In silico design and molecular docking study of CDK2 inhibitors with potent cytotoxic activity against HCT116 colorectal cancer cell line

Molecular interaction and MD-simulations: investigation of Sizofiran as a promising anti-cancer agent targeting eIF4E in colorectal cancer

CRC has a major global health impact due to high mortality rates. CRC shows high expression of eukaryotic translation initiation factor (eIF4E) protein, the rapid development of lung, bladder, colon, prostate, breast, head, and neck cancer is attributed to the dysregulation of eIF4E making an important target for treatment. Targeting eIF4E-mediated translation is a promising anti-cancer strategy. Many organic compounds that inhibit eIF4E are being studied clinically. The compound Sizofiran has emerged as a promising eIF4E inhibitor candidate, but its exact mechanism of action is unclear. In an effort to close this discrepancy by clarifying the mechanism of the interactions between phytochemical substances and eIF4E, molecular docking and dynamics studies were conducted. Molecular docking studies found Sizofiran (- 12.513 kcal/mol) has the most affinity eIF4E binding energy out of 93 phytochemicals, 5 current drugs, and 4 known inhibitors. This positions it as a top eIF4E inhibitor candidate. An alignment of eIF4E protein sequences from multiple pathogens revealed that the glutamate103 interacting residues are evolutionarily conserved across the different eIF4E proteins. Further insights from 100 ns of MD simulations supported Sizofiran having superior stability and eIF4E inhibition compared to reference compounds. Designed Sizofiran-related compounds showed better activity than the current drugs such as Camptosar, Sorafenib, Regorafenib, Doxorubicin, and Kenpaullone, indicating strong potential to suppress CRC progression by targeting eIF4E. This research aims to significantly aid development of improved eIF4E-targeting drugs for cancer treatment.

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Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00206-3.

Nitroimidazole derivatives potentiated against tumor hypoxia: Design, synthesis, antitumor activity, molecular docking study, and QSAR study

A hypoxic environment occurs predominantly in tumors. During the growth phase of a tumor, it grows until it exceeds its blood supply, leaving regions of the tumor in which the oxygen pressure is dramatically low. They are virtually absent in normal tissues, thus creating perfect conditions for selective bioreductive therapy of tumors. To this aim, a novel series of cytotoxic radiosensitizer agents were synthesized by linking the nitroimidazole scaffold with oxadiazole or triazole rings. The majority of the compounds exhibited moderate to excellent antiproliferative activities toward HCT116 cell line under normoxic and hypoxic conditions. The structure-activity relationship study revealed that compounds containing the free thiol group either in the oxadiazoles 11a,b or the triazoles 21a,b-23a,b demonstrated the strongest antiproliferative activity, which proves that the free thiol group plays a crucial role in the antiproliferative activity of our compounds under both normoxic (half-maximal inhibitory concentration [IC50 ] = 12.50-24.39 µM) and hypoxic conditions (IC50  = 4.69-11.56 µM). Radiosensitizing assay of the four most active cytotoxic compounds 11b and 21-23b assured the capability of the compounds to enhance the sensitivity of the tumor cells to the DNA damaging activity of γ-radiation (IC50  = 2.23-5.18 µM). To further investigate if the cytotoxicity of our most active compounds was due to a specific signaling pathway, the online software SwissTargetPrediction was exploited and a molecular docking study was done that proposed cyclin-dependent kinase 2 (CDK2) enzyme to be the most promising target. The CDK2 inhibitory assay assured this assumption as five out of six compounds demonstrated a comparable inhibitory activity with roscovitine, among which compound 21b showed threefold more potent inhibitory activity in comparison with the reference compound. A further biological evaluation proved compound 21b to have an apoptotic activity and cell cycle arrest activity at the G1 and S phases. During the AutoQSAR analysis, the model demonstrated excellent regression between the predicted and experimental activity with r2  = 0.86. Subsequently, we used the model to predict the activity of the test set compounds that came with r2  = 0.95.

Computational identification of potential inhibitors targeting cdk1 in colorectal cancer

Introduction: Despite improved treatment options, colorectal cancer (CRC) remains a huge public health concern with a significant impact on affected individuals. Cell cycle dysregulation and overexpression of certain regulators and checkpoint activators are important recurring events in the progression of cancer. Cyclin-dependent kinase 1 (CDK1), a key regulator of the cell cycle component central to the uncontrolled proliferation of malignant cells, has been reportedly implicated in CRC. This study aimed to identify CDK1 inhibitors with potential for clinical drug research in CRC. Methods: Ten thousand (10,000) naturally occurring compounds were evaluated for their inhibitory efficacies against CDK1 through molecular docking studies. The stability of the lead compounds in complex with CDK1 was evaluated using molecular dynamics simulation for one thousand (1,000) nanoseconds. The top-scoring candidates' ADME characteristics and drug-likeness were profiled using SwissADME. Results: Four hit compounds, namely, spiraeoside, robinetin, 6-hydroxyluteolin, and quercetagetin were identified from molecular docking analysis to possess the least binding scores. Molecular dynamics simulation revealed that robinetin and 6-hydroxyluteolin complexes were stable within the binding pocket of the CDK1 protein. Discussion: The findings from this study provide insight into novel candidates with specific inhibitory CDK1 activities that can be further investigated through animal testing, clinical trials, and drug development research for CRC treatment.

Identification of hits as anti-obesity agents against human pancreatic lipase via docking, drug-likeness, in-silico ADME(T), pharmacophore, DFT, molecular dynamics, and MM/PB(GB)SA analysis

Obesity, characterized by excessive fat accumulation, is a major health concern. Inhibition of human pancreatic lipase, an enzyme involved in fat digestion, offers a potential strategy for weight loss and obesity treatment. This study aimed to identify polyphenols capable of forming stable complexes with human pancreatic lipase to block its activity. Molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and MMPBGBSA calculations were employed to evaluate ligand binding, stability, and energy profiles. Pharmacophore modeling was also performed to identify key structural features for effective inhibition. Virtual screening identified ZINC000015120539, ZINC000000899200, ZINC000001531702, and ZINC000013340267 as potential candidates, exhibiting favorable binding and stable interactions over 100 ns MD simulations. These findings provide insights into the inhibitory potential of selected polyphenols on human pancreatic lipase and support further experimental investigations for obesity treatment.Communicated by Ramaswamy H. Sarma.

Innovative screening for functional improved aromatic amine derivatives: Toxicokinetics, free radical oxidation pathway and carcinogenic adverse outcome pathway

Aromatic amines, one of the most widely used low-cost antioxidants in rubbers, have been regarded as pollutants with human health concerns. To overcome this problem, this study developed a systematic molecular design, screening, and performance evaluation method to design functionally improved, environmentally friendly and synthesizable aromatic amine alternatives for the first time. Nine of 33 designed aromatic amine derivatives have improved antioxidant property (lower bond dissociation energy of N-H), and their environmental and bladder carcinogenicity impacts were evaluated through toxicokinetic model and molecular dynamics simulation. The environmental fate of the designed AAs-11-8, AAs-11-16, and AAs-12-2 after antioxidation (i.e., peroxyl radicals (ROO·), hydroxyl radicals (HO·), superoxide anion radicals (O₂·^-) and ozonation reaction) was also analyzed. Results showed that the by-products of AAs-11-8 and AAs-12-2 have less toxicity after antioxidation. In addition, human bladder carcinogenicity of the screened alternatives was also evaluated through adverse outcome pathway. The carcinogenic mechanisms were analyzed and verified through amino acid residue distribution characteristics, 3D-QSAR and 2D-QSAR models. AAs-12-2, with high antioxidation property, low environmental impacts and carcinogenicity, was screened as the optimum alternative for 3,5-Dimethylbenzenamine. This study provided theoretical support for designing environmentally friendly and functionally improved aromatic amine alternatives from toxicity evaluation and mechanism analysis.

Exploring biogenic chalcones as DprE1 inhibitors for antitubercular activity via in silico approach

Cases of drug-resistant tuberculosis (TB) have increased worldwide in the last few years, and it is a major threat to global TB control strategies and the human population. Mycobacterium tuberculosis is a common causative agent responsible for increasing cases of TB and as reported by WHO, approximately, 1.5 million death occurred from TB in 2020. Identification of new therapies against drug-resistant TB is an urgent need to be considered primarily. The current investigation aims to find the potential biogenic chalcone against the potential targets of drug-resistant TB via in silico approach. The ligand library of biogenic chalcones was screened against DprE1. Results of molecular docking and in silico ADMET prediction revealed that ZINC000005158606 has lead-like properties against the targeted protein. Pharmacophore modeling was done to identify the pharmacophoric features and their geometric distance present in ZINC000005158606. The binding stability study performed using molecular dynamics (MD) simulation of the DprE1-ZINC000005158606 complex revealed the conformational stability of the complex system over 100 ns with minimum deviation. Further, the in silico anti-TB sensitivity of ZINC000005158606 was found to be higher as compared to the standards against Mycobacterium tuberculosis. The overall in silico investigation indicated the potential of identified hit to act as a lead molecule against Mycobacterium tuberculosis.

Computational Exploration of Anti-cancer Potential of Flavonoids against Cyclin-Dependent Kinase 8: An In Silico Molecular Docking and Dynamic Approach

Over the centuries, cancer has been considered one of the significant health threats. It holds the position in the list of deadliest diseases over the globe. In women, breast cancer is the most common among many cancers and is the second most common cancer all over the world, while lung cancer is the first. Cyclin-dependent kinase 8 (CDK8) has been identified as a critical oncogenic driver that is found in breast cancer and associated with tumor progression. Flavonoids were virtually screened against CDK8 using molecular docking, drug-likeness, ADMET prediction, and a molecular dynamics (MD) simulation approach to determine the potential flavonoid structure against CDK8. The results indicated that ZINC000005854718 showed the highest negative binding affinity of -10.7 kcal/mol with the targeted protein and passed all the drug-likeness parameters. Performed molecular dynamics simulation showed that docked complex systems have good conformational stability over 100 ns in different temperatures (298, 300, 305, 310, and 320 K). The comparison between calculated binding free energy via MM/PB(GB)SA methods and binding affinity calculated via molecular docking suggested tight binding of ZINC000005854718 with targeted protein. The results concluded that ZINC000005854718 has drug-like properties with tight and stable binding with the targeted protein.

Predicting the Anticancer Activity of 2-alkoxycarbonylallyl Esters against MDA-MB-231 Breast Cancer - QSAR, Machine Learning and Molecular Docking

BACKGROUND

The continuous increase in mortality of breast cancer and other forms of cancer due to the failure of current drugs, resistance, and associated side effects calls for the development of novel and potent drug candidates.

METHODS

In this study, we used the QSAR and extreme learning machine models in predicting the bioactivities of some 2-alkoxycarbonylallyl esters as potential drug candidates against MDA-MB-231 breast cancer. The lead candidates were docked at the active site of a carbonic anhydrase target.

RESULTS

The QSAR model of choice satisfied the recommended values and was statistically significant. The R²_pred (0.6572) was credence to the predictability of the model. The extreme learning machine ELM-Sig model showed excellent performance superiority over other models against MDAMB- 231 breast cancer. Compound 22 with a docking score of 4.67 kcal mol^-1 displayed better inhibition of the carbonic anhydrase protein, interacting through its carbonyl bonds.

CONCLUSION

The extreme learning machine's ELM-Sig model showed excellent performance superiority over other models and should be exploited in the search for novel anticancer drugs.

KUALA: a machine learning-driven framework for kinase inhibitors repositioning

The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Another important aspect of binding similarity is the possibility of exploit it for repositioning of drugs on targets of the same family. In this study, we propose our approach called Kinase drUgs mAchine Learning frAmework (KUALA) to automatically identify kinase active ligands by using specific sets of molecular descriptors and provide a multi-target priority score and a repurposing threshold to suggest the best repurposable and non-repurposable molecules. The comprehensive list of all kinase-ligand pairs and their scores can be found at https://github.com/molinfrimed/multi-kinases .

Synthesis and Evaluation of Some New 4H-Pyran Derivatives as Antioxidant, Antibacterial and Anti-HCT-116 Cells of CRC, with Molecular Docking, Antiproliferative, Apoptotic and ADME Investigations

Colorectal cancer oncogenesis is linked to dysbiosis, oxidative stress and overexpression of CDK2. The 4H-pyran scaffold is considered an antitumoral, antibacterial and antioxidant lead as well as a CDK2 inhibitor. Herein, certain 4H-pyran derivatives were evaluated as antibacterial, antioxidant and cytotoxic agents against HCT-116 cells. Derivatives 4g and 4j inhibited all the tested Gram-positive isolates, except for B. cereus (ATCC 14579), with lower IC₅₀ values (µM) than ampicillin. In addition, 4g and 4j demonstrated the strongest DPPH scavenging and reducing potencies, with 4j being more efficient than BHT. In cell viability assays, 4d and 4k suppressed the proliferation of HCT-116 cells, with the lowest IC₅₀ values being 75.1 and 85.88 µM, respectively. The results of molecular docking simulations of 4d and 4k, inhibitory kinase assays against CDK2, along with determination of CDK2 protein concentration and the expression level of CDK2 gene in the lysates of HCT-116 treated cells, suggested that these analogues blocked the proliferation of HCT-116 cells by inhibiting kinase activity and downregulating expression levels of CDK2 protein and gene. Moreover, 4d and 4k were found to induce apoptosis in HCT-116 cells via activation of the caspase-3 gene. Lastly, compounds 4g, 4j, 4d and 4k were predicted to comply with Lipinski’s rule of five, and they are expected to possess excellent physiochemical and pharmacokinetic properties suitable for in vivo bioavailability, as predicted by the SwissADME web tool.

Evaluation of Potential Anti-Hepatitis A Virus 3C Protease Inhibitors Using Molecular Docking

Hepatitis A virus (HAV) infection is a major cause of acute hepatitis worldwide and occasionally causes acute liver failure and can lead to death in the absence of liver transplantation. Although HAV vaccination is available, the prevalence of HAV vaccination is not adequate in some countries. Additionally, the improvements in public health reduced our immunity to HAV infection. These situations motivated us to develop potentially new anti-HAV therapeutic options. We carried out the in silico screening of anti-HAV compounds targeting the 3C protease enzyme using the Schrodinger Modeling software from the antiviral library of 25,000 compounds to evaluate anti-HAV 3C protease inhibitors. Additionally, in vitro studies were introduced to examine the inhibitory effects of HAV subgenomic replicon replication and HAV HA11-1299 genotype IIIA replication in hepatoma cell lines using luciferase assays and real-time RT-PCR. In silico studies enabled us to identify five lead candidates with optimal binding interactions in the active site of the target HAV 3C protease using the Schrodinger Glide program. In vitro studies substantiated our hypothesis from in silico findings. One of our lead compounds, Z10325150, showed 47% inhibitory effects on HAV genotype IB subgenomic replicon replication and 36% inhibitory effects on HAV genotype IIIA HA11-1299 replication in human hepatoma cell lines, with no cytotoxic effects at concentrations of 100 μg/mL. The effects of the combination therapy of Z10325150 and RNA-dependent RNA polymerase inhibitor, favipiravir on HAV genotype IB HM175 subgenomic replicon replication and HAV genotype IIIA HA11-1299 replication showed 64% and 48% inhibitory effects of HAV subgenomic replicon and HAV replication, respectively. We identified the HAV 3C protease inhibitor Z10325150 through in silico screening and confirmed the HAV replication inhibitory activity in human hepatocytes. Z10325150 may offer the potential for a useful HAV inhibitor in severe hepatitis A.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

BACKGROUND

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity.

OBJECTIVE

This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2.

METHOD

We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs.

RESULTS

Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina.

CONCLUSION

All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.