Design of novel anti-cancer drugs targeting TRKs inhibitors based 3D QSAR, molecular docking and molecular dynamics simulation

First report on exploration of structural features of natural compounds (NPACT database) for anti-breast cancer activity (MCF-7): QSAR-based virtual screening, molecular docking, ADMET, MD simulation, and DFT studies

Due to the high toxicity, poor efficacy and resistance associated with current anti-breast cancer drugs, there's growing interest in natural products (NPs) for their potential anti-cancer properties. Computational modelling of NPs to identify key structural features can aid in developing novel natural inhibitors. In this study, we developed statistically significant QSAR models based on NPs from the NPACT database, which have shown potential anticancer activity against the MCF-7 cancer cell lines. All the developed QSAR models were statistically robust, meeting both internal (R 2  = 0.666-0.669, R 2 adj  = 0.657-0.660, Q 2 Loo  = 0.636-0.638) and external (Q 2 F n  = 0.686-0.714, CCC ext = 0.830-0.847) validation criteria. Consequently, they were utilized to virtually screen a series of NPs from the COCONUT database in the search for novel natural inhibitors. Molecular docking studies were conducted on the identified compounds against the human HER2 protein (PDB ID: 3PP0), which is a crucial target in breast cancer. Molecular docking analysis demonstrated that compounds 4608 and 2710 achieved the highest docking scores, with CDOCKER interaction energies of -72.67 kcal/mol and - 72.63 kcal/mol respectively. Compounds 4608 and 2710 were identified as the most promising candidates upon performing triplicate 100 ns MD simulation study using the CHARMM36 force field. DFT studies was performed to evaluate their stability and reactivity as potential drug molecules. This research contributes to the development of new natural inhibitors for breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00266-5.

Design of novel potent selective survivin inhibitors using 2D-QSAR modeling, molecular docking, molecular dynamics, and ADMET properties of new MX-106 hydroxyquinoline scaffold derivatives

Given the critical role of survivin (BIRC5) in tumor cell regulation, developing novel inhibitors represents a promising approach for cancer therapy. This study details the design of innovative survivin inhibitors based on the hydroxyquinoline scaffold of our previously reported lead compound, MX-106. Our study identified nine compounds whose inhibitory activity is expected to be superior to that of the most active molecule in the series. These compounds demonstrated potent suppression of MDA-MB-435 breast cancer cell proliferation in vitro and exhibited enhanced metabolic stability compared to the series' most active member. To evaluate these derivatives as potential survivin inhibitors, we employed a multi-faceted approach combining 2D-QSAR methods, molecular docking, molecular dynamics, and ADMET property assessment. Our molecular modeling studies led to the design of nine novel compounds (Pred1-Pred9) predicted to exhibit potent survivin inhibitory activity based on MLR models. To assess their suitability as drug candidates, we recommend a thorough evaluation of their ADMET properties. These compounds hold promise as innovative anticancer agents targeting survivin, similar to the established MX-106.

Classification models for predicting the bioactivity of pan-TRK inhibitors and SAR analysis

Tropomyosin receptor kinases (TRKs) are important broad-spectrum anticancer targets. The oncogenic rearrangement of the NTRK gene disrupts the extracellular structural domain and epitopes for therapeutic antibodies, making small-molecule inhibitors essential for treating NTRK fusion-driven tumors. In this work, several algorithms were used to construct descriptor-based and nondescriptor-based models, and the models were evaluated by outer 10-fold cross-validation. To find a model with good generalization ability, the dataset was partitioned by random and cluster-splitting methods to construct in- and cross-domain models, respectively. Among the 48 models built, the model with the combination of the deep neural network (DNN) algorithm and extended connectivity fingerprints 4 (ECFP4) descriptors achieved excellent performance in both dataset divisions. The results indicate that the DNN algorithm has a strong generalization prediction ability, and the richness of features plays a vital role in predicting unknown spatial molecules. Additionally, we combined the clustering results and decision tree models of fingerprint descriptors to perform structure-activity relationship analysis. It was found that nitrogen-containing aromatic heterocyclic and benzo heterocyclic structures play a crucial role in enhancing the activity of TRK inhibitors.

An in-silico investigation based on molecular simulations of novel and potential brain-penetrant GluN2B NMDA receptor antagonists as anti-stroke therapeutic agents

GluN2B-induced activation of NMDA receptors plays a key function in central nervous system (CNS) disorders, including Parkinson, Alzheimer, and stroke, as it is strongly involved in excitotoxicity, which makes selective NMDA receptor antagonists one of the potential therapeutic agents for the treatment of neurodegenerative diseases, especially stroke. The present study aims to examine a structural family of thirty brain-penetrating GluN2B N-methyl-D-aspartate (NMDA) receptor antagonists, using virtual computer-assisted drug design (CADD) to discover highly candidate drugs for ischemic strokes. Initially, the physicochemical and ADMET pharmacokinetic properties confirmed that C13 and C22 compounds were predicted as non-toxic inhibitors of CYP2D6 and CYP3A4 cytochromes, with human intestinal absorption (HIA) exceeding 90%, and designed to be as efficient central nervous system (CNS) agents due to the highest probability to cross the blood-brain barrier (BBB). Compared to ifenprodil, a co-crystallized ligand complexed with the transport protein encoded as 3QEL.pdb, we have noticed that C13 and C22 chemical compounds were defined by good ADME-Toxicity profiles, meeting Lipinski, Veber, Egan, Ghose, and Muegge rules. The molecular docking results indicated that C22 and C13 ligands react specifically with the amino acid residues of the NMDA receptor subunit GluN1 and GluN2B. These intermolecular interactions produced between the candidate drugs and the targeted protein in the B chain remain stable over 200 nanoseconds of molecular dynamics simulation time. In conclusion, C22 and C13 ligands are highly recommended as anti-stroke therapeutic drugs due to their safety and molecular stability towards NMDA receptors.Communicated by Ramaswamy H. Sarma.

In silico insights into the design of novel NR2B-selective NMDA receptor antagonists: QSAR modeling, ADME-toxicity predictions, molecular docking, and molecular dynamics investigations

Based on a structural family of thirty-two NR2B-selective N-Methyl-D-Aspartate receptor (NMDAR) antagonists, two phenylpiperazine derivatives labeled C37 and C39 were conceived thanks to molecular modeling techniques, as novel NMDAR inhibitors exhibiting the highest analgesic activities (of pIC50 order) against neuropathic pain, with excellent ADME-toxicity profiles, and good levels of molecular stability towards the targeted protein of NMDA receptor. Initially, the quantitative structure-activity relationships (QSARs) models were developed using multiple linear regression (MLR), partial least square regression (PLSR), multiple non-linear regression (MNLR), and artificial neural network (ANN) techniques, revealing that analgesic activity was strongly correlated with dipole moment, octanol/water partition coefficient, Oxygen mass percentage, electronegativity, and energy of the lowest unoccupied molecular orbital, whose the correlation coefficients of generated models were: 0.860, 0.758, 0.885 and 0.977, respectively. The predictive capacity of each model was evaluated by an external validation with correlation coefficients of 0.703, 0.851, 0.778, and 0.981 respectively, followed by a cross-validation technique with the leave-one-out procedure (CVLOO) with Q2cv of 0.785, more than Y-randomization test, and applicability domain (AD), in addition to Fisher's and Student's statistical tests. Thereafter, ten novel molecules were designed based on MLR QSAR model, then predicted with their ADME-Toxicity profiles and subsequently examined for their similarity to the drug candidates. Finally, two of the most active compounds (C37 and C39) were chosen for molecular docking and molecular dynamics (MD) investigations during 100 ns of MD simulation time in complex with the targeted protein of NMDA receptor (5EWJ.pdb).

QSAR, ADMET, molecular docking, and dynamics studies of 1,2,4-triazine-3(2H)-one derivatives as tubulin inhibitors for breast cancer therapy

Breast cancer remains a leading cause of cancer-related deaths among women globally, necessitating the development of more effective therapeutic agents with minimal side effects. This study explores novel 1,2,4-triazine-3(2H)-one derivatives as potential inhibitors of Tubulin, a pivotal protein in cancer cell division, highlighting a targeted approach in cancer therapy. Using an integrated computational approach, we combined quantitative structure-activity relationship (QSAR) modeling, ADMET profiling, molecular docking, and molecular dynamics simulations to evaluate and predict the efficacy and stability of these compounds. Our QSAR models, developed through rigorous statistical analysis, revealed that descriptors such as absolute electronegativity and water solubility significantly influence inhibitory activity, achieving a predictive accuracy (R2) of 0.849. Molecular docking studies identified compounds with high binding affinities, particularly Pred28, which exhibited the best docking score of - 9.6 kcal/mol. Molecular dynamics simulations conducted over 100 ns provided further insights into the stability of these interactions. Pred28 demonstrated notable stability, with the lowest root mean square deviation (RMSD) of 0.29 nm and root mean square fluctuation (RMSF) values indicative of a tightly bound conformation to Tubulin. The novelty of this work lies in its methodological rigor and the integration of multiple advanced computational techniques to pinpoint compounds with promising therapeutic potential. Our findings advance the current understanding of Tubulin inhibitors and open avenues for the synthesis and experimental validation of these compounds, aiming to offer new solutions for breast cancer treatment.

Exploring isoindolin-1-ones as potential CDK7 inhibitors using cheminformatic tools

In women who die from cancer, breast cancer is the most common cause of death. The development of small molecular scaffolds as specific Cyclin-dependent kinase (CDK) inhibitors is a promising strategy in the discovery of anti-breast cancer drugs. Isoindolin-1-ones are heterocyclic compounds with useful therapeutic properties. In this study, a library of 48 isoindolinones has been virtually screened by molecular docking that showed high binding affinity up to - 10.1 kcal/mol and conventional hydrogen bonding interactions with active amino acid residues of CDK7. The molecular dynamics simulation (MDS), fragment molecular orbital (FMO), density functional theory (DFT), and pharmacokinetics studies of the best two docked scored ligands 7 and 14 have been studied. Examining the ligand root mean square deviation and hydrogen bonding occupancy of the 100 ns MDS trajectory, both ligands 7 and 14 showed docked pose stability. FMO calculations displayed that LYS139 and LYS41 are majorly contributing to the binding interactions with ligands 7 and 14 in the docked poses. DFT studies of ligands 7 and 14 showed high values of global softness and low values of global hardness and chemical potential thus displaying chemically reactive soft molecules and this influences their anti-cancer activity. Our hits exhibited superior qualities to known CDK7 inhibitors, according to the comprehensive pharmacokinetic parameters that were predicted. The results indicate that isoindolin-1-one moieties are good candidates for anti-cancer action and could serve as effective CDK7 inhibitors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00225-0.

3D computer modeling of inhibitors targeting the MCF-7 breast cancer cell line

This study focused on developing new inhibitors for the MCF-7 cell line to contribute to our understanding of breast cancer biology and various experimental techniques. 3D QSAR modeling was used to design new tetrahydrobenzo[4, 5]thieno[2, 3-d]pyrimidine derivatives with good characteristics. Two robust 3D-QSAR models were developed, and their predictive capacities were confirmed through high correlations [CoMFA (Q2 = 0.62, R 2 = 0.90) and CoMSIA (Q2 = 0.71, R 2 = 0.88)] via external validations (R2 ext = 0.90 and R2 ext = 0.91, respectively). These successful evaluations confirm the potential of the models to provide reliable predictions. Six candidate inhibitors were discovered, and two new inhibitors were developed in silico using computational methods. The ADME-Tox properties and pharmacokinetic characteristics of the new derivatives were evaluated carefully. The interactions between the new tetrahydrobenzo[4, 5]thieno[2, 3-d]pyrimidine derivatives and the protein ERα (PDB code: 4XO6) were highlighted by molecular docking. Additionally, MM/GBSA calculations and molecular dynamics simulations provided interesting information on the binding stabilities between the complexes. The pharmaceutical characteristics, interactions with protein, and stabilities of the inhibitors were examined using various methods, including molecular docking and molecular dynamics simulations over 100 ns, binding free energy calculations, and ADME-Tox predictions, and compared with the FDA-approved drug capivasertib. The findings indicate that the inhibitors exhibit significant binding affinities, robust stabilities, and desirable pharmaceutical characteristics. These newly developed compounds, which act as inhibitors to mitigate breast cancer, therefore possess considerable potential as prospective drug candidates.

QSAR modelling, molecular docking, molecular dynamic and ADMET prediction of pyrrolopyrimidine derivatives as novel Bruton's tyrosine kinase (BTK) inhibitors

In recent years, there has been a focus on developing and discovering novel Bruton's tyrosine kinase (BTK) inhibitors, as they offer an effective treatment strategy for B-cell malignancies. BTK plays a crucial role in B cell receptor (BCR)-mediated activation and proliferation by regulating downstream factors such as the NF-κB and MAP kinase pathways. To address this challenge and propose potential therapeutic options for B-cell lymphomas, researchers conducted 2D-QSAR and ADMET studies on pyrrolopyrimidine derivatives that act as inhibitors of the BCR site in cytochrome b. These studies aim to improve and identify new compounds that could serve as more potent potential BTK inhibitors, which would lead to the identification of new drug candidates in this field. In our study, we used 2D-QSAR (multiple linear regression, multiple nonlinear regression, and artificial neural networks), molecular docking, molecular dynamics, and ADMET properties to investigate the potential of 35 pyrrolopyrimidine derivatives as BTK inhibitors. A molecular docking study and molecular dynamics simulations of molecule 13 over 10 ns revealed that it establishes multiple hydrogen bonds with several residues and exhibits frequent stability throughout the simulation period. Based on the results obtained by molecular modeling, we proposed six new compounds (Pred1, Pred2, Pred3, Pred4, Pred5, and Pred6) with highly significant predicted activity by MLR models. A study based on the in silico evaluation of the predicted ADMET properties of the new candidate molecules is strongly recommended to classify these molecules as promising candidates for new anticancer agents specifically designed to target Bruton's tyrosine kinase (BTK) inhibition.

Computational Studies to Understand the Neuroprotective Mechanism of Action Basil Compounds

Neurodegenerative diseases, such as Alzheimer's and Parkinson's, pose a significant global health challenge, emphasizing the need for novel neuroprotective agents. Basil (Ocimum spp.) has been recognized for its therapeutic potential, and numerous studies have reported neuroprotective effects. In this manuscript, we present a computational protocol to extricate the underlying mechanism of action of basil compounds in neuroprotective effects. Molecular docking-based investigation of the chemical interactions between selected bioactive compounds from basil and key neuroprotective targets, including AChE, GSK3β, γ-secretase, and sirtuin2. Our results demonstrate that basil compound myricerone caffeoyl ester possesses a high affinity of -10.01 and -8.85 kcal/mol against GSK3β and γ-secretase, respectively, indicating their potential in modulating various neurobiological processes. Additionally, molecular dynamics simulations were performed to explore the protein-ligand complexes' stability and to analyze the bound basil compounds' dynamic behavior. This comprehensive computational investigation enlightens the putative mechanistic basis for the neuroprotective effects of basil compounds, providing a rationale for their therapeutic use in neurodegenerative disorders after further experimental validation.

In-Silico Design, Synthesis, and Pharmacological Evaluation of Oxadiazole-Based Selective Cyclo-oxygenase-2 Inhibitors

A series of oxadiazole-based five-membered heterocyclic derivatives was designed and synthesized with the intent of exclusive cyclo-oxygenase-2 (COX-2) inhibition to acquire anti-inflammatory activity without the presence of gastric toxicity. Oxadiazole-based novel analogs were designed by using bioisosteric substitutions and were screened against the macromolecular target by using docking-based virtual screening to identify their potential inhibitors. These selective COX-2 inhibitors were further evaluated for their stability within the binding cavity of macromolecular complex by performing molecular dynamic simulation for 100 ns. Selected compounds were synthesized by using Naphthalene-2-yl-acetic acid as a starting material based on the fundamental structure of naphthalene. The naphthalene ring and methylene bridge of naphthalene-2-yl-acetic acid were retained in the rational molecular design by replacing the carboxyl group with biologically significant groups like 1,3,4-oxadiazoles, with the goal of obtaining a novel, superior, and relatively safe anti-inflammatory molecule with better efficacy and optimized pharmacokinetics. Anti-inflammatory as well as analgesic properties of the compounds were evaluated experimentally for their pharmacological efficiency.