Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Computer-Aided Design of VEGFR-2 Inhibitors as Anticancer Agents: A Review

Due to its intricate molecular and structural characteristics, vascular endothelial growth factor receptor 2 (VEGFR-2) is essential for the development of new blood vessels in various pathological processes and conditions, especially in cancers. VEGFR-2 inhibitors have demonstrated significant anticancer effects by blocking many signaling pathways linked to tumor growth, metastasis, and angiogenesis. Several small compounds, including the well-tolerated sunitinib and sorafenib, have been approved as VEGFR-2 inhibitors. However, the widespread side effects linked to these VEGFR-2 inhibitors-hypertension, epistaxis, proteinuria, and upper respiratory infection-motivate researchers to search for new VEGFR-2 inhibitors with better pharmacokinetic profiles. The key molecular interactions required for the interaction of the small molecules with the protein target to produce the desired pharmacological effects are identified using computer-aided drug design (CADD) methods such as pharmacophore and QSAR modeling, structure-based virtual screening, molecular docking, molecular dynamics (MD) simulation coupled with MM/PB(GB)SA, and other computational strategies. This review discusses the applications of these methods for VEGFR-2 inhibitor design. Future VEGFR-2 inhibitor designs may be influenced by this review, which focuses on the current trends of using multiple screening layers to design better inhibitors.

Breakthroughs in AI and multi-omics for cancer drug discovery: A review

Cancer is one of the biggest medical challenges we face today. It is characterized by abnormal, uncontrolled growth of cells that can spread to different parts of the body. Cancer is extremely complex, with genetic variations and the ability to adapt and evolve. This means we must continuously pursue innovative approaches to developing new cancer drugs. While traditional drug discovery methods have led to important breakthroughs, they also have significant limitations that make it difficult to efficiently create new, cost-effective cancer therapies. Integrating computational tools into the cancer drug discovery process is a major step forward. By harnessing computing power, we can overcome some of the inherent barriers of traditional methods. This review examines the range of computational techniques now being used, such as molecular docking, QSAR models, virtual screening, and pharmacophore modeling. It looks at recent advances in areas like machine learning and molecular simulations. The review also discusses the current challenges with these technologies and envisions future directions, underscoring how transformative these computational tools can be for creating targeted, new cancer treatments.

Advances in Cancer Therapy: A Comprehensive Review of CDK and EGFR Inhibitors

Protein kinases have essential responsibilities in controlling several cellular processes, and their abnormal regulation is strongly related to the development of cancer. The implementation of protein kinase inhibitors has significantly transformed cancer therapy by modifying treatment strategies. These inhibitors have received substantial FDA clearance in recent decades. Protein kinases have emerged as primary objectives for therapeutic interventions, particularly in the context of cancer treatment. At present, 69 therapeutics have been approved by the FDA that target approximately 24 protein kinases, which are specifically prescribed for the treatment of neoplastic illnesses. These novel agents specifically inhibit certain protein kinases, such as receptor protein-tyrosine kinases, protein-serine/threonine kinases, dual-specificity kinases, nonreceptor protein-tyrosine kinases, and receptor protein-tyrosine kinases. This review presents a comprehensive overview of novel targets of kinase inhibitors, with a specific focus on cyclin-dependent kinases (CDKs) and epidermal growth factor receptor (EGFR). The majority of the reviewed studies commenced with an assessment of cancer cell lines and concluded with a comprehensive biological evaluation of individual kinase targets. The reviewed articles provide detailed information on the structural features of potent anticancer agents and their specific activity, which refers to their ability to selectively inhibit cancer-promoting kinases including CDKs and EGFR. Additionally, the latest FDA-approved anticancer agents targeting these enzymes were highlighted accordingly.

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Identification of Potential Tryptase Inhibitors from FDA-Approved Drugs Using Machine Learning, Molecular Docking, and Experimental Validation

This study explores the innovative use of machine learning (ML) to identify novel tryptase inhibitors from a library of FDA-approved drugs, with subsequent confirmation via molecular docking and experimental validation. Tryptase, a significant mediator in inflammatory and allergic responses, presents a therapeutic target for various inflammatory diseases. However, the development of effective tryptase inhibitors has been challenging due to the enzyme's complex activation and regulation mechanisms. Utilizing a machine learning model, we screened an extensive FDA-approved drug library to identify potential tryptase inhibitors. The predicted compounds were then subjected to molecular docking to assess their binding affinity and conformation within the tryptase active site. Experimental validation was performed using RBL-2H3 cells, a rat basophilic leukemia cell line, where the efficacy of these compounds was evaluated based on their ability to inhibit tryptase activity and suppress β-hexosaminidase activity and histamine release. Our results demonstrated that several FDA-approved drugs, including landiolol, laninamivir, and cidofovir, significantly inhibited tryptase activity. Their efficacy was comparable to that of the FDA-approved mast cell stabilizer nedocromil and the investigational agent APC-366. These findings not only underscore the potential of ML in accelerating drug repurposing but also highlight the feasibility of this approach in identifying effective tryptase inhibitors. This research contributes to the field of drug discovery, offering a novel pathway to expedite the development of therapeutics for tryptase-related pathologies.

GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph

BACKGROUND

Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery.

RESULTS

We proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs.

CONCLUSIONS

GSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo's dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.

Peptide-Mediated Nanocarriers for Targeted Drug Delivery: Developments and Strategies

Effective drug delivery is essential for cancer treatment. Drug delivery systems, which can be tailored to targeted transport and integrated tumor therapy, are vital in improving the efficiency of cancer treatment. Peptides play a significant role in various biological and physiological functions and offer high design flexibility, excellent biocompatibility, adjustable morphology, and biodegradability, making them promising candidates for drug delivery. This paper reviews peptide-mediated drug delivery systems, focusing on self-assembled peptides and peptide-drug conjugates. It discusses the mechanisms and structural control of self-assembled peptides, the varieties and roles of peptide-drug conjugates, and strategies to augment peptide stability. The review concludes by addressing challenges and future directions.

Network pharmacology and experimental validation for deciphering the action mechanism of Fritillaria cirrhosa D. Don constituents in suppressing breast carcinoma

Fritillaria cirrhosa D. Don is a well-known medicinal plant of Kashmir Himalaya. Traditionally, it has been used to treat several diseases, including cancer. However, the molecular mechanism behind anticancer activity remains unclear. Therefore, in the present study, we have performed high performance-liquid chromatography-mass spectrometry (HR-LC/MS), network pharmacology, molecular docking and molecular dynamic (MD) simulation methods were used to explore the underlying molecular mechanism of F. cirrhosa for the treatment of breast cancer (BC). The targets of F. cirrhosa for treating BC were predicted using databases like SwissTargetPrediction, Gene Cards and OMIM. Protein-protein interaction analysis and network construction were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins programme, and analysis of Gene Ontology term enrichment and Kyoto Encyclopedia of Genes and Genomes pathway enrichment was done using the Cytoscape programme. In addition, molecular docking was used to investigate intermolecular interactions between the compounds and the proteins using the Autodock tool. MD simulations studies were also used to explore the stability of the representative AKT1 gene peiminine and Imperialine-3-β-glucoside. In addition, experimental treatment of F. cirrhosa was also verified. HR-LC/MS detected the presence of several secondary metabolites. Afterward, molecular docking was used to verify the effective activity of the active ingredients against the prospective targets. Additionally, Peiminine and Imperialine-3-β-glucoside showed the highest binding energy score against AKT-1 (-12.99 kcal/mol and -12.08 kcal/mol). AKT1 with Peiminine and Imperialine-3-β-glucoside was further explored for MD simulations. During the MD simulation study at 100 nanoseconds, a stable complex formation of AKT1 + Peiminine and Imperialine-3-β-glucoside was observed. The binding free energy calculations using MM/GBSA showed significant binding of the ligand with protein (ΔG: -79.83 ± 3.0 kcal/mol) between AKT1 + Peiminine was observed. The principal component analysis exhibited a stable converged structure by achieving global motion. Lastly, F. cirrhosa extracts also exhibited momentous anticancer activity through in vitro studies. Therefore, present study revealed the molecular mechanism of F. cirrhosa constituents for the effective treatment of BC by deactivating various multiple gene targets, multiple pathways particularly the PI3K-Akt signaling pathway. These findings emphasized the momentous anti-BC activity of F. cirrhosa constituents.Communicated by Ramaswamy H. Sarma.

Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success

Precision and organization govern the cell cycle, ensuring normal proliferation. However, some cells may undergo abnormal cell divisions (neosis) or variations of mitotic cycles (endopolyploidy). Consequently, the formation of polyploid giant cancer cells (PGCCs), critical for tumor survival, resistance, and immortalization, can occur. Newly formed cells end up accessing numerous multicellular and unicellular programs that enable metastasis, drug resistance, tumor recurrence, and self-renewal or diverse clone formation. An integrative literature review was carried out, searching articles in several sites, including: PUBMED, NCBI-PMC, and Google Academic, published in English, indexed in referenced databases and without a publication time filter, but prioritizing articles from the last 3 years, to answer the following questions: (i) "What is the current knowledge about polyploidy in tumors?"; (ii) "What are the applications of computational studies for the understanding of cancer polyploidy?"; and (iii) "How do PGCCs contribute to tumorigenesis?"

The Role of Natural and Semi-Synthetic Compounds in Ovarian Cancer: Updates on Mechanisms of Action, Current Trends and Perspectives

Ovarian cancer represents a major health concern for the female population: there is no obvious cause, it is frequently misdiagnosed, and it is characterized by a poor prognosis. Additionally, patients are inclined to recurrences because of metastasis and poor treatment tolerance. Combining innovative therapeutic techniques with established approaches can aid in improving treatment outcomes. Because of their multi-target actions, long application history, and widespread availability, natural compounds have particular advantages in this connection. Thus, effective therapeutic alternatives with improved patient tolerance hopefully can be identified within the world of natural and nature-derived products. Moreover, natural compounds are generally perceived to have more limited adverse effects on healthy cells or tissues, suggesting their potential role as valid treatment alternatives. In general, the anticancer mechanisms of such molecules are connected to the reduction of cell proliferation and metastasis, autophagy stimulation and improved response to chemotherapeutics. This review aims at discussing the mechanistic insights and possible targets of natural compounds against ovarian cancer, from the perspective of medicinal chemists. In addition, an overview of the pharmacology of natural products studied to date for their potential application towards ovarian cancer models is presented. The chemical aspects as well as available bioactivity data are discussed and commented on, with particular attention to the underlying molecular mechanism(s).

Amomum subulatum: A treasure trove of anti-cancer compounds targeting TP53 protein using in vitro and in silico techniques

Cancer is a primary global health concern, and researchers seek innovative approaches to combat the disease. Clinical bioinformatics and high-throughput proteomics technologies provide powerful tools to explore cancer biology. Medicinal plants are considered effective therapeutic agents, and computer-aided drug design (CAAD) is used to identify novel drug candidates from plant extracts. The tumour suppressor protein TP53 is an attractive target for drug development, given its crucial role in cancer pathogenesis. This study used a dried extract of Amomum subulatum seeds to identify phytocompounds targeting TP53 in cancer. We apply qualitative tests to determine its phytochemicals (Alkaloid, Tannin, Saponin, Phlobatinin, and Cardic glycoside), and found that alkaloid composed of 9.4% ± 0.04% and Saponin 1.9% ± 0.05% crude chemical constituent. In the results of DPPH Analysis Amomum subulatum Seeds founded antioxidant activity, and then we verified via observing methanol extract (79.82%), BHT (81.73%), and n-hexane extract (51.31%) found to be positive. For Inhibition of oxidation, we observe BHT is 90.25%, and Methanol (83.42%) has the most significant proportion of linoleic acid oxidation suppression. We used diverse bioinformatics approaches to evaluate the effect of A. subulatum seeds and their natural components on TP53. Compound-1 had the best pharmacophore match value (53.92), with others ranging from 50.75 to 53.92. Our docking result shows the top three natural compounds had the highest binding energies (-11.10 to -10.3 kcal/mol). The highest binding energies (-10.9 to -9.2 kcal/mol) compound bonded to significant sections in the target protein's active domains with TP53. Based on virtual screening, we select top phytocompounds for targets which highly fit based on pharmacophore score and observe these compounds exhibited potent antioxidant activity and inhibited cancer cell inflammation in the TP53 pathway. Molecular Dynamics (MD) simulations indicated that the ligand was bound to the protein with some significant conformational changes in the protein structure. This study provides novel insights into the development of innovative drugs for the treatment of cancer disorders.

Inhibitory Effect and Mechanism of Ursolic Acid on Cisplatin-Induced Resistance and Stemness in Human Lung Cancer A549 Cells

The survival rate of lung cancer patients remains low largely due to chemotherapy resistance during treatment, and cancer stem cells (CSCs) may hold the key to targeting this resistance. Cisplatin is a chemotherapy drug commonly used in cancer treatment, yet the mechanisms of intrinsic cisplatin resistance have not yet been determined because lung CSCs are hard to identify. In this paper, we proposed a mechanism relating to the function of ursolic acid (UA), a new drug, in reversing the cisplatin resistance of lung cancer cells regulated by CSCs. Human lung cancer cell line A549 was selected as the model cell and treated to become a cisplatin-resistant lung cancer cell line (A549-CisR), which was less sensitive to cisplatin and showed an enhanced capability of tumor sphere formation. Furthermore, in the A549-CisR cell line expression, levels of pluripotent stem cell transcription factors Oct-4, Sox-2, and c-Myc were increased, and activation of the Jak2/Stat3 signaling pathway was promoted. When UA was applied to the cisplatin-resistant cells, levels of the pluripotent stem cell transcription factors were restrained by the inhibition of the Jak2/Stat3 signaling pathway, which reduced the enrichment of tumor stem cells, and in turn, reversed cisplatin resistance in lung cancer cells. Hence, as a potential antitumor drug, UA may be able to inhibit the enrichment of the lung CSC population by inhibiting the activation of the Jak2-Stat3 pathway and preventing the resistance of lung cancer cells to cisplatin.

2,3-Dihydro-Quinazolin-4(1H)-One as a Fluorescent Sensor for Hg2+ Ion and its Docking Studies in Cancer Treatment

The 2,3-dihydro-quinazolin-4(1H)-one was synthesised via the deployment of SBA-Pr-SO3H and its application was explored as a highly selective fluorescent sensor for Hg2+ ion; fluorescence intensity was decreased selectively by Hg2+ ions. Furthermore, this compound also indicated for its superb anti-interference ability among other ions. It is important to mention that this compound could be employed to detect a very low amount of Hg2+ ions, which are highly toxic and general contaminants. The docking study shows that the molecule, 2,3-dihydro-quinazolin-4(1H)-one, is a good inhibitor for the 5ACC enzyme.

Insights into the Promising Prospect of G Protein and GPCR-Mediated Signaling in Neuropathophysiology and Its Therapeutic Regulation

G protein-coupled receptors (GPCRs) are intricately involved in the conversion of extracellular feedback to intracellular responses. These specialized receptors possess a crucial role in neurological and psychiatric disorders. Most nonsensory GPCRs are active in almost 90% of complex brain functions. At the time of receptor phosphorylation, a GPCR pathway is essentially activated through a G protein signaling mechanism via a G protein-coupled receptor kinase (GRK). Dopamine, an important neurotransmitter, is primarily involved in the pathophysiology of several CNS disorders; for instance, bipolar disorder, schizophrenia, Parkinson's disease, and ADHD. Since dopamine, acetylcholine, and glutamate are potent neuropharmacological targets, dopamine itself has potential therapeutic effects in several CNS disorders. GPCRs essentially regulate brain functions by modulating downstream signaling pathways. GPR6, GPR52, and GPR8 are termed orphan GPCRs because they colocalize with dopamine D1 and D2 receptors in neurons of the basal ganglia, either alone or with both receptors. Among the orphan GPCRs, the GPR52 is recognized for being an effective psychiatric receptor. Various antipsychotics like aripiprazole and quetiapine mainly target GPCRs to exert their actions. One of the most important parts of signal transduction is the regulation of G protein signaling (RGS). These substances inhibit the activation of the G protein that initiates GPCR signaling. Developing a combination of RGS inhibitors with GPCR agonists may prove to have promising therapeutic potential. Indeed, several recent studies have suggested that GPCRs represent potentially valuable therapeutic targets for various psychiatric disorders. Molecular biology and genetically modified animal model studies recommend that these enriched GPCRs may also act as potential therapeutic psychoreceptors. Neurotransmitter and neuropeptide GPCR malfunction in the frontal cortex and limbic-related regions, including the hippocampus, hypothalamus, and brainstem, is likely responsible for the complex clinical picture that includes cognitive, perceptual, emotional, and motor symptoms. G protein and GPCR-mediated signaling play a critical role in developing new treatment options for mental health issues, and this study is aimed at offering a thorough picture of that involvement. For patients who are resistant to current therapies, the development of new drugs that target GPCR signaling cascades remains an interesting possibility. These discoveries might serve as a fresh foundation for the creation of creative methods for pharmacologically useful modulation of GPCR function.

Emerging Role of Neuron-Glia in Neurological Disorders: At a Glance

Based on the diverse physiological influence, the impact of glial cells has become much more evident on neurological illnesses, resulting in the origins of many diseases appearing to be more convoluted than previously happened. Since neurological disorders are often random and unknown, hence the construction of animal models is difficult to build, representing a small fraction of people with a gene mutation. As a result, an immediate necessity is grown to work within in vitro techniques for examining these illnesses. As the scientific community recognizes cell-autonomous contributions to a variety of central nervous system illnesses, therapeutic techniques involving stem cells for treating neurological diseases are gaining traction. The use of stem cells derived from a variety of sources is increasingly being used to replace both neuronal and glial tissue. The brain's energy demands necessitate the reliance of neurons on glial cells in order for it to function properly. Furthermore, glial cells have diverse functions in terms of regulating their own metabolic activities, as well as collaborating with neurons via secreted signaling or guidance molecules, forming a complex network of neuron-glial connections in health and sickness. Emerging data reveals that metabolic changes in glial cells can cause morphological and functional changes in conjunction with neuronal dysfunction under disease situations, highlighting the importance of neuron-glia interactions in the pathophysiology of neurological illnesses. In this context, it is required to improve our understanding of disease mechanisms and create potential novel therapeutics. According to research, synaptic malfunction is one of the features of various mental diseases, and glial cells are acting as key ingredients not only in synapse formation, growth, and plasticity but also in neuroinflammation and synaptic homeostasis which creates critical physiological capacity in the focused sensory system. The goal of this review article is to elaborate state-of-the-art information on a few glial cell types situated in the central nervous system (CNS) and highlight their role in the onset and progression of neurological disorders.

Perspectives on the Molecular Mediators of Oxidative Stress and Antioxidant Strategies in the Context of Neuroprotection and Neurolongevity: An Extensive Review

Molecules with at least one unpaired electron in their outermost shell are known as free radicals. Free radical molecules are produced either within our bodies or by external sources such as ozone, cigarette smoking, X-rays, industrial chemicals, and air pollution. Disruption of normal cellular homeostasis by redox signaling may result in cardiovascular, neurodegenerative diseases and cancer. Although ROS (reactive oxygen species) are formed in the GI tract, little is known about how they contribute to pathophysiology and disease etiology. When reactive oxygen species and antioxidants are in imbalance in our bodies, they can cause cell structure damage, neurodegenerative diseases, diabetes, hypercholesterolemia, atherosclerosis, cancer, cardiovascular diseases, metabolic disorders, and other obesity-related disorders, as well as protein misfolding, mitochondrial dysfunction, glial cell activation, and subsequent cellular apoptosis. Neuron cells are gradually destroyed in neurodegenerative diseases. The production of inappropriately aggregated proteins is strongly linked to oxidative stress. This review's goal is to provide as much information as possible about the numerous neurodegenerative illnesses linked to oxidative stress. The possibilities of multimodal and neuroprotective therapy in human illness, using already accessible medications and demonstrating neuroprotective promise in animal models, are highlighted. Neuroprotection and neurolongevity may improve from the use of bioactive substances from medicinal herbs like Allium stadium, Celastrus paniculatus, and Centella asiatica. Many neuroprotective drugs' possible role has been addressed. Preventing neuroinflammation has been demonstrated in several animal models.