Small Molecule Screening in Human Induced Pluripotent Stem Cell-derived Terminal Cell Types

A need for better clinical outcomes has heightened interest in the use of physiologically relevant human cells in the drug discovery process. Patient-specific human induced pluripotent stem cells may offer a relevant, robust, scalable, and cost-effective model of human disease physiology. Small molecule high throughput screening in human induced pluripotent stem cell-derived cells with the intent of identifying novel therapeutic compounds is starting to influence the drug discovery process; however, the use of these cells presents many high throughput screening development challenges. This technology has the potential to transform the way drug discovery is performed.     

Therapeutic potential of natural product signal transduction agents

Modern drug discovery embraces a strategy of targeting cellular signal transduction pathways as a means of finding new therapeutic agents. Historically, natural products derived from microorganisms have played an important role as drug leads and clinical candidates under this paradigm. The future drug potential of natural products as signal transduction agents looks promising, as illustrated by two key examples. First, substantial advances have been made in the development of inhibitors based on immunophilin ligand polyketides, which target the TOR-mediated pathways and can modulate processes including cell proliferation and cell-cycle arrest. Second, the discovery of natural product inhibitors of the ubiquitin-proteasome proteolytic signal transduction pathway represents an emerging field. Given these examples, together with the diversity of as yet undiscovered agents, natural product signal transduction agents offer great potential for future drug discovery efforts.     

Applications of high-throughput ADME in drug discovery

Assessment of physicochemical and pharmacological properties is now conducted at very early stages of drug discovery for the purpose of accelerating the conversion of hits and leads into qualified development candidates. In particular, in vitro absorption, distribution, metabolism and elimination (ADME) assays and in vivo drug metabolism pharmacokinetic (DMPK) studies are being conducted throughout the discovery process, from hit generation through to lead optimization, with the goal of reducing the attrition rate of these potential drug candidates as they progress through development. Because the continuing trend in drug discovery has been to access ADME information earlier and earlier in the discovery process, the need has arisen within the analytical community to introduce faster and better analytical methods to enhance the 'developability' of drug leads. Strategies for streamlined ADME assessment of drug candidates in discovery and pre-clinical development are presented within.     

Functional assays for screening GPCR targets

G-protein-coupled receptors (GPCRs) are valuable molecular targets for drug discovery. An important aspect of the early drug discovery process is the design and implementation of high-throughput GPCR functional assays that allow the cost-effective screening of large compound libraries to identify novel drug candidates. Several functional assay kits based on fluorescence and/or chemiluminescence detection are commercially available for convenient screen development, each having advantages and disadvantages. In addition, new GPCR biosensors and high-content imaging technologies have recently been developed that hold promise for the development of functional GPCR screens in living cells.     

Bioinformatics approaches for new drug discovery: a review

Prolonged antibiotic therapy for the bacterial infections has resulted in high levels of antibiotic resistance. Initially, bacteria are susceptible to the antibiotics, but can gradually develop resistance. Treating such drug-resistant bacteria remains difficult or even impossible. Hence, there is a need to develop effective drugs against bacterial pathogens. The drug discovery process is time-consuming, expensive and laborious. The traditionally available drug discovery process initiates with the identification of target as well as the most promising drug molecule, followed by the optimization of this, in-vitro, in-vivo and in pre-clinical studies to decide whether the compound has the potential to be developed as a drug molecule. Drug discovery, drug development and commercialization are complicated processes. To overcome some of these problems, there are many computational tools available for new drug discovery, which could be cost effective and less time-consuming. In-silico approaches can reduce the number of potential compounds from hundreds of thousands to the tens of thousands which could be studied for drug discovery and this results in savings of time, money and human resources. Our review is on the various computational methods employed in new drug discovery processes.     

Natural product-like synthetic libraries

There is a paucity of chemical matter suitably poised for effective drug development. Improving the quality and efficiency of research early on in the drug discovery process has been a long standing objective for the drug industry and improvements to the accessibility and quality of compound screening decks might have a significant and positive impact. In the absence of specific molecular information that can be modeled and used predicatively we are far from identifying which small molecules are most relevant to emerging biological targets such as protein-protein interactions. Natural products have been historically successful as an entry point for drug discovery and recently screening libraries are being synthesized to emulate natural product like features.     

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.     

Biomarker discovery in biological fluids

Discovery of novel protein biomarkers is essential for successful drug discovery and development. These novel protein biomarkers may aid accelerated drug efficacy, response, or toxicity decision making based on their enhanced sensitivity and/or specificity. These biomarkers, if necessary, could eventually be converted into novel diagnostic marker assays. Proteomic platforms developed over the past few years have given us the ability to rapidly identify novel protein biomarkers in various biological matrices from cell cultures (lysates, supernatants) to human clinical samples (serum, plasma, and urine). In this article, we delineate an approach to biomarker discovery. This approach is divided into three steps, (i) identification of markers, (ii) prioritization of identified markers, and (iii) preliminary validation (qualification) of prioritized markers. Using drug-induced idiosyncratic hepatotoxicity as a case study, the article elaborates methods and techniques utilized during the three steps of biomarker discovery process. The first step involves identification of markers using multi-dimensional protein identification technology. The second step involves prioritization of a subset of marker candidates based on several criteria such as availability of reagent set for assay development and literature association to disease biology. The last step of biomarker discovery involves development of preliminary assays to confirm the bio-analytical measurements from the first step, as well as qualify the marker(s) in pre-clinical models, to initiate future marker validation and development.     

Predictive markers in early research and companion diagnostic developments in oncology

Predictive biomarkers are discovered and used in oncology research to formulate hypotheses aimed at the identification of patients benefiting from specific therapeutic intervention(s). They pave the way to the development of companion diagnostic tests which are tools readily implemented in the clinic and serve to qualify a patient for treatment with a particular targeted drug or the continued use of a particular drug, thus maximizing the benefit to risk ratio of the medical intervention to the patient. Predictive biomarkers are defined by biological characteristics of the patient's or tumor status that can be measured objectively and correlated with clinical outcome: these can be molecular, cellular or biochemical features. Predictive markers need extensive analytical validation - specific for the tool utilized for their assessment - as well as rigorous clinical qualification in the context of the drug treatment for which they define clinical utility. The process of companion diagnostic development is a highly interdisciplinary and complex one, driven by key crucial milestones and accompanying the same and typical process of a whole drug discovery and development continuum, from marker discovery and validation, assay development, clinical qualification until test approval and commercialization.     

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     

New approaches to molecular cancer therapeutics

Cancer drug development is leading the way in exploiting molecular biological and genetic information to develop "personalized" medicine. The new paradigm is to develop agents that target the precise molecular pathology driving the progression of individual cancers. Drug developers have benefited from decades of academic cancer research and from investment in genomics, genetics and automation; their success is exemplified by high-profile drugs such as Herceptin (trastuzumab), Gleevec (imatinib), Tarceva (erlotinib) and Avastin (bevacizumab). However, only 5% of cancer drugs entering clinical trials reach marketing approval. Cancer remains a high unmet medical need, and many potential cancer targets remain undrugged. In this review we assess the status of the discovery and development of small-molecule cancer therapeutics. We show how chemical biology approaches offer techniques for interconnecting elements of the traditional linear progression from gene to drug, thereby providing a basis for increasing speed and success in cancer drug discovery.     

The role of modern biology and medicine in drug development in academia and industry

This symposium addresses careers in drug development in industry; the performance of translational research by academia, industry, and both; and numerous factors pertinent to alliances essential to drug discovery and development. Drug development is a complex process that regularly involves effective collaborations between academic and physician scientists and industry. There are specific occupational factors affecting recruitment of scientists and physicians in drug development programs in industry; ideal backgrounds for successful applicants for positions in industry in drug development; ethical and regulatory considerations particularly germane to the performance of scientists and physicians in drug development programs in industry and at universities; and particular gratifications available to scientists in industry working on drug development. Both similarities and differences characterize the performance of translational research in industry compared with academia. In industry, logistic, operational, and scientific oversight is complex, especially because it often involves relationships with clinical enterprises outside of the corporation. The process is long and arduous from formulation of a good idea in discovery to acceptance of a novel drug in the marketplace. Collaborations and partnerships by industry often involving academia and confrontation of multiple issues are pivotal.     

基于深度学习与多层次信息融合的药物靶标亲和力预测*

药物研发是非常重要但也十分耗费人力物力的过程。利用计算机辅助预测药物与蛋白质亲和力的方法可以极大地加快药物研发过程。药物靶标亲和力预测的关键在于对药物和蛋白质进行准确详细地信息表征。提出一种基于深度学习与多层次信息融合的药物靶标亲和力的预测模型,试图通过综合药物与蛋白质的多层次信息,来获得更好的预测表现。首先将药物表述成分子图和扩展连接指纹两种形式,分别利用图卷积神经网络模块和全连接层进行学习;其次将蛋白质序列和蛋白质K-mer特征分别输入卷积神经网络模块和全连接层来学习蛋白质潜在特征;随后将4个通道学习到的特征进行融合,再利用全连接层进行预测。在两个基准药物靶标亲和力数据集上验证了所提方法的有效性,并与其他已有模型作对比研究。结果说明提出的模型相比基准模型能得到更好的预测性能,表明提出的综合药物与蛋白质多层次信息的药物靶标亲和力预测策略是有效的。     

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.     

Modern approaches to drug discovery and design: setting the scene

The raison d'ítre for the drug discovery and development process is to provide safe and effective treatments for diseases. Bringing a new drug to market, however, is a time-consuming and expensive process and it remains an imperative for drug companies that they identify ways in which they can accelerate the identification of potential targets and their screening and development in order to maintain a competitive edge. Successful drug discovery efforts include biochemical, biophysical, genetic and immunological approaches, targeting such processes as signal transduction, cell cycle control, apoptosis, gene regulation and metastasis. As the number of these biological targets increases, reliance on bioinformatics and chemoinformatics to improve decision making, by identifying characteristics of successful drugs and sharing knowledge gained within the scientific community, has become a burgeoning area in the post-genomic era of drug discovery.     

现代生物学对药物发现的影响

随着后基因组时代的到来,药物发现研究领域不断涌现出一系列新思路、新技术、新方法,从而迅速推进药物发现的多元化发展。一方面,基因组学、蛋白质组学、转录组学、代谢组学、生物信息学、系统生物学等新兴学科的崛起与发展,为药物发现提供更为广泛而深刻的理论基础;另一方面,计算机辅助药物设计、高通量筛选、高内涵筛选、生物芯片、转基因和RNA干扰等高新技术的发展和完善,为药物发现提供了新的技术手段和有力工具,极大地拓宽了药物发现的途径。本文结合近年来现代生物学的研究进展,综述现代生物学对药物发现过程的影响。     

CNS Drug Design Based on Principles of Blood-Brain Barrier Transport

Abstract: Lipid-soluble small molecules with a molecular mass under a 400–600-Da threshold are transported readily through the blood-brain barrier in vivo owing to lipid-mediated transport. However, other small molecules lacking these particular molecular properties, antisense drugs, and peptide-based pharmaceuticals generally undergo negligible transport through the blood-brain barrier in pharmacologically significant amounts. Therefore, if present day CNS drug discovery programs are to avoid termination caused by negligible blood-brain barrier transport, it is important to merge CNS drug discovery and CNS drug delivery as early as possible in the overall CNS drug development process. Strategies for special formulation that enable drug transport through the blood-brain barrier arise from knowledge of the molecular and cellular biology of blood-brain barrier transport processes.     

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.     

The SARS-CoV-2 main protease as drug target

The unprecedented pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is threatening global health. The virus emerged in late 2019 and can cause a severe disease associated with significant mortality. Several vaccine development and drug discovery campaigns are underway. The SARS-CoV-2 main protease is considered a promising drug target, as it is dissimilar to human proteases. Sequence and structure of the main protease are closely related to those from other betacoronaviruses, facilitating drug discovery attempts based on previous lead compounds. Covalently binding peptidomimetics and small molecules are investigated. Various compounds show antiviral activity in infected human cells.