTOR inhibitors as drug candidates. Results of the 3D-QSAR studies were confirmed by structure-based virtual screening protocol that identified selected designed compounds as a best fit for PI3K and mTOR receptors. Molecular docking studies on PI3K and mTOR crystal structures revealed the key active site residues involved in binding of PI3K/mTOR ligands. CONCLUSION: After combining the results of 3D-QSAR, ADMET profiling, virtual screening and docking, compounds 56-57 and 56-62 were chosen as the most promising new dual PI3K/mTOR inhibitors.

Evaluation of Biological Activity and Computer-Aided Design of New Soft Glucocorticoids.

Soft glucocorticoids are compounds that are biotransformed to inactive and non-toxic metabolites and have fewer side effects than traditional glucocorticoids. A new class of 17ß-carboxamide steroids has been recently introduced by our group. In this study, local anti-inflammatory activity of these derivatives was evaluated by use of the croton oil-induced ear edema test. Glucocorticoids with the highest maximal edema inhibition (MEI) were pointed out, and the systemic side effects of those with the lowest EC50 values were significantly lower in comparison to dexamethasone. A 3D-QSAR model was created and employed for the design of 27 compounds. By use of the sequential combination of ligand-based and structure-based virtual screening, three compounds were selected from the ChEMBL library and used as a starting point for the design of 15 derivatives. Molecular docking analysis of the designed derivatives with the highest predicted MEI and relative glucocorticoid receptor binding affinity (20, 22, 24-1, 25-1, 27, VS7, VS13, and VS14) confirmed the presence of interactions with the glucocorticoid receptor that are important for the activity.

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies.

HIGHLIGHTS Many CNS targets are being explored for multi-target drug designNew databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compoundsQSAR, virtual screening and docking methods increase the potential of rational drug design The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer's disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D1-R/D2-R/5-HT2A -R/H3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.

A combined ligand- and structure-based approach for the identification of rilmenidine-derived compounds which synergize the antitumor effects of doxorubicin.

The clonidine-like central antihypertensive agent rilmenidine, which has high affinity for I1-type imidazoline receptors (I1-IR) was recently found to have cytotoxic effects on cultured cancer cell lines. However, due to its pharmacological effects resulting also from α2-adrenoceptor activation, rilmenidine cannot be considered a suitable anticancer drug candidate. Here, we report the identification of novel rilmenidine-derived compounds with anticancer potential and devoid of α2-adrenoceptor effects by means of ligand- and structure-based drug design approaches. Starting from a large virtual library, eleven compounds were selected, synthesized and submitted to biological evaluation. The most active compound 5 exhibited a cytotoxic profile similar to that of rilmenidine, but without appreciable affinity to α2-adrenoceptors. In addition, compound 5 significantly enhanced the apoptotic response to doxorubicin, and may thus represent an important tool for the development of better adjuvant chemotherapeutic strategies for doxorubicin-insensitive cancers.

Synthesis of the vitamin E amino acid esters with an enhanced anticancer activity and in silico screening for new antineoplastic drugs.

Tocopherols and tocotrienols belong to the family of vitamin E (VE) with the well-known antioxidant properties. For certain α-tocopherol and γ-tocotrienol derivatives used as the lead compounds in this study, antitumor activities against various cancer cell types have been reported. In the course of the last decade, structural analogs of VE (esters, ethers and amides) with an enhanced antiproliferative and proapoptotic activity against various cancer cells were synthesized. Within the framework of this study, seven amino acid esters of α-tocopherol (4a-d) and γ-tocotrienol (6a-c) were prepared using the EDC/DMAP reaction conditions and their ability to inhibit proliferation of the MCF-7 and MDA-MB-231 breast cancer cells and the A549 lung cancer cells was evaluated. Compound 6a showed an activity against all three cell lines (IC50: 20.6µM, 28.6µM and 19µM for the MCF-7, MDA-MB-231 and A549 cells, respectively), while compound 4a inhibited proliferation of the MCF-7 (IC50=8.6µM) and A549 cells (IC50=8.6µM). Ester 4d exerted strong antiproliferative activity against the estrogen-unresponsive, multi-drug resistant MDA-MB-231 breast cancer cell line, with IC50 value of 9.2µM. Compared with the strong activity of compounds 4a, 4d and 6a, commercial α-tocopheryl succinate and γ-tocotrienol showed only a limited activity against all three cell lines, with IC50 values >50µM. Investigation of the cell cycle phase distribution and the cell death induction confirmed an apoptosis of the MDA-MB-231 cells treated with 4d, as well as a synergistic effect of 4d with the known anticancer drug doxorubicin. This result suggests a possibility of a combined therapy of breast cancer in order to improve the therapeutic response and to lower the toxicity associated with a high dose of doxorubicin. The stability study of 4d in human plasma showed that ca. 83% initial concentration of this compound remains in plasma in the course of six hours incubation. The ligand based virtual screening of the ChEMBL database identified new compounds with a potential antiproliferative activity on MCF-7 and on multi-drug resistant MDA-MB 231 breast cancer cells.

Quantitative structure-retention relationship of selected imidazoline derivatives on α1-acid glycoprotein column.

The retention behaviour of 22 selected imidazoline drugs and derivatives was investigated on α1-acid glycoprotein (AGP) column using Sørensen phosphate buffer (pH 7.0) and 2-propanol as organic modifier. Quantitative Structure-Retention Relationships (QSRR) models were built using extrapolated logkw values as well as isocratic retention factors (logk5, logk8, logk10, logk12, logk15 obtained for 5%, 8%, 10%, 12%, and 15%, of 2-propanol in mobile phase, respectively) as dependant variables and calculated physicochemical parameters as independant variables. The established QSRR models were built by stepwise multiple linear regression (MLR) and partial least squares regression (PLS). The performance of the stepwise and PLS models was tested by cross-validation and the external test set prediction. The validated QSRR models were compared and the optimal PLS-QSRR model for logkw and each isocratic retention factors (PLS-QSRR(logk5), PLS-QSRR(logk8), PLS-QSRR(logk10), MLR-QSRR(logk12), MLR-QSRR(logk15)) were selected. The QSRR results were further confirmed by Linear Solvation Energy Relationships (LSER). LSER analysis indicated on hydrogen bond basicity, McGowan volume and excess molar refraction as the most significant parameters for all AGP chromatographic retention factors and logkw values of 22 selected imidazoline drugs and derivatives.

Preclinical discovery and development of maraviroc for the treatment of HIV.

INTRODUCTION: Maraviroc is a first-in-class antiretroviral (ARV) drug acting on a host cell target (CCR5), which blocks the entry of the HIV virus into the cell. Maraviroc is currently indicated for combination ARV treatment in adults infected only with CCR5-tropic HIV-1. AREAS COVERED: This drug discovery case history focuses on the key studies that led to the discovery and approval of maraviroc, as well as on post-launch clinical reports. The article is based on the data reported in published preclinical and clinical studies, conference posters and on drug package data. EXPERT OPINION: The profound understanding of HIV's entry mechanisms has provided a strong biological rationale for targeting the chemokine receptor CCR5. The CCR5-antagonist mariviroc, with its unique mode of action and excellent safety profile, is an important therapeutic option for HIV patients. In general, the authors believe that targeting host factors is a useful approach for combating new and re-emerging transmissible diseases, as well as pathogens that easily become resistant to common antiviral drugs. Maraviroc, offering a potent and safe cellular receptor-mediated pharmacological response to HIV, has paved the way for the development of a new generation of host-targeting antivirals.

Prediction of blood-brain barrier permeation of α-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis.

Imidazoline receptor ligands are a numerous family of biologically active compounds known to produce central hypotensive effect by interaction with both α2-adrenoreceptors (α2-AR) and imidazoline receptors (IRs). Recent hypotheses connect those ligands with several neurological disorders. Therefore some IRs ligands are examined as novel centrally acting antihypertensives and drug candidates for treatment of various neurological diseases. Effective Blood-Brain Barrier (BBB) permeability (P(e)) of 18 IRs/α-ARs ligands and 22 Central Nervous System (CNS) drugs was experimentally determined using Parallel Artificial Membrane Permeability Assay (PAMPA) and studied by the Quantitative-Structure-Permeability Relationship (QSPR) methodology. The dominant molecules/cations species of compounds have been calculated at pH = 7.4. The analyzed ligands were optimized using Density Functional Theory (B3LYP/6-31G(d,p)) included in ChemBio3D Ultra 13.0 program and molecule descriptors for optimized compounds were calculated using ChemBio3D Ultra 13.0, Dragon 6.0 and ADMET predictor 6.5 software. Effective permeability of compounds was used as dependent variable (Y), while calculated molecular parametres were used as independent variables (X) in the QSPR study. SIMCA P+ 12.0 was used for Partial Least Square (PLS) analysis, while the stepwise Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) modeling were performed using STASTICA Neural Networks 4.0. Predictive potential of the formed models was confirmed by Leave-One-Out Cross- and external-validation and the most reliable models were selected. The descriptors that are important for model building are identified as well as their influence on BBB permeability. Results of the QSPR studies could be used as time and cost efficient screening tools for evaluation of BBB permeation of novel α-adrenergic/imidazoline receptor ligands, as promising drug candidates for treatment of hypertension or neurological diseases.