创新药物体外ADMET 的高通量筛选技术研究进展

目前的新药研发策略已从传统的以活性为主导的筛选模式,转变为在测定药效的同时平行评价化合物的ADMET性质,并建立了系列体外ADMET高通量筛选技术。本文综述了近年来药物体外ADMET高通量筛选技术的研究进展。     

抗体成药性筛选流程及评价策略

自1986年OKT3作为第一个治疗性单克隆抗体获批上市后,抗体技术及抗体药物迅猛发展。单克隆抗体、抗体片段、双(多)特异性抗体、融合蛋白、纳米抗体以及抗体偶联药物(antibody-drug conjugates, ADCs)等推陈出新,在肿瘤、血液、免疫、呼吸和代谢等相关疾病的治疗领域发挥着举足轻重的作用。抗体药物的发现过程,是通过多轮生物学功能评估和可成药性评估,筛选出具有安全、有效、稳定和可工艺放大的最佳候选序列,从而提高药物开发和临床研究的效率及成功率。抗体药物发现阶段的“成药性筛选与评估”已日益受到关注和重视,从药物发现和设计、先导分子筛选到候选分子确认,可及时发现分子潜在的物理化学风险因素,并评估可控性,保证后续药物开发过程中的质量稳定性。本文对抗体发现阶段的成药性筛选评估流程进行了分类和定义,涉及单克隆抗体、双特异性抗体、纳米抗体和ADCs等相关技术和药物形式,同时总结了成药性筛选评估中应重点关注的质量属性和高通量检测技术;系统性地阐述成药性开发流程和策略,为不断涌现的创新型药物的成药性筛选评估提供参考,大幅提高抗体药物开发的效率和成功率。     

海洋糖类创新药物研究进展

海洋生物资源作为一种可持续利用的再生性资源,为我们提供了丰富的海洋特征化合物,特别是糖类化合物已经成为寻找和发现海洋创新药物的重要源泉。海洋糖类化合物依据其来源可分为:海洋植物来源、动物来源及微生物来源糖类分子,而不同来源的糖类化合物由于其结构存在较大差异,可被用于不同功能的糖类药物研发。综述了海洋来源糖类化合物的结构与活性特征、修饰与衍生方法以及相关糖类药物研发的最新进展。尽管我国有丰富的海洋生物资源,且在海洋糖药物研发方面走在世界前列,然而目前糖类药物开发仍面临巨大挑战,亟需有效解决糖类先导化合物的作用靶点、作用机制、药代动力学性质以及安全性评价等方面问题,进而建立完善的糖类药物研发技术平台,加快推进我国具有自主知识产权的海洋糖类创新药物的研究与开发。     

化合物药物-靶标蛋白互作关联预测算法进展分析

药物的使用极大地提高了人类的生存质量。药物的有效性是药物发现研究中的关键环节。药物的有效性通过识别药物与其作用的靶标蛋白来判断。然而,通过高通量筛选的实验方法分析确定化合物药物-靶标蛋白互作关联是一个十分昂贵、耗时且富有挑战性的任务。基于计算方法的化合物药物-靶标蛋白互作关联预测研究具有效率高、成本低的特点,越来越受到人们的重视。相比实验验证方法,化合物药物-靶标蛋白互作关联的计算方法可为药物发现研究后续的生物药学实验提供更为准确的潜在化合物药物-靶标蛋白候选对,达到减少生物实验的时间和成本的目的。本文回顾了近20年来基于计算方法的化合物药物-靶标蛋白互作关联预测算法所涉及的生物医学特征数据、预测方法和技术,并分析研究过程中所面临的生物医学特征数据高维稀疏,以及多源生物医学数据融合程度不高等问题,为进一步研究提供有价值的参考。     

降解致病亨廷顿蛋白的新策略

靶向"不可成药靶点"已成为近年原创性药物开发的一个新方向,其中,通过降解致病蛋白是最具前景的方向,目前已有方法主要是PROTAC(proteolysis targeting chimera)等技术。复旦大学鲁伯埙、费义艳及丁澦合作研究团队的最新研究通过基于化合物芯片和前沿光学方法的筛选发现了特异性靶向自噬降低亨廷顿病致病蛋白的小分子化合物,并在小鼠神经元、亨廷顿病病人细胞以及亨廷顿病果蝇模型中得到验证。以上基于自噬小体绑定化合物(autophagosome tethering compounds, ATTEC)的药物研发原创概念有望为亨廷顿病和其他多种疾病的临床治疗提供了切入点。     

基于生物传感器Biacore S51技术的新药发现与开发

Biacore S51是目前世界上最先进的基于SPR技术的生物传感器,它专门针对药物的研发.该文介绍其在新药发现和开发方面的突出优势,以及在分子水平上研究药物候选物ADME方面的性质.     

基于机器学习的药物-靶标相互作用预测*

近年来,随着计算机硬件、软件工具和数据丰度的不断突破,以机器学习为代表的人工智能技术在生物、基础医学和药学等领域的应用不断拓展和融合,极大地推动了这些领域的发展,尤其是药物研发领域的变革。其中,药物-靶标相互作用(drug-target interactions, DTI)的识别是药物研发领域中的重要难题和人工智能技术交叉融合的热门方向,研究人员在DTI预测方面做了大量的工作,构建了许多重要的数据库,开发或拓展了各类机器学习算法和工具软件。对基于机器学习的DTI预测的基本流程进行了介绍,并对利用机器学习预测DTI的研究进行了回顾,同时对不同的机器学习方法运用于DTI预测的优缺点进行了简单总结,以期对开发更加有效的预测算法和DTI预测的发展提供帮助。     

军事医学科学院药物毒理学研究30年回顾

军事医学科学院的药物毒理学研究源远流长、与时俱进,经过近30年的积累与发展,已建立一整套服务于新药发现、临床前开发、临床实验及上市后监督再评价等完整研发链条的药物毒理学研究体系和学科群,涵盖新药早期发现毒理学、非临床安全性评价以及药物毒性作用机制研究等内容,通过与新药研发体系中其他学科互动与协作,为军事医学科学院乃至全国的新药研发提供了良好的非临床安全性评价的技术平台和保障.     

计算机辅助药物筛选平台及应用

先导化合物发现是创新药物研发的最重要环节之一。针对目前海量功能不明确的小分子化合物,本文构建了一个用来实现快速发现先导化合物,有效降低药物研发成本的计算机辅助药物筛选平台。该平台采用分布式架构思想,集成了Auto Dock Vina和多个小分子库,具有数据安全、计算与存储的负载均衡以及实时监控的特点。应用平台进行先导化合物筛选,在较短时间发现了有针对性的活性小分子化合物,命中率高,大大缩短先导化合物发现周期。该平台具有很好的实用性和良好的扩展性。     

实时细胞反应技术

高通量和高信息量的筛选方法在药物开发中起关键作用。虽然研究者用的方法、工具和策略各不相同,但他们都增加了细胞水平试验技术的投资,以期获得更高质量的首选化合物,并且在药物开发早期更多的测定化合物的药理学性质。最近的一些研究表明药物开发实验室在寻求更多生物学相关、对抑制剂敏感、以及能够研究复杂靶物的高效试验体系。     

<Emphasis Type="Italic">ADDME – Avoiding Drug Development Mistakes Early</Emphasis>: central nervous system drug discovery perspective

The advent of early absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening has increased the attrition rate of weak drug candidates early in the drug-discovery process, and decreased the proportion of compounds failing in clinical trials for ADMET reasons. This paper reviews the history of ADMET screening and its place in pharmaceutical development, and central nervous system drug discovery in particular. Assays that have been developed in response to specific needs and improvements in technology that result in higher throughput and greater accuracy of prediction of human mechanisms of absorption and toxicity are discussed. The paper concludes with the authors' forecast of new models that will better predict human efficacy and toxicity.     

Binding-Site Assessment by Virtual Fragment Screening

The accurate prediction of protein druggability (propensity to bind high-affinity drug-like small molecules) would greatly benefit the fields of chemical genomics and drug discovery. We have developed a novel approach to quantitatively assess protein druggability by computationally screening a fragment-like compound library. In analogy to NMR-based fragment screening, we dock ∼11000 fragments against a given binding site and compute a computational hit rate based on the fraction of molecules that exceed an empirically chosen score cutoff. We perform a large-scale evaluation of the approach on four datasets, totaling 152 binding sites. We demonstrate that computed hit rates correlate with hit rates measured experimentally in a previously published NMR-based screening method. Secondly, we show that the in silico fragment screening method can be used to distinguish known druggable and non-druggable targets, including both enzymes and protein-protein interaction sites. Finally, we explore the sensitivity of the results to different receptor conformations, including flexible protein-protein interaction sites. Besides its original aim to assess druggability of different protein targets, this method could be used to identifying druggable conformations of flexible binding site for lead discovery, and suggesting strategies for growing or joining initial fragment hits to obtain more potent inhibitors.     

Predictive ADMET studies, the challenges and the opportunities

Predictive ADMET is the new 'hip' area in drug discovery. The aim is to use large databases of ADMET data associated with structures to build computational models that link structural changes with changes in response, from which compounds with improved properties can be designed and predicted. These databases also provide the means to enable predictions of human ADMET properties to be made from human in vitro and animal in vivo ADMET measurements. Both methods are limited by the amount of data available to build such predictive models, the limitations of modelling methods and our understanding of the systems we wish to model. The current failures, successes and opportunities are reviewed.     

High-content single-cell drug screening with phosphospecific flow cytometry

Drug screening is often limited to cell-free assays involving purified enzymes, but it is arguably best applied against systems that represent disease states or complex physiological cellular networks. Here, we describe a high-content, cell-based drug discovery platform based on phosphospecific flow cytometry, or phosphoflow, that enabled screening for inhibitors against multiple endogenous kinase signaling pathways in heterogeneous primary cell populations at the single-cell level. From a library of small-molecule natural products, we identified pathway-selective inhibitors of Jak-Stat and MAP kinase signaling. Dose-response experiments in primary cells confirmed pathway selectivity, but importantly also revealed differential inhibition of cell types and new druggability trends across multiple compounds. Lead compound selectivity was confirmed in vivo in mice. Phosphoflow therefore provides a unique platform that can be applied throughout the drug discovery process, from early compound screening to in vivo testing and clinical monitoring of drug efficacy.     

Beyond Profiling: Using ADMET Models to Guide Decisions

ADMET Models, whether in silico or in vitro, are commonly used to ‘profile’ molecules, to identify potential liabilities or filter out molecules expected to have undesirable properties. While useful, this is the most basic application of such models. Here, we will show how models may be used to go ‘beyond profiling’ to guide key decisions in drug discovery. For example, selection of chemical series to focus resources with confidence or design of improved molecules targeting structural modifications to improve key properties. To prioritise molecules and chemical series, the success criteria for properties and their relative importance to a project's objective must be defined. Data from models (experimental or predicted) may then be used to assess each molecule's balance of properties against those requirements. However, to make decisions with confidence, the uncertainties in all of the data must also be considered. In silico models encode information regarding the relationship between molecular structure and properties. This is used to predict the property value of a novel molecule. However, further interpretation can yield information on the contributions of different groups in a molecule to the property and the sensitivity of the property to structural changes. Visualising this information can guide the redesign process. In this article, we describe methods to achieve these goals and drive drug‐discovery decisions and illustrate the results with practical examples.     

FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects

Background  

Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performedin silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making.     

Structure-based maximal affinity model predicts small-molecule druggability

Lead generation is a major hurdle in small-molecule drug discovery, with an estimated 60% of projects failing from lack of lead matter or difficulty in optimizing leads for drug-like properties. It would be valuable to identify these less-druggable targets before incurring substantial expenditure and effort. Here we show that a model-based approach using basic biophysical principles yields good prediction of druggability based solely on the crystal structure of the target binding site. We quantitatively estimate the maximal affinity achievable by a drug-like molecule, and we show that these calculated values correlate with drug discovery outcomes. We experimentally test two predictions using high-throughput screening of a diverse compound collection. The collective results highlight the utility of our approach as well as strategies for tackling difficult targets.     

Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described.     

Structure-based virtual screening,molecular docking,ADMET and molecular simulations to develop benzoxaborole analogs as potential inhibitor against Leishmania donovani trypanothione reductase

Visceral leishmaniasis (VL) is the most fatal form of leishmaniasis and it affects 70 countries worldwide. Increasing drug resistant for antileishmanial drugs such as miltefosine, sodium stibogluconate and pentamidine has been reported in the VL endemic region. Amphotericin B has shown potential antileishmanial activity in different formulations but its cost of treatment and associated nephrotoxicity have limited its use by affected people living in the endemic zone. To control the VL infection in the affected countries, it is necessary to develop new antileishmanial compounds with high efficacy and negligible toxicity. Computer aided programs such as binding free energy estimation; ADMET prediction and molecular dynamics simulation can be used to investigate novel antileishmanial molecules in shorter duration. To develop antileishmanial lead molecule, we performed standard precision (SP) docking for 1160 benzoxaborole analogs along with reference inhibitors against trypanothione reductase of Leishmania parasite. Furthermore, extra precision (XP) docking, ADMET prediction, prime MM-GBSA was conducted over 115 ligands, showing better docking score than reference inhibitors to get potential antileishmanial compounds. Simultaneously, area under the curve (AUC) was estimated using ROC plot to validate the SP and XP docking protocol. Later on, two benzoxaborole analogs with best MM-GBSA ΔG-bind were subjected to molecular simulation and docking confirmation to ensure the ligand interaction with TR. The presented drug discovery based on computational study confirms that BOB27 can be used as a potential drug candidate and warrants further experimental investigation to fight against VL in endemic areas.