Handbook of experimental pharmacology最新文献

{"title":"Role of Antibody Glycosylation in Health, Disease, and Therapy.","authors":"Falk Nimmerjahn","doi":"10.1007/164_2025_744","DOIUrl":"https://doi.org/10.1007/164_2025_744","url":null,"abstract":"<p><p>Immunoglobulin G (IgG) antibodies are an essential component of humoral immunity protecting the host from recurrent infections. Among all antibody isotypes, IgG antibodies have a uniquely long half-life, can basically reach any tissue in the body, and have the ability to kill opsonized target cells, which has made them the molecule of choice for therapeutic interventions in cancer and autoimmunity. Moreover, IgG antibodies in the form of pooled serum IgG preparations from healthy donors are used to treat chronic inflammatory and autoimmune diseases, providing evidence that serum IgG antibodies can have an active immunomodulatory activity. Research over the last two decades has established that the single sugar moiety attached to each IgG heavy chain plays a very important role in modulating the pro- and anti-inflammatory activities of IgG. Moreover, specific sugar moieties such as sialic acid and galactose residues can serve as highly specific biomarkers for ongoing inflammatory processes. This chapter will summarize how different sugar residues in the IgG sugar moiety change upon inflammation and how such changes may translate to altered IgG function and hence maybe useful for optimizing or modulating the function of therapeutic antibodies.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Systems Pharmacology Development and Application in Neuroscience.","authors":"Hugo Geerts","doi":"10.1007/164_2024_739","DOIUrl":"https://doi.org/10.1007/164_2024_739","url":null,"abstract":"<p><p>Successful clinical development of therapeutics in neurology and psychiatry is challenging due to the complexity of the brain, the lack of validated surrogate markers and the nature of clinical assessments. On the other hand, tremendous advances have been made in unraveling the neurophysiology of the human brain thanks to technical developments in noninvasive biomarkers in both healthy and pathological conditions.Quantitative systems pharmacology (QSP) aims to integrate this increasing knowledge into a mechanistic model of key biological processes that drive clinical phenotypes with the objective to support research and development of successful therapies. This chapter describes both modeling of molecular pathways resulting in measurable biomarker changes, similar to modeling in other indications, as well as extrapolating in a mechanistic way these biomarker outcomes to predict changes in relevant functional clinical scales.Simulating the effect of therapeutic interventions on clinical scales uses the modeling methodology of computational neurosciences, which is based on the premise that human behavior is driven by firing activity of specific neuronal networks. While driven by pathology, the clinical behavior can also be influenced by various medications and common genotype variants. To address this occurrence, computational neuropharmacology QSP models can be developed and, in principle, applied as virtual twins, which are in silico clones of real patients.Overall, central nervous system (CNS) QSP is an important additional tool for supporting research and development from the preclinical stage to post-marketing studies and clinical practice. Overall, CNS QSP is an important additional tool for supporting research and development from the preclinical stage to post-marketing studies and clinical practice.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Framework for Quantitative Systems Pharmacology Model Execution.","authors":"Victor Sokolov, Kirill Peskov, Gabriel Helmlinger","doi":"10.1007/164_2024_738","DOIUrl":"https://doi.org/10.1007/164_2024_738","url":null,"abstract":"<p><p>A mathematical model can be defined as a theoretical approximation of an observed pattern. The specific form of the model and the associated mathematical methods are typically dictated by the question(s) to be addressed by the model and the underlying data. In the context of research and development of new medicines, these questions often focus on the dose-exposure-response relationship.The general workflow for model development and application can be delineated in three major elements: defining the model, qualifying the model, and performing simulations. These elements may vary significantly depending on modeling objectives. Quantitative systems pharmacology (QSP) models address the formidable challenge of quantitatively and mechanistically characterizing human and animal biology, pathophysiology, and therapeutic intervention.QSP model development, by necessity, relies heavily on preexisting knowledge, requires a comprehensive understanding of current physiological concepts, and often makes use of heterogeneous and aggregated datasets from multiple sources. This reliance on diverse datasets presents an upfront challenge: the determination of an optimal model structure while balancing model complexity and uncertainty. Additionally, QSP model calibration is arduous due to data scarcity (particularly at the human subject level), which necessitates the use of a variety of parameter estimation approaches and sensitivity analyses, earlier in the modeling workflow as compared to, for example, population modeling. Finally, the interpretation of model-based predictions must be thoughtfully aligned with the data and the mathematical methods applied during model development.The purpose of this chapter is to provide readers with a high-level yet comprehensive overview of a QSP modeling workflow, with an emphasis on the various challenges encountered in this process. The workflow is centered around the construction of ordinary differential equation models and may be extended beyond this framework. It includes the fundamentals of systematic literature reviews, the selection of appropriate structural model equations, the analysis of system behavior, model qualification, and the application of various types of model-based simulations. The chapter concludes with details on existing software options suitable for implementing the described methodologies.This workflow may serve as a valuable resource to both newcomers and experienced QSP modelers, offering an introduction to the field as well as operating procedures and references for routine analyses.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesis of Sulfated Carbohydrates - Glycosaminoglycans.","authors":"Rakesh Raigawali, Sharath S Vishweshwara, Saurabh Anand, Raghavendra Kikkeri","doi":"10.1007/164_2025_742","DOIUrl":"https://doi.org/10.1007/164_2025_742","url":null,"abstract":"<p><p>Glycosaminoglycans (GAG) are polysaccharides that are ubiquitous on the surface of all mammalian cells, interacting with a multitude of proteins and orchestrating essential physiological and pathological processes. Among various GAG structures, heparan sulfate (HS) stands out for its intricate structure, positioning it as a significant cell-surface molecule capable of regulating wide range of cellular functions. Consequently, investigating the structure-activity relationships (SARs) with well-defined HS ligands emerges as an attractive avenue advancing drug discovery and biosensors. This chapter outlines a modular divergent strategy for synthesizing HS oligosaccharides to elucidate SARs. Here, we provide a literature overview on the synthesis of disaccharide building blocks, employing different orthogonal protecting groups, promoters, and optimization conditions to improve their suitability for subsequent oligosaccharide synthesis. Further, we highlight the synthesis of universal disaccharide building blocks derived from natural polysaccharides. We also provide insights of one-pot method and automated solid-phase synthesis of HS oligosaccharides. Finally, we review the status of SARs of popular heparan sulfate binding proteins (HSBPs).</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Cross-Institutional and Interdisciplinary Collaboration in Defining and Executing a Quantitative Systems Pharmacology Strategy.","authors":"Paolo Vicini, Piet H van der Graaf","doi":"10.1007/164_2024_736","DOIUrl":"10.1007/164_2024_736","url":null,"abstract":"<p><p>The application of quantitative systems pharmacology (QSP) has enabled substantial progress and impact in many areas of therapeutic discovery and development. This new technology is increasingly accepted by industry, academia, and solution providers, and is enjoying greater interest from regulators. In this chapter, we summarize key aspects regarding how effective collaboration among institutions and disciplines can support the growth of QSP and expand its application domain. We exemplify these considerations through a selection of successful cross-institutional or cross-functional collaborations, which resulted in reuse, repurposing, or extension of QSP modeling results or infrastructure, with important and novel results.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143004318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future Directions for Quantitative Systems Pharmacology.","authors":"Birgit Schoeberl, Cynthia J Musante, Saroja Ramanujan","doi":"10.1007/164_2024_737","DOIUrl":"10.1007/164_2024_737","url":null,"abstract":"<p><p>In this chapter, we envision the future of Quantitative Systems Pharmacology (QSP) which integrates closely with emerging data and technologies including advanced analytics, novel experimental technologies, and diverse and larger datasets. Machine learning (ML) and Artificial Intelligence (AI) will increasingly help QSP modelers to find, prepare, integrate, and exploit larger and diverse datasets, as well as build, parameterize, and simulate models. We picture QSP models being applied during all stages of drug discovery and development: During the discovery stages, QSP models predict the early human experience of in silico compounds created by generative AI. In preclinical development, QSP will integrate with non-animal \"new approach methodologies\" and reverse-translated datasets to improve understanding of and translation to the human patient. During clinical development, integration with complementary modeling approaches and multimodal patient data will create multidimensional digital twins and virtual populations for clinical trial simulations that guide clinical development and point to opportunities for precision medicine. QSP can evolve into this future by (1) pursuing high-impact applications enabled by novel experimental and quantitative technologies and data types; (2) integrating closely with analytical and computational advancements; and (3) increasing efficiencies through automation, standardization, and model reuse. In this vision, the QSP expert will play a critical role in designing strategies, evaluating data, staging and executing analyses, verifying, interpreting, and communicating findings, and ensuring the ethical, safe, and rational application of novel data types, technologies, and advanced analytics including AI/ML.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural Products and Diabetes: (-)-Epicatechin and Mechanisms Involved in the Regulation of Insulin Sensitivity.","authors":"Cesar G Fraga, Eleonora Cremonini, Monica Galleano, Patricia I Oteiza","doi":"10.1007/164_2024_707","DOIUrl":"10.1007/164_2024_707","url":null,"abstract":"<p><p>Type 2 diabetes (T2D) is a disease that occurs when cells do not respond normally to insulin, a condition called insulin resistance, which leads to high blood glucose levels. Although it can be treated pharmacologically, dietary habits beyond carbohydrate restriction can be highly relevant in the management of T2D. Emerging evidence supports the possibility that natural products (NPs) could contribute to managing blood glucose or counteract the undesirable effects of hyperglycemia and insulin resistance. This chapter summarizes the relevant preclinical evidence involving the flavonoid (-)-epicatechin (EC) in the optimization of glucose homeostasis, reducing insulin resistance and/or diabetes-associated disorders. Major effects of EC are observed on (i) intestinal functions, including digestive enzymes, glucose transporters, microbiota, and intestinal permeability, and (ii) redox homeostasis, including oxidative stress and inflammation. There is still a need for further clinical studies to confirm the in vitro and rodent data, allowing recommendations for EC, particularly in prediabetic and T2D patients. The collection of similar data and the lack of clinical evidence for EC is also applicable to other NPs.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":"159-173"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139989857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacology of Non-Psychoactive Phytocannabinoids and Their Potential for Treatment of Cardiometabolic Disease.","authors":"Cherry L Wainwright, Sarah K Walsh","doi":"10.1007/164_2024_731","DOIUrl":"10.1007/164_2024_731","url":null,"abstract":"<p><p>The use of Cannabis sativa by humans dates back to the third millennium BC, and it has been utilized in many forms for multiple purposes, including production of fibre and rope, as food and medicine, and (perhaps most notably) for its psychoactive properties for recreational use. The discovery of Δ⁹-tetrahydrocannabinol (Δ⁹-THC) as the main psychoactive phytocannabinoid contained in cannabis by Gaoni and Mechoulam in 1964 (J Am Chem Soc 86, 1646-1647), was the first major step in cannabis research; since then the identification of the chemicals (phytocannabinoids) present in cannabis, the classification of the pharmacological targets of these compounds and the discovery that the body has its own endocannabinoid system (ECS) have highlighted the potential value of cannabis-derived compounds in the treatment of many diseases, such as neurological disorders and cancers. Although the use of Δ⁹-THC as a therapeutic agent is constrained by its psychoactive properties, there is growing evidence that non-psychoactive phytocannabinoids, derived from both Cannabis sativa and other plant species, as well as non-cannabinoid compounds found in Cannabis sativa, have real potential as therapeutics. This chapter will focus on the possibilities for using these compounds in the prevention and treatment of cardiovascular disease and related metabolic disturbances.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":"61-93"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142132558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Vivo and Clinical Studies of Natural Products Targeting the Hallmarks of Cancer.","authors":"Mohamed Elbadawi, Thomas Efferth","doi":"10.1007/164_2024_716","DOIUrl":"10.1007/164_2024_716","url":null,"abstract":"<p><p>Despite more than 200 approved anticancer agents, cancer remains a leading cause of death worldwide due to disease complexity, tumour heterogeneity, drug toxicity, and the emergence of drug resistance. Accordingly, the development of chemotherapeutic agents with higher efficacy, a better safety profile, and the capability of bypassing drug resistance would be a cornerstone in cancer therapy. Natural products have played a pivotal role in the field of drug discovery, especially for the pharmacotherapy of cancer, infectious, and chronic diseases. Owing to their distinctive structures and multiple mechanistic activities, natural products and their derivatives have been utilized for decades in cancer treatment protocols. In this review, we delve into the potential of natural products as anticancer agents by targeting cancer's hallmarks, including sustained proliferative signalling, evading growth suppression, resisting apoptosis and cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. We highlight the molecular mechanisms of some natural products, in vivo studies, and promising clinical trials. This review emphasizes the significance of natural products in fighting cancer and the need for further studies to uncover their fully therapeutic potential.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":"95-121"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural Products in the Treatment of Lower Urinary Tract Dysfunction and Infection.","authors":"Ann-Kathrin Lederer, Martin C Michel","doi":"10.1007/164_2024_708","DOIUrl":"10.1007/164_2024_708","url":null,"abstract":"<p><p>The popularity of natural products for the treatment of lower urinary tract symptoms (LUTS) differs considerably between countries. Here we discuss the clinical evidence for efficacy in two indications, male LUTS suggestive of benign prostatic hyperplasia and urinary tract infections, and the mechanistic evidence from experimental studies. Most evidence for male LUTS is based on extracts from saw palmetto berries, stinging nettle roots, and pumpkin seeds, whereas most evidence for urinary tract infection is available for European golden rod and combined preparations although this field appears more fragmented with regard to extract sources. Based on differences in sample collection and extraction, extracts from the same plants are likely to exhibit at least quantitative differences in potential active ingredients, which makes extrapolation of findings with one extract to those of others potentially difficult. While only limited information is available for most individual extracts, some extracts have been compared to placebo and/or active controls in adequately powered trials.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":"295-323"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139971648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}