Handbook of experimental pharmacology最新文献

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.

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.

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.

Metabolic syndrome (MetS) is a prevalent health condition that requires significant attention and intervention due to its multifaceted nature. It encompasses a variety of ailments such as diabetes mellitus, hypertension, obesity, and dyslipidemia. Despite extensive research, the underlying pathophysiology of MetS is not entirely understood, and current synthetic drugs used to treat it have adverse effects and can be expensive. Therefore, natural products are being investigated as a potential alternative treatment for MetS. This chapter provides an overview of studies on natural products as a treatment for MetS. The available evidence suggests that bioactive phytochemicals and herbal medicines, such as curcumin, resveratrol, Nigella sativa, Hibiscus sabdariffa, and Theobroma cacao, have the potential to treat MetS effectively. Furthermore, natural products can be explored as a novel drug discovery approach for MetS. However, it is imperative to conduct well-designed randomized controlled trials with large sample sizes to confirm these findings. Based on our review, we conclude that natural products could be a promising alternative for treating MetS. Further research is warranted to explore this potential fully. The use of natural products for MetS treatment could reduce the reliance on synthetic drugs, many of which have harmful side effects and are costly. The development of natural products as a treatment for MetS could have significant implications for public health, and we encourage further research in this area.

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.

Maintaining good vascular health is a major component in healthy ageing as it reduces the risk of cardiovascular diseases. Endothelial dysfunction, in particular, is a key mechanism in the development of major cardiovascular diseases including hypertension, atherosclerosis and diabetes. Recently, endothelial senescence has emerged as a pivotal early event in age-related endothelial dysfunction. Endothelial function is characterized by an imbalance between the endothelial formation of vasoprotective mechanisms, including the formation of nitric oxide (NO) and endothelium-dependent hyperpolarization responses, and an increased level of oxidative stress involving several pro-oxidant enzymes such as NADPH oxidases and, often also, the appearance of cyclooxygenase-derived vasoconstrictors. Pre-clinical studies have indicated that natural products, in particular several polyphenol-rich foods, can trigger activating pathways in endothelial cells promoting an increased formation of NO and endothelium-dependent hyperpolarization. In addition, some can even exert beneficial effects on endothelial senescence. Moreover, some of these products have been associated with the prevention and/or improvement of established endothelial dysfunction in several experimental models of cardiovascular diseases and in humans with cardiovascular diseases. Therefore, intake of certain natural products, such as dietary and plant-derived polyphenol-rich products, appears to be an attractive approach for a healthy vascular system in ageing.

Mass spectrometry-based techniques have emerged as a gamechanger for the analysis of carbohydrates. Here, we summarize state-of-the-art glycan analysis in vacuo, demonstrating how mass spectrometry, tandem mass spectrometry, ion mobility spectrometry, and gas-phase infrared spectroscopy, supported by chromatography methods and computational modelling, facilitate the structural characterization of isolated glycans and the analysis of the entire glycome. After an introduction to glycans and techniques, the sugar classes of N-glycans, O-glycans, human milk oligosaccharides, and glycosaminoglycans will be discussed in detail, before the chapter ends with an outlook on the future of mass spectrometry-based glycan analysis.

Structural alterations of the glycan chains attached to glycoproteins and glycolipids are present in all types of malignomas investigated to date, including adenocarcinomas, sarcomas and haematological malignancies. They occur in humans as well as in animals including experimental models of malignancy, regardless of the type, cause, or stage of the tumour. The biochemical and genomic characterization of the enzymatic machineries involved in glycan biosynthesis in cancer cells shows that tumour-associated glycosylation changes are a critical part of tumour initiation and progression. Experimental studies and epidemiological findings give clear evidence that tumour-associated glycans bear functional significance in the invasive and metastatic growth of malignancies, for immunological tumour defence and, hence, influence the clinical outcome and the prognosis of cancer patients. Tumour-associated glycan changes are, moreover, targets for new pharmacological and immunological therapy methods and serve as important clinical biomarkers for diagnosis, particularly for monitoring disease progression and therapeutic efficacy. This chapter provides an overview of the major types of changes of glycosylation, genetic and biochemical mechanisms contributing to cancer-associated glycosylation, functional consequences for tumour growth and the clinical significance in cancer diagnosis, monitoring and treatment.