Current Topics in Medicinal Chemistry - Volume 19, Issue 13, 2019

Background: Considering the need for the development of new antitumor drugs, associated with the great antitumor potential of thiophene and thiosemicarbazonic derivatives, in this work we promote molecular hybridization approach to synthesize new compounds with increased anticancer activity. Objective: Investigate the antitumor activity and their likely mechanisms of action of a series of N-substituted 2-(5-nitro-thiophene)-thiosemicarbazone derivatives. Methods: Methods were performed in vitro (cytotoxicity, cell cycle progression, morphological analysis, mitochondrial membrane potential evaluation and topoisomerase assay), spectroscopic (DNA interaction studies), and in silico studies (docking and molecular modelling). Results: Most of the compounds presented significant inhibitory activity; the NCIH-292 cell line was the most resistant, and the HL-60 cell line was the most sensitive. The most promising compound was LNN-05 with IC50 values ranging from 0.5 to 1.9 μg.mL-1. The in vitro studies revealed that LNN-05 was able to depolarize (dose-dependently) the mitochondrial membrane, induceG1 phase cell cycle arrest noticeably, promote morphological cell changes associated with apoptosis in chronic human myelocytic leukaemia (K-562) cells, and presented no topoisomerase II inhibition. Spectroscopic UV-vis and molecular fluorescence studies showed that LNN compounds interact with ctDNA forming supramolecular complexes. Intercalation between nitrogenous bases was revealed through KI quenching and competitive ethidium bromide assays. Docking and Molecular Dynamics suggested that 5-nitro-thiophene-thiosemicarbazone compounds interact against the larger DNA groove, and corroborating the spectroscopic results, may assume an intercalating interaction mode. Conclusion: Our findings highlight 5-nitro-thiophene-thiosemicarbazone derivatives, especially LNN-05, as a promising new class of compounds for further studies to provide new anticancer therapies.

Background: Tubulin polymerization inhibitors interfere with microtubule assembly and their functions lead to mitotic arrest, therefore they are attractive target for design and development of novel anticancer compounds. Objective: The proposed novel and effective structures following the use of three-dimensionalquantitative structure activity relationship (3D-QSAR) pharmacophore based virtual screening clearly demonstrate the high efficiency of this method in modern drug discovery. Methods: Combined computational approach was applied to extract the essential 2D and 3D features requirements for higher activity as well as identify new anti-tubulin agents. Results: The best quantitative pharmacophore model, Hypo1, exhibited good correlation of 0.943 (RMSD=1.019) and excellent predictive power in the training set compounds. Generated model AHHHR, was well mapped to colchicine site and three-dimensional spatial arrangement of their features were in good agreement with the vital interactions in the active site. Total prediction accuracy (0.92 for training set and 0.86 for test set), enrichment factor (4.2 for training set and 4.5 for test set) and the area under the ROC curve (0.86 for training set and 0.94 for the test set), the developed model using Extended Class FingerPrints of maximum diameter 4 (ECFP_4) was chosen as the best model. Conclusion: Developed computational platform provided a better understanding of requirement features for colchicine site inhibitors and we believe the results of this study might be useful for the rational design and optimization of new inhibitors.

Background: The discovery of novel potent molecules for both cancer prevention and treatment has been continuing over the past decade. In recent years, identification of new, potent, and safe anticancer agents through drug repurposing has been regarded as an expeditious alternative to traditional drug development. The cyclooxygenase-2 is known to be over-expressed in several types of human cancer. For this reason cyclooxygenase-2 inhibition may be useful tool for cancer chemotherapy. Objective: The first aim of the study was to develop a validated linear model to predict antitumor activity. Subsequently, applicability of the model for repurposing these cyclooxygenase-2 inhibitors as antitumor compounds to abridge drug development process. Methods: We performed a quantitative structure-toxicity relationship (QSTR) study on a set of coumarin derivatives using a large set of molecular descriptors. A linear model predicting growth inhibition on leukemia CCRF cell lines was developed and consequently validated internally and externally. Accordingly, the model was applied on a set of 143 cyclooxygenase-2 inhibitor coumarin derivatives to explore their antitumor activity. Results: The results indicated that the developed QSAR model would be useful for estimating inhibitory activity of coumarin derivatives on leukemia cell lines. Electronegativity was found to be a prominent property of the molecules in describing antitumor activity. The applicability domain of the developed model highlighted the potential antitumor compounds. Conclusion: The promising results revealed that applied integrated in silico approach for repurposing by combining both the biological activity similarity and the molecular similarity via the computational method could be efficiently used to screen potential antitumor compounds among cyclooxygenase-2 inhibitors.

Background: Lung cancer is the most common among all the types of cancer worldwide with 1.8 million people diagnosed every year, leading to 1.6 million deaths every year according to the American cancer society. The involvement of mutated Anaplasic Lymphoma Kinase (ALK) positive fusion protein in the progression of NSCLC has made a propitious target to inhibit and treat NSCLC. In the present study, the main motif is to screen the most effective inhibitor against ALK protein with the potential pharmacological profile. The ligands selected were docked with Molegro Virtual Docker (MVD) and CEP-37440 (PubChem CID- 71721648) was the best docked pre-established compound with a permissible pharmacological profile. Methods: The selected ligands were docked with Molegro Virtual Docker (MVD). With reference to the obtained compound with the lowest re-rank score, PubChem database was virtually screened to retrieve a large set of similar compounds which were docked to find the compound with higher affinity. Further comparative studies and in silico prediction included pharmacophore studies, proximity energy parameters, ADMET and BOILED-egg plot analysis. Results: CEP-37440 (PubChem CID- 71721648) was the best docked pre-established compound with preferable pharmacological profile and PubChem compound CID-123449015 came out as the most efficient virtually screened inhibitor. Interestingly, the contours of the virtual screened compound PubChem CID- 123449015 fall within our desired high volume cavity of protein having admirable property to control the ALK regulation to prevent carcinogenesis in NSCLC. BOILED-Egg plot analysis depicts that both the compounds have analogous characteristics in the divergent aspects. Moreover, in the evaluations of Blood Brain Barrier, Human Intestinal Absorption, AMES toxicity, and LD50, the virtually screened compound (PubChem CID-123449015) was found within high optimization. Conclusion: These investigations denote that the virtually screened compound (PubChem CID- 123449015) is more efficient to be a better prospective candidate for NSCLC treatment having good pharmacological profile than the pre-established compound CEP-37440 (PubChem CID- 71721648) with low re-rank score. The identified virtually screened compound has high potential to act as an ALK inhibitor and can show promising results in the research of non-small cell lung cancer (NSCLC).

Background: Griseofulvin - a mold metabolite produced by Penisilium griseofulvum is known as an antifungal drug. Objective: Thus, the goal of this paper is the design and synthesis of new griseofulvin derivatives and evaluation of their antifungal activity. Methods: Forty-two new compounds were synthesized using classical methods of organic synthesis and evaluated for their antimicrobial activity by microdilution method. Results: All forty-two new compounds exhibited very good activity against eight tested micromycetes with MIC ranging from 0.0075-0.055 mg/ml and MFC from 0.02-024 mg/ml. All compounds exhibited better activity than reference drugs ketoconazole (7-42 times) and bifonazole (3-16 fold). The most promising was compound 15. The most sensitive fungal was found to be T. viride, while the most resistant, as was expected, was A. fumigatus. It should be mentioned that most of compounds exhibited better activity than griseofulvin. The molecular docking studies revealed that the most active compound have the same hydrophobic and H-bonding interactions with Thr276 residue observed for griseofulvin forming 3 hydrogen bonds while griseofulvin only one. In general, the molecular docking results coincide with experimental. Conclusion: Forty-two giseofulvin derivatives were designed, synthesized and evaluated for antimicrobial activity. These derivatives revealed good antifungal activity, better than reference drugs ketoconazole, bifonazole, and griseofulvin as well.

Background: Though virtual screening methods have proven to be potent in various instances, the technique is practically incomplete to quench the need of drug discovery process. Thus, the quest for novel designing approaches and chemotypes for improved efficacy of lead compounds has been intensified and logistic approaches such as scaffold hopping and hierarchical virtual screening methods were evolved. Till now, in all the previous attempts these two approaches were applied separately. Objective: In the current work, we made a novel attempt in terms of blending scaffold hopping and hierarchical virtual screening. The prime objective is to assess the hybrid method for its efficacy in identifying active lead molecules for emerging PPI target Bcl-2 (B-cell Lymphoma 2). Methods: We designed novel scaffolds from the reported cores and screened a set of 8270 compounds using both scaffold hopping and hierarchical virtual screening for Bcl-2 protein. Also, we enumerated the libraries using clustering, PAINS filtering, physicochemical characterization and SAR matching. Results: We generated a focused library of compounds towards Bcl-2 interface, screened the 8270 compounds and identified top hits for seven families upon fine filtering with PAINS algorithm, features, SAR mapping, synthetic accessibility and similarity search. Our approach retrieved a set of 50 lead compounds. Conclusion: Finding rational approach meeting the needs of drug discovery process for PPI targets is the need of the hour which can be fulfilled by an extended scaffold hopping approach resulting in focused PPI targeting by providing novel leads with better potency.

Background: Alzheimers Disease (AD) is a neurodegenerative disease which is characterized by the deposition of amyloid plaques in the brain- a concept supported by most of the researchers worldwide. The main component of the plaques being amyloid-beta (Aβ42) results from the sequential cleavage of Amyloid precursor protein (APP) by beta and gamma secretase. This present study intends to inhibit the formation of amyloid plaques by blocking the action of gamma secretase protein with Inhibitors (GSI). Methods: A number of Gamma Secretase Inhibitors (GSI) were targeted to the protein by molecular docking. The inhibitor having the best affinity was used as a subject for further virtual screening methods to obtain similar compounds. The generated compounds were docked again at the same docking site on the protein to find a compound with higher affinity to inhibit the protein. The highlights of virtually screened compound consisted of Pharmacophore Mapping of the docking site. These steps were followed by comparative assessments for both the compounds, obtained from the two aforesaid docking studies, which included interaction energy descriptors, ADMET profiling and PreADMET evaluations. Results: 111 GSI classified as azepines, sulfonamides and peptide isosteres were used in the study. By molecular docking an amorpholino-amide, compound (22), was identified to be the high affinity compound GSI along with its better interaction profiles.The virtually screened pubchem compound AKOS001083915 (CID:24462213) shows the best affinity with gamma secretase. Collective Pharmacophore mapping (H bonds, electrostatic profile, binding pattern and solvent accesibility) shows a stable interaction. The resulting ADMETand Descriptor values were nearly equivalent. Conclusion: These compounds identified herein hold a potential as Gamma Secretase inhibitors.According to PreADMET values the compound AKOS001083915 is effective and specific to the target protein. Its BOILED-egg plot analysis infers the compound permeable to blood brain barrier.Comparative study for both the compounds resulted in having nearly equivalent properties. These compounds have the capacity to inhibit the protein which is indirectly responsible for the formation of amyloid plaques and can be further put to in vitro pharmacokinetic and dynamic studies.