Natural products drug discovery: accelerating the clinical candidate development using reverse pharmacology approaches

The pharmaceutical industry is facing serious challenges as the drug discovery process is becoming extremely expensive, riskier and critically inefficient. A significant shift from single to multi targeted drugs especially for polygenic syndromes is being witnessed. Strategic options based on natural product drug discovery, ethnopharmacology and traditional medicines are re-emerging to offer good base as an attractive discovery engine. Approaches based on reverse pharmacology may offer efficient development platforms for herbal formulations. Relevant case studies from India and other countries where such approaches have expedited the drug discovery and development process by reducing time and economizing investments with better safety are discussed.     

Microarray technology GEM microarrays and drug discovery

Incyte Genomics' GEM™ Gene Expression Microarray is a proven genomics tool used by a large number of pharmaceutical companies to speed up the drug discovery and development process. The development and integration of this technology, together with Incyte's sequence databases and clone resources, have resulted in GEM microarrays that span approximately 60,000 human genes as well as approximately 60,000 plant, rat, mouse, yeast, and bacterial genes. The technology underlying the use of these arrays and their application to the drug discovery process is highlighted. Journal of Industrial Microbiology & Biotechnology (2002) 28, 180–185 DOI: 10.1038/sj/jim/7000136 Received 16 November 2000/ Accepted in revised form 01 March 2001     

Drug discovery for malaria: a very challenging and timely endeavor

The prevalence of resistance to known antimalarial drugs has resulted in the expansion of antimalarial drug discovery efforts. Academic and nonprofit institutions are partnering with the pharmaceutical industry to develop new antimalarial drugs. Several new antimalarial agents are undergoing clinical trials, mainly those resurrected from previous antimalarial drug discovery programs. Novel antimalarials are being advanced through the drug development process, of course, with the anticipated high failure rate typical of drug discovery. Many of these are summarized in this review. Mechanisms for funding antimalarial drug discovery and genomic information to aid drug target selection have never been better. It remains to be seen whether ongoing efforts will be sufficient for reducing malaria burden in the developing world.     

High-throughput human metabolism and toxicity analysis

Poor drug candidate safety profiles are often identified late in the drug development process, manifesting themselves in the preclinical and clinical phases and significantly contributing to the high cost and low yield of drug discovery. As a result, new tools are needed to accelerate the assessment of drug candidate toxicity and human metabolism earlier in the drug development process, from primary drug candidate screening to lead optimization. Although high-throughput screens exist for much of the discovery phase of drug development, translating such screening techniques into platforms that can accurately mimic the human in vivo response and predict the impact of drug candidates on human toxicology has proven difficult. Nevertheless, some success has been achieved in recent years, which may ultimately yield widespread acceptance in the pharmaceutical industry.     

Toxicogenomics in drug discovery: from preclinical studies to clinical trials

Gene expression analysis applied to toxicology studies, also referred to as toxicogenomics, is rapidly being embraced by the pharmaceutical industry as a useful tool to identify safer drugs in a quicker, more cost-effective manner. Studies have already demonstrated the benefits of applying gene expression profiling towards drug safety evaluation, both for identifying mechanisms underlying toxicity, as well as for providing a means to identify safety liabilities early in the drug discovery process. Furthermore, toxicogenomics has the potential to better identify and assess adverse drug reactions of new drug candidates or marketed products in humans. While much still remains to be learned about the relevance and the application of gene expression changes in human toxicology, the next few years should see gene expression technologies applied to more stages and more programs of the drug discovery and development process. This review will focus on how toxicogenomics can or has been applied in drug discovery and development, and will discuss some of the challenges that still remain.     

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.     

Pharmacologically relevant proteins

The emergence of structure-based drug design as a tool for drug discovery and development has focused increased attention on pharmacologically relevant proteins and the use of their three-dimensional structures to design novel pharmaceutical agents. This review describes recent structural studies of selected macromolecules that have been identified as targets for drug development. Several examples of the successful application of structure-based drug design techniques are also described.     

Structure-directed combinatorial library design

In recent years pharmaceutical companies have utilized structure-based drug design and combinatorial library design techniques to speed up their drug discovery efforts. Both approaches are routinely used in the lead discovery and lead optimization stages of the drug discovery process. Fragment-based drug design, a new power tool in the drug design toolbox, is also gaining acceptance across the pharmaceutical industry. This review will focus on the interplay between these three design techniques and recent developments in computational methodologies that enhance their integration. Examples of successful synergistic applications of these three techniques will be highlighted. Opinion regarding possible future directions of the field will be given.     

Literature mining in support of drug discovery

The drug discovery enterprise provides strong drivers for data integration. While attention in this arena has tended to focus on integration of primary data from omics and other large platform technologies contributing to drug discovery and development, the scientific literature remains a major source of information valuable to pharmaceutical enterprises, and therefore tools for mining such data and integrating it with other sources are of vital interest and economic impact. This review provides a brief overview of approaches to literature mining as they relate to drug discovery, and offers an illustrative case study of a 'lightweight' approach we have implemented within an industrial context.     

虚拟筛选与新药发现

虚拟筛选是创新药物研究的新方法和新技术,近年来引起了研究机构和制药公司的高度重视,并且已经成为一种与高通量筛选互补的实用化工具,加入到了创新药物研究的工作流程(pipeline)中。本文介绍国际上虚拟筛选及其在创新药物发现中应用的研究进展,特别介绍了我国这方面研究的状况。     

Systems biology in drug discovery

The hope of the rapid translation of 'genes to drugs' has foundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Systems biology aims to describe and to understand the operation of complex biological systems and ultimately to develop predictive models of human disease. Although meaningful molecular level models of human cell and tissue function are a distant goal, systems biology efforts are already influencing drug discovery. Large-scale gene, protein and metabolite measurements ('omics') dramatically accelerate hypothesis generation and testing in disease models. Computer simulations integrating knowledge of organ and system-level responses help prioritize targets and design clinical trials. Automation of complex primary human cell-based assay systems designed to capture emergent properties can now integrate a broad range of disease-relevant human biology into the drug discovery process, informing target and compound validation, lead optimization, and clinical indication selection. These systems biology approaches promise to improve decision making in pharmaceutical development.     

Why is big Pharma getting out of antibacterial drug discovery?

Since the advent of the antibiotic era in the late 1940s drug discovery and development has evolved into an expensive, time consuming, cumbersome and bureaucratic process involving multiple interest groups such as pharmaceutical manufacturers, governmental regulatory authorities, patent officers, academic and clinical researchers and trial lawyers. It would seem that the least involved among the interest groups are the consumers of health care themselves. Politicians and the public alike complain loudly about drug prices although fewer and fewer new therapies are being developed. The cost and complexities of drug discovery and development have shifted the investment equation away from the development of drugs targeting short course therapies for acute diseases and towards long-term treatment of chronic conditions. Coupled with the failure of large investments into target-based approaches to produce novel antibacterial agents, companies large and small have exited from this field despite a growing clinical need.     

Process biology: integrated genomics and bioinformatics tools for improved target assessment

The availability of the human genome sequence has greatly increased the number of potential drug targets in recent years. As a result, the way targets are assessed has become crucial to the success of the drug discovery process. Unfortunately, the traditional methods relied on by the pharmaceutical industry to identify and validate targets are too slow and labor-intensive to be useful in the current environment. One solution to this dilemma is to adopt a new paradigm, which we call Process Biology. Process Biology integrates genomics and bioinformatics tools into automated, optimized modules that can be applied readily to a wide range of biological questions. By using organizational principles not usually applied to biological experiments, Process Biology can significantly impact target assessment and assist in decision-making throughout the drug discovery process.     

Statistical decoding of potent pools based on chemical structure

Pooling experiments are used as a cost-effective approach for screening chemical compounds as part of the drug discovery process in pharmaceutical companies. When a biologically potent pool is found, the goal is to decode the pool, i.e., to determine which of the individual compounds are potent. We propose augmenting the data on pooled testing with information on the chemical structure of compounds in order to complete the decoding process. This proposal is based on the well-known relationship between biological potency of a compound and its chemical structure. Application to real data from a drug discovery process at GlaxoSmithKline reveals a 100% increase in hit rate, namely, the number of potent compounds identified divided by the number of tests required.     

Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach

It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure-activity and quantitative structure-property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve.     

Will genomics revolutionize antimicrobial drug discovery?

Utilizing genome sequence data from bacterial and fungal pathogens for the discovery of new antimicrobial agents has received considerable attention, both practical and critical, from the pharmaceutical and biotechnological communities. Although no new drugs derived from genomics-based discovery have been reported to be in a development pipeline, the utilization of genomics has revolutionized many aspects of drug discovery. The application, utility, opportunity, and challenges afforded by many of these new approaches are discussed.     

Discovery and validation of novel peptide agonists for G-protein-coupled receptors

G-protein-coupled receptors (GPCRs) represent an important group of targets for pharmaceutical therapeutics. The completion of the human genome revealed a large number of putative GPCRs. However, the identification of their natural ligands, and especially peptides, suffers from low discovery rates, thus impeding development of therapeutics based on these potential drug targets. We describe the discovery of novel GPCR ligands encrypted in the human proteome. Hundreds of potential peptide ligands were predicted by machine learning algorithms. In vitro screening of selected 33 peptides on a set of 152 GPCRs, including a group of designated orphan receptors, was conducted by intracellular calcium measurements and cAMP assays. The screening revealed eight novel peptides as potential agonists that specifically activated six different receptors in a dose-dependent manner. Most of the peptides showed distinct stimulatory patterns targeted at designated and orphan GPCRs. Further analysis demonstrated a significant in vivo effect for one of the peptides in a mouse inflammation model.     

Plant natural products: Back to the future or into extinction?

Natural product substances have historically served as the most significant source of new leads for pharmaceutical development. However, with the advent of robotics, bioinformatics, high throughput screening (HTS), molecular biology-biotechnology, combinatorial chemistry, in silico (molecular modeling) and other methodologies, the pharmaceutical industry has largely moved away from plant derived natural products as a source for leads and prospective drug candidates. Can, or will, natural products ever recapture the preeminent position they once held as a foundation for drug discovery and development? The challenges associated with development of natural products as pharmaceuticals are illustrated by the Taxol^® story. Several misconceptions, which constrain utilization of plant natural products, for discovery and development of pharmaceuticals, are addressed to return natural products to the forefront.     

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。