Ligand- and structural-based discovery of potential small molecules that target the colchicine site of tubulin for cancer treatment

In-silico design strategies for tubulin inhibitors for the development of anticancer therapies

INTRODUCTION

Microtubules, composing of α, β-tubulin dimers, are important for cellular processes like proliferation and transport, thereby they become suitable targets for research in cancer. Existing candidates often exhibit off-target effects, necessitating the quest for safer alternatives.

AREA COVERED

The authors explore various aspects of computer-aided drug design (CADD) for tubulin inhibitors. The authors review various techniques like molecular docking, QSAR analysis, molecular dynamic simulations, and machine learning approaches for predicting drug efficacy and modern computational methods utilized in the design and discovery of agents with anticancer potential. This article is based on a comprehensive search of literature utilizing Scopus, PubMed, Google Scholar, and Web of Science, covering the period from 2018 to 2025.

EXPERT OPINION

CADD is crucial in the pursuit of new cancer treatments, particularly by merging computer algorithms with experimental data. CADD predicts small molecule activity against tubulin related targets, expediting drug candidate identification and optimization for enhanced efficacy with reduced toxicity. Challenges include limited predictive models and the need for sophisticated ones to capture complex interactions among targets and pathways. Despite relying on cancer cell line transcriptome profiles, CADD remains pivotal for future anticancer drug discovery efforts.

An N-linked imidazo[1,2-a]pyridine benzoheterobicyclic hybrid inhibits mitosis and cancer cell proliferation by targeting tubulin

Colchicine-site agents have strong potential to be used as tubulin-targeted anticancer agents. In this study, a series of imidazo[1,2-a]pyridine-benzoheterobicyclic hybrids linked by a nitrogen atom as N-heterocyclic imines were designed as colchicine site binding agents. Cell-based assays identified two compounds, 6b (N-(3-(4-chlorophenyl)imidazo[1,2-a](pyridin-2-yl)benzo[d]thiazol-2(3H)-imine) and 6c (N-(6-chloro-3-phenylimidazo[1,2-a]pyridin-2-yl)-1,3-dihydro-2H-benzo[d]imidazol-2-imine), as the most potent antiproliferative compounds against cervical cancer (HeLa) cells. Compound 6c inhibited purified tubulin polymerization in vitro and depolymerized microtubules in HeLa and MCF-7 cells. Additionally, 6c arrested HeLa cells in the mitotic phase, increased the production of reactive oxygen species, and induced cell death. The compound also exhibited a strong binding affinity towards the colchicine binding site on tubulin. Quantum chemical analysis and molecular docking indicated that 6c preferentially binds to tubulin in its iminic tautomeric state. The chemoinformatic analysis further revealed that 6c occupies a unique and therapeutically relevant chemical space with a favorable profile regarding physicochemical properties, ADMET, and pharmacokinetics.

Discovery of a novel microtubule destabilizing agent targeting the colchicine site based on molecular docking

Although a number of microtubule-targeting agents have been used in tumour therapy, their resistance and drug toxicity pose clinical challenges. Microtubule destabilizing agents (MDAs) targeting the colchicine site were found to have the advantage of being able to overcome drug resistance. In our previous studies, we identified a novel MDA from the compound database based on virtual screening methods. Its chemical formula is C23H19N3O5S, abbreviated as C10. In this study, molecular docking methods confirmed that the binding pattern of C10 to tubulin is similar to that of colchicine. Immunofluorescence staining and tubulin polymerization experiments showed that C10 disrupts the microtubule network and reduces the polymerization efficiency of tubulin. Cell proliferation and toxicity assay showed that C10 could effectively inhibit the growth of tumour cells. After 72 h treatment, the semi-inhibitory concentrations of A549, MCF-7 and HepG2 were 18.83 μM, 16.32 μM and 16.92 μM. Colony formation assay and EdU staining also showed that C10 significantly inhibited the proliferative capacity of tumour cells. Meanwhile, it was found by wound healing and transwell assay that the migration and invasive ability of tumour cells were relatively weakened after treatment with C10. Furthermore, flow cytometry analysis and western blot revealed that C10 reduced the expression of the B-cell lymphoma 2 (BCL-2) and upregulated the level of the Bcl-2-associated X protein (BAX), which in turn activated caspase-3 to promote apoptosis. Finally, the vivo studies in animal showed that C10 significantly inhibited the growth of tumour in nude mice without significant drug toxicity. Thus, C10 may be a colchicine binding site inhibitor with anticancer potential. Abbreviations: BAX, bcl-2-associated X protein; BCL-2, the B-cell lymphoma 2; BSA, bovine albumin; CBSIs, colchicine binding site inhibitors; CCK-8, cell counting kits-8; DAPI, 4',6-diamidino-2-phenylindole; DMSO, dimethyl sulfoxide; EdU, 5-ethynyl-2'-deoxyuridine; FITC, fluorescein Isothiocyanate; GAPDH,glyceraldehyde-3-phosphate dehydrogenase; H-E, hematoxylin-eosin; HRP, horseradish Peroxidase; IHC, immunohistochemistry; MDAs, microtubule destabilizing agents; MSAs, microtubule stabilizing agents; MTAs, microtubule-targeting agents; PBS, phosphate buffered saline; PI, propidium Iodide; PVDF, polyvinylidenefluoride.

Highly Accurate and Explainable Predictions of Small-Molecule Antioxidants for Eight In Vitro Assays Simultaneously through an Alternating Multitask Learning Strategy

Small molecule antioxidants can inhibit or retard oxidation reactions and protect against free radical damage to cells, thus playing a key role in food, cosmetics, pharmaceuticals, the environment, as well as materials. Experimentally driven antioxidant discovery is a major paradigm, and computationally assisted antioxidants are rarely reported. In this study, a functional-group-based alternating multitask self-supervised molecular representation learning method is proposed to simultaneously predict the antioxidant activities of small molecules for eight commonly used in vitro antioxidant assays. Extensive evaluation results reveal that compared with the baseline models, the multitask FG-BERT model achieves the best overall predictive performance, with the highest average F1, BA, ROC-AUC, and PRC-AUC values of 0.860, 0.880, 0.954, and 0.937 for the test sets, respectively. The Y-scrambling testing results further demonstrate that such a deep learning model was not constructed by accident and that it has reliable predictive capabilities. Additionally, the excellent interpretability of the multitask FG-BERT model makes it easy to identify key structural fragments/groups that contribute significantly to the antioxidant effect of a given molecule. Finally, an online antioxidant activity prediction platform called AOP (freely available at https://aop.idruglab.cn/) and its local version were developed based on the high-quality multitask FG-BERT model for experts and nonexperts in the field. We anticipate that it will contribute to the discovery of novel small-molecule antioxidants.

Isomer-sourced structure iteration methods for in silico development of inhibitors: Inducing GTP-bound NRAS-Q61 oncogenic mutations to an "off-like" state

The NRAS-mutant subset of melanoma represent some of the most aggressive and deadliest types associated with poor overall survival. Unfortunately, for more than 40 years, no therapeutic agent directly targeting NRAS mutations has been clinically approved. In this work, based on microsecond scale molecular dynamics simulations, the effect of Q61 mutations on NRAS conformational characteristics is revealed at the atomic level. The GTP-bound NRAS-Q61R and Q61K mutations show a specific targetable pocket between Switch-II and α-helix 3 whereas the NRAS-Q61L non-polar mutation category shows a different targetable pocket. Moreover, a new isomer-sourced structure iteration method has been developed for the in silico design of potential inhibitor prototypes for oncogenes. We show the possibility of a designed prototype HM-387 to target activated NRAS-Q61R and that it can gradually induce the transition from the activated NRAS-Q61R to an "off-like" state.

Structure-based discovery and rational design of microtubule-targeting agents

Microtubule-targeting agents (MTAs) have demonstrated remarkable efficacy as antitumor, antifungal, antiparasitic, and herbicidal agents, finding applications in the clinical, veterinary, and agrochemical industry. Recent advances in tubulin and microtubule structural biology have provided powerful tools that pave the way for the rational design of innovative small-molecule MTAs for future basic and applied life science applications. In this mini-review, we present the current status of the tubulin and microtubule structural biology field, the recent impact it had on the discovery and rational design of MTAs, and exciting avenues for future MTA research.

Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors

Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP ( https://kipp.idruglab.cn ) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.

Mechanism of anticancer effect of ETP-45658, a PI3K/AKT/mTOR pathway inhibitor on HT-29 Cells

The PI3K pathway plays a crucial role in tumor cell proliferation across various cancers, including colon cancer, making it a promising treatment target. This study aims to investigate the antiproliferative activity of ETP-45658, a PI3K/AKT/mTOR pathway inhibitor, on colon cancer and elucidate the underlying mechanisms. HT-29 colon cancer cells were treated with varying doses of ETP 45658 and its cytotoxic effect assessed using the XTT cell viability assay.ELISA was also used to measure TAS, TOS, Bax, BCL-2, cleaved caspase 3, cleaved PARP, and 8-oxo-dG levels. Flow cytometry was performed to investigate apoptosis, cell cycle, caspase 3/7 activity, and mitochondrial membrane potential. Additionally, following the administration of DAPI (4,6-diamidino-2-phenylindole) dye, the cells were visualized using an immunofluorescence microscope. It was observed that ETP-45658 exerted a dose-dependent and statistically significant antiproliferative effect on HT-29 colon cancer cells. Further investigations using the IC50 dose showed that ETP-45658 decreased TAS levels and increased TOS levels and revealed that it upregulated apoptotic proteins while downregulating anti-apoptotic proteins. Our findings also showed that it increased Annexin V binding, arrested the cell cycle at G0/G1 phase, induced caspase 3/7 activity, impaired mitochondrial membrane potential, and ultimately triggered apoptosis in HT-29 cells. ETP-45658 shows promise against colon cancer by inducing cell death, and oxidative stress, and arresting the cell cycle. Targeting the PI3K/AKT/mTOR pathway with ETP-45658 offers exciting potential for colon cancer treatment.

Computational Approaches to the Rational Design of Tubulin-Targeting Agents

Microtubules are highly dynamic polymers of α,β-tubulin dimers which play an essential role in numerous cellular processes such as cell proliferation and intracellular transport, making them an attractive target for cancer and neurodegeneration research. To date, a large number of known tubulin binders were derived from natural products, while only one was developed by rational structure-based drug design. Several of these tubulin binders show promising in vitro profiles while presenting unacceptable off-target effects when tested in patients. Therefore, there is a continuing demand for the discovery of safer and more efficient tubulin-targeting agents. Since tubulin structural data is readily available, the employment of computer-aided design techniques can be a key element to focus on the relevant chemical space and guide the design process. Due to the high diversity and quantity of structural data available, we compiled here a guide to the accessible tubulin-ligand structures. Furthermore, we review different ligand and structure-based methods recently used for the successful selection and design of new tubulin-targeting agents.

Consensus Virtual Screening Protocol Towards the Identification of Small Molecules Interacting with the Colchicine Binding Site of the Tubulin-microtubule System

Modification of the tubulin-microtubule (Tub-Mts) system has generated effective strategies for developing different treatments for cancer. A huge amount of clinical data about inhibitors of the tubulin-microtubule system have supported and validated the studies on this pharmacological target. However, many tubulin-microtubule inhibitors have been developed from representative and common scaffolds that cover a small region of the chemical space with limited structural innovation. The main goal of this study is to develop the first consensus virtual screening protocol for natural products (ligand- and structure-based drug design methods) tuned for the identification of new potential inhibitors of the Tub-Mts system. A combined strategy that involves molecular similarity, molecular docking, pharmacophore modeling, and in silico ADMET prediction has been employed to prioritize the selections of potential inhibitors of the Tub-Mts system. Five compounds were selected and further studied using molecular dynamics and binding energy predictions to characterize their possible binding mechanisms. Their structures correspond to 5-[2-(4-hydroxy-3-methoxyphenyl) ethyl]-2,3-dimethoxyphenol (1), 9,10-dihydro-3,4-dimethoxy-2,7-phenanthrenediol (2), 2-(3,4-dimethoxyphenyl)-5,7-dihydroxy-6-methoxy-4H-1-benzopyran-4-one (3), 13,14-epoxyparvifoline-4',5',6'-trimethoxybenzoate (4), and phenylmethyl 6-hydroxy-2,3-dimethoxybenzoate (5). Compounds 1-3 have been associated with literature reports that confirm their activity against several cancer cell lines, thus supporting the utility of this protocol.

Discovery of potent microtubule-destabilizing agents targeting for colchicine site by virtual screening, biological evaluation, and molecular dynamics simulation

Microtubule has been considered as attractive therapeutic target for various cancers. Although numerous of chemically diverse compounds targeting to colchicine site have been reported, none of them was approved by Food and Drug Administration. In this investigation, the virtual screening methods, including pharmacophore model, molecular docking, and interaction molecular fingerprints similarity, were applied to discover novel microtubule-destabilizing agents from database with 324,474 compounds. 22 compounds with novel scaffolds were identified as microtubule-destabilizing agents, and then submitted to the biological evaluation. Among these 22 hits, hit4 with novel scaffold represents the best anti-proliferative activity with IC₅₀ ranging from 4.51 to 14.81 μM on four cancer cell lines. The in vitro assays reveal that hit4 can effectively inhibit tubulin assembly, and disrupt the microtubule network in MCF-7 cell at a concentration-dependent manner. Finally, the molecular dynamics simulation analysis exhibits that hit4 can stably bind to colchicine site, interact with key residues, and induce αT5 and βT7 regions changes. The values of ΔG_bind for the tubulin-colchicine and tubulin-hit4 are -172.9±10.5 and -166.0±12.6 kJ·mol^-1, respectively. The above results indicate that the hit4 is a novel microtubule destabilizing agent targeting to colchicine-binding site, which could be developed as a promising tubulin polymerization inhibitor with higher activity for cancer therapy.

FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction

Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especially for drug design and discovery. In this study, we advanced a novel deep learning architecture, termed FP-GNN (fingerprints and graph neural networks), which combined and simultaneously learned information from molecular graphs and fingerprints for molecular property prediction. To evaluate the FP-GNN model, we conducted experiments on 13 public datasets, an unbiased LIT-PCBA dataset and 14 phenotypic screening datasets for breast cell lines. Extensive evaluation results showed that compared to advanced deep learning and conventional machine learning algorithms, the FP-GNN algorithm achieved state-of-the-art performance on these datasets. In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model. Analysis of the anti-noise ability and interpretation ability also indicated that FP-GNN was competitive in real-world situations. Collectively, FP-GNN algorithm can assist chemists, biologists and pharmacists in predicting and discovering better molecules with desired functions or properties.

Ligand- and structure-based identification of novel CDK9 inhibitors for the potential treatment of leukemia

Cyclin-dependent kinase 9 (CDK9) plays a vital role in controlling cell transcription and has been an attractive target for cancer treatment. Herein, ten predictive models derived from 1330 unique molecules against CDK9 were constructed based on molecular fingerprints and graphs using two conventional machine learning and four deep learning methods. The evaluation results showed that FP-GNN deep learning architecture performed best for CDK9 inhibitors prediction with the highest BA and F1 values of 0.681 and 0.912 for testing set. We then performed virtual screening to identify new CDK9 inhibitors by incorporating the optimal established predictive model and molecular docking. Five compounds were identified to show broad anticancer activity against various cancer cell lines through bioassays. For example, C9 exhibited antiproliferative activities against HeLa, MOLM-13 and MDA-MB-231 with IC₅₀ values of 2.53, 3.92 and 11.65 μM. Kinase inhibition assay results demonstrated that these compounds displayed submicromolar (214 ∼ 504 nM) inhibitory activities against CDK9. Further cellular mechanism evaluation revealed that C9 suppressed the activity of CDK9 and interfered with the expression of Mcl-1 and cleaved PARP in MOLM-13 cells, resulting in the induction of cellular apoptosis. In addition, C9 displayed a good stability in rat liver microsomes, artificial gastrointestinal fluid and plasm. An online platform (called DEEPCDK9Pred) was developed based on the FP-GNN models to predict or design new CDK9 inhibitors. Collectively, our findings demonstrated that FP-GNN algorithm can achieve accurate prediction of CDK9 inhibitors and the subsequent discovery of C9 as a new potential CDK9 inhibitor deserves further structural modification for the treatment of leukemia.

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors

PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.

DenovoProfiling: A webserver for de novo generated molecule library profiling

Various deep learning-based architectures for molecular generation have been proposed for de novo drug design. The flourish of the de novo molecular generation methods and applications has created a great demand for the visualization and functional profiling for the de novo generated molecules. An increasing number of publicly available chemogenomic databases sets good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization module for chemical structure visualization and identify the reported structures, (2) chemical space module for chemical space exploration using similarity maps, principal components analysis (PCA), drug-like properties distribution, and scaffold-based clustering, (3) ADMET prediction module for predicting the ADMET properties of the de novo molecules, (4) molecular alignment module for three dimensional molecular shape analysis, (5) drugs mapping module for identifying structural similar drugs, and (6) target & pathway module for identifying the reported targets and corresponding functional pathways. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their de novo library and could guide the further selection of candidates for chemical synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

Oncological drug discovery: AI meets structure-based computational research

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.

Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells

Breast cancer (BC) has surpassed lung cancer as the most frequently occurring cancer, and it is the leading cause of cancer-related death in women. Therefore, there is an urgent need to discover or design new drug candidates for BC treatment. In this study, we first collected a series of structurally diverse datasets consisting of 33,757 active and 21,152 inactive compounds for 13 breast cancer cell lines and one normal breast cell line commonly used in in vitro antiproliferative assays. Predictive models were then developed using five conventional machine learning algorithms, including naïve Bayesian, support vector machine, k-Nearest Neighbors, random forest, and extreme gradient boosting, as well as five deep learning algorithms, including deep neural networks, graph convolutional networks, graph attention network, message passing neural networks, and Attentive FP. A total of 476 single models and 112 fusion models were constructed based on three types of molecular representations including molecular descriptors, fingerprints, and graphs. The evaluation results demonstrate that the best model for each BC cell subtype can achieve high predictive accuracy for the test sets with AUC values of 0.689–0.993. Moreover, important structural fragments related to BC cell inhibition were identified and interpreted. To facilitate the use of the model, an online webserver called ChemBC (http://chembc.idruglab.cn/) and its local version software (https://github.com/idruglab/ChemBC) were developed to predict whether compounds have potential inhibitory activity against BC cells.

Bryodulcosigenin a natural cucurbitane-type triterpenoid attenuates dextran sulfate sodium (DSS)-induced colitis in mice

BACKGROUND

Bryodulcosigenin (BDG) a cucurbitane-type triterpenoid has been isolated from the roots of Bryonia dioca and possesses marked anti-inflammatory effects, although its beneficial effect against intestinal disorders remains unclear.

PURPOSE

To explore the underlying mechanism of BDG on the dysbiosis of chronic ulcerative colitis (UC) and its associated side-effects on lung tissues.

METHODS

A chronic UC model was established using 2.5% dextran sulfate sodium (DSS) in mice treated for 64 days and diagnostic assessments, western blot analysis and quantitative real time-PCR were employed to determine the protective mechanism of BDG.

RESULTS

Oral administration of BDG (10 mg/kg/day) significantly improved colon length, disease activity index, and alleviated colonic histopathological damage in the DSS-induced colitis  mice. BDG not only reversed the TNF-α-induced degradation of tight junction proteins (occludin and ZO-1) but also suppressed the elevated apoptosis seen in intestinal epithelial cells (NCM460). In addition, BDG significantly attenuated damage in alveolar epithelial cells (MLE-12) co-cultured with NCM460 cells under inflammatory conditions. Furthermore, BDG in vivo significantly prevented the symptoms of respiratory disorders and repressed alveolar inflammation by regulating DSS-induced chronic colitis in mice.

CONCLUSION

BDG effectively inhibited the apoptosis of intestinal epithelial cells and suppressed the activation of the NLRP3 inflammasome which resulted in the restoration of the intestinal barrier. Therefore, the enhanced integrity of intestinal epithelial cells produced by BDG intervention contributed to its anti-colitis effects, indicating its great potential as an inhibitor of UC and lung injury. Therefore, restoring intestinal integrity may represent a promising strategy in the prevention of pulmonary disease.

Design, synthesis and biological evaluation of novel hybrids targeting mTOR and HDACs for potential treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major contributor to global cancer incidence and mortality. Many pathways are involved in the development of HCC and various proteins including mTOR and HDACs have been identified as potential drug targets for HCC treatment. In the present study, two series of novel hybrid molecules targeting mTOR and HDACs were designed and synthesized based on parent inhibitors (MLN0128 and PP121 for mTOR, SAHA for HDACs) by using a fusion-type molecular hybridization strategy. In vitro antiproliferative assays demonstrated that these novel hybrids with suitable linker lengths exhibited broad cytotoxicity against various cancer cell lines, with significant activity against HepG2 cells. Notably, DI06, an MLN0128-based hybrid, exhibited antiproliferative activity against HepG2 cells with an IC₅₀ value of 1.61 μM, which was comparable to those of both parent drugs (MLN0128, IC₅₀ = 2.13 μM and SAHA, IC₅₀ = 2.26 μM). In vitro enzyme inhibition assays indicated that DI06, DI07 and DI17 (PP121-based hybrid) exhibited nanomolar inhibitory activity against mTOR kinase and HDACs (e.g., HDAC1, HDAC2, HDAC3, HADC6 and HADC8). Cellular studies and western blot analyses uncovered that in HepG2 cells, DI06 and DI17 induced cell apoptosis by targeting mTOR and HDACs, blocked the cell cycle at the G0/G1 phase and suppressed cell migration. The potential binding modes of the hybrids (DI06 and DI17) with mTOR and HDACs were investigated by molecular docking. DI06 displayed better stability in rat liver microsomes than DI07 and DI17. Collectively, DI06 as a novel mTOR and HDACs inhibitor presented here warrants further investigation as a potential treatment of HCC.

Design, Hemiysnthesis, crystal structure and anticancer activity of 1, 2, 3-triazoles derivatives of totarol

A new series of diverse triazoles linked to the hydroxyl group of totarol were synthesized using click chemistry approach. The structures of these compounds were elucidated by HRMS, IR and NMR spectroscopy. The structure of compound 3 g was also confirmed by x-ray single crystal diffraction. The cytotoxicity of these compounds was evaluated by the MTT method against four cancer cell lines, including fibrosarcoma HT-1080, lung carcinoma A-549 and breast adenocarcinoma (MDA-MB-231 and MCF-7), and the results indicated that all compounds showed weak to moderate activities against all cancer cell lines with IC₅₀ values ranging from 14.44 to 46.25 μM. On the basis of our research the structure-activity relationships (SAR) of these compounds were discussed. This work provides some important hints for further structural modification of totarol towards developing novel and highly effective anticancer drugs respectively. It is interesting to note that compound 3 g indicated a very significant cytotoxicity against HT-1080 and A-549 cell lines. The molecular docking showed that compound 3 g activated the caspase-3 and inhibited tubulin by forming stable protein-ligand complexes.

Design and discovery of new antiproliferative 1,2,4-triazin-3(2H)-ones as tubulin polymerization inhibitors targeting colchicine binding site

Thirty-five new colchicine binding site inhibitors have been designed and synthesized based on the 1,2,4-triazin-3(2H)-one nucleus. Such molecules were synthesized through a cascade reaction between readily accessible α-amino ketones and phenyl carbazate as a masked N-isocyanate precursor. The synthesized derivatives are cisoid restricted combretastatin A4 analogues containing 1,2,4-triazin-3(2H)-one in place of the olefinic bond, and they have the same essential pharmacophoric features of colchicine binding site inhibitors. The synthesized compounds were evaluated in vitro for their antiproliferative activities against a panel of three human cancer cell lines (MCF-7, HepG-2, and HCT-116), using colchicine as a positive control. Among them, two compounds 5i and 6i demonstrated a significant antiproliferative effect against all cell lines with IC₅₀ ranging from 8.2 - 18.2 µM. Further investigation was carried out for the most active cytotoxic agents as tubulin polymerization inhibitors. Compounds 5i and 6i effectively inhibited microtubule assembly with IC₅₀ values ranging from 3.9 to 7.8 µM. Tubulin polymerization assay results were found to be comparable with the cytotoxicity results. The cell cycle analysis revealed significant G2/M cell cycle arrest of the analogue 5i in HepG-2 cells. The most active compounds 4i, 4j, 5 g, 5i and 6i did not induce significant cell death in normal human lung cells Wl-38, suggesting their selectivity against cancer cells. Also, These compounds upregulated the level of active caspase-3 and boosted the levels of the pro-apoptotic protein Bax by five to seven folds in comparison to the control. Moreover, apoptosis analyses were conducted for compound 5i to evaluate its apoptotic potential. Finally, in silico studies were conducted to reveal the probable interaction with the colchicine binding site. ADME prediction study of the designed compounds showed that they are not only with promising tubulin polymerization inhibitory activity but also with favorable pharmacokinetic and drug-likeness properties.

Tubulin Inhibitors: A Chemoinformatic Analysis Using Cell-Based Data

Inhibiting the tubulin-microtubules (Tub-Mts) system is a classic and rational approach for treating different types of cancers. A large amount of data on inhibitors in the clinic supports Tub-Mts as a validated target. However, most of the inhibitors reported thus far have been developed around common chemical scaffolds covering a narrow region of the chemical space with limited innovation. This manuscript aims to discuss the first activity landscape and scaffold content analysis of an assembled and curated cell-based database of 851 Tub-Mts inhibitors with reported activity against five cancer cell lines and the Tub-Mts system. The structure-bioactivity relationships of the Tub-Mts system inhibitors were further explored using constellations plots. This recently developed methodology enables the rapid but quantitative assessment of analog series enriched with active compounds. The constellations plots identified promising analog series with high average biological activity that could be the starting points of new and more potent Tub-Mts inhibitors.

Investigation of molecular mechanisms underlying the antiproliferative effects of colchicine against PC3 prostate cancer cells

This work examined the cytotoxic effects of colchicine on PC3 cells and elucidated the possible underlying mechanisms of its cytotoxicity. The cells were exposed to colchicine at different concentrations ranging from 1 to 100 ng/mL for 24 h, and it showed considerable cytotoxicity with an IC₅₀ value of 22.99 ng/mL. Mechanistic studies also exhibited that colchicine treatment results in cell cycle arrest at the G2/M phase as well as decreased mitochondrial membrane potential and increased early and late apoptotic cells. The apoptotic and DNA-damaging effects of colchicine have also been verified by fluorescence imaging and ELISA experiments, and they revealed that while colchicine treatment significantly modulated expression as increases in Bax, cleaved caspase 3, cleaved PARP, and 8-hydroxy-desoxyguanosine levels and as a decrease of BCL-2 protein expression. Besides, colchicine treatment significantly increased the total oxidant (TOS) level, which is a signal of oxidative stress and potential cause of DNA damage. Finally, the results of quantitative real-time PCR experiments demonstrated that colchicine treatment concentration-dependently suppressed MMP-9 mRNA expression. Overall, colchicine provides meaningful cytotoxicity on PC3 cells due to induced oxidative stress, reduced mitochondrial membrane potential, increased DNA damage, and finally increased apoptosis in PC3 cells. Nevertheless, further research needs to be conducted to assess the potential of colchicine as an anticancer drug for the treatment of prostate cancer.