定量药理学在疫苗研发中应用的概述与展望

本文从抗原提呈、佐剂、淋巴系统等方面介绍了疫苗的作用机制与产品特点,并总结了定量药理学的QSP、PK/PD、Dose-Response、MBMA等核心工具在疫苗研发的量效关系、临床前与临床转化、生物标记物与有效性相关性等研究中的关键应用。期待在药物研发中成功应用的模型引导药物开发理念能够在疫苗领域促成模型引导的疫苗研发,进而为更安全、有效、可控的疫苗产品研发做出其应有的贡献。     

新药临床试验的计算机模拟

新药临床试验模拟在新药研发中发挥越来越重要的作用,其对相关信息（尤其是药物的药理学知识）进行综合,用于研究假设并改进试验设计,目的是为了减少试验数目、降低成本、缩短开发时间、提高试验成功率、使数据中的信息最大化。另外,通过模拟还可获得对药物处置和作用的深刻理解。在新药研发中,临床试验模拟可用于对变异进行建模、评估临床操作因素的影响、评估假设、比较和优化试验设计、区分竞争性药物等。本文介绍临床试验模拟的概念、作用、发展历程、方法学,重点说明其在新药开发中的应用及意义,最后做了展望。模拟可用于临床试验的很多方面,这里讨论的模拟主要基于药动学/药效学模型（反映药物的处置和作用）。     

第三届定量药理学与新药评价国际会议通知(第三轮)

自从美国FDA(2004年)提出了基于模型的药物研发(Model Based Drug Development,MBDD)模式,定量药理学在全球范围内成为一个热门学科,MBDD已经列入中国十二五国家科技重大专项研     

定量药理学与新药注册

定量药理学（Pharmacometrics）作为一门学科,其定义随着自身的发展而逐渐趋于统一。美国FDA于2009年将这门学科定义为一门用来量化药物的药理、疾病本身以及临床试验设计的学科,而其目的是为了促进药物研发,监管决策以及合理用药。     

基于网络药理学的药物研发新模式

多向药理学（polypharmacology）与网络药理学（networkpharmacology）的兴起,为困境中的新药研发带来新的光明。     

肾脏药理学研究进展及趋势

目前慢性肾病发病率不断上升,但缺乏有效防治肾病药物。发现和确认治疗肾病新的药物靶点、研发和合理应用防治肾病药物是人类健康的迫切需求,也是肾脏药理学的热点研究领域。近年来,肾脏药理学领域的基础研究热点和临床关注重点包括治疗免疫相关肾病药物、新型糖皮质激素、治疗多囊肾病药物、治疗尿酸性肾病药物、治疗肾结石药物、治疗肾性贫血药物和利尿药等新药研发,以及对肾有损伤作用的药物和肾损伤标志物研究。本文对肾脏药理学未来发展趋势亦予以了展望。     

定量药理学与儿童药物研发和合理用药

1引言儿童合理用药是摆在世界各国临床药理学工作者和儿科医生面前一个亟待解决而又悬而难决的课题。长期以来,儿童作为弱势群体和人类的未来是社会大众的保护对象,伦理上不接受儿童作为药物试验的受试者。即使是常用于儿童的药物,也很少在儿童中试验过。     

第三届定量药理学与新药评价国际会议通知(第二轮)

自从美国FDA(2004年)提出了"基于模型的药物研发(Model Based Drug Development,MBDD)"模式,定量药理学在全球范围内成为一个热门学科,MBDD已经列入中国"十二五"国家科技重大专项研究。中国药理学会数学药理专业委员会分别于2007年和2010年在南京和厦门成功举办了两届"定量药理学与新药评价国际学术会议"(International Sympo-siumof Quantitative Pharmacologyin Drug Development and Regulation,ISQP),吸引了国内外众多学者参加。为促进我国定量药理学的发展,增进国内外交流,第三届"定量药理学与新药评价国际学术会议"将于2011年11月3-6日在上海     

定量药理学研究文献的可视化分析

目的：分析近10年国际上定量药理学的研究现状、知识基础、研究热点及研究前沿变化,为未来开展定量药理学的研究和应用提供参考。方法：检索Web of Science数据库2009-2018年发表的关于定量药理学的文献,利用CiteSpace 5.5.R2软件对定量药理学的研究文献进行可视化分析,从时间、国家、机构、作者、期刊、学科分布分析定量药理学的研究现状,通过知识图谱分析该领域的知识基础、研究热点与研究前沿变化。结果：共纳入3 513篇文献,年发文量呈逐年递增的趋势,美国的发文量最多,发文量最多的机构是瑞典的乌普萨拉大学,来自乌普萨拉大学的学者Karlsson MO发文量最多,《Clinical Pharmacokinetics》杂志被引频次最高。研究热点主要包括模型的建立、模型化与仿真、新药研发、治疗药物监测及特殊群体的研究等。未来的研究趋势以指导临床合理用药为主。结论：定量药理学正处于蓬勃发展阶段,其研究的重点主要集中在模型的建立、模型化与仿真、新药研发、治疗药物监测等方面。     

模型引导下免疫检查点抑制剂的研发

免疫检查点抑制剂作为一种新型的抗肿瘤治疗药物,因其对多种肿瘤卓越的疗效及良好的安全性得到广泛认可。基于定量药理学的发展应运而生的模型引导的药物研发(model-informed drug development,MIDD),能加速新药临床试验的进程,提高新药研究过程中决策的正确率,尤其是针对研发难度较大而需求甚广的免疫检查点抑制剂类新药。本文主要以帕博利珠单抗为例,阐述MIDD方法在免疫检查点抑制剂研发过程中的具体应用,包括研发早期有效给药方案的拟定,研发晚期评估临床疗效和验证给药方案的可行性,再至上市后给药方案的再评估及变更,为MIDD指导抗肿瘤新药的研发提供参考。     

Quantitative clinical pharmacology practice for optimal use of antibiotics during the neonatal period

Introduction: For safe and effective neonatal antibiotic therapy, knowledge of the pharmacokinetic parameters of antibacterial agents in neonates is a prerequisite. Fast maturational changes during the neonatal period influence pharmacokinetic and pharmacodynamic parameters and their variability. Consequently, the need for applying quantitative clinical pharmacology and determining optimal drug dosing regimens in neonates has become increasingly recognized.

Areas covered: Modern quantitative approaches, such as pharmacometrics, are increasingly utilized to characterize, understand and predict the pharmacokinetics of a drug and its effect, and to quantify the variability in the neonatal population. Individual factors, called covariates in modeling, are integrated in such approaches to explain inter-individual pharmacokinetic variability. Pharmacometrics has been shown to be a relevant tool to evaluate, optimize and individualize drug dosing regimens.

Expert opinion: Challenges for optimal use of antibiotics in neonates can largely be overcome with quantitative clinical pharmacology practice. Clinicians should be aware that there is a next step to support the clinical decision-making based on clinical characteristics and therapeutic drug monitoring, through Bayesian-based modeling and simulation methods. Pharmacometric modeling and simulation approaches permit us to characterize population average, inter-subject and intra-subject variability of pharmacokinetic parameters such as clearance and volume of distribution, and to identify and quantify key factors that influence the pharmacokinetic behavior of antibiotics during the neonatal period.     

Challenges in the transition to model-based development

Practitioners of the art and science of pharmacometrics are well aware of the considerable effort required to successfully complete modeling and simulation activities for drug development programs. This is particularly true because of the current, ad hoc implementation wherein modeling and simulation activities are piggybacked onto traditional development programs. This effort, coupled with the failure to explicitly design development programs around modeling and simulation, will continue to be an important obstacle to the successful transition to model-based drug development. Challenges with timely data availability, high data discard rates, delays in completing modeling and simulation activities, and resistance of development teams to the use of modeling and simulation in decision making are all symptoms of an immature process capability for performing modeling and simulation. A process that will fulfill the promise of model-based development will require the development and deployment of three critical elements. The first is the infrastructure--the data definitions and assembly processes that will allow efficient pooling of data across trials and development programs. The second is the process itself--developing guidelines for deciding when and where modeling and simulation should be applied and the criteria for assessing performance and impact. The third element concerns the organization and culture--the establishment of truly integrated, multidisciplinary, and multiorganizational development teams trained in the use of modeling and simulation in decision-making. Creating these capabilities, infrastructure, and incentivizations are critical to realizing the full value of modeling and simulation in drug development.     

Using quantitative systems pharmacology for novel drug discovery

Introduction: Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.

Areas covered: This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider ‘systems-level’ context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.

Expert opinion: The use of network biology produces valuable information such as new indications for approved drugs, drug–drug interactions, proteins–drug side effects and pathways–gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from ‘one molecule for one target to give one therapeutic effect’ to the ‘big systems-based picture’ seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.     

Regulatory considerations on the role of general pharmacology studies in the development of therapeutic agents

In contrast to the well-defined regulatory requirements for the conduct of animal toxicology studies, FDA regulations and guidelines for nonclinical pharmacodynamic studies are relatively general and do not require that any specific studies be conducted. General pharmacology studies are conducted to identify actions of a new agent in addition to those associated with the primary therapeutic utility. General pharmacology studies aimed at determining drug effects on cardiovascular, central nervous system, gastrointestinal, respiratory and pulmonary, renal, endocrine and metabolism, autonomic nervous system, and drug-receptor functions were among the types of general pharmacology studies included in a sample of recent investigational new drug application (IND) and new drug application (NDA) submissions. Assessment of drug effects on cardiovascular, autonomic nervous system, and drug-receptor interactions were given the greatest individual importance in identifying drug effects relevant to the assessment of a product's safety at the initial IND stage. At the NDA stage, general pharmacology studies find their greatest value in predicting drug-drug interactions, defining mechanisms of action, characterizing the pharmacological correlates of drug-overdose, identifying dose-limiting effects for the chronic toxicity studies, and associating animal toxicity findings with known pharmacotoxic effects. General pharmacology studies provide valuable information to complement animal toxicity studies for evaluating a drug's potential risk to humans.     

疾病进程模型的研究进展

疾病进程模型是一种描述疾病状态随时间动态变化的模型。它作为基于模型的药物研发的新趋势,正迅速成为研究药物如何影响疾病的重要手段。通过对疾病进程模型的研究能对未来疾病的变化趋势作出预测,结合个体信息及时调整治疗方案。本文对近年来被成功应用的几种疾病进程模型及药物干预模式进行了总结,并探讨了对疾病进程建模及模拟的应用前景。     

《基于药代动力学方法支持PD-1/PD-L1单抗治疗肿瘤患者的替代剂量方案选择指南》解读

近年来,以定量药理学为基础的模型化与仿真技术在新药研发中的地位日益凸显。2021年8月,FDA发布了《基于药代动力学方法支持PD-1/PD-L1单抗治疗肿瘤患者的替代剂量方案选择指南》征求意见稿(以下简称"《指南》"),提出基于群体PK(Pop-PK)模型仿真寻找替代方案的必要性和具体实施标准。本文首先对PD-1/PD-L1单抗的现有临床方案以及该指南的内容进行了总结,随后列举了基于Pop-PK仿真方法辅助替代方案获批的既往实际案例,并进一步分析了该指南用于PD-1/PD-L1单抗替代方案优化的要点,展望其对PD-1/PD-L1单抗临床研发的意义和价值,以期为国内同行提供参考。     

Model-Based Drug Development: The Road to Quantitative Pharmacology

High development costs and low success rates in bringing new medicines to the market demand more efficient and effective approaches. Identified by the FDA as a valuable prognostic tool for fulfilling such a demand, model-based drug development is a mathematical and statistical approach that constructs, validates, and utilizes disease models, drug exposure-response models, and pharmacometric models to facilitate drug development. Quantitative pharmacology is a discipline that learns and confirms the key characteristics of new molecular entities in a quantitative manner, with goal of providing explicit, reproducible, and predictive evidence for optimizing drug development plans and enabling critical decision making. Model-based drug development serves as an integral part of quantitative pharmacology. This work reviews the general concept, basic elements, and evolving role of model-based drug development in quantitative pharmacology. Two case studies are presented to illustrate how the model-based drug development approach can facilitate knowledge management and decision making during drug development. The case studies also highlight the organizational learning that comes through implementation of quantitative pharmacology as a discipline. Finally, the prospects of quantitative pharmacology as an emerging discipline are discussed. Advances in this discipline will require continued collaboration between academia, industry and regulatory agencies.     

Understanding the molecular pharmacology of the serotonergic system: using fluoxetine as a model

Objectives Serotonin is a monoamine neurotransmitter that is widely distributed in the body and plays an important role in a variety of psychological and other body functions such as mood, sexual desire and function, appetite, sleep, memory and learning, temperature regulation and social behaviour. This review will assess the use of fluoxetine, one of the most commonly used selective serotonin reuptake inhibitors, as a model for understanding the molecular pharmacology of the serotoninergic system. Key findings Seven serotonin receptor families have been discovered to date. All serotonin receptors, except 5‐HT₃, are G‐protein coupled, seven transmembrane receptors that activate an intracellular second messenger cascade. The 5‐HT₃ receptor is a ligand‐gated ion channel. Furthermore, 5‐HT_1A receptors are known as autoreceptors since their stimulation inhibits the release serotonin in nerve terminals. A transporter protein found in the plasma membrane of serotonergic neurones is responsible for the reuptake of this neurotransmitter. Selective serotonin reuptake inhibitors, such as fluoxetine, act primarily at the serotonin transporter protein and have limited, if any, reaction with other neurotransmitter systems. Selective serotonin reuptake inhibitors appear to bind with the serotonin transporter with different rates of occupancy, duration and potency. Summary The following review focuses on the interaction of serotonin with this membrane transporter in the body and assesses the use of fluoxetine as a reference drug in the understanding of this interaction.