中药网络药理学研究中的生物信息学方法

为了更有效地治疗癌症、心血管疾病、免疫系统疾病等复杂疾病,基于分子网络的多靶点药物发现理念逐渐成为一种新的趋势,而中药整体、辨证、协同的用药观再一次引起了药物发现领域的极大兴趣。中药在治疗复杂慢性疾病方面有确切的疗效和较小的毒副作用。中药网络药理学从分子网络调控的水平上阐明中药的作用机制,为多靶点药物发现提供有益的启示和借鉴,并有可能从临床有效的中药反向开发现代多组分、多靶点新药。针对基于生物分子网络的中药药理学研究路线中的4 个步骤,介绍近年来中药网络药理学研究中相关的生物信息学方法。     

Multifunctional synthetic poly(L-glutamic acid)-based cancer therapeutic and imaging agents

Modern polymer chemistry has led to the generation of a number of biocompatible synthetic polymers that have been increasingly studied as efficient carriers for drugs and imaging agents. Synthetic biocompatible polymers have been used to improve the efficacy of both small-molecular-weight therapeutics and imaging agents. Furthermore, multiple targeted anticancer agents and/or imaging reporters can be attached to a single polymer chain, allowing multifunctional and/or multimodality therapy and molecular imaging. Having both an anticancer drug and an imaging reporter in a single polymer chain allows noninvasive real-time visualization of the pharmacokinetics of polymeric drug delivery systems, which can uncover and explain the complicated mechanisms of in vivo drug delivery and their correlation to pharmacodynamics. This review examines the use of the synthetic biocompatible polymer poly(L-glutamic acid) (PG) as an efficient carrier of cancer therapeutics and imaging agents. This review summarizes and updates our recent research on the use of PG as a platform for drug delivery and molecular imaging, including recent clinical findings with respect to PG-paclitaxel (PG-TXL), the combination of PG-TXL with radiotherapy, mechanisms of action of PG-TXL, and noninvasive visualization of in vivo delivery of polymeric conjugates with contrast-enhanced magnetic resonance imaging, optical imaging, and multimodality imaging.     

Complex diseases require complex therapies

Understanding disease causes and drug action at the molecular and systems levels could help to identify combinations of drugs that are more effective than individual drugs alone.Since the rise of modern pharmaceutical research and industry in the 1950s, drugs have been used to treat an increasingly wide range of diseases. From antibiotics for treating infections, to antivirals to treat HIV/AIDS, to drugs for hypertension and cancer, drug-based therapies have had enormous effects in curing or converting often fatal diseases into manageable conditions. Even pathophysiologies, such as peptic ulcers, that once required surgery are now routinely treated by drugs.Along with the many successes, several limitations have also become evident. Many diseases, especially those that progress in severity, remain difficult to treat with drugs. The list of such disorders is long and includes aneurysms, congestive heart failure, diabetes, kidney disease and many types of cancer. Even drugs that are efficacious do not work for everybody. Effective drugs cause serious adverse events in a subset of users. As we often cannot predict who might suffer from these side effects, the drug is typically taken off the market.These problems have generated a sense that our current approaches might have reached their limits and that we need new thinking to drive both drug discovery and usage. The extensive advances in our understanding of the basic molecular and cell biology of humans, other mammalian organisms and model organisms indicate that there are probably many more cellular components that could be targeted by drugs to fight disease. Another general insight is that cellular components interact with one another to form extensive networks. These networks have the capability to regulate and coordinate a range of subcellular functions, which gives rise to cellular phenotypes [1,2]. These cellular phenotypes underlie the tissue and organ functions that are characteristics of both health and disease.Malfunctions at the molecular level, when propagated to a higher level of organization, give rise to diseaseGenomics, molecular and cell biology and biochemistry are steadily becoming the basic elements for systems biology. As we continue to identify and characterize parts of cells and tissues, the next step in biology is to understand how these parts come together to form functional systems. The focus is not only to understand the characteristics and functions of individual entities, such as genes, proteins, lipids, sugars and so on, but also to understand how these entities interact with one another and what functions emerge from these interactions [3]. In this line of reasoning, almost all tissue and organ functions as well as organismal behaviour arise from molecular interactions. This has been explicitly demonstrated for coupled biochemical components that form positive feedback loops, which function as bistable switches. Such switches underlie, for instance, long-term depression of synaptic responses in the hippocampus [4] or hunger in mice [5].The systems-biology view that complex networks underlie many diseases is being increasingly demonstrated for many diseases, including heart disease, kidney disease, diabetes, metabolic diseases and cancers. To cast systems of interacting entities as networks is useful because it allows the use of graph theory, a branch of mathematics that analyses how complex systems are organized and how such organization enables system-level functions. When one thinks of complex regulatory networks, we often tend to think of molecular networks, but it is important to remember that networks exist at the level of tissues and organs and between organs at the level of organisms. Tissue-level networks are best recognized in the brain, where the activity of circuits—that is, networks of neurons—can be correlated with the behaviour of animals.The combining of drugs that act on different targets within a network could be more efficacious than treating disease with one drugAt the organismal level, current therapies for hypertension, which include multiple drugs acting at various tissues and organs—β-blockers on the heart, angiotensin-converting-enzyme inhibitors on blood vessels and diuretics on the kidney—provide compelling evidence of how blood pressure is a function of interactions between multiple tissues and organs in the body. Overall, it is reasonable to conclude that there are networks at different levels of organization: molecular networks within and between cells, cellular networks within tissues and organs, and networks of organs that functionally give rise to organismal physiology. Between each of these networks there are multiple connections, which are essential for a healthy organism (Fig 1). Malfunctions at the molecular level, when propagated to a higher level of organization, give rise to disease. Sometimes these malfunctions differ from person to person owing to variations and changes in the person''s genome. These variations indicate that different malfunctions can give rise to the same disease and knowing the molecular malfunctions is essential for developing personalized therapy. The various streams of data show overall that there is reasonable evidence to support a systems-biology approach that uses a network perspective of disease genes and mechanisms [6].Open in a separate windowFigure 1A schematic representation of the layers of networks that underlie organismal function, such as control of blood pressure (hypertension) or glucose levels in the blood (type 2 diabetes). Organismal functions arise from functional interactions between multiple organs. Organ and tissue functions arise from the functions of the multiple cell types of which they are comprised. Molecular networks exist within and between cell types that give rise to cellular functions. Drugs typically change the activity of the molecular components, and this change in activity percolates up to eventually affect organismal functions or malfunctions in disease states.Drugs, by and large, work at the molecular level, just as diseases originate from molecular malfunctions. From penicillin, which inhibits enzymes that make the bacterial cell wall, to β-blockers, such as propranolol, that inhibit β-adrenergic receptors to regulate heart function, to cancer drugs, such as imatinib, that block tyrosine kinases to inhibit the proliferation of cells, the effects of drugs start with molecular interactions. These effects are propagated across scales of organization to alter tissue or organ function to cure or relieve disease. The transmission of the drug effect is not linear. Rather, it occurs through the networks at each level of organization. This type of percolation at various scales of organization can sometimes have harmful consequences in addition to the intended good effect of treating the disease. These are called side effects, where effective treatment of one disease or its symptoms is associated with occurrence of a different type of disease in some individuals taking the drug.Such systems-biology-based approaches are likely to be of increasing value in the treatment of cancer because most cancers undergo multiple molecular changes as they progressWell-known examples of side effects are the occurrence of heart attacks and strokes associated with rofecoxib, which is used to treat osteoarthritis, and rosiglitazone, which is used to treat type 2 diabetes mellitus. In each case the drug is efficacious in treating the disease it is intended to treat but the risk of a serious side effect is too great and these drugs have been largely withdrawn from the market. In both cases, it appears that the side effects were a result of the networks in which the intended drug targets participate in different cell types and tissues.Sometimes, drugs bind to unintended targets and such interactions can lead to serious side effects. Many classes of drug, for reasons that are not always clear, cause arrhythmias by binding to the HERG channel in the heart. As one of its preclinical safety checks, the US Food and Drug Administration (FDA) therefore recommends that the developers of new drugs demonstrate that their drug does not interact with the HERG channel protein. Unintended targets of drugs are also part of cellular networks and, therefore, effects on these targets can be propagated through networks.Drug combinations can also cause unanticipated side effects. Analysis of the FDA Adverse Event Reporting System database (FAERS) by Altman and colleagues [7] showed that paroxetine, an antidepressant, and pravastatin, a cholesterol-lowering drug, raised blood glucose levels when administered in combination, whereas each drug on its own did not. Such an increase in blood glucose is an important consideration for patients with diabetes. This study showed the potential usefulness of analysing large databases, such as FAERS, to identify unanticipated biological effects associated with drug combinations and provided support for the idea that systems biology underlies combination drug therapy.Systems pharmacology is the name that is increasingly being used for the new systems-based approach that is being used to understand drug actions and for drug discovery. Systems pharmacology will take into account genomic variations and molecular complexity in defining physiological and pathophysiological responses at the tissue, organ and organism levels. My colleagues and I have used it to understand drug actions by studying how drug targets function within cellular networks. One hypothesis we have pursued is that, in addition to networks enabling drugs to do bad things, they can also enable good effects.Combining drugs that act on different targets within a network could be more efficacious than treating disease with one drug. Sometimes, complex diseases cannot be treated effectively by modulating a single target. Asthma is a good example: long-acting stimulators of the β-adrenergic receptors and corticosteroids together are effective and are widely used in combination. The combined effects are through drug action at varying timescales in cellular and tissue networks: the long-acting β2-adrenergic activator acutely relaxes the airways while the corticosteroids suppress inflammation with a slower time course.The combination of long-acting β2-adrenergic activators with muscarinic-receptor blockers is going through the approval process for treatment of chronic obstructive pulmonary disease [8]. These drug combinations are based on knowledge of how the targets of these drugs work in the context of cellular regulatory networks, and represent good examples of how systems-level thinking can lead to useful therapies. Such systems-biology-based approaches are likely to be of increasing value in the treatment of cancer because most cancers undergo multiple molecular changes as they progress. The combination of drugs that block the effects of multiple activators and inhibitors of cell growth are likely to become efficacious targeted therapy as we start to obtain detailed knowledge of the molecular networks underlying many cancers.Not all drug combinations are based on network logic. The commonly used antibacterial, Augmentin, combines the antibiotic amoxicillin with clavulanic acid, an inhibitor of the β-lactamase that breaks down the antibiotic. Here, the second drug extends the life of the first drug thus making it more efficacious.A novel systems approach in cancer has been described for treatment of some types of leukaemia and involves the use of genetically engineered T cells, which produce a cytokine storm that can kill off cancerous cells [9]. However, there are serious life-threatening side effects. An article in the New York Times describes how physicians have combined the genetically engineered T cells with antibodies against interleukin-6 by using tocilizumab to keep the effects of the T cells within a therapeutic range [10]. Although the news report suggests that this combination was developed empirically for a medical emergency, post hoc it is clear that the physicians have used an implicit systems approach to select a second drug to manage the risk–benefit ratio of the first drug by considering the source and target cells as part of a multicellular response network.A study from my laboratory [11] also shows that combination therapy can substantially reduce the serious adverse effects associated with a useful drug. We analysed FAERS and found many cases in which a drug B was given for a different reason and reduced a serious adverse event associated with drug A. We studied the combination of rosiglitazone and exenatide in some depth. Patients who were prescribed rosiglitazone and exenatide had a greatly reduced risk of heart attack than did patients prescribed rosiglitazone in combination with other drugs. This finding suggests that exenatide selectively reduces the risk of heart attacks and stroke associated with rosiglitazone. We were able to build molecular networks to show how signals from the targets of these two drugs might intersect and found that the blood protein PAI1 might be involved. PAI1 regulates the protease that breaks down blood clots. Increases in PAI1 levels lead to an increased risk of clots. We validated the network-based molecular mechanisms underlying the drug combination effects in a mouse model of diabetes.…the studies described here and many others are starting to show that systems-level analysis can be a powerful driver for understanding drug actionThis case is not unique. We identified nearly 19,000 other drug combinations in FAERS in which a second drug mitigated a serious adverse event associated with a first drug. Some of these combinations and effects are surprising. H2 antagonists, typically given for acid reflux diseases, were associated with a decreased number of suicides associated with selective serotonin reuptake inhibitors, and the blood-pressure medication lisinopril reduces statin-associated muscle wasting. We have been able to build plausible molecular networks for several of these drug combinations, suggesting that current molecular and cell biological knowledge could be used to develop a network-based understanding of the beneficial effects of drug combinations. Of note, the second drug is often given to treat an entirely different disease and the decreased side effects are unanticipated benefits of drug combination.At a general level, the studies described here and many others show that systems-level analysis can be a powerful driver for understanding drug action. One can envisage three kinds of new knowledge coming from such analyses (Fig 2). First is the identification of unanticipated adverse events that each drug might not produce on its own. Identification and prediction of such adverse effects could prove useful to guide physicians regarding which medicines can be co-prescribed. The second kind of knowledge is the opposite of the first: identification of unanticipated beneficial effects by drug combinations, such as mitigation of side effects. This type of knowledge might lead to repurposing of approved drugs if their efficacy in suppressing adverse events could be established in rigorous clinical trials. The third kind of knowledge, which is the most forward-looking, is that network biology can be used for the discovery of new drugs. Network analysis can provide a rational basis for identifying targets, which, when modulated together by drug combinations, might be distinctively efficacious in treating complex diseases.Open in a separate windowFigure 2A flow chart of how systems biology can affect various facets of pharmacology and therapeutics.Combination therapy based on network biology could become efficacious for the treatment of progressive diseases, such as type 2 diabetes, kidney disease, congestive heart failure and, of course, many cancers. While the necessary knowledge is not yet available, the path forward can be readily seen. Large databases, such as FAERs, can provide empirical knowledge of good and bad outcomes associated with combination therapies in humans. As large amounts of genomic and molecular data are integrated with clinical data when electronic medical records become more widely used and molecular characterization of patients becomes more standardized, it will probably generate a wealth of systems-level information to analyse and generate hypotheses. These hypotheses might help with the design of studies to better understand the progression of diseases, and design new drugs or repurpose existing drugs that, in combination, are more effective for treating complex diseases.? Open in a separate windowRavi Iyengar     

Glycosylation of monoclonal antibody products: Current status and future prospects

Protein based therapeutics have started to dominate the pharmaceutical landscape primarily due to the high efficacy that they have demonstrated against complex diseases. Despite the significant success, issues with regards to safety, efficacy and quality of biotherapeutics have been a point of considerable debate and concern among the various stakeholders. The correlation between the glycoform profile and the safety and efficacy of a drug, in particular, has garnered significant attention of researchers worldwide. An escalated emphasis is presently given to develop an understanding of how the various process parameters as well molecular biology considerations contribute to glycan heterogeneity. This paper aims to review the major developments that have occurred in this area over the last decade. The impact of the various process parameters on glycan expression, methods for obtaining a pre‐determined glycan levels/profiles of a protein therapeutic and developments in the area of real‐time glycan monitoring and control are discussed. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1091–1102, 2016     

Mapping Molecular Association Networks of Nervous System Diseases via Large-Scale Analysis of Published Research

Network medicine has been applied successfully to elicit the structure of large-scale molecular interaction networks. Its main proponents have claimed that this approach to integrative medical investigation should make it possible to identify functional modules of interacting molecular biological units as well as interactions themselves. This paper takes a significant step in this direction. Based on a large-scale analysis of the nervous system molecular medicine literature, this study analyzes and visualizes the complex structure of associations between diseases on the one hand and all types of molecular substances on the other. From this analysis it then identifies functional co-association groups consisting of several types of molecular substances, each consisting of substances that exhibit a pattern of frequent co-association with similar diseases. These groups in turn exhibit interlinking in a complex pattern, suggesting that such complex interactions between functional molecular modules may play a role in disease etiology. We find that the patterns exhibited by the networks of disease – molecular substance associations studied here correspond well to a number of recently published research results, and that the groups of molecular substances identified by statistical analysis of these networks do appear to be interesting groups of molecular substances that are interconnected in identifiable and interpretable ways. Our results not only demonstrate that networks are a convenient framework to analyze and visualize large-scale, complex relationships among molecular networks and diseases, but may also provide a conceptual basis for bridging gaps in experimental and theoretical knowledge.     

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.     

Comparison between molecularly defined and conventional therapeutics in a conditional BCR-ABL cell culture model

Accumulating knowledge about the molecular mechanisms causing human diseases can support the development of targeted therapies such as imatinib, a BCR-ABL-specific tyrosine kinase inhibitor to treat chronic myeloid leukemia (CML). Here, we use lentivirus-mediated RNA interference (RNAi) targeting BCR-ABL and the downstream signaling molecules SHP2, STAT5, and Gab2 to compare the efficacy and specificity of molecularly defined therapeutics with that of conventional cytotoxic drugs (cytarabine, doxorubicin, etoposide) in a conditional BCR-ABL cell culture model. IC(50) values were determined for each drug in TonB cells cultured either with interleukin-3 (IL-3) or BCR-ABL, and molecularly defined therapies were studied using lentivirally expressed shRNAs. We demonstrate that conventional anti-leukemic drugs have small or no differential effects under different cell culture conditions, whereas both imatinib and specific RNAi significantly inhibit proliferation of TonB cells in the presence of BCR-ABL but not IL-3. To study molecularly defined combination therapy, we evaluated either imatinib in TonB cells with target-specific RNAi or we used lentiviral vectors to induce combinatorial RNAi through simultaneous expression of two shRNAs. These combination therapies result in increased efficacy without loss in specificity. Interestingly, combinatorial RNAi can specifically deplete TonB cell cultures in the presence of BCR-ABL, even without targeting the oncogene itself. This model provides a tool to evaluate potential therapeutic targets and to quantify efficacy and specificity preclinically of new combination therapies in BCR-ABL-positive cells.     

Shedding light on designing potential meprin β inhibitors through ligand-based robust validated computational approaches: A proposal to chemists!

Human meprin (EC 3.4.24.18) is a member of the metzincin superfamily. It correlates with matrix metalloproteinases and ADAMs (a disintegrin and metalloproteinase). Overexpression of meprin β is implicated in fibrosis, inflammatory diseases and cancers. However, selective meprin β inhibition is crucial to reduce cancer metastasis and adverse effects in inflammation. It also plays critical roles in modulating several interleukins and growth factors. Moreover, meprin β cleaves amyloid precursor protein, thought to be involved in the progression of Alzheimer’s disease. Therefore, meprin β inhibitors are considered to be emerging therapeutics with paramount importance in the treatment of kidney failure, fibrosis, inflammatory bowel diseases and cancer. Despite its crucial implication in several diseases, no meprin β inhibitors are available as drug candidates till date. Therefore, it is an urgent need to identify new potential meprin β inhibitors as prospective therapeutics. In this article, a series of meprin β inhibitors has been analysed through multiple molecular modelling studies as the first initiative to get an idea about their structural, physicochemical and pharmacophoric requirements for higher activity. All in silico approaches performed here are statistically validated and subsequently adjudicated each other. Compounds with p-carboxylic acid substituted arylsulphonamide moiety attached with m-carboxylic acid substituted benzyl group along with a methylene hydroxamate function may be crucial for imparting potential meprin β inhibition. Depending on the results obtained, 14 molecules have been proposed by QSAR model that predicted a minimum of 4-fold higher activity compared to these compounds of the current study.     

Dual targeting strategies with bispecific antibodies

Monoclonal antibodies are widely used for the treatment of cancer, inflammatory and infectious diseases and other disorders. Most of the marketed antibodies are monospecific and therefore capable of interacting and interfering with a single target. However, complex diseases are often multifactorial in nature, and involve redundant or synergistic action of disease mediators or upregulation of different receptors, including crosstalk between their signaling networks. Consequently, blockade of multiple, different pathological factors and pathways may result in improved therapeutic efficacy. This result can be achieved by combining different drugs, or use of the dual targeting strategies applying bispecific antibodies that have emerged as an alternative to combination therapy. This review discusses the various dual targeting strategies for which bispecific antibodies have been developed and provides an overview of the established bispecific antibody formats.     

Dynamical modelling of pattern formation during embryonic development

The combination of genetic and molecular biology techniques has uncovered the intricacies of several gene networks controlling developmental processes. In the face of such complex regulatory networks, developmental geneticists cannot rely on reasoning alone; a thorough understanding of the spatio-temporal properties of these networks clearly requires the use of proper computational tools and methods.     

A systems biology approach to identify effective cocktail drugs

Background

Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive.

Results

In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes.

Conclusions

The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.

     

Drug repositioning for orphan diseases

The need and opportunity to discover therapeutics for rare or orphan diseases are enormous. Due to limited prevalence and/or commercial potential, of the approximately 6000 orphan diseases (defined by the FDA Orphan Drug Act as <200 000 US prevalence), only a small fraction (5%) is of interest to the biopharmaceutical industry. The fact that drug development is complicated, time-consuming and expensive with extremely low success rates only adds to the low rate of therapeutics available for orphan diseases. An alternative and efficient strategy to boost the discovery of orphan disease therapeutics is to find connections between an existing drug product and orphan disease. Drug Repositioning or Drug Repurposing--finding a new indication for a drug--is one way to maximize the potential of a drug. The advantages of this approach are manifold, but rational drug repositioning for orphan diseases is not trivial and poses several formidable challenges--pharmacologically and computationally. Most of the repositioned drugs currently in the market are the result of serendipity. One reason the connection between drug candidates and their potential new applications are not identified in an earlier or more systematic fashion is that the underlying mechanism 'connecting' them is either very intricate and unknown or indirect or dispersed and buried in an ever-increasing sea of information, much of which is emerging only recently and therefore is not well organized. In this study, we will review some of these issues and the current methodologies adopted or proposed to overcome them and translate chemical and biological discoveries into safe and effective orphan disease therapeutics.     

Biotech news and views

Engineering antibody-based therapeutics for the 21st century Proteopedia – 3D images of biomacromolecules Identifying proteins inside cells: New technology in sight European research and networks: Develop new cures for complex diseases     

以T细胞受体为基础的免疫疗法研究进展

T细胞是机体抗肿瘤免疫的核心,以T细胞功能调控为基础的免疫检查点疗法已经在多种肿瘤的临床治疗中取得了重大突破,以基因工程化T细胞为基础的过继性免疫细胞疗法在血液瘤治疗中取得了重要进展,免疫治疗已经对肿瘤的临床治疗产生了深刻变革,成为肿瘤临床治疗策略的重要组成部分。T细胞受体（T cell receptor,TCR）赋予了T细胞识别肿瘤抗原的特异性,能够识别由主要组织相容性复合体（major histocompatibility complex,MHC）呈递的包括胞内抗原在内的广泛肿瘤抗原,具有高度的抗原敏感性,因而具有广泛的抗肿瘤应用前景。2022年第一款TCR药物的上市开启了TCR药物开发的新纪元,多项TCR药物临床研究表现出潜在的肿瘤治疗价值。本文综述了以TCR为基础的免疫治疗策略研究进展,包括T细胞受体工程化T细胞（T cell receptor-engineered T cell,TCR-T）和TCR蛋白药物,以及基于TCR信号的其他免疫细胞疗法,以期为以TCR为基础的免疫治疗策略开发提供参考。     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.     

A quantitative model for metabolic intervention using gut microbes

As medicine shifts toward precision-based and personalized therapeutics, utilizing more complex biomolecules to treat increasingly difficult and rare conditions, microorganisms provide an avenue for realizing the production and processing necessary for novel drug pipelines. More so, probiotic microbes can be co-opted to deliver therapeutics by oral administration as living drugs, able to survive and safely transit the digestive tract. As living therapeutics are in their nascency, traditional pharmacokinetic–pharmacodynamic (PK–PD) models for evaluating drug candidates are not appropriate for this novel platform. Using a living therapeutic in late-stage clinical development for phenylketonuria (PKU) as a case study, we adapt traditional oral drug delivery models to properly evaluate and inform the engineering of living therapeutics. We develop the adapted for living therapeutics compartmental absorption and transit (ALT-CAT) model to provide metrics for drug efficacy across nine age groups of PKU patients and evaluate model parameters that are influenced by patient physiology, microbe selection and therapeutic production, and dosing formulations. In particular, the ALT-CAT model describes the mathematical framework to model the behavior of orally delivered engineered bacteria that act as living therapeutics by adapting similar methods that have been developed and widely-used for small molecular drug delivery and absorption.     

Drug repurposing in oncology: Compounds,pathways, phenotypes and computational approaches for colorectal cancer

The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.     

Mapping Cellular Polarity Networks Using Mass Spectrometry-based Strategies

Cell polarity is a vital biological process involved in the building, maintenance and normal functioning of tissues in invertebrates and vertebrates. Unsurprisingly, molecular defects affecting polarity organization and functions have a strong impact on tissue homeostasis, embryonic development and adult life, and may directly or indirectly lead to diseases. Genetic studies have demonstrated the causative effect of several polarity genes in diseases; however, much remains to be clarified before a comprehensive view of the molecular organization and regulation of the protein networks associated with polarity proteins is obtained. This challenge can be approached head-on using proteomics to identify protein complexes involved in cell polarity and their modifications in a spatio-temporal manner. We review the fundamental basics of mass spectrometry techniques and provide an in-depth analysis of how mass spectrometry has been instrumental in understanding the complex and dynamic nature of some cell polarity networks at the tissue (apico-basal and planar cell polarities) and cellular (cell migration, ciliogenesis) levels, with the fine dissection of the interconnections between prototypic cell polarity proteins and signal transduction cascades in normal and pathological situations. This review primarily focuses on epithelial structures which are the fundamental building blocks for most metazoan tissues, used as the archetypal model to study cellular polarity. This field offers broad perspectives thanks to the ever-increasing sensitivity of mass spectrometry and its use in combination with recently developed molecular strategies able to probe in situ proteomic networks.     

Network pharmacology and drug discovery

The pharmaceutical industry has historically relied on particular families of 'druggable' proteins against which to develop compounds with desired actions. But proteins rarely function in isolation in and outside the cell; rather, proteins operate as part of highly interconnected cellular networks. Network pharmacology is an emerging area of pharmacology which utilizes network analysis of drug action. By considering drug actions in the context of the cellular networks, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. Network pharmacology can provide new approaches for drug discovery for complex diseases. This review introduced the recent progress of network pharmacology and its importance to understand the mechanism of drug actions and drug discovery.