Handbook of experimental pharmacology最新文献

Quantitative systems toxicology (QST) is emerging as an independent field of model-informed drug development (MIDD) with a focus on predicting toxicity endpoints. To enable toxicological predictions, QST models require incorporation of mechanistic details specific to safety applications including the ability to accurately model supratherapeutic doses and appropriately represent safety endpoints. Unique to the field of toxicology, mechanistic knowledge is often described through the use of adverse outcome pathways (AOPs), which formally represent existing knowledge about mechanisms of toxicity. The toxicities represented by QST models can arise from exaggerated or adverse pharmacological effects of engaging the drug's intended target (on-target toxicity) or from adverse events due to modulation of additional targets beyond the primary target (off-target toxicity). In cases of on-target toxicity, QST models can be considered as a type of Quantitative Systems Pharmacology (QSP) model that incorporates safety biomarkers and often includes simulations performed outside the therapeutic dose range to explore potential adverse consequences of exaggerated pharmacology in a pre-clinical or clinical setting. QST models assessing off-target toxicities can be considered distinct from QSP models in that they are typically applicable across molecules of a given modality which can (and often do) have different primary therapeutic targets. Off-target QST models commonly focus on the interrogation of general (e.g. pan-compound) toxicity mechanisms, often within a specific organ system. It can be difficult to categorize a model as purely QSP or QST (given that some models can be considered as both a QSP and a QST model), and therefore, we encourage readers to refer to a model based on its context of use and application. Thus, throughout this chapter, we refer to models as QST models when the context of use is to understand safety-related questions. To illustrate QST modeling approaches, examples of QST model applications for on-target and off-target toxicities at different stages of the drug discovery and development pipeline are presented and discussed. Additionally, contexts of use, triggers, key objectives, and potential impacts of QST models including the types of decisions QST applications can inform across drug discovery and development are reviewed. The chapter concludes with an overview of key challenges and future perspectives in the field of QST.

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.

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.

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.

Quantitative systems pharmacology (QSP) is a modeling approach employed in drug research and development that combines mechanistic representations of biological processes with drug pharmacology to deepen biological understanding and predict the responses to novel drugs or protocols. QSP has evolved from and is related to other modeling approaches, but has a number of unique attributes and applications. Here, we clarify the definition of QSP and its key features, trace its evolution, briefly compare it to other approaches, and explain why and how it can be used to reduce risk and improve efficiency in drug research and development.

The basic principles of pharmacokinetics and pharmacodynamics represent the foundational knowledge base upon which complex quantitative systems pharmacology models of drug action are built. This chapter provides a high-level overview of fundamental factors that determine the disposition and physiological responses to drugs and the application of compartmental models to characterize the time-course of drug exposure and pharmacological effects. Many of these processes are subject to capacity-limitation, which is defined by a nonlinear function containing a driving substrate concentration and parameters representing the capacity of the process and a substrate affinity constant. Most contemporary mechanism-based pharmacodynamic models are developed by integrating an appropriate drug exposure forcing function, a mathematical model of the interaction between the drug and its target (i.e., binding and transduction), and the physiological turnover (or production and loss) of the biomarker of drug response. Numerous complexities can be introduced to basic models, such as homeostatic feedback, tolerance mechanisms, disease progression, drug interactions, circadian rhythms, and many others. These basic and advanced models can be viewed as the groundwork for the development of comprehensive quantitative systems pharmacology models that are applicable across biological spatiotemporal scales.

Parasitic diseases including malaria, leishmaniasis, and trypanosomiasis have received significant attention due to their severe health implications, especially in developing countries. Marine natural products from a vast and diverse range of marine organisms such as sponges, corals, molluscs, and algae have been found to produce unique bioactive compounds that exhibit promising potent properties, including antiparasitic, anti-Plasmodial, anti-Leishmanial, and anti-Trypanosomal activities, providing hope for the development of effective treatments. Furthermore, various techniques and methodologies have been used to investigate the mechanisms of these antiparasitic compounds. Continued efforts in the discovery and development of marine natural products hold significant promise for the future of novel treatments against parasitic diseases.

Chronic airway inflammatory diseases like asthma, chronic obstructive pulmonary disease (COPD), and their associated exacerbations cause significant socioeconomic burden. There are still major obstacles to effective therapy for controlling severe asthma and COPD progression. Advances in understanding the pathogenesis of the two diseases at the cellular and molecular levels are essential for the development of novel therapies. In recent years, significant efforts have been made to identify natural products as potential drug leads for treatment of human diseases and to investigate their efficacy, safety, and underlying mechanisms of action. Many major active components from various natural products have been extracted, isolated, and evaluated for their pharmacological efficacy and safety. For the treatment of asthma and COPD, many promising natural products have been discovered and extensively investigated. In this chapter, we will review a range of natural compounds from different chemical classes, including terpenes, polyphenols, alkaloids, fatty acids, polyketides, and vitamin E, that have been demonstrated effective against asthma and/or COPD and their exacerbations in preclinical models and clinical trials. We will also elaborate in detail their underlying mechanisms of action unraveled by these studies and discuss new opportunities and potential challenges for these natural products in managing asthma and COPD.

Embryonic stem cells are pluripotent stem cells originally derived from the inner cell mass of blastocysts and have the essential characteristics of pluripotency and self-renewal. Pluripotent stem cells can differentiate into all of the cell types constituting the adult body. Our current understanding is that pluripotent stem cells transition through three stages: a naïve state, a formative state, and a primed state. The stemness and differentiation of pluripotent stem cells depend on cell-surface glycans, which work as essential modulators in ligand-receptor interactions, cell-cell interactions, and cell-extracellular matrix interactions. Cell-surface glycans bind to various signal ligands, including Wnt, fibroblast growth factors, and bone morphogenetic proteins, and are tissue-specific and developmentally regulated. In addition, intracellular O-linked N-acetylglucosamine, a modification found on only nuclear or cytoplasmic proteins, regulates core transcription factors involved in stemness, phosphorylation of downstream signal components, epigenetics, and liquid-liquid phase separation. Thus, various kinds of glycans regulate each stem cell status; furthermore, different glycan structures at each stage are simultaneously epigenetically regulated by the polycomb repressive complex PRC2. Understanding the functions of glycans in stemness and differentiation is increasingly important for both innovative clinical applications and basic research. This chapter focuses on the roles of glycans in mouse and human pluripotent stem cells.

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.