Handbook of experimental pharmacology最新文献

{"title":"Decrypting Glycosaminoglycan \"sulfation code\" with Computational Approaches.","authors":"Sergey A Samsonov, Mateusz P Marcisz","doi":"10.1007/164_2025_741","DOIUrl":"https://doi.org/10.1007/164_2025_741","url":null,"abstract":"<p><p>Glycosaminoglycans (GAGs), linear anionic periodic polysaccharides, play pivotal roles in various biologically relevant processes within the extracellular matrix (ECM). These processes encompass cell development, proliferation, signaling, ECM assembly, coagulation, and angiogenesis. GAGs perform their functions through their interactions with specific protein partners, rendering them attractive targets for regenerative medicine and drug design. However, the molecular mechanisms governing protein-GAG interactions remain unclear. Classical structure determination techniques face significant challenges when dealing with protein-GAG complexes. This is due to GAGs' unique properties, including their extensive length, flexibility, periodicity, symmetry, multipose binding, and the high heterogeneity of their sulfation patterns constituting the \"sulfation code.\" Consequently, only a limited number of experimental protein-GAG structures have been elucidated. Hence, theoretical approaches are particularly promising in deciphering the code for understanding the structure-function relationship of these complex molecules. In this chapter, we focus on the particularities, challenges, and advances of computational methods such as molecular docking, molecular dynamics, and free-energy calculations when applied to GAG-containing systems. These computational approaches offer valuable insights into the enigmatic world of protein-GAG interactions, paving the way for their enhanced understanding and potential therapeutic applications.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752383","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":"Diagnostic and Therapeutic Approaches in Congenital Disorders of Glycosylation.","authors":"Alexandre Raynor, Élodie Lebredonchel, François Foulquier, François Fenaille, Arnaud Bruneel","doi":"10.1007/164_2025_745","DOIUrl":"https://doi.org/10.1007/164_2025_745","url":null,"abstract":"<p><p>Congenital disorders of glycosylation (CDG) constitute an increasing group of inborn metabolic disorders, with more than 170 described diseases to date. A disturbed glycosylation process characterizes them, with molecular defects localized in distinct cell compartments. In CDG, N-glycosylation, O-glycosylation, glycosylation of lipids (including phosphatidylinositol) as well as the glycosaminoglycan synthesis can be affected. Owing to the importance of glycosylation for the function of concerned proteins and lipids, glycosylation defects have diverse clinical consequences. CDG affected individuals often present with a non-specific multivisceral syndrome including neurological involvement, intellectual disability, dysmorphia, and hepatopathy. As CDG are rare diseases frequently lacking distinctive symptoms, biochemical and genetic testing bear important and complementary diagnostic roles.After an introduction on glycosylation and CDG, we review current biomarkers and analytical techniques in the field. Furthermore, we illustrate their interests in the follow-up of proven therapeutic approaches including D-mannose in MPI-CDG, D-galactose in PGM1-CDG, and manganese (MnSO₄) in TMEM165-CDG.</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":"143676968","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":"Integrating QSP and ML to Facilitate Drug Development and Personalized Medicine.","authors":"Tongli Zhang","doi":"10.1007/164_2024_740","DOIUrl":"https://doi.org/10.1007/164_2024_740","url":null,"abstract":"<p><p>In this chapter, the potential integration between quantitative systems pharmacology (QSP) and machine learning (ML) is explored. ML models are in their nature \"black boxes\", since they make predictions based on data without explicit system definitions, while on the other hand, QSP models are \"white boxes\" that describe mechanistic biological interactions and investigate the systems properties emerging from such interactions. Despite their differences, both approaches have unique strengths that can be leveraged to form a powerful integrated tool. ML's ability to handle large datasets and make predictions is complemented by QSP's detailed mechanistic insights into drug actions and biological systems. The chapter discusses basic ML techniques and their application in drug development, including supervised and unsupervised learning methods. It also illustrates how combining QSP with ML can facilitate the design of combination therapies against cancer resistance to single therapies. The synergy between these two methodologies shows promise to accelerate the drug development process, making it more efficient and tailored to individual patient needs.</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":"143673435","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":"Activity-Based Profiling of Retaining Glycosidases in Disease Diagnosis and Their Application in Drug Discovery.","authors":"Yevhenii Radchenko, Johannes M F G Aerts, Gideon J Davies, Jeroen D C Codée, Herman S Overkleeft","doi":"10.1007/164_2025_743","DOIUrl":"https://doi.org/10.1007/164_2025_743","url":null,"abstract":"<p><p>Retaining glycosidases employ a two-step double displacement mechanism to hydrolyze their substrate glycosides. This mechanism involves a covalent enzyme-substrate adduct, and irreversible retaining glycosidase inhibitors have been designed based on this mechanism. Tagging such inhibitors with a reported moiety (biotin, fluorophore, bioorthogonal tag) provides activity-based retaining glycosidase probes. This chapter describes research on such activity-based probes that are inspired by the natural product retaining β-glucosidase inhibitor, cyclophellitol. Modulation of the configuration and substitution pattern yielded a suite of probes with which a host of retaining glycosidases are inhibited, and reported on, including enzymes involved in human pathologies (cancer, inherited lysosomal storage disorders). This chapter provides insights into their design and synthesis, their application in disease diagnosis, and their application in drug discovery, both as tools to uncover competitive inhibitors and as starting point for the design of covalent inhibitors.</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":"143676963","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":"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}