Quantitative structure–activity relationship study on potent anticancer compounds against MOLT-4 and P388 leukemia cell lines

Machine learning liquid chromatography retention time prediction model augments the dansylation strategy for metabolite analysis of urine samples

Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC-MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12C-reagent labeled individual samples spiked with a 13C-reagent labeled reference or pooled sample, LC-MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC-RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC-RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC-RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman.

Design, Synthesis, ADME, and Anticancer Studies of Newer N-Aryl-5-(3,4,5-Trifluorophenyl)-1,3,4-Oxadiazol-2-Amines: An Insight into Experimental and Theoretical Investigations

In continuance of our investigation into the anticancer activity of oxadiazoles, we report here the preparation of 10 new 1,3,4-oxadiazole analogues using the scaffold hopping technique. We have prepared the oxadiazoles having a common pharmacophoric structure (oxadiazole linked aryl nucleus) as seen in the reported anticancer agents IMC-038525 (tubulin inhibitor), IMC-094332 (tubulin inhibitor), and FATB (isosteric replacement of the S of thiadiazole with the O of oxadiazole). All of the oxadiazole analogues were predicted for their absorption, distribution, metabolism, and excretion (ADME) profiles and toxicity studies. All of the compounds were found to follow Lipinski's rule of 5 with a safe toxicity profile (Class IV compound) against immunotoxicity, mutagenicity, and toxicity. All of the compounds were synthesized and characterized using spectral data, followed by their anticancer activity tested in a single-dose assay at 10 μM as reported by the National Cancer Institute (NCI US) Protocol against nearly 59 cancer cell lines obtained from nine panels, including non-small-cell lung, ovarian, breast, central nervous system (CNS), colon, leukemia, prostate, and cancer melanoma. N-(2,4-Dimethylphenyl)-5-(3,4,5-trifluorophenyl)-1,3,4-oxadiazol-2-amine (6h) displayed significant anticancer activity against SNB-19, OVCAR-8, and NCI-H40 with percent growth inhibitions (PGIs) of 86.61, 85.26, and 75.99 and moderate anticancer activity against HOP-92, SNB-75, ACHN, NCI/ADR-RES, 786-O, A549/ATCC, HCT-116, MDA-MB-231, and SF-295 with PGIs of 67.55, 65.46, 59.09, 59.02, 57.88, 56.88, 56.53, 56.4, and 51.88, respectively. The compound 6h also registered better anticancer activity than Imatinib against CNS, ovarian, renal, breast, prostate, and melanoma cancers with average PGIs of 56.18, 40.41, 36.36, 27.61, 22.61, and 10.33, respectively. Molecular docking against tubulin, one of the appealing cancer targets, demonstrated an efficient binding within the binding site of combretastatin A4. The ligand 6h (docking score = -8.144 kcal/mol) interacted π-cationically with the residue Lys352 (with the oxadiazole ring). Furthermore, molecular dynamic (MD) simulation studies in complex with the tubulin-combretastatin A4 protein and ligand 6h were performed to examine the dynamic stability and conformational behavior.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Quantitative structure-activity relationship for the oxidation of organic contaminants by peracetic acid using GA-MLR method

Peracetic acid (PAA) is considered as an effective and powerful oxidant for eliminating organic contaminants in wastewater treatment. The second-order rate constant (k_app) for the reaction of PAA with organic contaminants is practically important for evaluating their removal efficiency in wastewater treatment, but only limited numbers of k_app values are available. In this study, 70 organic compounds with various structures were selected, and the k_app of PAA with each organic compound was used to develop two quantitative structure-activity relationship (QSAR) models based on three kinds of descriptors including constitutional, quantum chemical, and the PaDEL descriptors. The genetic algorithm (GA) was applied to select the molecular descriptors, then the models developed by multiple linear regression (MLR). The most important descriptors that explain the reactivity of organic compounds with PAA are the E_HOMO for the model with the constitutional and quantum chemical descriptors. The maxHdsCH and minHdCH₂ are two most important descriptors for the model with only PaDEL descriptors. The developed models can be used to predict k_app for a wide range of organic contaminants. The accuracy of the developed models was proved by the internal, external validation and the Y-scrambling technique. The developed QSAR models using the GA-MLR method can be used as a screening tool for predicting the elimination of organic contaminants by PAA and increasing the understanding of chemical pollutant fate.

Polyethylene-water partition coefficients for polychlorinated biphenyls: Application of QSPR predictions models with experimental validation

The water environmental recalcitrance and ecotoxicity caused by polychlorinated biphenyls (PCBs) are international issues of common concern. The partition coefficients with PCBs between low-density polyethylene (LDPE) and water (K_PE-w) are significant to assess their environmental transport and/or fate in aquatic environment. Even moderately hydrophobic PCBs, however, possess large K_PE-w values, which makes directly experimental measurement labored. Here, based on the combination of quantitative structure-property relationships (QSPRs) and machine-learning algorithms, 10 in-silico models are developed to provide a quick estimate of K_PE-w. These models exhibit good goodness-of-fit (R²_adj: 0.919-0.975), robustness (Q²_LOO: 0.870-0.954) and external prediction performances (Q²_ext: 0.880-0.971), providing a speedy feasibility to close data gaps for limited or absent experimental information, especially the RF-2 model. Particularly, an additional experimental verification is performed for models by a rapid and accurate three-phase system (aqueous phase, surfactant micelles and LDPE). The results of the experiments for 16 PCBs show the modeling results agree well with experimental values, within or approaching the residuals of ± 0.3 log unit. Mechanism interpretations imply that the number of chlorine atoms and ortho-substituted chlorines are the great effect parameters for K_PE-w. This result also heightens interest in measuring and predicting the K_PE-w values of chemicals containing halogen atoms in water.

Azasteroids from diosgenin: Synthesis and evaluation of their antiproliferative activity

In this work, we report the synthesis of two new azasteroids through the modification of the A and B rings of diosgenin 1. The 4-azasteroid derivative 12 was prepared in three steps using the α,β-insaturated-3-keto compound 11 as a precursor, which was first oxidized with KMnO4/KIO4 followed by an oxidative cleavage of ring A, and subsequently cyclized with an ammonium salt, under focused microwave irradiation for a short time of 3 min. A second azasteroid was synthesized, for which the key step was the Beckmann rearrangement of ring B of the oxime 16, affording the lactam-type enamide 17 in good yield. The methodologies developed for the synthesis of the precursors derivatives 10 and 11 contribute to improved yields, compared to those reported in the literature. The biological activity of the azasteroidal compounds 12 and 17 and their precursors has been evaluated in cervical cancer cells (HeLa), colon (HCT-15), and triple negative breast cancer (MDA-MB-231) lines.

Quantitative structure-activity relationship (QSAR) and design of novel ligands that demonstrate high potency and target selectivity as protein tyrosine phosphatase 1B (PTP 1B) inhibitors as an effective strategy used to model anti-diabetic agents

Diabetes and obesity have increased dramatically in recent decades worldwide. Diabetes mainly emerged as a major health care burden disease in both the US and other industrialized countries, among which type II diabetes is the most common. Discovering new and effective treatments for diabetes is currently a high international health priority. In the present study a computational technique was used to model 97 compounds with PTP-1B inhibitory activity, in order to demonstrate the Quantitative structure-activity relationship (QSAR) of these compounds a genetic function approximation (GFA) algorithm was applied to pick the best descriptors and multiple linear regression (MLR) was used to establish a relationship between the PTP-1B inhibitory activity of these compounds and the best molecular descriptors. This QSAR study allowed investigating the influence of very simple and easy-to-compute descriptors in determining biological activities, which shed light on the key factors that aid in the design of novel potent molecules using computer-aided drug design tools.

In silico studies of novel pyrazole-furan and pyrazole-pyrrole carboxamide as fungicides against Sclerotinia sclerotiorum

Background

Pyrazole-furan and pyrazole-pyrrole moiety are among the molecular structures that were found to have an extensive range of applications in the field of medicine and agrochemical due to their wide spectrum of biological activities. These include antimicrobial activity, anti-glaucoma activity, ocular hypertension activity, and antifungal activity.

Results

An in silico study was carried out on 37 compounds of pyrazole-furan and pyrazole-pyrrole carboxamide derivatives against Sclerotinia sclerotiorum. Using Spartan 14 software, optimization of the compounds was performed at the DFT/B3LYP/6-31G* quantum mechanical method. PaDEL descriptor software was used to calculate the molecular descriptors, and a Generic Function Approximation (GFA) was employed to generate the model. Out of four models generated, model 1 was found to be the optimal and has the following statistical parameters; R2 = 0.83485, R2adj = 0.793563, cross-validated R2 = 0.74037, and external R2 = 0.58479. Molecular docking study was carried out between the antifungal compounds, and the binding site of S. sclerotiorum (PDB CODE 2X2S) in which compound 7 was identified to have the highest binding energy of − 7.5kcal/mol. This compound “7” has a strong affinity with the macromolecular target point of the S. sclerotiorum (2x2s), producing H-bond and as well as the hydrophobic interaction at target point of the amino acid residue. Considering compound 7 as our scaffold, four (4) more potent compounds (7a, 7b, 7c, and 7d) were designed using optimization method of structure-based designed which have the following docking score, − 7.7, − 7.8, − 7.7, and − 7.7kcal/mol.

Conclusion

Statistical analyses including variance inflation factor (VIF), mean effect (ME), and applicability domain were conducted on the model. Considering an interpretation of the descriptors given in the discussion, the QSAR model provided an idea of ligand-based design while the molecular docking gave an insight on structure-based design of the new compounds with better activity against S. sclerotiorum in which four (4) compounds 7a, 7b, 7c, and 7d were designed and discovered to be of high quality and have greater binding affinity compared to the one obtained from the literature (compound 7).

QSAR and molecular docking studies of 1,3-dioxoisoindoline-4-aminoquinolines as potent antiplasmodium hybrid compounds

Quantitative structure-activity relationships (QSAR) provides a model that link biological activities of compounds to thier chemical stuctures and molecular docking study reveals the interaction between drug and its target enzyme. These studies were conducted on 1,3-dioxoisoindoline-4-aminoquinolines with the aim of producing a model that could be used to design highly potent antiplasmodium. The compounds were first optimized using Density Functional Theory (DFT) with basis set B3LYP/6-31G∗ then their descriptors calculated. Genetic Function Algorithm (GFA) was used to select descriptors and build the model. One of the four models generated was found to be the best having internal and external squared correlation coefficient (R 2) of 0.9459 and 0.7015 respectively, adjusted squared correlation coefficient (R adj) of 0.9278, leave-one-out (LOO) cross-validation coefficient (Q 2 cv) of 0.8882. The model shows that antiplasmodial activities of 1,3-dioxoisoindoline-4-aminoquinolines depend on ATSC5i, GATS8p, minHBint3, minHBint5, MLFER_A and topoShape descriptors. The model was validated to be predictive, robust and reliable. Hence, it can predict the antiplasmodium activities of new 1,3-dioxoisoindoline-4-aminoquinolines.The docking result indicates strong binding between 1,3-dioxoisoindoline-4-aminoquinolines and Plasmodium falciparum lactate dehydrogenase (pfLDH), and revealed the important of the morpholinyl substituent and amide linker in inhibiting pfLDH. These results could serve as a model for designing novel 1,3-dioxoisoindoline-4-aminoquinolines as inhibitors of PfLDH with higher antiplasmodial activities.

Discovery and development of azasteroids as anticancer agents

Cancer is the second leading cause of death worldwide following cardiovascular diseases. Cancer can be treated by a variety of techniques including surgery, radiation therapy, immunotherapy, and chemotherapy. Choice of the method can be made based on type, physiologic location and the stage of disease progression. Among chemical methods, steroids find broad applications. Azasteroids have N- substitutions in steroidal rings. This structural modification renders azasteroids advantageous in increased effectiveness and reduced side effects. Numerous accounts of cancer efficacy of this family of compounds are available in literature. The progress made in the discovery, synthetic efforts and development of azasteroids as anticancer agents is broadly outlined in this review.

Theoretical QSAR modelling and molecular docking studies of some 4-hydroxyphenylpyruvate dioxygenase (HPPD) enzyme inhibitors potentially used as herbicides

Computational QSAR studies together with molecular docking calculations have been performed on 118 different derivatives of organic molecules potentially used as herbicides. The Becke's three parameter exchange functional (B3) hybrid with Lee, Yang and Parr correlation functional (LYP), termed as B3LYP hybrid function and 6-31G* basis set (B3LYP/6-31G*) were used to develop five models of QSAR using the GFA technique. Models 1, was preferred as the best model because it possesses certain statistical implications (Friedman LOF = 0.52567, R2 = 0.9034, Radjst2= 0.8943, QCV2= 0.87 98 and Rpred.2= 0.8403).” The prepared model was validated internally and externally using training and test inhibitors. The molecular docking studies conducted in this study has actually outline the binding affinities of the 10 selected compounds (5, 25, 26, 27, 29, 35, 52, 55, 98 and 114) which were all in good correlation with their pIC₅₀ values. The binding affinities of the 10 selected compounds range between -5.9 kcal/mol to -10.1 kcal/mol. The compounds 25 and 27 with binding affinities of -10.1 kcal/mol and -9.7 kcal/mol formed the most stable complexes with the receptor (HPPD) as compared to other inhibitors. The complexes of these inhibitors show two most important types of bonding; Hydrogen bonding and hydrophobic bond interaction with the target amino acid residues. The computational QSAR study together with the molecular docking has actually provided a valuable approach for agrochemical researchers in synthesizing and developing new herbicides with high potency against the target enzyme.

Quantitative Structure-Activity Relationship Study of Camptothecin Derivatives as Anticancer Drugs Using Molecular Descriptors

Aim and Objective:

A Quantitative Structure-Activity Relationship (QSAR) has been widely developed to derive a correlation between chemical structures of molecules to their known activities. In the present investigation, QSAR models have been carried out on 76 Camptothecin (CPT) derivatives as anticancer drugs to develop a robust model for the prediction of physicochemical properties.

Materials and Methods:

A training set of 60 structurally diverse CPT derivatives was used to construct QSAR models for the prediction of physiochemical parameters such as Van der Waals surface area (SvdW), Van der Waals Volume (VvdW), Molar Refractivity (MR) and Polarizability (α). The QSAR models were optimized using Multiple Linear Regression (MLR) analysis. A test set of 16 compounds was evaluated using the defined models.

:

The Genetic Algorithm And Multiple Linear Regression Analysis (GA-MLR) were used to select the descriptors derived from the Dragon software to generate the correlation models that relate the structural features to the studied properties.

Results:

QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF) and the Durbin–Watson (DW) statistics.

Conclusion:

The predictive ability of the models was found to be satisfactory. Thus, QSAR models derived from this study may be helpful for modeling and designing some new CPT derivatives and for predicting their activity.

First Report on the Validated Classification-Based Chemometric Modeling of Human Rhinovirus 3C Protease (HRV 3Cpro) Inhibitors

Human rhinoviruses (HRVs), a major cause of common cold and upper respiratory infections, may trigger severe respiratory complications like asthma and COPD. To date, no drugs are available in the market which are designed as novel HRV inhibitors despite the involvement of some pharmaceutical companies' due to economical and clinical constraints. HRV 3C protease may be a potential target for drug design as it plays crucial role in viral RNA replication and virion assembly process. Therefore, designing novel HRV 3Cpro inhibitors is necessary and demanding in the field of antiviral drug design. In this article, statistically significant and validated classification-based QSARs of a series of HRV 3Cpro inhibitors were performed for the first time as per the authors' knowledge. Results suggest that oxopyrrolidine and piperidinone rings are favored whereas carboxybenzyl and unsubstituted benzyl functions may be unfavorable. Moreover, this group, along with cyclic alkyl or aryl ring structures may favor HRV 3Cpro inhibition. These observations may be utilized for the design of a higher active anti-HRV agent in future.

In silico frameworks for systematic pre-clinical screening of potential anti-leukemia therapeutics

INTRODUCTION

Leukemia is a collection of highly heterogeneous cancers that arise from neoplastic transformation and clonal expansion of immature hematopoietic cells. Post-treatment recurrence is high, especially among elderly patients, thus necessitating more effective treatment modalities. Development of novel anti-leukemic compounds relies heavily on traditional in vitro screens which require extensive resources and time. Therefore, integration of in silico screens prior to experimental validation can improve the efficiency of pre-clinical drug development. Areas covered: This article reviews different methods and frameworks used to computationally screen for anti-leukemic agents. In particular, three approaches are discussed including molecular docking, transcriptomic integration, and network analysis. Expert opinion: Today's data deluge presents novel opportunities to develop computational tools and pipelines to screen for likely therapeutic candidates in the treatment of leukemia. Formal integration of these methodologies can accelerate and improve the efficiency of modern day anti-leukemic drug discovery and ease the economic and healthcare burden associated with it.