Using bioinformatics techniques for gene identification in drug discovery and development

As more and more evidence has become available, the link between gene and emergent disease has been made including cancer, heart disease and parkinsonism. Analyzing the diseases and designing drugs with respect to the gene and protein level obviously help to find the underlying causes of the diseases, and to improve their rate of cure. The development of modern molecular biology, biochemistry, data collection and analysis techniques provides the scientists with a large amount of gene data. To draw a link between genes and their relation to disease outcomes and drug discovery is a big challenge: How to analyze large datasets and extract useful knowledge? Combining bioinformatics with drug discovery is a promising method to tackle this issue. Most techniques of bioinformatics are used in the first two phases of drug discovery to extract interesting information and find important genes and/or proteins for speeding the process of drug discovery, enhancing the accuracy of analysis and reducing the cost. Gene identification is a very fundamental and important technique among them. In this paper, we have reviewed gene identification algorithms and discussed their usage, relationships and challenges in drug discovery and development.

Identifying targets for drug discovery using bioinformatics

Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. Objective: The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time. Methods: Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with. Results: We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently.

Nanotechnology based platforms for survivin targeted drug discovery

Introduction: Development of an effective, safe and targeted drug delivery system to fight cancer and other diseases is a prime focus in the area of drug discovery. The emerging field of nanotechnology has revolutionised the way cancer therapy and diagnosis is achieved primarily due to the recent advances in material engineering and drug availability. Further, the recognition of the crucial role played by anti-apoptotic proteins such as survivin, has initiated the development of therapeutics that can target this protein as an attempt to develop alternative cancer therapies. However, a key challenge faced in drug development is the efficient delivery of survivin-targeted molecules to specific areas in the body.Areas covered: This review primarily focuses on the different strategies employing nanotechnology for targeting survivin expressed in human cancers. Different nanomaterials incorporating nucleic molecules or drugs targeted at survivin are discussed and the results obtained from studies are highlighted.Expert opinion: There are extensive studies reporting different treatment regimens for cancer, however, they still result in systemic toxicity, reduced bioavailability and ineffective delivery. Novel approaches involve the use of biocompatible nanomaterials together with gene or drug molecules to target proteins such as survivin, which is overexpressed in cancerous cells. These nanoformulations allow the benefits of protecting easily degradable molecules, allow controlled release, and enhance targeted delivery and effectiveness. Hence, nanotherapy utilizing survivin targeting can be considered to play a key role in the development of personalized nanomedicine for cancer.

Pathways to new drug discovery in neuropsychiatry

There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success.

Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.

Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular networks or pathways. Drug discovery processes should delineate new drugs targeting the intracellular networks and immune-related pathways.

The use of mixed methods in drug discovery: integrating qualitative methods into clinical trials

Contemporary methods in clinical trials are pivoted around hypothesis confirmation, not generation. This is a problem for new drug discovery, since the pharmacokinetic or receptor profile of most novel agents do not link to pathophysiology, which is very poorly understood. Therefore, it is difficult to impute the therapeutic potential of a candidate agent. Most psychotropic agents were discovered serendipitously, either through careful clinical observation or by researchers finding unexpected associations in datasets. Methods that increase the ability to detect latent signals in data are needed. These include mixed methods that incorporate qualitative methods into randomized controlled trials.

This chapter proposes a methodology for the integration of mixed methods in clinical trials, fusing qualitative and quantitative methods, and presents an exemplar using this approach.

Mixed methods show potential for signal detection, hypothesis generation, and associations that may be otherwise undetected in traditional clinical trials.

Industrial lab-on-a-chip: design, applications and scale-up for drug discovery and delivery

Microfluidics is an emerging and promising interdisciplinary technology which offers powerful platforms for precise production of novel functional materials (e.g., emulsion droplets, microcapsules, and nanoparticles as drug delivery vehicles- and drug molecules) as well as high-throughput analyses (e.g., bioassays, detection, and diagnostics). In particular, multiphase microfluidics is a rapidly growing technology and has beneficial applications in various fields including biomedicals, chemicals, and foods. In this review, we first describe the fundamentals and latest developments in multiphase microfluidics for producing biocompatible materials that are precisely controlled in size, shape, internal morphology and composition. We next describe some microfluidic applications that synthesize drug molecules, handle biological substances and biological units, and imitate biological organs. We also highlight and discuss design, applications and scale up of droplet- and flow-based microfluidic devices used for drug discovery and delivery.

Zebrafish as a genetic model in pre-clinical drug testing and screening

The traditional drug discovery pipeline for the identification and development of compounds that selectively target specific molecules to ameliorate disease remains a major focus for medical research. However, the zebrafish is increasingly providing alternative strategies for various components of this pipeline. Zebrafish and their embryos are small, easily accessible and relatively low cost, making them applicable to high-throughput, small molecule screening. Zebrafish can also be manipulated by a range of forward and reverse genetics techniques to facilitate gene discovery and functional studies. Moreover, their physiological and developmental complexity provides accurate models of human disease to underpin mechanism of action and in vivo validation studies. Finally, several of these biological characteristics make zebrafish eminently suitable for toxicity testing, including eco-toxicology. Here we review the application of zebrafish to preclinical drug development and toxicity testing, including recent advances in mutant generation, drug screening and toxicology that serve to further enhance the capabilities of this valuable model organism in drug discovery.

Auranofin: repurposing an old drug for a golden new age

Drug discovery, development and registration is an expensive and time-consuming process associated with a high failure rate [Pessetto et al. (Mol Cancer Ther 12:1299-1309, 2013), Woodcock and Woosley (Annu Rev Med 59:1-12, 2008)]. Drug 'repurposing' is the identification of new therapeutic purposes for already approved drugs and is more affordable and achievable than novel drug discovery [Pessetto et al. (Mol Cancer Ther 12:1299-1309, 2013)]. Auranofin is a drug that is approved for the treatment of rheumatoid arthritis but is being investigated for potential therapeutic application in a number of other diseases including cancer, neurodegenerative disorders, HIV/AIDS, parasitic infections and bacterial infections [Tejman-Yarden et al. (Antimicrob Agents Chemother 57:2029-2035, 2013)]. The main mechanism of action of auranofin is through the inhibition of reduction/oxidation (redox) enzymes that are essential for maintaining intracellular levels of reactive oxygen species. Inhibition of these enzymes leads to cellular oxidative stress and intrinsic apoptosis [Pessetto et al. (Mol Cancer Ther 12:1299-1309, 2013), Fan et al. (Cell Death Dis 5:e1191, 2014), Fiskus et al. (Cancer Res 74:2520-2532, 2014), Marzano et al. (Free Radic Biol Med 42:872-881, 2007)]. Drugs such as auranofin that have already been approved for human use [Tejman-Yarden et al. (Antimicrob Agents Chemother 57:2029-2035, 2013)] can be brought into clinical use for other diseases relatively quickly and for a fraction of the cost of new drugs.

A similarity search algorithm to predict protein structures

Accurate prediction of protein structures is very important for many applications such as drug discovery and biotechnology. Building side chains is an essential to get any reliable prediction of the protein structure for any given a protein main chain conformation. Most of the methods that predict side chain conformations use statistically generated data from known protein structures. It is a computationally intractable problem to search suitable side chains from all possible rotamers simultaneously using information of known protein structures. Reducing the number of possibility is a main issue to predict side chain conformation. This paper proposes an enumeration based similarity search algorithm to predict side chain conformations. By introducing “beam search” technique, a significant number of unrelated side chain rotamers can easily be eliminated. As a result, we can search for suitable residue side chains from all possible side chain conformations.

Focusing in on structural genomics : the University of Queensland structural biology pipeline

The flood of new genomic sequence information together with technological innovations in protein structure determination have led to worldwide structural genomics (SG) initiatives. The goals of SG initiatives are to accelerate the process of protein structure determination, to fill in protein fold space and to provide information about the function of uncharacterized proteins. In the long-term, these outcomes are likely to impact on medical biotechnology and drug discovery, leading to a better understanding of disease as well as the development of new therapeutics. Here we describe the high throughput pipeline established at the University of Queensland in Australia. In this focused pipeline, the targets for structure determination are proteins that are expressed in mouse macrophage cells and that are inferred to have a role in innate immunity. The aim is to characterize the molecular structure and the biochemical and cellular function of these targets by using a parallel processing pipeline. The pipeline is designed to work with tens to hundreds of target gene products and comprises target selection, cloning, expression, purification, crystallization and structure determination. The structures from this pipeline will provide insights into the function of previously uncharacterized macrophage proteins and could lead to the validation of new drug targets for chronic obstructive pulmonary disease and arthritis.

Lab-on-a-chip : a component view

Miniaturization is being increasingly applied to biological and chemical analysis processes. Lab-on-a-chip systems are direct creation of the advancement in the miniaturization of these processes. They offer a host of exciting applications in several areas including clinical diagnostics, food and environmental analysis, and drug discovery and delivery studies. This paper reviews lab-on-a-chip systems from their components perspective. It provides a categorization of the standard functional components found in lab-on-a-chip devices together with an overview of the latest trends and developments related to lab-on-a-chip technologies and their application in nanobiotechnology. The functional components include: injector, transporter, preparator, mixer, reactor, separator, detector, controller, and power supply. The components are represented by appropriate symbols allowing designers to present their lab-on-a-chip products in a standard manner. Definition and role of each functional component are included and complemented with examples of existing work. Through the approach presented in this paper, it is hoped that modularity and technology transfer in lab-on-a-chip systems can be further facilitated and their application in nanobiotechnology be expanded.

Apoptosis goes on a chip : advances in the microfluidic analysis of programmed cell death

Recent years have brought enormous progress in cell-based lab-on-a-chip technologies, allowing dynamic studies of cell death with an unprecedented accuracy. As interest in the microfabricated technologies for cell-based bioassays is rapidly gaining momentum, we highlight the most promising technologies that provide a new outlook for the rapid assessment of programmed and accidental cell death and are applicable in drug discovery, high-content drug screening, and personalized clinical diagnostics.