In Silico Insights: QSAR Modeling of TBK1 Kinase Inhibitors for Enhanced Drug Discovery.

TBK1, or TANK-binding kinase 1, is an enzyme that functions as a serine/threonine protein kinase. It plays a crucial role in various cellular processes, including the innate immune response to viruses, cell proliferation, apoptosis, autophagy, and antitumor immunity. Dysregulation of TBK1 activity can lead to autoimmune diseases, neurodegenerative disorders, and cancer. Due to its central role in these critical pathways, TBK1 is a significant focus of research for therapeutic drug development. In this paper, we explore data from the CAS Content Collection regarding TBK1 and its implication in a large assortment of diseases and disorders. With the demand for developing efficient TBK1 inhibitors being outlined, we focus on utilizing a machine learning approach for developing predictive models for TBK1 inhibition, derived from the fragment-functional analysis descriptors. Using the extensive CAS Content Collection, we assembled a training set of TBK1 inhibitors with experimentally measured IC50 values. We explored several machine learning techniques combined with various molecular descriptors to derive and select the best TBK1 inhibitor QSAR models. Certain significant structural alerts that potentially contribute to inhibition of TBK1 are outlined and discussed. The merit of the article stems from identifying the most adequate TBK1 QSAR models and subsequent successful development of advanced positive training data to facilitate and enhance drug discovery for an important therapeutic target such as TBK1 inhibitors, based on an extensive, wide-ranging set of scientific information provided by the CAS Content Collection.

Unveiling Trends: Nanoscale Materials Shaping Emerging Biomedical Applications.

The realm of biomedical materials continues to evolve rapidly, driven by innovative research across interdisciplinary domains. Leveraging big data from the CAS Content Collection, this study employs quantitative analysis through natural language processing (NLP) to identify six emerging areas within nanoscale materials for biomedical applications. These areas encompass self-healing, bioelectronic, programmable, lipid-based, protein-based, and antibacterial materials. Our Nano Focus delves into the multifaceted utilization of nanoscale materials in these domains, spanning from augmenting physical and electronic properties for interfacing with human tissue to facilitating intricate functionalities like programmable drug delivery.

Navigating Antibacterial Frontiers: A Panoramic Exploration of Antibacterial Landscapes, Resistance Mechanisms, and Emerging Therapeutic Strategies.

The development of effective antibacterial solutions has become paramount in maintaining global health in this era of increasing bacterial threats and rampant antibiotic resistance. Traditional antibiotics have played a significant role in combating bacterial infections throughout history. However, the emergence of novel resistant strains necessitates constant innovation in antibacterial research. We have analyzed the data on antibacterials from the CAS Content Collection, the largest human-curated collection of published scientific knowledge, which has proven valuable for quantitative analysis of global scientific knowledge. Our analysis focuses on mining the CAS Content Collection data for recent publications (since 2012). This article aims to explore the intricate landscape of antibacterial research while reviewing the advancement from traditional antibiotics to novel and emerging antibacterial strategies. By delving into the resistance mechanisms, this paper highlights the need to find alternate strategies to address the growing concern.

Emerging Targets and Therapeutics in Immuno-Oncology: Insights from Landscape Analysis.

In the ever-evolving landscape of cancer research, immuno-oncology stands as a beacon of hope, offering novel avenues for treatment. This study capitalizes on the vast repository of immuno-oncology-related scientific documents within the CAS Content Collection, totaling over 350,000, encompassing journals and patents. Through a pioneering approach melding natural language processing with the CAS indexing system, we unveil over 300 emerging concepts, depicted in a comprehensive "Trend Landscape Map". These concepts, spanning therapeutic targets, biomarkers, and types of cancers among others, are hierarchically organized into eight major categories. Delving deeper, our analysis furnishes detailed quantitative metrics showcasing growth trends over the past three years. Our findings not only provide valuable insights for guiding future research endeavors but also underscore the merit of tapping the vast and unparalleled breadth of existing scientific information to derive profound insights.

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers.

Cancer is one of the leading causes of death worldwide. Early cancer detection is critical because it can significantly improve treatment outcomes, thus saving lives, reducing suffering, and lessening psychological and economic burdens. Cancer biomarkers provide varied information about cancer, from early detection of malignancy to decisions on treatment and subsequent monitoring. A large variety of molecular, histologic, radiographic, or physiological entities or features are among the common types of cancer biomarkers. Sizeable recent methodological progress and insights have promoted significant developments in the field of early cancer detection biomarkers. Here we provide an overview of recent advances in the knowledge related to biomolecules and cellular entities used for early cancer detection. We examine data from the CAS Content Collection, the largest human-curated collection of published scientific information, as well as from the biomarker datasets at Excelra, and analyze the publication landscape of recent research. We also discuss the evolution of key concepts and cancer biomarkers development pipelines, with a particular focus on pancreatic and liver cancers, which are known to be remarkably difficult to detect early and to have particularly high morbidity and mortality. The objective of the paper is to provide a broad overview of the evolving landscape of current knowledge on cancer biomarkers and to outline challenges and evaluate growth opportunities, in order to further efforts in solving the problems that remain. The merit of this review stems from the extensive, wide-ranging coverage of the most up-to-date scientific information, allowing unique, unmatched breadth of landscape analysis and in-depth insights.

The Evolving Landscape of Antibody-Drug Conjugates: In Depth Analysis of Recent Research Progress.

Antibody-drug conjugates (ADCs) are targeted immunoconjugate constructs that integrate the potency of cytotoxic drugs with the selectivity of monoclonal antibodies, minimizing damage to healthy cells and reducing systemic toxicity. Their design allows for higher doses of the cytotoxic drug to be administered, potentially increasing efficacy. They are currently among the most promising drug classes in oncology, with efforts to expand their application for nononcological indications and in combination therapies. Here we provide a detailed overview of the recent advances in ADC research and consider future directions and challenges in promoting this promising platform to widespread therapeutic use. We examine data from the CAS Content Collection, the largest human-curated collection of published scientific information, and analyze the publication landscape of recent research to reveal the exploration trends in published documents and to provide insights into the scientific advances in the area. We also discuss the evolution of the key concepts in the field, the major technologies, and their development pipelines with company research focuses, disease targets, development stages, and publication and investment trends. A comprehensive concept map has been created based on the documents in the CAS Content Collection. We hope that this report can serve as a useful resource for understanding the current state of knowledge in the field of ADCs and the remaining challenges to fulfill their potential.

Amyloidogenic Properties of Peptides Derived from the VHL Tumor Suppressor Protein.

The von Hippel-Lindau tumor suppressor protein (pVHL) is involved in maintaining cellular oxygen homeostasis through the regulated degradation of HIF-α. The intrinsically disordered nature of pVHL makes it prone to aggregation that impairs its function, and this is further aggravated in mutant versions of the protein, thus promoting tumor development. By using in silico analysis, we predicted six peptide fragments from pVHL to be amyloidogenic. This was verified for two of the peptides by biophysical approaches, which demonstrated self-assembly and formation of ß-sheet-rich aggregates, which, under transmission electron microscopy, atomic force microscopy, and X-ray diffraction, displayed typical fibrillar amyloid characteristics. These motifs may serve as proxies for exploring the nature of pVHL aggregation.

Carbamylation promotes amyloidogenesis and induces structural changes in Tau-core hexapeptide fibrils.

BACKGROUND: Carbamylation is a non-enzymatic post-translational modification (PTM), which involves the covalent modification of N-terminus of protein or ε-amino group of Lys. The role of carbamylation in several age-related disorders is well documented, however, the relationship between carbamylation and neurodegenerative disorders including Alzheimer's disease remains uncharted. METHODS: In the present study, using aggregation-prone tau-core hexapeptide fragments 306VQIVYK311 (PHF6) and 275VQIINK280 (PHF6*) as models, we have elucidated the effect of carbamylation on aggregation kinetics and the changes occurring in the 3-dimensional architecture of fibrils using biophysical assays and molecular dynamics simulations. RESULTS: We found that carbamylation aids in amyloid formation and can convert the unstructured off-pathway aggregates into robust amyloids, which were toxic to cells. Electron microscopy images and molecular dynamics simulations of PHF6 fibrils showed that carbamylated peptides can form excess hydrogen bonds and modulate the pitch length and twist of peptides fibrils. We have also compared N-terminal carbamylation to acetylation and further extended our finding to full length tau that exhibits aggregation upon carbamylation even in the absence of any external inducer. CONCLUSION: Our in vitro and in silico results together suggest that carbamylation can modulate the aggregation pathway of the amyloidegenic sequences and cause structural changes in fibril assemblies. GENERAL SIGNIFICANCE: Carbamylation acts as a switch, which triggers the aggregation in short amyloidogenic peptide fragments and modulate the structural changes in resulting amyloid fibrils.