Molecular docking and machine learning analysis of Abemaciclib in colon cancer

Exploring bile acid transporters as key players in cancer development and treatment: Evidence from preclinical and clinical studies

Bile acid transporters (BATs) are integral membrane proteins belonging to various families, such as solute carriers, organic anion transporters, and ATP-binding cassette families. These transporters play a crucial role in bile acid transportation within the portal and systemic circulations, with expression observed in tissues, including the liver, kidney, and small intestine. Bile acids serve as signaling molecules facilitating the absorption and reabsorption of fats and lipids. Dysregulation of bile acid concentration has been implicated in tumorigenesis, yet the role of BATs in this process remains underexplored. Emerging evidence suggests that BATs may modulate various stages of cancer progression, including initiation, development, proliferation, metastasis, and tumor microenvironment regulation. Targeting BATs using siRNAs, miRNAs, and small compound inhibitors in preclinical models and their polymorphisms are well-studied for transporters like BSEP, MDR1, MRP2, OATP1A2, etc., and have shed light on their involvement in tumorigenesis, particularly in cancers such as those affecting the liver and gastrointestinal tract. While BATs' role in diseases like Alagille syndrome, biliary atresia, and cirrhosis have been extensively studied, their implications in cancer warrant further investigation. This review highlights the expression and function of BATs in cancer development and emphasizes the potential of targeting these transporters as a novel therapeutic strategy for various malignancies.

In silico approaches for drug repurposing in oncology: a scoping review

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

Recent Advances on Small-Molecule Inhibitors of Lipocalin-like Proteins

Lipid transfer proteins (LTPs) are crucial players in nonvesicular lipid trafficking. LTPs sharing a lipocalin lipid transfer domain (lipocalin-like proteins) have a wide range of biological functions, such as regulating immune responses and cell proliferation, differentiation, and death as well as participating in the pathogenesis of inflammatory, metabolic, and neurological disorders and cancer. Therefore, the development of small-molecule inhibitors targeting these LTPs is important and has potential clinical applications. Herein, we summarize the structure and function of lipocalin-like proteins, mainly including retinol-binding proteins, lipocalins, and fatty acid-binding proteins and discuss the recent advances on small-molecule inhibitors for these protein families and their applications in disease treatment. The findings of our Perspective can provide guidance for the development of inhibitors of these LTPs and highlight the challenges that might be faced during the procedures.

Computational molecular perspectives on novel carbazole derivative as an anti-cancer molecule against CDK1 of breast and colorectal cancers via gene expression studies, novel two-way docking strategies, molecular mechanics and dynamics

With increase in cancer incidences, alternative strategies for disease management are of utmost importance. Carbazole, is a compound that is being studied extensively as an anti-cancer compound. In this work, we aimed to investigate a carbazole derivative against specific cancer types such as breast and colorectal, based on the off-target analyses of carbazole derivative. The present work shortlisted 6 proteins that have an association in both cancer types, and then employed two different molecular docking strategies to examine the binding stability of carbazole derivative: a blind-docking state, where the pockets were undefined and mutation-docking state, where possible mutations were induced within the proteins. The results showed that CDK1 bound best in both states to carbazole derivative, and performed better than an array of positive controls. Molecular dynamic simulations at 100 ns further proved its stability, with carbazole derivative-CDK1-blind and mutated complex having RMSD values between 3.2 and 3.6 Å, and 2.8-3.2 Å respectively. Molecular-mechanics generalized born and surface area solvation disclosed free energy of binding for the complexes as -28.79 ± 3.97 kcal/mol and -31.86 ± 5.09 kcal/mol respectively, with carbazole derivative bound stably within the binding pocket at every 10 ns of the 100 ns trajectory. Radial distribution functions showed that the bell curve was well within 6 Å, thus showing that carbazole derivative and its atoms do not deviate away from the pocket, suggesting its ability to be used as a good anti-cancer compound against breast and colorectal.

Current trends and future prospects of drug repositioning in gastrointestinal oncology

Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

Kruppel-like factor 5 enhances proliferation, lipid droplet formation and oxaliplatin resistance in colorectal cancer by promoting fatty acid binding protein 6 transcription

Oxaliplatin (OXA) is a standard agent for colorectal cancer (CRC) adjuvant chemotherapy. However, acquired and intrinsic OXA resistance is a primary challenge for CRC treatment. This study investigates the function of the Kruppel-like factor 5/fatty acid binding proteins 6 (KLF5/FABP6) axis in CRC proliferation, lipid droplet formation and OXA resistance. OXA-resistant CRC cell lines were constructed, and FABP6 and KLF5 expression was assessed in parental and OXA-resistant CRC cells. Subsequent to gain- and loss-of-function experiments, CRC cell proliferation was assessed by cell counting kit-8 (CCK-8) and clone formation assays, the intracellular lipid synthesis by oil red O staining and the protein expression of lipid metabolism genes by western blot. OXA resistance of CRC cells was assessed by CCK-8 assay. The binding of KLF5 to FABP6 was analyzed by the dual-luciferase reporter and ChIP assays. A tumorigenicity assay in nude mice was adopted to examine the impact of KLF5 on CRC tumor growth and OXA resistance in vivo . FABP6 and KLF5 expression was high in CRC cell lines. Downregulation of FABP6 or KLF5 restrained CRC cell proliferation and lipid droplet formation in vitro . FABP6 and KLF5 expression was elevated in OXA-resistant CRC cells. Downregulation of FABP6 or KLF5 repressed the OXA resistance of OXA-resistant CRC cells. Mechanistically, KLF5 facilitated the transcription of FABP6. FABP6 overexpression counteracted the suppressive effects of KLF5 downregulation on CRC cell growth, lipid droplet formation and OXA resistance. KLF5 downregulation restrained CRC tumor growth and OXA resistance in vivo . In conclusion, KLF5 knockdown reduced FABP6 transcription to protect against proliferation, lipid droplet formation and OXA resistance in CRC.

Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies

Protein kinase p38γ is an attractive target against cancer because it plays a pivotal role in cancer cell proliferation by phosphorylating the retinoblastoma tumour suppressor protein. Therefore, inhibition of p38γ with active small molecules represents an attractive alternative for developing anti-cancer drugs. In this work, we present a rigorous and systematic virtual screening framework to identify potential p38γ inhibitors against cancer. We combined the use of machine learning-based quantitative structure activity relationship modelling with conventional computer-aided drug discovery techniques, namely molecular docking and ligand-based methods, to identify potential p38γ inhibitors. The hit compounds were filtered using negative design techniques and then assessed for their binding stability with p38γ through molecular dynamics simulations. To this end, we identified a promising compound that inhibits p38γ activity at nanomolar concentrations and hepatocellular carcinoma cell growth in vitro in the low micromolar range. This hit compound could serve as a potential scaffold for further development of a potent p38γ inhibitor against cancer.

Computational studies of potential antiviral compounds from some selected Nigerian medicinal plants against SARS-CoV-2 proteins

The challenges posed by COVID-19's emergence have led to a search for its therapies. There is no cure for COVID-19 infection yet, but there is significant progress in vaccine formulation for prophylaxis and drug development (such as paxlovid) for high-risk patients. As a contribution to the ongoing quest for solutions, this study shows potent phytocompounds identification as inhibitors of SARS-CoV-2 targets using in silico methods. We used virtual screening, molecular docking, and molecular dynamics (MD) simulations to investigate the interaction of some phytochemicals with 3CLpro, ACE2, and PLpro proteins crucial to the SARS-CoV-2 viral cycle. The predicted docking scores range from −5.5 to −9.4 kcal/mol, denoting appreciable binding of these compounds to the SARS-CoV-2 proteins and presenting a multitarget inhibition for COVID-19. Some phytocompounds interact favorably at non-active sites of the enzymes. For instance, MD simulation shows that an identified site on PLpro is stable and likely an allosteric region for inhibitor binding and modulation. These phytocompounds could be developed into effective therapy against COVID-19 and probed as potential multitarget-directed ligands and drug candidates against the SARS-CoV-2 virus. The study unveils drug repurposing, selectivity, allosteric site targeting, and multitarget-directed ligand in one piece. These concepts are three distinct approaches in the drug design and discovery pipeline.

A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Cyclin-Dependent Kinase 4/6 Inhibitors: A Potential Breakthrough Therapy for Malignancies of Gastrointestinal Tract

Cancer is the leading cause of death worldwide for which effective treatments remain limited. This article aimed to critically review and discuss the potential of targeting cell cycle machineries as a vital tool for cancer treatment. Cyclin dependent kinase (CDK) 4/6 inhibitors were originally approved by the United State Food and Drug Administration (US FDA) for advanced-stage breast cancer treatment. The nearly double-prolonged survival time in patients who received CDK4/6 inhibitors are superior to the conventional chemotherapy or endocrine therapy alone and, thus, these medications have been designated a breakthrough therapy by the US FDA. The requirement of CDK4/6 in the progression of cancer cells, but probably dispensable in normal cells, makes CDK4/6 a popular target for cancer treatment. The effects of CDK4/6 inhibitors in cancer may also involve the tumor microenvironment in which the therapeutic effects are synergistically pronounced. These emerging roles, hence, prompt investigations regarding their therapeutic potential in other cancers, including gastrointestinal cancer. Many preclinical and clinical studies of CDK4/6 inhibitors in gastrointestinal cancers are underway and, as a result, several new potentials are gradually reported. Contrariwise, the primary effect of this drug group is arresting the cell cycle rather than inducing cell death. The efficacy of using CDK4/6 inhibitors as a single regimen in clinical practice is then limited. In this article, the effects of CDK4/6 inhibitors on the progression of gastrointestinal cancers, at both preclinical and clinical levels are reviewed. The future directions for research and the possibility of CDK4/6 inhibitors being "breakthrough therapy" for gastrointestinal cancers are also discussed.

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.

Gamma-mangostin isolated from garcinia mangostana suppresses colon carcinogenesis and stemness by downregulating the GSK3β/β-catenin/CDK6 cancer stem pathway

BACKGROUND

Despite advances in chemotherapies and targeted drugs, colorectal cancer (CRC) remains challenging to treat due to drug resistance. Emerging evidence indicates that cancer-associated fibroblasts (CAFs) facilitate the generation of cancer stem-like cells (CSCs) and drug resistance. Glycogen synthase kinase-3 (GSK) associated signaling pathways have been implicated in the generation of CSCs and represent a target for therapeutics development.

HYPOTHESIS

Gamma-mangostin (gMG) isolated from Garcinia mangostana was evaluated for its ability to downregulate GSK3β-associated signaling in CRC cells and overcome CAF-induced 5-fluorouracil resistance and CSC generation.

METHODS

Bioinformatics analysis, in silico molecular docking, in vitro assays, including cell viability tests, colony- and tumor sphere-formation assays, transwell migration assays, ELISA, SDS-PAGE, Western blotting, miR expression, qPCR, and flow cytometry, as well as in vivo mouse xenograft models were used to evaluate the antitumor effects of gMG.

RESULTS

Bioinformatics analyses indicated that GSK3β/CDK6/β-catenin mRNA signature was significantly higher in colon cancer patients. Additional algorithms predicted a higher miR-26b level was associated with significantly higher survival in CRC patients and GSK3β and CDK6 as targets of miR-26b-5p. To validate these findings in vitro, we showed that CAF-cocultured CRC cells expressed an increased expression of GSK3β, β-catenin, CDK6, and NF-κB. Therapeutically, we demonstrated that gMG treatment suppressed GSK3β-associated signaling pathways while concomitantly increased the miR-26b-5p level. Using a xenograft mouse model of CAFs cocultured HCT116 tumorspheres, we showed that gMG treatment reduced tumor growth and overcame CAF-induced 5-fluorouracil resistance.

CONCLUSIONS

Pharmacological intervention with gMG suppressed CRC carcinogenesis and stemness via downregulating GSK3/β-catenin/CDK6 and upregulating the miR-26b-5p tumor suppressor. Thus, gMG represents a potential new CRC therapeutic agent and warrants further investigation.

Systems-level biomarkers identification and drug repositioning in colorectal cancer

Colorectal cancer (CRC) is the most commonly diagnosed fatal cancer in both women and men worldwide. CRC ranked second in mortality and third in incidence in 2020. It is difficult to diagnose CRC at an early stage as there are no clinical symptoms. Despite advances in molecular biology, only a limited number of biomarkers have been translated into routine clinical practice to predict risk, prognosis and response to treatment. In the last decades, systems biology approaches at the omics level have gained importance. Over the years, several biomarkers for CRC have been discovered in terms of disease diagnosis and prognosis. On the other hand, a few drugs are being developed and used in clinics for the treatment of CRC. However, the development of new drugs is very costly and time-consuming as the research and development takes about 10 years and more than $1 billion. Therefore, drug repositioning (DR) could save time and money by establishing new indications for existing drugs. In this review, we aim to provide an overview of biomarkers for the diagnosis and prognosis of CRC from the systems biology perspective and insights into DR approaches for the prevention or treatment of CRC.

Characterizing Immune Responses in Whole Slide Images of Cancer With Digital Pathology and Pathomics

Purpose of Review

Our goal is to show how readily available Pathomics tissue analytics can be used to study tumor immune interactions in cancer. We provide a brief overview of how Pathomics complements traditional histopathologic examination of cancer tissue samples. We highlight a novel Pathomics application, Tumor-TILs, that quantitatively measures and generates maps of tumor infiltrating lymphocytes in breast, pancreatic, and lung cancer by leveraging deep learning computer vision applications to perform automated analyses of whole slide images.

Recent Findings

Tumor-TIL maps have been generated to analyze WSIs from thousands of cases of breast, pancreatic, and lung cancer. We report the availability of these tools in an effort to promote collaborative research and motivate future development of ensemble Pathomics applications to discover novel biomarkers and perform a wide range of correlative clinicopathologic research in cancer immunopathology and beyond.

Summary

Tumor immune interactions in cancer are a fascinating aspect of cancer pathobiology with particular significance due to the emergence of immunotherapy. We present simple yet powerful specialized Pathomics methods that serve as powerful clinical research tools and potential standalone clinical screening tests to predict clinical outcomes and treatment responses for precision medicine applications in immunotherapy.