Engineered Antibodies to Improve Efficacy against Neurodegenerative Disorders.

Antibodies that can selectively remove rogue proteins in the brain are an obvious choice to treat neurodegenerative disorders (NDs), but after decades of efforts, only two antibodies to treat Alzheimer's disease are approved, dozens are in the testing phase, and one was withdrawn, and the other halted, likely due to efficacy issues. However, these outcomes should have been evident since these antibodies cannot enter the brain sufficiently due to the blood-brain barrier (BBB) protectant. However, all products can be rejuvenated by binding them with transferrin, preferably as smaller fragments. This model can be tested quickly and at a low cost and should be applied to bapineuzumab, solanezumab, crenezumab, gantenerumab, aducanumab, lecanemab, donanemab, cinpanemab, and gantenerumab, and their fragments. This paper demonstrates that conjugating with transferrin does not alter the binding to brain proteins such as amyloid-ß (Aß) and α-synuclein. We also present a selection of conjugate designs that will allow cleavage upon entering the brain to prevent their exocytosis while keeping the fragments connected to enable optimal binding to proteins. The identified products can be readily tested and returned to patients with the lowest regulatory cost and delays. These engineered antibodies can be manufactured by recombinant engineering, preferably by mRNA technology, as a more affordable solution to meet the dire need to treat neurodegenerative disorders effectively.

Innovative Therapeutic Strategies in Alzheimer's Disease: A Synergistic Approach to Neurodegenerative Disorders.

Alzheimer's disease (AD) remains a significant challenge in the field of neurodegenerative disorders, even nearly a century after its discovery, due to the elusive nature of its causes. The development of drugs that target multiple aspects of the disease has emerged as a promising strategy to address the complexities of AD and related conditions. The immune system's role, particularly in AD, has gained considerable interest, with nanobodies representing a new frontier in biomedical research. Advances in targeting antibodies against amyloid-ß (Aß) and using messenger RNA for genetic translation have revolutionized the production of antibodies and drug development, opening new possibilities for treatment. Despite these advancements, conventional therapies for AD, such as Cognex, Exelon, Razadyne, and Aricept, often have limited long-term effectiveness, underscoring the need for innovative solutions. This necessity has led to the incorporation advanced technologies like artificial intelligence and machine learning into the drug discovery process for neurodegenerative diseases. These technologies help identify therapeutic targets and optimize lead compounds, offering a more effective approach to addressing the challenges of AD and similar conditions.

Glucagon-like peptide agonists: A prospective review.

BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have emerged as promising therapeutic options for addressing Type-2 diabetes, obesity, and related conditions. Among these, semaglutide, tirzepatide, liraglutide etc., all notable GLP-1RA, have gained attention owing to their favourable pharmacological properties and clinical efficacy. AIMS: This comprehensive review aims to provide a detailed analysis of both the currently available GLP-1RAs in the market and those undergoing clinical trials. The focus is on examining their mechanism of action, pharmacokinetics, efficacy in glycemic control and weight management, safety profile, and potential applications. MATERIALS & METHODS: The review employs a systematic approach to gather information on GLP-1RAs. Relevant literature from the currently literature and ongoing clinical trials is thoroughly examined. Detailed scrutiny is applied to understand the mechanism of action, pharmacokinetic properties, and clinical outcomes of these agents. RESULTS: The review presents a comprehensive overview of the GLP-1RAs, highlighting their distinct mechanisms of action, pharmacokinetic profiles, and clinical effectiveness in glycemic control and weight management. Safety profiles are also discussed, providing a holistic understanding of these therapeutic agents. DISCUSSION: The findings are discussed in the context of advancements in the field of GLP-1RAs. Potential applications beyond diabetes and obesity are explored, shedding light on the broader implications of these agents in managing related conditions. CONCLUSION: In conclusion, this review underscores the significance of GLP-1RAs, with a specific focus on semaglutide, in the management of type 2 diabetes, obesity, and beyond. By synthesizing information on their mechanisms, pharmacokinetics, efficacy, and safety, this review provides valuable insights into the potential benefits these agents offer, contributing to the ongoing discourse in the field.

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review.

In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis.

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.