Providing data science support for systems pharmacology and its implications to drug discovery

ABSTRACT

Introduction: The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology.

Areas covered: This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction.

Expert opinion: Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.     

Fifty years of Biochemical Pharmacology: the discipline and the journal

The discipline of biochemical pharmacology emerged in the late 1940s as a result of an increasing emphasis on understanding drug mechanisms at the cellular level. This research approach has contributed significantly to the development of many new drug classes including antihypertensive, antifective, cholesterol lowering, anti-inflammatory, and anticancer agents, as well as antipsychotics, antidepressants and anxiolytics. Biochemical pharmacology remains a major tool in drug discovery, being employed in the search for novel therapeutics for the above and other conditions and clinical challenges, such as neurodegenerative disorders, for the treatment of pain, and for development of agents that do not induce, or can overcome, antibiotic/antiviral resistance. Together with chemical, molecular, genetic, physiological, and clinical sciences, biochemical pharmacology will in the coming decades continue to be a critical component of the drug discovery process.     

Botanical drugs, synergy, and network pharmacology: forth and back to intelligent mixtures

For centuries the science of pharmacognosy has dominated rational drug development until it was gradually substituted by target-based drug discovery in the last fifty years. Pharmacognosy stems from the different systems of traditional herbal medicine and its "reverse pharmacology" approach has led to the discovery of numerous pharmacologically active molecules and drug leads for humankind. But do botanical drugs also provide effective mixtures? Nature has evolved distinct strategies to modulate biological processes, either by selectively targeting biological macromolecules or by creating molecular promiscuity or polypharmacology (one molecule binds to different targets). Widely claimed to be superior over monosubstances, mixtures of bioactive compounds in botanical drugs allegedly exert synergistic therapeutic effects. Despite evolutionary clues to molecular synergism in nature, sound experimental data are still widely lacking to support this assumption. In this short review, the emerging concept of network pharmacology is highlighted, and the importance of studying ligand-target networks for botanical drugs is emphasized. Furthermore, problems associated with studying mixtures of molecules with distinctly different pharmacodynamic properties are addressed. It is concluded that a better understanding of the polypharmacology and potential network pharmacology of botanical drugs is fundamental in the ongoing rationalization of phytotherapy.     

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—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。     

中国临床药理学发展概况与展望

临床药理学是研究药物与人体之间相互作用及其规律的一门科学,是涉及药理学、医学、药物学、生物学、护理学、毒理学、流行病学、遗传学、数学、统计学、经济学、社会和个体行为学等多学科的交叉学科或综合学科.主要研究内容包括人体(正常,病人)的药动学、药效学和遗传药理学、临床药物评价和Ⅰ、Ⅱ、Ⅲ、Ⅳ期临床试验、治疗药物检测、药物警戒、药物利用、药物流行病学、药物经济学、新药发现与开发等.以往认为临床药理学是药理学的分支学科,还有将药物临床试验等同于临床药理学,均难以涵盖临床药理学的研究内容.临床药理学是指导药物临床合理使用、新药临床安全性有效性评价以及新药发现与开发的科学基础.改革开放以来,中国临床药理学研究经历了知识普及与队伍培育、规范研究与不断提高、蓬勃发展与着力创新三个阶段,在指导临床合理用药、药物临床研究、新药创制、人才培养、国内外学术交流、著作教材和期刊建设以及学术组织建设等方面发挥了重要作用.该文就改革开放以来我国临床药理学发展概况与进展作一简要综述.     

British Pharmacology Society 2nd Focused meeting on Cell Signalling: GPCRs have sunny days in springtime Leicester

The British Pharmacology Society recently held its 2nd Focused meeting on Cell Signalling in April 2007 at which a number of leading investigators from throughout the world presented findings that emphasized cellular and molecular aspects of G-protein-coupled receptor (GPCR) biology. Although the presentations highlighted both in vitro and in vivo studies, there was renewed and increased attention paid to systems with physiological (or near-physiological) levels of receptor expression. Mechanisms involved in regulating receptor expression, receptor recognition and activation by different classes of drugs, and in the interaction of G-protein-coupled receptor with other molecular entities, including heterotrimeric G proteins, were major themes of the presentations. Several speakers emphasized the challenges of drug discovery in relation to G-protein-coupled receptor, as well as their continuing importance in pharmacology and therapeutics.     

Stem cells as screening tools in drug discovery

All physiologic processes operate in a cellular setting. Therefore, drug discoverers need the highest quality cells as they pursue the next generation of safe and effective medicines. Recently, investigators have begun to consider stem cells as a new source of predictive, cell-based assays in drug discovery. Stem cell technology still has hurdles to overcome before these cells are fully accepted as decision-making reagents and amenable to high-throughput screening. However, with global research interest in stem cell biology, significant advances in the application of these cells in drug discovery have been reported. These advances are aligned with three important stages of pharmaceutical research: target discovery and validation, identification of efficacious chemical leads, and drug safety pharmacology. This concise review describes the application of stem cells in these areas of drug discovery with emphasis on molecular screening opportunities.     

British Pharmacology Society 2nd Focused Meeting on Cell Signalling: GPCRs have sunny days in springtime Leicester

The British Pharmacology Society recently held its 2^nd Focused meeting on Cell Signalling in April 2007 at which a number of leading investigators from throughout the world presented findings that emphasized cellular and molecular aspects of G-protein-coupled receptor (GPCR) biology. Although the presentations highlighted both in vitro and in vivo studies, there was renewed and increased attention paid to systems with physiological (or near-physiological) levels of receptor expression. Mechanisms involved in regulating receptor expression, receptor recognition and activation by different classes of drugs, and in the interaction of G-protein-coupled receptor with other molecular entities, including heterotrimeric G proteins, were major themes of the presentations. Several speakers emphasized the challenges of drug discovery in relation to G-protein-coupled receptor, as well as their continuing importance in pharmacology and therapeutics.     

Personalized medicine: the impact on chemistry

An effective strategy for personalized medicine requires a major conceptual change in the development and application of therapeutics. In this article, we argue that further advances in this field should be made with reference to another conceptual shift, that of network pharmacology. We examine the intersection of personalized medicine and network pharmacology to identify strategies for the development of personalized therapies that are fully informed by network pharmacology concepts. This provides a framework for discussion of the impact personalized medicine will have on chemistry in terms of drug discovery, formulation and delivery, the adaptations and changes in ideology required and the contribution chemistry is already making. New ways of conceptualizing chemistry's relationship with medicine will lead to new approaches to drug discovery and hold promise of delivering safer and more effective therapies.     

A manifesto for clinical pharmacology from principles to practice

1 This is a manifesto for UK clinical pharmacology.2 A clinical pharmacologist is a medically qualified practitioner who teaches, does research, frames policy, and gives information and advice about the actions and proper uses of medicines in humans and implements that knowledge in clinical practice. Those without medical qualifications who practise some aspect of clinical pharmacology could be described as, say, ‘applied pharmacologists’.3 Clinical pharmacology is operationally defined as a translational discipline in terms of the basic tools of human pharmacology (e.g. receptor pharmacology) and applied pharmacology (e.g. pharmacokinetics) and how they are used in drug discovery and development and in solving practical therapeutic problems in individuals and populations.4 Clinical pharmacologists are employed by universities, health-care services, private organizations (such as drug companies), and regulatory agencies. They are• mentors and teachers, teaching laboratory science, clinical science, and all aspects of practical drug therapy as underpinned by the science of pharmacology; they write and edit didactic and reference texts;• researchers, covering research described by the operational definition;• clinicians, practising general medicine, clinical toxicology, other medical specialties, and general practice;• policy makers, framing local, national, and international medicines policy, including formularies, licensing of medicines and prescribing policies.5 The future of clinical pharmacology depends on the expansion and maintenance of a central core of practitioners (employed by universities or health-care services), training clinical pharmacologists to practise in universities, health-care services, private organizations, and regulatory agencies, and training other clinicians in the principles and practice of clinical pharmacology.     

Can pharmacology possibly have a role for bioinformatics?

In today’s information-driven culture, there is virtually no walk of life that is not impacted on by computing. As a bridging discipline in the health sciences with activities that span both basic science and clinical interests, modern pharmacology is no exception. As the plethora of data and databases spawned by the ‘omics’ generation expand in number and complexity, bioinformatics is necessary to manage, integrate and exploit this cohort of data so that the appropriate links to molecular pathology and therapeutic response can be made. Bioinformatics is now an integral part of drug discovery and development. This article reviews the use of bioinformatics in this process, from target identification and validation, to pharmacogenomics, toxicogenomics and systems biology.     

Embryonic stem cells as a source of models for drug discovery

Embryonic stem cells (ESCs) will become a source of models for a wide range of adult differentiated cells, providing that reliable protocols for directed differentiation can be established. Stem-cell technology has the potential to revolutionize drug discovery, making models available for primary screens, secondary pharmacology, safety pharmacology, metabolic profiling and toxicity evaluation. Models of differentiated cells that are derived from mouse ESCs are already in use in drug discovery, and are beginning to find uses in high-throughput screens. Before analogous human models can be obtained in adequate numbers, reliable methods for the expansion of human ESC cultures will be needed. For applications in drug discovery, involving either species, protocols for directed differentiation will need to be robust and affordable. Here, we explore current challenges and future opportunities in relation to the use of stem-cell technology in drug discovery, and address the use of both mouse and human models.     

Troubleshooting and deconvoluting label-free cell phenotypic assays in drug discovery

Introduction: Central to drug discovery and development is to comprehend the target(s), potency, efficacy and safety of drug molecules using pharmacological assays. Owing to their ability to provide a holistic view of drug actions in native cells, label-free biosensor-enabled cell phenotypic assays have been emerging as new generation phenotypic assays for drug discovery. Despite the benefits associated with wide pathway coverage, high sensitivity, high information content, non-invasiveness and real-time kinetics, label-free cell phenotypic assays are often viewed to be a blackbox in the era of target-centric drug discovery. Methods: This article first reviews the biochemical and biological complexity of drug-target interactions, and then discusses the key characteristics of label-free cell phenotypic assays and presents a five-step strategy to troubleshooting and deconvoluting the label-free cell phenotypic profiles of drugs. Results: Drug-target interactions are intrinsically complicated. Label-free cell phenotypic signatures of drugs mirror the innate complexity of drug-target interactions, and can be effectively deconvoluted using the five-step strategy. Discussion: The past decades have witnessed dramatic expansion of pharmacological assays ranging from molecular to phenotypic assays, which is coincident with the realization of the innate complexity of drug-target interactions. The clinical features of a drug are defined by how it operates at the system level and by its distinct polypharmacology, ontarget, phenotypic and network pharmacology. Approaches to examine the biochemical, cellular and molecular mechanisms of action of drugs are essential to increase the efficiency of drug discovery and development. Label-free cell phenotypic assays and the troubleshooting and deconvoluting approach presented here may hold great promise in drug discovery and development.     

An assessment of the present and future roles of non-ligand gated ion channel modulators as CNS therapeutics

Few approved drugs have, as their primary known mechanism of action, modulation of non-ligand gated ion channels. However, these proteins are important regulators of neuronal function through their control of sodium, potassium, calcium and chloride flux, and are ideal candidates as drug discovery targets. Recent progress in the molecular biology and pharmacology of ion channels suggests that many will be associated with specific pharmacological profiles that will include both activators and inhibitors. Ion channels, through their regulation by G-proteins, are a major component of the final common pathway of many drugs acting at classical neuronal receptors. Thus, targeting of the ion channels themselves may confer different profiles of efficacy and specificity to drug action in the brain and spinal cord. Three areas for drug discovery are profiled that the authors consider prime targets for ion channel based therapies, anticonvulsant drugs, cognition enhancing drugs and drugs for improving neurone survival following ischaemia.     

虚拟筛选辅助新药发现的研究进展

近年来从中药和天然药物中发现新药已成为药物研究领域的热点。虽然从中药和天然药物中能够分离得到大量新化合物,但通常这些新化合物的作用靶点难于确定,而且由于其收率较低,限制了进一步的药理学机制研究。因此,应用基于蛋白质结构的虚拟筛选方法,结合蛋白质生物化学和药理学加以验证,能够大大缩短研究时间并降低对化合物产量的需求。综述基于蛋白质结构-分子对接的天然产物活性筛选方法、虚拟筛选常用软件、天然产物数据库、结构生物学基本方法、蛋白质结构数据库（PDB）、筛选结果的实验性验证方法,以及虚拟筛选方法发现天然产物为先导化合物的成功应用实例,以期为天然药物活性成分的快速发现提供参考。

     

蛋白质组学技术在网络药理学中的应用

蛋白质组学发展至今已日趋成熟,在生物医药相关领域研究中的应用显著增加,与之相关的样品制备技术、蛋白定量方法及先进的质谱仪器也得到了快速发展。网络药理学是近年来提出的新药发现新策略,是药理学的新兴分支学科,它从整体的角度探索药物与疾病的关联性,发现药物靶标,指导新药研发。将蛋白质组学技术应用于网络药理学研究用,加速药物靶点的确认,从而设计多靶点药物或药物组合。综述了蛋白质组学技术的新近研究进展,并简单概述了其在网络药理学中的应用。     

An assessment of the present and future roles of non-ligand gated ion channel modulators as CNS therapeutics

Few approved drugs have, as their primary known mechanism of action, modulation of non-ligand gated ion channels. However, these proteins are important regulators of neuronal function through their control of sodium, potassium, calcium and chloride flux, and are ideal candidates as drug discovery targets. Recent progress in the molecular biology and pharmacology of ion channels suggests that many will be associated with specific pharmacological profiles that will include both activators and inhibitors. Ion channels, through their regulation by G-proteins, are a major component of the final common pathway of many drugs acting at classical neuronal receptors. Thus, targeting of the ion channels themselves may confer different profiles of efficacy and specificity to drug action in the brain and spinal cord. Three areas for drug discovery are profiled that the authors consider prime targets for ion channel based therapies, anticonvulsant drugs, cognition enhancing drugs and drugs for improving neurone survival following ischaemia.