Targeting AraC-Resistant Acute Myeloid Leukemia by Dual Inhibition of CDK9 and Bcl-2: A Systematic Review and Meta-Analysis

Therapeutic Path to Triple Knockout: Investigating the Pan-inhibitory Mechanisms of AKT, CDK9, and TNKS2 by a Novel 2-phenylquinazolinone Derivative in Cancer Therapy- An In-silico Investigation Therapy

BACKGROUND

Blocking the oncogenic Wnt//β-catenin pathway has of late been investigated as a viable therapeutic approach in the treatment of cancer. This involves the multi-targeting of certain members of the tankyrase-kinase family; Tankyrase 2 (TNKS2), Protein Kinase B (AKT), and Cyclin- Dependent Kinase 9 (CDK9), which propagate the oncogenic Wnt/β-catenin signalling pathway.

METHODS

During a recent investigation, the pharmacological activity of 2-(4-aminophenyl)-7-chloro- 3H-quinazolin-4-one was repurposed to serve as a 'triple-target' inhibitor of TNKS2, AKT and CDK9. Yet, the molecular mechanism that surrounds its multi-targeting activity remains unanswered. As such, this study aims to explore the pan-inhibitory mechanism of 2-(4-aminophenyl)-7-chloro-3H-quinazolin- 4-one towards AKT, CDK9, and TNKS2, using in silico techniques.

RESULTS

Results revealed favourable binding affinities of -34.17 kcal/mol, -28.74 kcal/mol, and -27.30 kcal/mol for 2-(4-aminophenyl)-7-chloro-3H-quinazolin-4-one towards TNKS2, CDK9, and AKT, respectively. Pan-inhibitory binding of 2-(4-aminophenyl)-7-chloro-3H-quinazolin-4-one is illustrated by close interaction with specific residues on tankyrase-kinase. Structurally, 2-(4-aminophenyl)-7-chloro- 3H-quinazolin-4-one had an impact on the flexibility, solvent-accessible surface area, and stability of all three proteins, which was illustrated by numerous modifications observed in the unbound as well as the bound states of the structures, which evidenced the disruption of their biological function. Prediction of the pharmacokinetics and physicochemical properties of 2-(4-aminophenyl)-7-chloro-3H-quinazolin-4- one further established its inhibitory potential, evidenced by the favourable absorption, metabolism, excretion, and minimal toxicity properties.

CONCLUSION

The following structural insights provide a starting point for understanding the paninhibitory activity of 2-(4-aminophenyl)-7-chloro-3H-quinazolin-4-one. Determining the criticality of the interactions that exist between the pyrimidine ring and catalytic residues could offer insight into the structure-based design of innovative tankyrase-kinase inhibitors with enhanced therapeutic effects.

Retracted: Targeting AraC-Resistant Acute Myeloid Leukemia by Dual Inhibition of CDK9 and Bcl-2: A Systematic Review and Meta-Analysis

[This retracts the article DOI: 10.1155/2022/2842066.].

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Acute lymphoblastic leukemia (ALL) is a rare type of blood cancer caused due to the overproduction of lymphocytes by the bone marrow in the human body. It is one of the common types of cancer in children, which has a fair chance of being cured. However, this may even occur in adults, and the chances of a cure are slim if diagnosed at a later stage. To aid in the early detection of this deadly disease, an intelligent method to screen the white blood cells is proposed in this study. The proposed intelligent deep learning algorithm uses the microscopic images of blood smears as the input data. This algorithm is implemented with a convolutional neural network (CNN) to predict the leukemic cells from the healthy blood cells. The custom ALLNET model was trained and tested using the microscopic images available as open-source data. The model training was carried out on Google Collaboratory using the Nvidia Tesla P-100 GPU method. Maximum accuracy of 95.54%, specificity of 95.81%, sensitivity of 95.91%, F1-score of 95.43%, and precision of 96% were obtained by this accurate classifier. The proposed technique may be used during the pre-screening to detect the leukemia cells during complete blood count (CBC) and peripheral blood tests.