Handbook of experimental pharmacology最新文献

Quantitative systems pharmacology (QSP) is an interdisciplinary approach that integrates mathematical modeling, biological knowledge, and experimental data to gain insights into the complex dynamics of drug action and disease progression on the human physiological system. Different types of data from multiple and often disparate sources are used throughout all phases of QSP model development. Herein, we explore the various types of data used to inform the development and application of QSP models, their sources, and how the quality of data impacts the interpretation of model-derived results.

Bacterial infections are a significant public health concern, and the emergence of antibiotic-resistant bacteria (ARB) has become a major challenge for modern medicine. The overuse and misuse of antibiotics have contributed to the development of ARB, which has led to the need for alternative therapies. Plant-derived natural products (PNPs) have been extensively studied for their potential as alternative therapies for the treatment of bacterial infections. The diverse chemical compounds found in plants have shown significant antibacterial properties, making them a promising source of novel antibacterial agents. The use of PNPs as antibacterial agents is particularly appealing because they offer a relatively safe and cost-effective approach to the treatment of bacterial infections. This chapter aims to provide an overview of the current state of research on PNPs as antibacterial agents. It will cover the mechanisms of action of the main PNPs against bacterial pathogens and discuss their potential to be used as complementary therapies to combat ARB. This chapter will also highlight the most common screening methodologies to discover new PNPs and the challenges and future prospects in the development of these compounds as antibacterial agents.

Neurocognitive disorders are characterized by a decline in various components of cognitive function, resulting in a high rate of morbidity and mortality. Despite multiple efforts, there is still a lack of practical preventive and therapeutic approaches for these diseases, and current pharmaceuticals have failed to manage their progression. Consequently, this chapter aims to provide a concise overview of the existing preclinical and clinical evidence that explores the impact of plant-based therapies on the prevention and treatment of neurocognitive disorders.We thoroughly searched different web databases to identify preclinical and clinical studies that investigate the effect of plant-based medicines on cognitive function in animal models, as well as individuals who are healthy, those with mild cognitive decline, or those with Alzheimer's disease. We included studies that examined plant extracts, multi-component herbal preparations, and phytochemicals such as Nigella sativa Linn., Rosmarinus officinalis L., Ginkgo biloba, and Melissa officinalis. The neuroprotective effects of these plants were associated with their anticholinesterase, anti-inflammatory, and antioxidative activities. None of the included studies reported severe adverse reactions.In conclusion, the results of the preclinical and clinical studies indicate the potential benefits of plant-based therapies on neurocognitive disorders. However, more extended and comprehensive clinical studies must confirm these findings thoroughly.

Nanopores have emerged as a powerful, label-free technique for single molecule analysis, offering high sensitivity and rapid analysis capabilities. Originally developed for DNA sequencing, nanopores have shown promise not only for the characterization of other biomolecules, such as RNA, proteins, and glycans but also of small inorganic compounds, such as nanoparticles. Glycosaminoglycans (GAGs) are a linear, highly charged subclass of glycans, which play essential roles in cell signaling, tissue development, and inflammation processes. The immense structural complexity of GAGs involving unique sulfation patterns renders their analysis challenging. This chapter provides a comprehensive overview on the application of biological and solid-state nanopores for the analysis of GAGs.

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.

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.