Restoring order at the cell cycle border: Co-targeting CDK4/6 and CDK2

Discovery of potent and selective CDK2 inhibitors with high safety and favorable bioavailability for the treatment of cancer

Targeting cyclin-dependent kinases (CDKs) to inhibit the cell proliferation is considered as a promising strategy for the treatment of cancer, and the success of selective CDK4/6 inhibitors proves this concept. CDK2 plays an important role in the cell cycle and proliferation for the CCNE1-amplifed cancers and CDK4/6 inhibitors resistant breast cancers. Therefore, selective inhibition of CDK2 become research hotspots. In our work, we achieved a potent and selective CDK2 inhibitor 46 through virtual screening and systematic structural modification. Compound 46 could arrest cell cycle, promote apoptosis, and induce senescence-related phenotypes for CCNE1-amplifed ovarian cancer OVCAR3 cell line, and also displayed potent antitumor activity against OVCAR3 xenografts. Furthermore, 46 hold promise in overcoming resistance to CDK4/6 inhibitor. More significantly, 46 exhibited great safety properties and favorable pharmacokinetic profiles in vivo. All these results demonstrated that 46 was a potential candidate of novel anticancer drugs.

Targeting cyclin-dependent kinases: From pocket specificity to drug selectivity

The development of selective modulators of cyclin-dependent kinases (CDKs), a kinase family with numerous members and functional variations, is a significant preclinical challenge. Recent advancements in crystallography have revealed subtle differences in the highly conserved CDK pockets. Exploiting these differences has proven to be an effective strategy for achieving excellent drug selectivity. While previous reports briefly discussed the structural features that lead to selectivity in individual CDK members, attaining inhibitor selectivity requires consideration of not only the specific structures of the target CDK but also the features of off-target members. In this review, we summarize the structure-activity relationships (SARs) that influence selectivity in CDK drug development and analyze the pocket features that lead to selectivity using molecular-protein binding models. In addition, in recent years, novel CDK modulators have been developed, providing more avenues for achieving selectivity. These cases were also included. We hope that these efforts will assist in the development of novel CDK drugs.

Mathematical Modeling Identifies Optimum Palbociclib-fulvestrant Dose Administration Schedules for the Treatment of Patients with Estrogen Receptor-positive Breast Cancer

Cyclin-dependent kinases 4/6 (CDK4/6) inhibitors such as palbociclib are approved for the treatment of metastatic estrogen receptor-positive (ER+) breast cancer in combination with endocrine therapies and significantly improve outcomes in patients with this disease. However, given the large number of possible pairwise drug combinations and administration schedules, it remains unclear which clinical strategy would lead to best survival. Here, we developed a computational, cell cycle-explicit model to characterize the pharmacodynamic response to palbociclib-fulvestrant combination therapy. This pharmacodynamic model was parameterized, in a Bayesian statistical inference approach, using in vitro data from cells with wild-type estrogen receptor (WT-ER) and cells expressing the activating missense ER mutation, Y537S, which confers resistance to fulvestrant. We then incorporated pharmacokinetic models derived from clinical data into our computational modeling platform. To systematically compare dose administration schedules, we performed in silico clinical trials based on integrating our pharmacodynamic and pharmacokinetic models as well as considering clinical toxicity constraints. We found that continuous dosing of palbociclib is more effective for lowering overall tumor burden than the standard, pulsed-dose palbociclib treatment. Importantly, our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment strategies in search of optimal combination dosing strategies of other cell-cycle inhibitors in ER+ breast cancer.

SIGNIFICANCE

We created a computational modeling platform to predict the effects of fulvestrant/palbocilib treatment on WT-ER and Y537S-mutant breast cancer cells, and found that continuous treatment schedules are more effective than the standard, pulsed-dose palbociclib treatment schedule.

Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors: existing and emerging differences

The cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors palbociclib, ribociclib, and abemaciclib are standard-of-care therapy for hormone receptor-positive advanced or metastatic breast cancer, based on randomized trials showing improved progression-free survival for all 3 drugs and overall survival for ribociclib and abemaciclib. Results in early breast cancer are discordant, with sustained improvement in invasive disease-free survival demonstrated for abemaciclib but not other CDK4/6 inhibitors to date. We review nonclinical studies exploring mechanistic differences between the drugs, the impact of continuous dosing on treatment effect, and translational research into potential resistance mechanisms and prognostic and predictive markers. We focus particularly on how emerging findings may help us understand similarities and differences between the available CDK4/6 inhibitors. Even at late-stage clinical development, there remains much to learn about how agents in this class exert their varying effects.

An Interpretable Multitask Framework BiLAT Enables Accurate Prediction of Cyclin-Dependent Protein Kinase Inhibitors

The cyclin-dependent protein kinases (CDKs) are protein-serine/threonine kinases with crucial effects on the regulation of cell cycle and transcription. CDKs can be a hallmark of cancer since their excessive expression could lead to impaired cell proliferation. However, the selectivity profile of most developed CDK inhibitors is not enough, which have hindered the therapeutic use of CDK inhibitors. In this study, we propose a multitask deep learning framework called BiLAT based on SMILES representation for the prediction of the inhibitory activity of molecules on eight CDK subtypes (CDK1, 2, 4-9). The framework is mainly composed of an improved bidirectional long short-term memory module BiLSTM and the encode layer of the Transformer framework. Additionally, the data enhancement method of SMILES enumeration is applied to improve the performance of the model. Compared with baseline predictive models based on three conventional machine learning methods and two multitask deep learning algorithms, BiLAT achieves the best performance with the highest average AUC, ACC, F1-score, and MCC values of 0.938, 0.894, 0.911, and 0.715 for the test set. Moreover, we constructed a targeted external data set CDK-Dec for the CDK family, which mainly contains bait values screened by 3D similarity with active compounds. This dataset was utilized in the subsequent evaluation of our model. It is worth mentioning that the BiLAT model is interpretable and can be used by chemists to design and synthesize compounds with improved activity. To further verify the generalization ability of the multitask BiLAT model, we also conducted another evaluation on three public datasets (Tox21, ClinTox, and SIDER). Compared with several currently popular models, BiLAT shows the best performance on two datasets. These results indicate that BiLAT is an effective tool for accelerating drug discovery.

CDK4/6 inhibitor resistance: A bibliometric analysis

Background

Cyclin-dependent kinases (CDKs) 4/6 inhibitors are a type of cell cycle regulation that prevents cell proliferation by blocking retinoblastoma protein (Rb) phosphorylation in the G1 to S phase transition. CDK 4/6 inhibitors are currently used mainly in patients with hormone receptor-positive/human epidermal growth factor receptor 2 (HER2) negative breast cancer in combination with endocrine therapy. However, primary or acquired resistance to drugs severely affect drug efficacy. Our study aims at summarizing and visualizing the current research direction and development trend of CDK4/6 inhibitor resistance to provide clinicians and research power with a summary of the past and ideas for the future.

Methods

The Web of Science Core Collection and PubMed was searched for all included articles on CDK4/6 inhibitor resistance for bibliometric statistics and graph plotting. The metrological software and graphing tools used were R language version 4.2.0, Bibliometrix 4.0.0, Vosviewer 1.6.18, GraphPad Prism 9, and Microsoft Excel 2019.

Results

A total of 1278 English-language articles related to CDK4/6 inhibitor resistance were included in the Web of Science core dataset from 1996-2022, with an annual growth rate of14.56%. In PubMed, a total of 1123 articles were counted in the statistics, with an annual growth rate of 17.41% Cancer Research is the most included journal (102/1278, 7.98%) with an impact factor of 13.312 and is the Q1 of the Oncology category of the Journal Citation Reports. Professor Malorni Luca from Italy is probably the most contributing author in the current field (Publications 21/1278, 1.64%), while Prof. Turner Nicholas C from the USA is perhaps the most authoritative new author in the field of CDK4/6 inhibitor resistance (Total Citations2584, M-index 1.429). The main research efforts in this field are currently focused on Palbociclib and Abemaciclib. Studies on drug resistance mechanisms or post-drug resistance therapies focus on MEK inhibitors and related pathways, PI3K-AKT-MTOR pathways or inhibitors, EGFR-related pathways, EGFR inhibitors, TKI inhibitors, MAPK pathways and inhibitors, and so on.

Conclusion

This study provides researchers with a reliable basis and guidance for finding authoritative references, understanding research trends, and mining research neglect directions.

Evaluation of Diagnostic and Prognostic Value of hsa_circ_0084927 and Analysis of Associated ceRNA Network in Colorectal Cancer

Object

This study aims to analyze the differentially expressed circRNA in colon adenocarcinoma (COAD) and evaluate its diagnostic and prognostic value. Analyze associated circRNA-miRNA-mRNA network in COAD.

Methods and Materials

Real-time quantitative PCR (RT-PCR) was used to verify differentially expressed circRNA in COAD tissues and cells; Receiver operator characteristic (ROC) and Cox regression analysis were used to evaluating its diagnostic and prognostic value; Meanwhile we conducted CCK-8, invasion, and migration experiments in cell lines to explore the function of circRNA. In addition, a competitive endogenous RNA (ceRNA) network was established using bioinformatics methods to explore its prognostic value and potential functional mechanisms.

Results

Our study found that hsa_circ_0084927 is highly expressed in COAD tissues and cell lines. Plasma hsa_circ_0084927 can be used as a diagnostic marker for COAD patients; hsa_circ_0084927 can promote the proliferation, migration and invasion of COAD cells. In addition, we effectively constructed a ceRNA: network has_circ_0084927/miR-106b-5p/VEGFA. The ceRNA network indicates that hsa_circ_0084927 may affect the prognosis of COAD through the regulation of cell cycle, apoptosis and other pathways.

Conclusion

Our research results indicate that hsa_circ_0084927 has a cancer-promoting effect and may be used as a circulating tumor marker for COAD prognosis. In addition, this study proposes a new ceRNA network to provide new insights for the targeted therapy of COAD.