Antidepressant Drug Design on TCAs and Phenoxyphenylpropylamines utilizing QSAR and Pharmacophore Modeling

Background: Depression is a mental illness caused by the imbalance of important neurotransmitters such as serotonin (5-HT) and norepinephrine (NE). It is a serious neurological disorder that could be treated by antidepressant drugs. Objective: There are two major classes such as TCAs and phenoxyphenylpropylamines which have been proven to be broad-spectrum antidepressant compounds. Several attempts were made to design, synthesize and discover potent antidepressant compounds having the least toxicity and most selectivity towards serotonin and norepinephrine transporters. But there is hardly any drug design based on quantitative structure-activity relationship (QSAR) and pharmacophore modeling attempted yet. Method: In the present study, many TCAs (dibenzoazepine) and phenoxyphenylpropylamine derivatives are taken into consideration for pharmacophore feature generation followed by pharmacophoric distant related descriptors based QSAR modeling. Further, several five new congeners have been designed which are subjected to the prediction of biological activities in terms of serotonin receptor affinity utilizing validated QSAR models developed by us. Results: An important pharmacophoric feature point C followed by the generation of a topography of the TCAs and phenoxyphenylpropylamine has been predicted. The developed pharmacophoric feature-based QSAR can explain 64.2% of the variances of 5-HT receptor antagonism. The best training model has been statistically validated by the prediction of test set compounds. This training model has been used for the prediction of some newly designed congeneric compounds which are comparable with the existed drugs. Conclusion: The newly designed compounds may be proposed for further synthesis and biological screening as antidepressant agents.

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An Introduction to the Basic Concepts in QSAR-Aided Drug Design

Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment - Advances in Chemical and Materials Engineering ◽

10.4018/978-1-4666-8136-1.ch001 ◽

2015 ◽

pp. 1-47 ◽

Cited By ~ 4

Author(s):

Maryam Hamzeh-Mivehroud ◽

Babak Sokouti ◽

Siavoush Dastmalchi

Keyword(s):

Drug Design ◽

Drug Development ◽

Biological Activities ◽

Model Development ◽

Quantitative Structure Activity Relationship ◽

Molecular Structures ◽

New Drugs ◽

Qsar Modeling ◽

Qsar Studies ◽

Modern Drug

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

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An Introduction to the Basic Concepts in QSAR-Aided Drug Design

Oncology ◽

10.4018/978-1-5225-0549-5.ch002 ◽

2017 ◽

pp. 20-66

Author(s):

Maryam Hamzeh-Mivehroud ◽

Babak Sokouti ◽

Siavoush Dastmalchi

Keyword(s):

Drug Design ◽

Drug Development ◽

Biological Activities ◽

Model Development ◽

Quantitative Structure Activity Relationship ◽

Molecular Structures ◽

New Drugs ◽

Qsar Modeling ◽

Qsar Studies ◽

Modern Drug

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Computer-aided Drug Design and Drug Pharmacokinetic Prediction: A Mini-review

Current Pharmaceutical Design ◽

10.2174/1381612824666180903123423 ◽

2018 ◽

Vol 24 (26) ◽

pp. 3014-3019 ◽

Cited By ~ 11

Author(s):

Jamshid Tabeshpour ◽

Amirhossein Sahebkar ◽

Mohammad Reza Zirak ◽

Majid Zeinali ◽

Mahmoud Hashemzaei ◽

...

Keyword(s):

Drug Design ◽

Target Prediction ◽

Pharmacokinetic Profile ◽

Quantitative Structure Activity Relationship ◽

The Body ◽

Qsar Modeling ◽

Computer Aided Drug Design ◽

Rational Drug ◽

Computer Aided ◽

Drug Target Prediction

Prediction of pharmacokinetics and drug targeting is a challenge in drug design. There are different types of software that can help to predict the pharmacokinetic profile of a drug. Quantitative structure-activity relationship (QSAR) modeling is used for drug design with less cost. Drug-excipient interactions are predicted by docking tools. Computerized drug target prediction and docking programs offer additional options to predict potential effects and adverse reactions of a given candidate as well as the best orientation of the compound on the receptor active site. Information on the absorption, distribution, metabolism and excretion of the drug in the body can enhance prediction of drug release and distribution in the blood and central nervous system (CNS). Computer- aided drug design and delivery can help to save the time and cost in the process of rational drug development.

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Prospects for the Treatment of Depression

Australian & New Zealand Journal of Psychiatry ◽

10.1080/j.1440-1614.2006.01814.x ◽

2006 ◽

Vol 40 (5) ◽

pp. 394-401 ◽

Cited By ~ 7

Author(s):

Trevor R. Norman

Keyword(s):

Mechanism Of Action ◽

Serotonin Receptor ◽

Antidepressant Drug ◽

Antidepressant Drugs ◽

Receptor System ◽

Phase Development ◽

Treatment Of Depression ◽

The Face ◽

Short And Long Term ◽

New Compounds

Antidepressant drugs represent the principal form of treatment for major depressive disorder. While there are a plethora of medications available for this task, current drugs have many shortcomings. In the face of these deficiencies there is an ongoing search for new agents. The search has been guided, in part, by drug design based on existing agents and their putative mechanism of action. This has been less than fruitful in addressing inadequacies of existing medications as it has not produced compounds which are novel in terms of pharmacological mechanisms. Recent insights from molecular biological approaches hold promise for the discovery of novel compounds, in particular the so-called neurogenesis hypothesis suggests novel therapeutic approaches. Although significantly modified over the years, the monoamine hypothesis of depression and antidepressant drug action still remains an important driving force behind the development of new compounds. Several recently marketed agents and some in early-phase development tend to conform to these existing mechanistic hypotheses. Clearly the place of these agents in the treatment of depression is dependent on issues such as short- and long-term safety and efficacy. Duloxetine has been developed as a dual monoamine re-uptake inhibitor. Agomelatine is a compound with major effects on the circadian system as well as effects on subtypes of the serotonin receptor system. While the mechanism of action of this compound is not certain, recent evidence would suggest that the drug exerts its effects through antagonist actions at serotonin receptors. Compounds based on the hypothalamic pituitary adrenal axis, substance P antagonism and other neuropeptides have potential application for the treatment of depression but require further development before that potential is realized.

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Comprehensive ensemble in QSAR prediction for drug discovery

BMC Bioinformatics ◽

10.1186/s12859-019-3135-4 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 4

Author(s):

Sunyoung Kwon ◽

Ho Bae ◽

Jeonghee Jo ◽

Sungroh Yoon

Keyword(s):

Drug Discovery ◽

Biological Activities ◽

Quantitative Structure Activity Relationship ◽

Ensemble Method ◽

Single Subject ◽

Learning Approaches ◽

Qsar Modeling ◽

Prediction Limit ◽

Meta Learning ◽

Individual Classifier

Abstract Background Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject. Results The proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at http://data.snu.ac.kr/QSAR/. Conclusions We propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.

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An Analysis of QSAR Research Based on Machine Learning Concepts

Current Drug Discovery Technologies ◽

10.2174/1570163817666200316104404 ◽

2020 ◽

Vol 17 ◽

Cited By ~ 2

Author(s):

Mohammad Reza Keyvanpour ◽

Mehrnoush Barani Shirzad

Keyword(s):

Machine Learning ◽

Learning Strategies ◽

Biological Activities ◽

Analytical Framework ◽

Quantitative Structure Activity Relationship ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Future Research ◽

Qsar Modeling ◽

Chemical Structures

: Quantitative Structure–Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called ‘ML-QSAR‘. This framework has been designed for future research to: a)facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.

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Appraisal of the role of In silico Methods in Pyrazole based drug design

Mini-Reviews in Medicinal Chemistry ◽

10.2174/1389557520666200901184146 ◽

2020 ◽

Vol 20 ◽

Author(s):

Smriti Sharma ◽

Vinayak Bhatia

Keyword(s):

Drug Design ◽

In Silico ◽

Md Simulations ◽

Quantitative Structure Activity Relationship ◽

Field Analysis ◽

Computational Techniques ◽

Neuroprotective Drugs ◽

Pharmacological Potential ◽

Available Information ◽

Anti Fungal

: Pyrazole and its derivatives are a pharmacologically significant active scaffold that have innumerable physiological and pharmacological activities. They can be very good targets for the discovery of novel anti-bacterial, anticancer, anti-inflammatory, anti-fungal, anti-tubercular, antiviral, antioxidant, antidepressant, anti-convulsant and neuroprotective drugs. This review focuses on the importance of in silico manipulations of pyrazole and its derivatives for medicinal chemistry. The authors have discussed currently available information on the use of computational techniques like molecular docking, structure-based virtual screening (SBVS), molecular dynamics (MD) simulations, quantitative structure activity relationship (QSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to drug design using pyrazole moieties. Pyrazole based drug design is mainly dependent on the integration of experimental and computational approaches. The authors feel that more studies need to be done to fully explore the pharmacological potential of the pyrazole moiety and in silico method can be of great help.

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Prospects for Discovering the Secondary Metabolites of Cordyceps Sensu Lato by the Integrated Strategy

Medicinal Chemistry ◽

10.2174/1573406416666191227120425 ◽

2020 ◽

Vol 17 (2) ◽

pp. 97-120

Author(s):

Shabana Bibi ◽

Yuan-Bing Wang ◽

De-Xiang Tang ◽

Mohammad Amjad Kamal ◽

Hong Yu

Keyword(s):

Drug Design ◽

Biological Activities ◽

Design Methods ◽

Chinese Herbs ◽

Active Metabolites ◽

Computer Aided Drug Design ◽

Design Concepts ◽

Conventional Drug ◽

Computer Aided ◽

Design Techniques

: Some species of Cordyceps sensu lato are famous Chinese herbs with significant biological activities, often used as edible food and traditional medicine in China. Cordyceps represents the largest entomopathogenic group of fungi, including 40 genera and 1339 species in three families and incertae sedis of Hypocreales. Objective: Most of the Cordyceps-derivatives have been approved clinically for the treatment of various diseases such as diabetes, cancers, inflammation, cardiovascular, renal and neurological disorders and are used worldwide as supplements and herbal drugs, but there is still need for highly efficient Cordyceps-derived drugs for fatal diseases with approval of the U.S. Food and Drug Administration. Methods: Computer-aided drug design concepts could improve the discovery of putative Cordyceps- derived medicine within less time and low budget. The integration of computer-aided drug design methods with experimental validation has contributed to the successful discovery of novel drugs. Results: This review focused on modern taxonomy, active metabolites, and modern drug design techniques that could accelerate conventional drug design and discovery of Cordyceps s. l. Successful application of computer-aided drug design methods in Cordyceps research has been discussed. Conclusion: It has been concluded that computer-aided drug design techniques could influence the multiple target-focused drug design, because each metabolite of Cordyceps has shown significant activities for the various diseases with very few or no side effects.

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Action of Thioglycosides of 1,2,4-Triazoles and Imidazoles on the Oxidative Stress and Glycosidases in Mice with Molecular Docking

Letters in Drug Design & Discovery ◽

10.2174/1573413715666181212150955 ◽

2019 ◽

Vol 16 (6) ◽

pp. 696-710

Author(s):

Mahmoud Balbaa ◽

Doaa Awad ◽

Ahmad Abd Elaal ◽

Shimaa Mahsoub ◽

Mayssaa Moharram ◽

...

Keyword(s):

Oxidative Stress ◽

Molecular Docking ◽

Active Sites ◽

Biological Activities ◽

Imidazole Ring ◽

Quantitative Structure Activity Relationship ◽

Binding Interaction ◽

Qsar Study

Background: ,2,3-Triazoles and imidazoles are important five-membered heterocyclic scaffolds due to their extensive biological activities. These products have been an area of growing interest to many researchers around the world because of their enormous pharmaceutical scope. Methods: The in vivo and in vitro enzyme inhibition of some thioglycosides encompassing 1,2,4- triazole N1, N2, and N3 and/or imidazole moieties N4, N5, and N6. The effect on the antioxidant enzymes (superoxide dismutase, glutathione S-transferase, glutathione peroxidase and catalase) was investigated as well as their effect on α-glucosidase and β-glucuronidase. Molecular docking studies were carried out to investigate the mode of the binding interaction of the compounds with α- glucosidase and β -glucuronidase. In addition, quantitative structure-activity relationship (QSAR) investigation was applied to find out the correlation between toxicity and physicochemical properties. Results: The decrease of the antioxidant status was revealed by the in vivo effect of the tested compounds. Furthermore, the in vivo and in vitro inhibitory effects of the tested compounds were clearly pronounced on α-glucosidase, but not β-glucuronidase. The IC50 and Ki values revealed that the thioglycoside - based 1,2,4-triazole N3 possesses a high inhibitory action. In addition, the in vitro studies demonstrated that the whole tested 1,2,4-triazole are potent inhibitors with a Ki magnitude of 10-6 and exhibited a competitive type inhibition. On the other hand, the thioglycosides - based imidazole ring showed an antioxidant activity and exerted a slight in vivo stimulation of α-glucosidase and β- glucuronidase. Molecular docking proved that the compounds exhibited binding affinity with the active sites of α -glucosidase and β-glucuronidase (docking score ranged from -2.320 to -4.370 kcal/mol). Furthermore, QSAR study revealed that the HBD and RB were found to have an overall significant correlation with the toxicity. Conclusion: These data suggest that the inhibition of α-glucosidase is accompanied by an oxidative stress action.

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EPID-36. ANTIDEPRESSANT DRUG USE IN GLIOBLASTOMA PATIENTS: AN EPIDEMIOLOGICAL VIEW

Neuro-Oncology ◽

10.1093/neuonc/noaa215.354 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii86-ii86

Author(s):

Dorothee Gramatzki ◽

James Rogers ◽

Marian Neidert ◽

Caroline Hertler ◽

Emilie Le Rhun ◽

...

Keyword(s):

Overall Survival ◽

Multivariate Analysis ◽

Drug Use ◽

Survival Data ◽

Selective Serotonin Reuptake Inhibitors ◽

Methylation Status ◽

Antidepressant Drug ◽

Antidepressant Drugs ◽

Population Level ◽

Disease Course

Abstract PURPOSE Antidepressant drugs have shown anti-tumor activity in preclinical glioblastoma studies. Antidepressant drug use, as well as its association with survival, in glioblastoma patients has not been well characterized on a population level. METHODS Patient characteristics, including the frequency of antidepressant drug use, were assessed in a glioblastoma cohort diagnosed in a 10-year time-frame between 2005 and 2014 in the Canton of Zurich, Switzerland. Cox proportional hazards regression models were applied for multivariate analysis. Kaplan-Meier survival curves were used to estimate overall survival data and the log-rank test was performed for comparisons. RESULTS Four hundred four patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma were included in this study. Sixty-five patients (16.1%) took antidepressant drugs at some point during the disease course. Patients were most commonly prescribed selective serotonin reuptake inhibitors at any time (N=46, 70.8%). Nineteen patients (29.2%) were on antidepressant drugs at the time of their tumor diagnosis. No differences were observed in overall survival between those patients who had taken antidepressants at some point in their disease course and those who had not (p=0.356). These data were confirmed in a multivariate analysis including age, Karnofsky performance status, gender, extent of resection, O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status, and first-line treatment as cofounders (p=0.315). Also, there was no association of use of drugs modulating voltage-dependent potassium channels (citalopram; escitalopram) with survival (p=0.639). CONCLUSIONS This signal-seeking study does not support the hypothesis that antidepressants have antitumor efficacy in glioblastoma on a population level.

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