基于机制的生理药动- 药效学模型研究进展

生理药动- 药效学模型通过对生物学过程的描述,可预测体内药物的吸收、分布、代谢、排泄以及进一步的生理生化反应。这类模型是通过已知的生化/ 生理基础来构建,采用了一种“自下而上”的建模方法,并利用数学方法对生理过程及药物的作用机制作出准确的描述。其中药效学部分,根据预测目的和药物作用的复杂性,可进一步分为基于经验的和基于机制的效应模型,这两种模型均可与基于机制的生理药动学模型相连接,最终预测药物的体内处置和效应。基于机制的生理药动- 药效学模型的优势在于：可外推性、新的影响因素的可纳入性以及可用于未知领域的预测等。随着实验技术的不断革新,药物作用机制的逐渐明确,这类模型越来越多的被成功应用于药物研发及风险评估中。综述生理药动- 药效学模型的构建策略与分类以及在药物研发和风险评估中的应用研究。     

卵巢癌类器官模型的构建与应用

该文详述了卵巢癌中病人来源的类器官(patient-derived organoids,PDOs)模型的构建及培养方式,以证实该实验方式具有可重复性与较高的性价比。分析药物筛选结果与临床药物反应性,旨在证明该模型对临床治疗的价值。同时,为扩展类器官模型的应用范围,采用了DNA损伤相关的彗星实验、DNA fiber实验以及免疫荧光、蛋白电泳等实验技术,探索以该模型为基础的各项实验技术的可行性。结果表明,PDOs模型中的药物筛选结果可以模拟临床病人对药物的反应性,铂类化疗治疗效果与PDOs模型中的药物敏感性具有显著相关性。除常规使用的化疗药物以外,也可以通过该模型的药物筛选摸索病人治疗的最佳用药方式,并推动未进入临床的新药的研发。     

血脑屏障体外模型研究进展

脑毛细血管上的特殊结构单元为大脑提供氧气和养分,与此同时形成一种限制性屏障,称为血脑屏障(BBB),该结构单元由单层脑微血管内皮细胞构成,内皮细胞外侧的周细胞、基膜以及星形胶质细胞的足突也参与了血脑屏障的形成.血脑屏障是一种选择性渗透屏障,大多数中枢神经系统候选药物在血脑屏障中的渗透性差,用实验动物进行药物筛选具有成本高、周期长、成功率低等缺点.此外,直接在人体中试验有违道德伦理,但建立可靠的体外血脑屏障模型可以简化实验过程、缩短试验周期、实验结果更易测定,因此建立体外BBB模型可以极大地加快中枢神经系统药物的研发.目前已研究的模型主要可以分为3类:单培养、共培养、三培养,这些模型由简单到复杂,与体内血脑屏障的相似性也越来越高.本文就目前现有的血脑屏障模型进行综述,以期未来在体外BBB模型设计中有新的思路.     

用于药物评价的体外心脏组织模型研究进展

候选药物对心脏的毒副作用是其在开发过程中被淘汰的重要原因之一.传统药物评价所采用的动物模型存在种属差异、成本高、效率低等缺陷.因此,近年来随着干细胞和生物打印等技术的快速发展,体外心脏组织模型的构建受到了越来越多地关注.本文追踪体外心脏模型构建的起源与发展,综述模型所利用的心肌细胞来源以及二维、三维模型构建的相关技术与方法,着重阐述心肌组织模型血管化的重要性及研究进展,并对该领域未来的发展方向进行展望,以期为体外心肌组织模型在药物评价方面的研究和应用提供新思路.     

皮肤刺激实验替代模型的研究新进展

皮肤刺激性是日常使用化妆品最常见的不良反应之一。人类健康相关产品危险性评价常做皮肤刺激性实验,皮肤刺激性试验是化妆品原料及产品安全性评价的主要项目。传统皮肤刺激试验采用实验动物进行,2013年3月11日欧盟已经禁止销售基于动物实验研发的化妆品原料及产品.随着组织工程技术和现代生物技术的发展,多种替代动物试验的体外模型被开发和应用,新的的皮肤刺激物陆续被发现。欧盟多采纳重组人表皮实验方法作为新体外皮肤实验指南(包括模型Episkin和模型Epiderm),随着体外模型重建技术的不断改善,不仅拓展了皮肤模型的临床应用范围,也必然推动新的敏感而特异的皮肤标志物的发现和应用。     

小鼠白血病实验模型

动物白血病模型包括自发性白血病、由物理、化学及生物致癌因子诱发的白血病以及在纯系动物中传代的细胞移植性白血病.它们在研究人类白血病和其它恶性肿瘤的病因学、发病学、实验治疗和新抗癌药物的筛选中都发挥了重要作用.早在1908年,有学者用鸡白血病组织的     

斑马鱼模型在药物筛选中的应用

斑马鱼具有子代数量多、体外受精、胚胎透明、可以做大规模遗传突变筛选等生物学特性, 因此成为一种良好的脊椎动物模式生物。随着研究的深入, 斑马鱼不仅应用于遗传学和发育生物学研究, 而且拓展和延伸到疾病模型和药物筛选领域。作为一种整体动物模型, 斑马鱼能够全面地检测评估化合物的活性和副作用, 实现高内涵筛选。近年来, 科学家们不断地发展出新的斑马鱼疾病模型和新的筛选技术, 并找到了一批活性化合物。这些化合物大多数在哺乳动物模型中也有相似的效果, 其中前列腺素E2(dmPGE2)和来氟米特(Leflunomide)已经进入临床实验, 分别用来促进脐带血细胞移植后的增殖和治疗黑素瘤。这些成果显示了斑马鱼模型很适合用于药物筛选。文章概括介绍了斑马鱼模型的特点和近年来在疾病模型和药物筛选方面的进展, 希望能够帮助人们了解斑马鱼在新药研发中的应用, 并开展基于斑马鱼模型的药物筛选。     

兔结核病模型的应用研究进展

结核病(tuberculosis,TB)由结核分枝杆菌(Mycobacterium tuberculosis,MTB)感染所致,是严重危害患者身心健康的慢性传染病。目前TB的发病机制尚未完全阐明。因此建立恰当的动物模型有助于进一步探索TB发病机制、研发全新高效疫苗,优化治疗方案提供可靠的实验和理论依据。兔TB模型是应用较为广泛的动物模型之一。综述了兔TB模型构建特点及应用进展,从免疫机制和可行性角度深入分析兔TB模型的优势及其局限性,同时梳理了近年来国内外通过构建兔TB模型用于药物研究、疫苗研发和TB治疗方案评价等方面发挥的作用,为临床研究提供参考。     

戊型肝炎病毒细胞培养模型的研究进展

戊型肝炎病毒（ HEV）体外细胞培养模型的建立有助于阐明HEV的感染、复制及其致病机理,有利于新型抗病毒药物和疫苗的研发与评估。近年来国内外对HEV细胞培养模型的报道很多,但所用细胞系、感染方法及病毒的复制效果等不尽相同。因此,对近年来HEV细胞培养模型的研究进展进行了综述。     

非哺乳动物模型与真菌病研究

许多非哺乳动物的免疫功能与哺乳动物免疫系统中的天然免疫很相似,而且一些在哺乳动物模型中表达的真菌毒力因子对非哺乳动物的致病也具有相似性。在医学与药学实验中,非哺乳动物具有结构简单、价格低廉以及不受伦理学制约等优势。因此,非哺乳动物可以作为研究宿主对真菌的天然免疫反应以及筛选抗真菌药物的简单而实用的实验模型。此外,这些宿主所具有的清晰的遗传学特征和保守的天然免疫系统为在分子上的水平研究提供了便利。     

The emergence of the genetically engineered animal models in carcinogenic risk assessment of pharmaceuticals: a case study of process innovation

In this article, we explain the emergence of new short-term tests for carcinogenicity involving genetically engineered animals for the purposes of pharmaceutical regulation. Drawing on some long-standing theories of technological innovation, we argue that the alteration of carcinogenic risk assessment of pharmaceuticals, which occurred from 1998, did not result solely, or perhaps even mainly, from internal logical and technical developments in the experimental sciences of toxicology or genetics. Rather, this process innovation in regulatory science resulted from a complex interaction between scientist activism around molecularization of toxicology in powerful US government institutions, on the one hand, and a powerful research-based trans-national pharmaceutical industry committed to deregulatory mobilization, on the other, seeking to reduce carcinogenicity testing of its products and capable of marshalling significant support from governments and regulators, especially in Europe and Japan, to achieve that goal. The new techno-scientific basis for regulatory decisions about whether pharmaceuticals are carcinogenic risks to the public was, in effect, an accommodation in “bio-political” trading between two power-blocks of expert scientists. Those from industry and their regulatory allies in Europe and Japan, who sought reductions in “the burden” of drug testing, on the one hand, and FDA scientists, who did not accept the simple “reduction” agenda, but were interested in shifting the paradigm of carcinogenicity testing toward geneticization, on the other.     

Generalized parametric cure models for relative survival

Cure models are used in time-to-event analysis when not all individuals are expected to experience the event of interest, or when the survival of the considered individuals reaches the same level as the general population. These scenarios correspond to a plateau in the survival and relative survival function, respectively. The main parameters of interest in cure models are the proportion of individuals who are cured, termed the cure proportion, and the survival function of the uncured individuals. Although numerous cure models have been proposed in the statistical literature, there is no consensus on how to formulate these. We introduce a general parametric formulation of mixture cure models and a new class of cure models, termed latent cure models, together with a general estimation framework and software, which enable fitting of a wide range of different models. Through simulations, we assess the statistical properties of the models with respect to the cure proportion and the survival of the uncured individuals. Finally, we illustrate the models using survival data on colon cancer, which typically display a plateau in the relative survival. As demonstrated in the simulations, mixture cure models which are not guaranteed to be constant after a finite time point, tend to produce accurate estimates of the cure proportion and the survival of the uncured. However, these models are very unstable in certain cases due to identifiability issues, whereas LC models generally provide stable results at the price of more biased estimates.     

遗传性白内障动物模型的研究进展

白内障是严重影响公众健康的重大疾病。人群中, 大部分遗传性白内障是外显率较高的常染色体显性遗传, 但也有X连锁和常染色体隐性遗传存在。随着分子生物学技术的发展, 出现了许多白内障遗传动物模型, 这些模型有助于揭示白内障的发病机制, 为晶状体的发育和生理学研究提供新的见解, 还有助于进一步了解遗传、环境和营养等因素对晶状体的作用方式, 并为白内障遗传病的诊断和治疗提供依据。文章综述了遗传性白内障动物模型的相关基因、突变形式及其研究进展。     

New substitution models for rooting phylogenetic trees

The root of a phylogenetic tree is fundamental to its biological interpretation, but standard substitution models do not provide any information on its position. Here, we describe two recently developed models that relax the usual assumptions of stationarity and reversibility, thereby facilitating root inference without the need for an outgroup. We compare the performance of these models on a classic test case for phylogenetic methods, before considering two highly topical questions in evolutionary biology: the deep structure of the tree of life and the root of the archaeal radiation. We show that all three alignments contain meaningful rooting information that can be harnessed by these new models, thus complementing and extending previous work based on outgroup rooting. In particular, our analyses exclude the root of the tree of life from the eukaryotes or Archaea, placing it on the bacterial stem or within the Bacteria. They also exclude the root of the archaeal radiation from several major clades, consistent with analyses using other rooting methods. Overall, our results demonstrate the utility of non-reversible and non-stationary models for rooting phylogenetic trees, and identify areas where further progress can be made.     

A case study on the choice,interpretation and checking of multilevel models for longitudinal binary outcomes

Recent advances in statistical software have led to the rapid diffusion of new methods for modelling longitudinal data. Multilevel (also known as hierarchical or random effects) models for binary outcomes have generally been based on a logistic-normal specification, by analogy with earlier work for normally distributed data. The appropriate application and interpretation of these models remains somewhat unclear, especially when compared with the computationally more straightforward semiparametric or 'marginal' modelling (GEE) approaches. In this paper we pose two interrelated questions. First, what limits should be placed on the interpretation of the coefficients and inferences derived from random-effect models involving binary outcomes? Second, what diagnostic checks are appropriate for evaluating whether such random-effect models provide adequate fits to the data? We address these questions by means of an extended case study using data on adolescent smoking from a large cohort study. Bayesian estimation methods are used to fit a discrete-mixture alternative to the standard logistic-normal model, and posterior predictive checking is used to assess model fit. Surprising parallels in the parameter estimates from the logistic-normal and mixture models are described and used to question the interpretability of the so-called 'subject-specific' regression coefficients from the standard multilevel approach. Posterior predictive checks suggest a serious lack of fit of both multilevel models. The results do not provide final answers to the two questions posed, but we expect that lessons learned from the case study will provide general guidance for further investigation of these important issues.     

Use of genome-scale metabolic models for understanding microbial physiology

The exploitation of microorganisms in industrial, medical, food and environmental biotechnology requires a comprehensive understanding of their physiology. The availability of genome sequences and accumulation of high-throughput data allows gaining understanding of microbial physiology at the systems level, and genome-scale metabolic models represent a valuable framework for integrative analysis of metabolism of microorganisms. Genome-scale metabolic models are reconstructed based on a combination of genome sequence information and detailed biochemical information, and these reconstructed models can be used for analyzing and simulating the operation of metabolism in response to different stimuli. Here we discuss the requirement for having detailed physiological insight in order to exploit microorganisms for production of fuels, chemicals and pharmaceuticals. We further describe the reconstruction process of genome-scale metabolic models and different algorithms that can be used to apply these models to gain improved insight into microbial physiology.     

Terrestrial models and global change: challenges for the future

A wide variety of models have illustrated the potential importance of terrestrial biological feedbacks on climate and climate change; yet our ability to make precise predictions is severely limited, due to a high degree of uncertainty. In this paper, after briefly reviewing current models, we present challenges for new terrestrial models and introduce a simple mechanistic approach that may complement existing approaches.     

Semiparametric transformation models for current status data with informative censoring

Current status data arise due to only one feasible examination such that the failure time of interest occurs before or after the examination time. If the examination time is intrinsically related to the failure time of interest, the examination time is referred to as an informative censoring time. Such data may occur in many fields, for example, epidemiological surveys and animal carcinogenicity experiments. To avoid severely misleading inferences resulted from ignoring informative censoring, we propose a class of semiparametric transformation models with log‐normal frailty for current status data with informative censoring. A shared frailty is used to account for the correlation between the failure time and censoring time. The expectation‐maximization (EM) algorithm combining a sieve method for approximating an infinite‐dimensional parameter is employed to estimate all parameters. To investigate finite sample properties of the proposed method, simulation studies are conducted, and a data set from a rodent tumorigenicity experiment is analyzed for illustrative purposes.     

Transgenic animals as models for human disease

Transgenic animals, especially mice, have been used quite extensively as models for various human diseases. At first, the level of scientific inquiry was driven by the need to establish the model. In many cases, these models may be considered quite crude because of their limitations. More recently, transgenic models of disease have become more refined and are currently being used to study the pathological mechanisms behind the disease rather than to just provide a model of the disease. Using some examples from the recent literature, we will document the current level and complexity of inquiry using transgenic animals. New techniques and techniques that may prove promising will be discussed. This revised version was published online in August 2006 with corrections to the Cover Date.     

A niche for biology in species distribution models

Why species are found where they are is a central question in biogeography. The most widely used tool for understanding the controls on distribution is species distribution modelling. Species distribution modelling is now a well‐established method in both the theoretical and applied ecological literature. In this special issue we examine the current state of the art in species distribution modelling and explore avenues for including more biological processes in such models. In particular we focus on physiological, demographic, dispersal, competitive and ecological‐modulation processes. This overview highlights opportunities for new species distribution model concepts and developments, as well as a statistical agenda for implementing such models.