High-throughput flow cytometry for drug discovery

High-throughput flow cytometry exploits a novel many-samples/one-file approach to dramatically speed data acquisition, limit aspirated sample volume to as little as 2 μl/well and produce multisample data sets that facilitate automated analysis of samples in groups as well as individually. It has been successfully applied to both cell- and microsphere-based bioassays in 96- and 384-well formats, to screen tens-of-thousands of compounds and identify novel bioactive structures. High-content multiparametric analysis capabilities have been exploited for assay multiplexing, allowing the assessment of biologic selectivity and specificity to be an integral component of primary screens. These and other advances in the last decade have contributed to the application of flow cytometry as a uniquely powerful tool for probing biologic and chemical diversity and complex systems biology.     

Antiviral drug discovery against SARS-CoV

Severe Acute Respiratory Syndrome (SARS) is a life-threatening infectious disease caused by SARS-CoV. In the 2003 outbreak, it infected more than 8,000 people worldwide and claimed the lives of more than 900 victims. The high mortality rate resulted, at least in part, from the absence of definitive treatment protocols or therapeutic agents. Although the virus spreading has been contained, due preparedness and planning, including the successful development of antiviral drugs against SARS-CoV, is necessary for possible reappearance of SARS. In this review, we have discussed currently available strategies for antiviral drug discovery and how these technologies have been utilized to identify potential antiviral agents for the inhibition of SARS-CoV replication. Moreover, progress in the drug development based on different molecular targets is also summarized, including 1) Compounds that block the S protein-ACE2-mediated viral entry; 2) Compounds targeting SARS-CoV M(pro); 3) Compounds targeting papain-like protease 2 (PLP2); 4) Compounds targeting SARS-CoV RdRp; 5) Compounds targeting SARS-CoV helicase; 6) Active compounds with unspecified targets; and 7) Research on siRNA. This review aims to provide a comprehensive account of drug discovery on SARS. The experiences with the SARS outbreak and drug discovery would certainly be an important lesson for the drug development for any new viral outbreaks that may emerge in the future.     

High-throughput X-ray crystallography for drug discovery

Knowledge of the three-dimensional structures of protein targets has the potential to greatly accelerate drug discovery, but technical challenges and time constraints have traditionally limited its use to lead optimization. Its application is now being extended beyond structure determination into new approaches for lead discovery. Structure-activity relationships by nuclear magnetic resonance have been widely used to detect ligand binding and to give some indication of the location of the binding site. X-ray crystallography has the advantage of defining ligand-binding sites with greater certainty. High-throughput approaches make this method applicable to screening to identify molecular fragments that bind protein targets, and to defining precisely their binding sites. X-ray crystallography can then be used as a rapid technique to guide the elaboration of the fragments into larger molecular weight compounds that might be useful leads for drug discovery.     

High-throughput kinase profiling as a platform for drug discovery

To fully exploit the potential of kinases as drug targets, novel strategies for the efficient discovery of inhibitors are required. In contrast to the traditional, linear process of inhibitor discovery, high-throughput kinase profiling enables a parallel approach by interrogating compounds against hundreds of targets in a single screen. Compound potency and selectivity are determined simultaneously, providing a choice of targets to pursue that is guided by the quality of lead compounds available, rather than by target biology alone.     

High-throughput gene expression analysis for drug discovery

The ability to rapidly survey and compare gene expression levels between reference and test samples is moving the drug discovery process towards a more genomic orientation. The success of the Human Genome Project and related private genomics initiatives, combined with new technologies to probe, image and access expression data, are responsible for this transformation. This article reviews the history, status and future direction of high-throughput gene expression analysis. It describes classical approaches, explains the development of methods such as differential display for discovering novel genes, and discusses how microarray technology is exploiting collections of known sequences to pinpoint drug targets.     

High-throughput drug screens for amyotrophic lateral sclerosis drug discovery

ABSTRACT

Introduction: Amyotrophic lateral sclerosis (ALS) is a rapid adult-onset neurodegenerative disorder characterised by the progressive loss of upper and lower motor neurons. Current treatment options are limited for ALS, with very modest effects on survival. Therefore, there is a unmet need for novel therapeutics to treat ALS.

Areas covered: This review highlights the many diverse high-throughput screening platforms that have been implemented in ALS drug discovery. The authors discuss cell free assays including in silico and protein interaction models. The review also covers classical in vitro cell studies and new cell technologies, such as patient derived cell lines. Finally, the review looks at novel in vivo models and their use in high-throughput ALS drug discovery

Expert opinion: Greater use of patient-derived in vitro cell models and development of better animal models of ALS will improve translation of lead compounds into clinic. Furthermore, AI technology is being developed to digest and interpret obtained data and to make ‘hidden knowledge’ usable to researchers. As a result, AI will improve target selection for high-throughput drug screening (HTDS) and aid lead compound optimisation. Furthermore, with greater genetic characterisation of ALS patients recruited to clinical trials, AI may help identify responsive genetic subtypes of patients from clinical trials.     

High-throughput crystallography for lead discovery in drug design

Knowledge of the three-dimensional structures of protein targets now emerging from genomic data has the potential to accelerate drug discovery greatly. X-ray crystallography is the most widely used technique for protein structure determination, but technical challenges and time constraints have traditionally limited its use primarily to lead optimization. Here, we describe how significant advances in process automation and informatics have aided the development of high-throughput X-ray crystallography, and discuss the use of this technique for structure-based lead discovery.     

High-throughput screening technologies for ion channel drug discovery

Ion channels are attractive targets for drug discovery as an increasing number of new ion channel targets have been uncovered in diseases, such as pain, cardiovascular disease, and neurological disorders. Despite their relevance in diseases and the variety of physiological functions they are involved in, ion channels still remain underexploited as drug targets. This, to a large extent, is attributed to the absence of screening technologies that ensure both the quality and the throughput of data. However, an increasing number of assays and technologies have evolved rapidly in the past decades. In this review, we summarized the currently available high-throughput screening technologies in ion channel drug discovery.     

High-throughput virtual screening for drug discovery in parallel

With the influx of targets generated by genomics and proteomics initiatives, a new drug discovery paradigm is emerging. Many companies are setting up target family platforms that tackle multiple targets and therapeutic areas simultaneously. Virtual screening (VS) techniques are a fundamental component of such platforms for in silico filtering of compound collections and prioritization of chemistry and screening efforts. At the heart of these, structure-based docking and scoring methods are especially effective in identifying bioactive molecules if the structure of a target is available. As structural genomics maps the structural space of the proteome, these techniques are expected to become commonplace. In light of this, an overview of the latest developments in VS methodology is given here. In particular, emphasis is placed on those techniques adaptable to high-throughput VS in parallel drug discovery platforms. The first examples of docking across multiple targets have already appeared in the literature and will be reviewed here.     

High-throughput electronic biology: mining information for drug discovery

The vast range of in silico resources that are available in life sciences research hold much promise towards aiding the drug discovery process. To fully realize this opportunity, computational scientists must consider the practical issues of data integration and identify how best to apply these resources scientifically. In this article we describe in silico approaches that are driven towards the identification of testable laboratory hypotheses; we also address common challenges in the field. We focus on flexible, high-throughput techniques, which may be initiated independently of 'wet-lab' experimentation, and which may be applied to multiple disease areas. The utility of these approaches in drug discovery highlights the contribution that in silico techniques can make and emphasizes the need for collaboration between the areas of disease research and computational science.     

High-throughput screening, metabolomics and drug discovery

The science of metabolomics has the potential to deliver wide-reaching benefits to the currently embattled pharmaceutical industry. Current applications for this field center around toxicological profiling and biomarker studies; however, the ability of metabolomics to quantitatively assess pharmacologically induced changes in biological systems at the phenotype level suggests that there would be value in its adoption at much earlier phases of the drug-discovery process. As is argued herein, this approach could be coupled with a re-organization of early drug-discovery paradigms to reduce both the rates of attrition and the costs of bringing a drug to market.     

High-throughput multiplexed capillary electrophoresis in drug discovery

The demand for high-throughput analytical tools to support drug discovery applications has led to the development of multiplexed capillary electrophoresis and multichannel microfluidic devices to characterize libraries of compounds and alleviate backlogs in the discovery process. The capability to analyze multiple samples in parallel, and the diverse separation conditions that are permissible, facilitates rapid turnaround times. Examples of high-throughput applications of multiplexed electrophoresis in drug discovery include: physicochemical profiling, enzyme analysis, chiral separations and protein/metabolite analysis. Many single capillary electrophoresis methods can be potentially adapted to a multiplexed format, therefore, we anticipate the development of other high-throughput applications in the near future, which should facilitate decreases in sample analysis time and help improve laboratory efficiency.     

High-throughput target discovery using cell-based genetics

High-throughput target discovery requires robust disease models and the ability to rapidly survey the genome for function. In the post-genomics era, there has been a strong emphasis placed upon "gene-to-function" approaches that take advantage of the large amount of gene sequence information now available. Here, we advocate a return to "function-to-gene" approaches as a first step in target discovery (and validation), followed by hypothesis-driven research to validate new targets identified by their activity in cell-based disease models.     

High-throughput fluorescence imaging approaches for drug discovery using in vitro and in vivo three-dimensional models

Introduction: High-resolution microscopy using fluorescent probes is a powerful tool to investigate individual cell structure and function, cell subpopulations and mechanisms underlying cellular responses to drugs. Additionally, responses to drugs more closely resemble those seen in vivo when cells are physically connected in three-dimensional (3D) systems (either 3D cell cultures or whole organisms), as opposed to traditional monolayer cultures. Combined, the use of imaging-based 3D models in the early stages of drug development has the potential to generate biologically relevant data that will increase the likelihood of success for drug candidates in human studies.

Areas covered: The authors discuss current methods for the culturing of cells in 3D as well as approaches for the imaging of whole-animal models and 3D cultures that are amenable to high-throughput settings and could be implemented to support drug discovery campaigns. Furthermore, they provide critical considerations when discussing imaging these 3D systems for high-throughput chemical screenings.

Expert opinion: Despite widespread understanding of the limitations imposed by the two-dimensional versus the 3D cellular paradigm, imaging-based drug screening of 3D cellular models is still limited, with only a few screens found in the literature. Image acquisition in high throughput, accurate interpretation of fluorescent signal, and uptake of staining reagents can be challenging, as the samples are in essence large aggregates of cells. The authors recognize these shortcomings that need to be overcome before the field can accelerate the utilization of these technologies in large-scale chemical screens.     

High-throughput purification platform in support of drug discovery

The application of parallel synthesis is an efficient approach to explore the chemical space and to rapidly develop meaningful structure activity relationship (SAR) data for drug discovery programs. However, the effectiveness of the parallel synthesis requires a high throughput purification workflow that can process a large number of crude samples within a meaningful time frame. This paper describes a high throughput purification platform that has been adopted at Merck's Rahway research site. The platform includes the evaluation of crude samples, mass-directed HPLC purification, fraction analysis, compound registration, final compound purity assessment and assay distribution. Assisting with the sample tracking and the data management is the internally designed laboratory information management system, Light Automation Framework (LAF). Using this process and the tools described herein, the group has successfully achieved purities of 95% or greater for 90% of samples.     

High-throughput and in silico screenings in drug discovery

Background: In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones. Objective: Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process. Conclusion: Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.     

High-throughput crystallization and structure determination in drug discovery

High-throughput protein X-ray crystallography offers an unprecedented opportunity to facilitate drug discovery. The key bottlenecks in the path from target gene to three-dimensional protein structure determination are defined. Special emphasis is placed on the concept that drug discovery projects are typically directed at a key protein target whose structure must be solved within a reasonable time frame to have an impact on the drug discovery process. The time-sensitive nature of structural data has placed growing pressure on the need to automate all aspects of protein crystallography, from gene identification to model building and refinement. Current technological innovations and strategies for automation are discussed with respect to the bottleneck they are intended to eliminate.     

High-throughput molecular pathology in human tissues as a method for driving drug discovery

To facilitate prioritization of potential drug targets, gene expression can be localized to individual cell types in normal and diseased tissues. Given the complexity of molecular physiology and pathology, the creation of large-scale molecular pathology databases collating data obtained from human tissues is a challenging marriage of old and new technologies, particularly when considering the many issues that preclude easy access to substantial quantities of human tissues. Molecular pathology databases are powerful tools and are essential for early-stage drug discovery, enabling informed decisions to be made with respect to scientific direction and follow-up research.     

High-throughput molecular dynamics: the powerful new tool for drug discovery

Molecular dynamics simulations are capable of resolving molecular recognition processes with chemical accuracy, but their practical application is popularly considered limited to the timescale accessible to a single simulation, which is far below biological timescales. In this perspective article, we propose that the true limiting factor for molecular dynamics is rather the high hardware and electrical power costs, which constrain not only the length of runs but also the number that can be performed concurrently. As a result of innovation in accelerator processors and high-throughput protocols, the cost of molecular dynamics sampling has been dramatically reduced and we argue that molecular dynamics simulation is now placed to become a key technology for in silico drug discovery in terms of binding pathways, poses, kinetics and affinities.