QSAR and DFT Studies of Some Tacrine-Hydroxamate Derivatives as Inhibitors of Cholinesterase (AChEs) in the Treatment of Alzheimer's Disease

Introduction: This work was devoted to an in silico investigation conducted on twenty-eight Tacrine-hydroxamate derivatives as a potential treatment for Alzheimer's disease using DFT and QSAR modeling techniques. Method(s): The data set was randomly partitioned into a training set (22 compounds) and a test set (6 compounds). Then, fourteen models were built and were used to compute the predicted pIC50 of compounds belonging to the test set. Result(s): Al built models were individualy validated using both internal and external validation methods, including the Y-Randomization test and Golbraikh and Tropsha's model acceptance criteria. Then, one model was selected for its higher R2, R2test, and Q2cv values (R2 = 0.768, R2adj = 0.713, MSE = 0.304, R2test=0.973, Q2cv = 0.615). From these outcomes, the activity of the studied compounds toward the main protease of Cholinesterase (AChEs) seems to be influenced by 4 descriptors, i.e., the total dipole moment of the molecule (mu), number of rotatable bonds (RB), molecular topology radius (MTR) and molecular topology polar surface area (MTPSA). The effect of these descriptors on the activity was studied, in particular, the increase in the total dipole moment and the topological radius of the molecule and the reduction of the rotatable bond and topology polar surface area increase the activity. Conclusion(s): Some newly designed compounds with higher AChEs inhibitory activity have been designed based on the best-proposed QSAR model. In addition, ADMET pharmacokinetic properties were carried out for the proposed compounds, the toxicity results indicate that 7 molecules are nontoxic.Copyright © 2023 Bentham Science Publishers.

TARGETING THE MAIN PROTEASE AND THE SPIKE PROTEIN OF SARS-CoV-2 WITH NATURALLY OCCURRING COMPOUNDS FROM SOME CAMEROONIAN MEDICINAL PLANTS: AN IN-SILICO STUDY FOR DRUG DESIGNING

Despite the social distancing and hygiene rules prescribed by the WHO, the novel Corona-virus is still on the way of a significant rapid rise in deaths. Therefore, identification of chemotherapeutic drugs against Corona Viral Infection all around the world is still requires. Some medicinal plants have a valuable therapeutic effect when mixt with honey, the obtained formulations are preliminary use in Cameroon against viral infection particularly respiratory infections. In this work, we looked for the potential anti-SARS-CoV-2 molecule throw execution of in silico computational studies of six Cameroonian plants intervening in the treatment respiratory infections in apiphytotherapy. AutoDock Vina was used for docking studies against SARS-CoV-2 main protease (Mpro) and spike (SP) proteins. We further conducted of pharmacokinetics properties and the safety profile of compounds with the top score in order to identify the best drug candidates. Totally 100 compounds were screened, of these, eighteen showed high binding affinity against SARS-CoV-2 Mpro and SP. The results suggest the effectiveness of compounds 10 and 17 obtained from Citrus Sinensis as potent drugs against SARS-CoV-2 as they tightly bind to its Mpro and SP with low binding energies. The stability of the two compounds complexed with Mpro and SP was validated through MD simulation. The availability of potent protein inhibitors and diverse of compounds from Cameroon flora scaffolds indicate the feasibility of developing potent Mpro and SP proteins inhibitors as antivirals for COVID-19. Based on further in vivo and in vitro experiments and clinical trials, some of these phytoconstituents could be proposed for effective inhibition of the replication of the SARS-CoV-2.

In Silico Approach for Designing Novel SARS-CoV-2 Inhibitors from Medicinal Plants

Several countries in the world, are still under the threat of SARS-CoV-2 propagation, although the majority of the population has received a vaccine. Some ethno-botanical surveys were conducted to document potential herbal remedies that can be used in the management of the COVID-19 pandemic in Cameroon. Medicinal plants belonging to Cameroon flora could be a source for the discovery of potential inhibitors of SARS-CoV-2Mpro and spike proteins. These two proteins play a pivotal role in mediating viral replication and transcription, making them attractive targets for drug design against SARS-CoV-2. The aim of this in silico study is to evaluate the behavior of the isolated secondary metabolites from Cameroonian medicinal plant species towards SARS-CoV-2Mpro and spike proteins. In the present study, six plant species are selected among the frequently used plants to treat COVID-19 and related symptoms in Cameroon. To highlight the interactions of studied secondary metabolites with SARS-CoV-2Mpro (6lu7) and spike (6m0j) proteins a molecular docking analysis is used. Among the one hundred and twenty-five screened compounds, thirty-five showed high binding affinity against the two targeted proteins. Furthermore, molecular dynamics simulations were performed to support the docking results. Additional investigations, including physicochemical properties, pharmacokinetics, and toxicological profile show that only twelve compounds bind tightly to Mpro (6lu7) and spike (6m0j) proteins and could be considered as promising drug candidates of SARS-CoV-2. The selected twelve compounds are evaluated for their acute and chronic toxicity, possible mutagenic, tumorigenic, irritant, and reproductive effectiveness. The outcomes of this study suggest the possibility of developing potent Mpro and spike proteins inhibitors from naturally occurring compounds belonging to Cameroon flora. © 2022,Physical Chemistry Research. All Rights Reserved.

QSAR study of a series of peptidomimetic derivatives towards MERS-CoV inhibitors

In this study, we report the quantitative structure activity relationships (QSAR) investigation to determine the relationship between the anti-MERS-CoV activity and a set of chemical descriptors computed using ChemSketch, MarvinSketch and ChemOffice software. Herein, the principal components analysis (PCA), multiple linear regression (MLR) and multiple non-linear regression (MNLR) methods were used with the intention to obtain a reliable QSAR model with good predictive capacity. The original data set of 43 peptidomimetic compounds was randomly divided into training and test set of 35 and 8 compounds, respectively. The values obtained by MLR and MNLR for the determination coefficient are 0.777 and 0.813, respectively. The predictive ability of the MLR model was assessed by external validation using the eight compounds of the test set with predicted determination coefficients R2test of 0.655.

QSAR Modelling of Peptidomimetic Derivatives towards HKU4-CoV 3CL(pro) Inhibitors against MERS-CoV

In this paper, we report the relationship between the anti-MERS-CoV activities of the HKU4 derived peptides for some peptidomimetic compounds and various descriptors using the quantitative structure activity relationships (QSAR) methods. The used descriptors were computed using ChemSketch, Marvin Sketch and ChemOffice software. The principal components analysis (PCA) and the multiple linear regression (MLR) methods were used to propose a model with reliable predictive capacity. The original data set of 41 peptidomimetic derivatives was randomly divided into training and test sets of 34 and 7 compounds, respectively. The predictive ability of the best MLR model was assessed by determination coefficient R-2 = 0.691, cross-validation parameter Q(cv)(2) = 0.528 and the external validation parameter R-test(2) = 0.794.