Handbook of experimental pharmacology最新文献

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.

Atherosclerosis is a common cardiovascular disease closely associated with factors such as hyperlipidaemia and chronic inflammation. Among them, endothelial dysfunction serves as a major predisposing factor. Vascular endothelial dysfunction is manifested by impaired endothelium-dependent vasodilation, enhanced oxidative stress, chronic inflammation, leukocyte adhesion and hyperpermeability, endothelial senescence, and endothelial-mesenchymal transition (EndoMT). Flavonoids are known for their antioxidant activity, eliminating oxidative stress induced by reactive oxygen species (ROS), thereby preventing the oxidation of low-density lipoprotein (LDL) cholesterol, reducing platelet aggregation, alleviating ischemic damage, and improving vascular function. Flavonoids have also been shown to possess anti-inflammatory activity and to protect the cardiovascular system. This review focuses on the protective effects of these naturally-occuring bioactive flavonoids against the initiation and progression of atherosclerosis through their effects on endothelial cells including, but not limited to, their antioxidant, anti-inflammatory, anti-thrombotic, and lipid-lowering properties. However, more clinical evidences are still needed to determine the exact role and optimal dosage of these compounds in the treatment of atherosclerosis.

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.

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.

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.

Cannabis sativa is one of the oldest medicinal plants in human history. Even ancient physicians from hundreds of years ago used Cannabis sativa to treat several conditions like pain. In the modern era, the research community, including health-care providers, have witnessed wide-scale changes in cannabis policy, legislation, and marketing, with a parallel increase in patient interest. A simple search in PubMed using "cannabis and pain" as keywords provides more than 2,400 articles, about 80% of which were published in the last 8-10 years. Several advancements have been achieved in understanding the complex chemistry of cannabis along with its multiple pharmacological activities. Preclinical data have demonstrated evidence for the promising potential of cannabis for pain management, and the continuous rise in the prevalence of pain increases the urgency to translate this into clinical practice. Despite the large body of cannabis literature, researchers still need to find rigorous answers for the questions about the efficacy and safety of cannabis in treatment of certain disorders such as pain. In the current chapter, we seek to present a critical overview about the current knowledge on cannabis with special emphasis on pain-related disorders.

The chapter provides a brief overview on current methodologies for the assembly of complex oligosaccharides by chemical synthesis. Following an introductory section describing the major factors and variables involved in glycosylation reactions, select examples for advanced approaches towards mammalian and bacterial glycans are discussed illustrating recent progress in the field of glycochemistry.

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.

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.

Despite an increasing number of clinical trials, cancer is one of the leading causes of death worldwide in the past decade. Among all complex diseases, clinical trials in oncology have among the lowest success rates, in part due to the high intra- and inter-tumoral heterogeneity. There are more than a thousand cancer drugs and treatment combinations being investigated in ongoing clinical trials for various cancer subtypes, germline mutations, metastasis, etc. Particularly, treatments relying on the (re)activation of the immune system have become increasingly present in the clinical trial pipeline. However, the complexities of the immune response and cancer-immune interactions pose a challenge to the development of these therapies. Quantitative systems pharmacology (QSP), as a computational approach to predict tumor response to treatments of interest, can be used to conduct in silico clinical trials with virtual patients (and emergent use of digital twins) in place of real patients, thus lowering the time and cost of clinical trials. In line with improved mechanistic understanding of the human immune system and promising results from recent cancer immunotherapy, QSP models can play critical roles in model-informed drug development in immuno-oncology. In this chapter, we discuss how QSP models were designed to serve different study objectives, including hypothesis testing, dose optimization, and efficacy prediction, via case studies in immuno-oncology.