Proteomic methods for drug target discovery

The field of drug target discovery is currently very popular with a great potential for advancing biomedical research and chemical genomics. Innovative strategies have been developed to aid the process of target identification, either by elucidating the primary mechanism-of-action of a drug, by understanding side effects involving unanticipated 'off-target' interactions, or by finding new potential therapeutic value for an established drug. Several promising proteomic methods have been introduced for directly isolating and identifying the protein targets of interest that are bound by active small molecules or for visualizing enzyme activities affected by drug treatment. Significant progress has been made in this rapidly advancing field, speeding the clinical validation of drug candidates and the discovery of the novel targets for lead compounds developed using cell-based phenotypic screens. Using these proteomic methods, further insight into drug activity and toxicity can be ascertained.     

Drug discovery in academia

Drug discovery and development is generally done in the commercial rather than the academic realm. Drug discovery involves target discovery and validation, lead identification by high-throughput screening, and lead optimization by medicinal chemistry. Follow-up preclinical evaluation includes analysis in animal models of compound efficacy and pharmacology (ADME: administration, distribution, metabolism, elimination) and studies of toxicology, specificity, and drug interactions. Notwithstanding the high-cost, labor-intensive, and non-hypothesis-driven aspects of drug discovery, the academic setting has a unique and expanding niche in this important area of investigation. For example, academic drug discovery can focus on targets of limited commercial value, such as third-world and rare diseases, and on the development of research reagents such as high-affinity inhibitors for pharmacological "gene knockout" in animal models ("chemical genetics"). This review describes the practical aspects of the preclinical drug discovery process for academic investigators. The discovery of small molecule inhibitors and activators of the cystic fibrosis transmembrane conductance regulator is presented as an example of an academic drug discovery program that has yielded new compounds for physiology research and clinical development. high-throughput screening; drug development; pharmacology; fluorescence; cystic fibrosis transmembrane conductance regulator     

Gene expression microarrays and the integration of biological knowledge

Large-scale parallel measurement of whole-genome RNA expression is now possible with high-density arrays of cDNA or oligonucleotides. Using this technology efficiently will require the integration of other sources of biological information, such as gene identity, biomedical literature and biochemical pathway for a given gene. Such integration is essential to understand the cellular program of gene expression and the molecular physiology of an organism. Advances in microarray technology, and the expected rapid rise in microarray data will lead to new insight into fundamental biological problems such as the prediction of gene function from expression profiles and the identification of potential drug targets from biologically active compounds.     

Target network analysis of adiponectin,a multifaceted adipokine

Application of network analysis to dissect the potential molecular mechanisms of biological processes and complicated diseases has been the new trend in biology and medicine in recent years. Among which, the protein–protein interactions (PPI) networks attract interests of most researchers. Adiponectin, a cytokine secreted from adipose tissue, participates in a number of metabolic processes, including glucose regulation and fatty acid metabolism and involves in a series of complicated diseases from head to toe. Hundreds of proteins including many identified and potential drug targets have been reported to be involved in adiponectin related signaling pathways, which comprised a complicated regulation network. Therapeutic target database (TTD) provides extensive information about the known and explored therapeutic protein targets and the signaling pathway information. In this study, adiponectin associated drug targets based PPI was constructed and its topological properties were analyzed, which might provide some insight into the dissection of adiponectin action mechanisms and promote adiponectin signaling based drug target identification and drug discovery. J. Cell. Biochem. 114: 1145–1152, 2013. © 2012 Wiley Periodicals, Inc.     

Structural proteomics in drug discovery

High-throughput, automated or semiautomated methodologies implemented by companies and structural genomics initiatives have accelerated the process of acquiring structural information for proteins via x-ray crystallography. This has enabled the application of structure-based drug design technologies to a variety of new structures that have potential pharmacologic relevance. Although there remain major challenges to applying these approaches more broadly to all classes of drug discovery targets, clearly the continued development and implementation of these structure-based drug design methodologies by the scientific community at large will help to address and provide solutions to these hurdles. The result will be a growing number of protein structures of important pharmacologic targets that will help to streamline the process of identification and optimization of lead compounds for drug development. These lead agonist and antagonist pharmacophores should, in turn, help to alleviate one of the current critical bottlenecks in the drug discovery process; that is, defining the functional relevance of potential novel targets to disease modification. The prospect of generating an increasing number of potential drug candidates will serve to highlight perhaps the most significant future bottleneck for drug development, the cost and complexity of the drug approval process.     

Structural proteomics in drug discovery

High-throughput, automated or semiautomated methodologies implemented by companies and structural genomics initiatives have accelerated the process of acquiring structural information for proteins via x-ray crystallography. This has enabled the application of structure-based drug design technologies to a variety of new structures that have potential pharmacologic relevance. Although there remain major challenges to applying these approaches more broadly to all classes of drug discovery targets, clearly the continued development and implementation of these structure-based drug design methodologies by the scientific community at large will help to address and provide solutions to these hurdles. The result will be a growing number of protein structures of important pharmacologic targets that will help to streamline the process of identification and optimization of lead compounds for drug development. These lead agonist and antagonist pharmacophores should, in turn, help to alleviate one of the current critical bottlenecks in the drug discovery process; that is, defining the functional relevance of potential novel targets to disease modification. The prospect of generating an increasing number of potential drug candidates will serve to highlight perhaps the most significant future bottleneck for drug development, the cost and complexity of the drug approval process.     

In vivo metabolite profiling as a means to identify uncharacterized lipase function: Recent success stories within the alpha beta hydrolase domain (ABHD) enzyme family

Genome sequencing efforts have identified many uncharacterized lipase/esterase enzymes that have potential to be drug targets for metabolic diseases such as obesity, diabetes, and atherosclerosis. However, sequence information and associated structural predictions provide only a loose framework for linking enzyme function to disease risk. We are now confronted with the challenge of functionally annotating a large number of uncharacterized lipases, with the goal of generating new therapies for metabolic diseases. This daunting challenge involves gathering not only sequence-driven predictions, but also more importantly structural, biochemical (substrates and products), and physiological data. At the center of such drug discovery efforts are accurately identifying physiologically relevant substrates and products of individual lipases, and determining whether newly identified substrates/products can modulate disease in appropriate preclinical animal model systems. This review describes the importance of coupling in vivo metabolite profiling to in vitro enzymology as a powerful means to assign lipase function in disease specific contexts using animal models. In particular, we highlight recent examples using this multidisciplinary approach to functionally annotate genes within the α/β hydrolase fold domain (ABHD) family of enzymes. These new discoveries within the ABHD enzyme family serve as powerful examples of linking novel lipase function to human disease. This article is part of a Special Issue entitled Tools to study lipid functions.     

Optimization of a higher throughput microsomal stability screening assay for profiling drug discovery candidates

Metabolic stability plays an important role in the success of drug candidates. First-pass metabolism is one of the major causes of poor oral bioavailability and short half-life. Traditionally, metabolic stability was evaluated at a later stage of drug discovery and required laborious manual manipulations. With the advance of high-throughput screening, combinatorial chemistry, and early profiling of drug-like properties, automated and rapid stability assays are needed to meet the increasing demand of throughput, speed, and reproducibility at earlier stages of drug discovery. The authors describe optimization of a simple, robust, high-throughput microsomal stability assay developed in a 96-well format. The assay consists of 2 automated components: robotic sample preparation for incubation and cleanup and rapid liquid chromatography/mass spectrometry/mass spectrometry (LC/MS/MS) analysis to determine percent remaining of the parent compound. The reagent solutions and procedural steps were optimized for automation. Variables affecting assay results were investigated. The variability introduced by microsome preparations from different sources (various vendors and batches) was studied and indicates the need for careful control. Quality control and normalization of the stability results are critical when applying the screening data, generated at different times or research sites, to discovery projects.     

Structural genomics and drug discovery: all in the family

Structural genomics is starting to have an impact on the early stages of drug discovery and target validation through the contribution of new structures of known and potential drug targets, their complexes with ligands and protocols and reagents for additional structural work within a drug discovery program. Recent progress includes structures of targets from bacterial, viral and protozoan human pathogens, and human targets from known or potential druggable protein families such as, kinases, phosphatases, dehydrogenases/oxidoreductases, sulfo-, acetyl- and methyl-transferases, and a number of other key metabolic enzymes. Importantly, many of these structures contained ligands in the active sites, including for example, the first structures of target-bound therapeutics. Structural genomics of protein families combined with ligand discovery holds particular promise for advancing early stage discovery programs.     

药物靶标蛋白在蛋白网络中的分布

药物靶标发现是目前生物学研究领域的热点和难点问题。从已有药物靶标中寻找规律可以为新靶标的发现总结规律,提供依据。随着功能基因组学的发展,这种组学数据的积累为这一问题的研究提供了契机。本文研究了已有靶标在蛋白网络中的分布,并分析了它们的蛋白功能域组成情况。结果显示靶标基因倾向位于网络的核心区域,并且集中在一些特定蛋白家族中。这些规律的总结将对药物研发过程中药物靶点的选择提供一定的帮助。     

BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation

We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. The platform offers prioritizations of putative disease genes, supported by functional hypotheses. We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge. Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at .     

Drug discovery of the future: the implications of the human genome project

The elucidation of the 3.2-gigabase human genome will have various impacts on drug discovery. The number of drug targets will increase by at least one order of magnitude and target validation will become a high-throughput process. To benefit from these opportunities, a theory-based integration of the vast amount of new biological data into models of biological systems is called for. The skills and knowledge required for genome-based drug discovery of the future go beyond the traditional competencies of the pharmaceutical industry. Cooperation with biotechnology firms and research institutions during drug discovery and development will become even more important.     

二氢乳清酸脱氢酶靶向抗疟药研究进展

疟疾是全球危害最严重的传染性疾病之一,尤其是在非洲,发病率与死亡率仍居高不下。抗药性的出现和发展使大多数现有抗疟药在临床上失去了效用,研究和开发新型抗疟药已成为当前疟疾防治研究的迫切需求。随着恶性疟原虫基因组测序的完成和对疟原虫生物学认知的不断深入,寻找抗疟新靶点的研究得以快速发展。嘧啶生物合成途径是经临床确证有效的抗疟靶点的典范。我们简要综述了近年来以恶性疟原虫嘧啶从头合成途径第四步关键酶——二氢乳清酸脱氢酶（DHODH）为靶点的抗疟新药研究。高通量筛选、药物化学等研究已获得若干对恶性疟原虫DHODH有选择性抑制作用的化合物结构,其中有些在恶性疟原虫体外培养试验中表现出了较强的抗疟作用,且其酶抑制活性与抗疟活性间具有良好的相关性。通过三唑并嘧啶类系列先导化合物的优化研究,已获得了具有良好代谢稳定性、对鼠疟模型有效的类似物。已有大量研究表明DHODH靶向抗疟药的研发具有广阔前景。     

Optimal control problems arising in cell-cycle-specific cancer chemotherapy

We explore mathematical properties of models of cancer chemotherapy including cell-cycle dependence. Using the mathematical methods of control theory, we demonstrate two assertions of interest for the biomedical community: 1 Periodic chemotherapy protocols are close to the optimum for a wide class of models and have additional favourable properties. 2 Two possible approaches, (a) to minimize the final count of malignant cells and the cumulative effect of the drug on normal cells, or (b) to maximize the final count of normal cells and the cumulative effect of the drug on malignant cells, lead to similar principles of optimization. From the mathematical viewpoint, the paper provides a catalogue of simplest mathematical models of cell-cycle dependent chemotherapy. They can be classified based on the number of compartments and types of drug action modelled. In all these models the optimal controls are complicated by the singular and periodic trajectories and multiple solutions. However, efficient numerical methods have been developed. In simpler cases, it is also possible to provide an exhaustive classification of solutions. We also discuss developments in estimation of cell cycle parameters and cell-cycle dependent drug action.     

药物蛋白质组学与药物发现

21世纪,科学家面临着从基因组到蛋白质组的转变,蛋白质组学是基因组和药物发现的效率。药物蛋白质组学研究不仅有助于发现治疗的可能靶点,也将明显提高药物发现的效率。药物蛋白质组学的研究内容,在临床前包括发现新的治疗靶点和发现针对所有靶点的全部化合物,在临床研究方面应包括药物作用的特异蛋白作为诊断和治疗的标志,或以蛋白质谱的差异来分类者。本文主要综述了蛋白质组学在药物靶点的发现和确认,以有药物发现过程中最有关的技术物研究进展。     

Observing local and global properties of metabolic pathways: 'load points' and 'choke points' in the metabolic networks

MOTIVATION: The local and global aspects of metabolic network analyses allow us to identify enzymes or reactions that are crucial for the survival of the organism(s), therefore directing us towards the discovery of potential drug targets. RESULTS: We demonstrate a new method ('load points') to rank the enzymes/metabolites in the metabolic network and propose a model to determine and rank the biochemical lethality in metabolic networks (enzymes/metabolites) through 'choke points'. Based on an extended form of the graph theory model of metabolic networks, metabolite structural information was used to calculate the k-shortest paths between metabolites (the presence of more than one competing path between substrate and product). On the basis of these paths and connectivity information, load points were calculated and used to empirically rank the importance of metabolites/enzymes in the metabolic network. The load point analysis emphasizes the role that the biochemical structure of a metabolite, rather than its connectivity (hubs), plays in the conversion pathway. In order to identify potential drug targets (based on the biochemical lethality of metabolic networks), the concept of choke points and load points was used to find enzymes (edges) which uniquely consume or produce a particular metabolite (nodes). A non-pathogenic bacterial strain Bacillus subtilis 168 (lactic acid producing bacteria) and a related pathogenic bacterial strain Bacillus anthracis Sterne (avirulent but toxigenic strain, producing the toxin Anthrax) were selected as model organisms. The choke point strategy was implemented on the pathogen bacterial network of B.anthracis Sterne. Potential drug targets are proposed based on the analysis of the top 10 choke points in the bacterial network. A comparative study between the reported top 10 bacterial choke points and the human metabolic network was performed. Further biological inferences were made on results obtained by performing a homology search against the human genome. AVAILABILITY: The load and choke point modules are introduced in the Pathway Hunter Tool (PHT), the basic version of which is available on http://www.pht.uni-koeln.de.     

Biomedical applications of protein chips

The development of microchips involving proteins has accelerated within the past few years. Although DNA chip technologies formed the precedent, many different strategies and technologies have been used because proteins are inherently a more complex type of molecule. This review covers the various biomedical applications of protein chips in diagnostics, drug screening and testing, disease monitoring, drug discovery (proteomics), and medical research. The proteomics and drug discovery section is further subdivided to cover drug discovery tools (on-chip separations, expression profiling, and antibody arrays), molecular interactions and signaling pathways, the identification of protein function, and the identification of novel therapeutic compounds. Although largely focused on protein chips, this review includes chips involving cells and tissues as a logical extension of the type of data that can be generated from these microchips.     

Driving efficiency in a high-throughput metabolic stability assay through a generic high-resolution accurate mass method and automated data mining

Improving analytical throughput is the focus of many quantitative workflows being developed for early drug discovery. For drug candidate screening, it is common practice to use ultra-high performance liquid chromatography (U-HPLC) coupled with triple quadrupole mass spectrometry. This approach certainly results in short analytical run time; however, in assessing the true throughput, all aspects of the workflow needs to be considered, including instrument optimization and the necessity to re-run samples when information is missed. Here we describe a high-throughput metabolic stability assay with a simplified instrument set-up which significantly improves the overall assay efficiency. In addition, as the data is acquired in a non-biased manner, high information content of both the parent compound and metabolites is gathered at the same time to facilitate the decision of which compounds to proceed through the drug discovery pipeline.     

Bringing Biocuration to China

Biocuration involves adding value to biomedical data by the processes of standardization, quality control and information transferring(also known as data annotation). It enhances data interoperability and consistency, and is critical in translating biomedical data into scientific discovery. Although China is becoming a leading scientific data producer, biocuration is still very new to the Chinese biomedical data community. In fact, there currently lacks an equivalent acknowledged word in Chinese for the word ‘‘curation". Here we propose its Chinese translation as ‘‘审编"(Pinyin: sheˇn bi"an), based on its implied meanings taken by biomedical data community.The 8th International Biocuration Conference to be held in China(http://biocuration2015.tilsi.org)next year bears the potential to raise the general awareness in China of the significant role of biocuration in scientific discovery. However, challenges are ahead in its implementation.     

Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.