Bioinformatics, in the drug development context, aims to facilitate the selection of drug targets by acquiring and presenting all available information to the drug developers. The Economics of Drug Discovery Let us turn to the economics of the drug discovery cycle. Of the about 5, - 10, compounds studied, only one drug gets onto the market. These phases constitute parts of the manufacturing,regulatory and cost factors of drug discovery.
Due to these factors - regulatory, cost-effectiveness of drug discovery and the supply and demand fundamentals - the process of drug discovery is undergoing a complete overhaul. Consequently, companies, which have been reaping a fortune from the sales of drugs are expected to shift their focus to tap into information. A case in point is managed healthcare. In the managed healthcare treatment of cancer, for example, the federal government might limit treatments to two per patient, instead of the age-old "physicians shall do whatever it takes" - the Hippocratic Oath.
For instance, a patient will be given chemotherapy, and then an operation, if necessary. If this still does not help, that will be it. Thus, companies which maintain good databases for diseases will be able to, via some intelligent software or otherwise, predict the best course treatment for individual patients depending on the ethnic background, progression and stage of illness, age, sex, previous history and others. Or that they can tap into bioinformation and cheminformation to shorten the cycle of drug discovery, and thus making drug discovery more cost-effective.
Future Pharmaceutical Discoveries Traditionally, large pharmaceutical companies have a cautious, mostly chemistry- and pharmacology-based approach to the discovery and preclinical development program and therefore, do not yet have expertise in-house to generate, evaluate and manage genetic data. The general consensus is that future pharmaceutical discoveries will stem from biological information. Major pharmaceutical companies develop new core products.
These companies are either slower in response; or they do not want to develop sequencing expertise nor maintain proprietary database in-house; or they do not want to commit the financial resources for such purposes. But they do want to respond quickly and do need access to comprehensive genetic, biological and chemical information for timely and accurate decision making. Modern drug discovery, on the other hand, has been transformed by the industrialization and automation of research. The resulting explosion in the quantity and complexity of biological, chemical, and experimental data has overwhelmed the ability of the drug discovery industry to make sense of it.
The data explosion, combined with the pressure to reduce costs and speed up drug discovery cycles, provides a strong demand for software and information products. Informatics integration is the key to unleashing the potential of modern drug discovery. Increasing reliance on genomic information about disease targets and on chemical information is creating a data-oriented research environment in which collaboration among molecular biologists, molecular modelers, drug chemists and computer scientists is essential for efficient drug discovery.
These disciplines are loosely coupled by computational science. The role of bioinformatics and cheminformatics has changed from a specialist niche tool to that of an essential corporate technology. The scope has also accordingly widened from a laboratory-based tool to an integrated corporate infrastructure.
Indeed, biology has become so data-intensive that the whole scenario has been paralleled to what happened to physics some fifty years ago. The technology is coming to fruition at a pace that outstrips the capacity of the current methodologies of managing and analyzing biological and chemical data.
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Genomics, combinatorial chemistry and high-throughput screening are recognized as the triumvirate of the new order of drug discovery. Thus we are seeing bioinformatics divisions springing up in all major pharmaceutical companies to either partake in this exciting new area, or to partner with smaller, more nimble companies.
Because of this, smaller companies are constantly being formed to take advantage of the window of opportunities, some of which survived, and many more of which floundered. In general, these small companies try to develop technologies, be it laboratory-based or information-based, produce a database of some form and then generate revenue from the database by either selling subscriptions to the database, or selling information derived from the database. As with any business, one has to be on the qui vive for quacksalvers. There are many companies out there trying to sell unproven technologies and many eager investors are misled into empty promises.
For example, a small biotechnology company may claim to have a core technology to do high throughput sequencing. More often than not, the company also uses a complementary and more proven technology, for example, an ABI machine, as a control. However, it will have no qualms in presenting results from the complementary technology as results from the core technology when the unproven core technology fails to live up to expectations. Or somehow by a legerdemain of skillful massaging selected data to make them look convincing; or to put up a Potemkin village with heavy machinery of moving parts, computers of blinking lights, foyers of chandeliers, offices of mahogany executive desks, etc, redolent of achievements, successes and wealth.
In other words, the turpitude of code of business ethics is redefined. Ultimately the PDF created with pdfFactory trial version www. Another pitfall is duplication of efforts, which can be quite bootless. For example, in cDNA sequencing, several companies are using different core technologies to sequence many of the same tissues when the resources can be better utilized to sequence other tissues. There are even instances in which companies do so just to prove the "higher" throughputness of their core technologies.
The bottomline is once the data has been obtained, no one really cares how it was obtained, or by which technology! The goals and mission may vary in accordance with local needs, and very much driven by applications and clients. Bioinfobahn Since bioinformatics is a marriage of computer and biology, it is not surprising that it is well kept abreast with advances in computer technology, in particular, the internet technology. The internet came into being about twenty years ago as a successor to ARPANET, a US military network disguised to provide networking capabilities with a high redundancy.
The principle behind has remained unchanged and has proven very powerful: to have every computer potentially talk to each other, regardless of what platform, what network path the communication actually takes. By going cybernized, information and knowledge disseminate at a much more timely rate. There are countless electronic publications on the net, as is obvious from the cited footnotes of this text. These publications appear in the form of regular ascii text, postscript, hypertext, Java and other derivations therefrom.
A good example of a biotech company that fully utilizes the internet technology is D'Trends, Inc. D'Trends, Inc. These products and technologies integrate and automate the full range of pharmaceutical business- critical processes to provide unprecedented levels of productivity. GenomeNet is a Japanese computer network for genome research and related research areas in molecular and cellular biology.
It provides public access services for database retrieval and analysis. GBF is characterized by long term projects for protecting the environment, and for dealing with the knowledge, diagnosis therapy and prophylaxis of diseases. Discussions and Conclusion Judging from the current prevailing trends in federal spending, healthcare and social reforms, and other force majeure, it is very likely that information, disease database maintenance, and intelligent software for extracting knowledge from these databases, will play a major role in the future of disease treatment.
Disease therapeutics will rely more on data, and information and knowledge derived therefrom, than on guess work, chemistry or pharmacology. Current successful therapeutics target initial causative agents such as infectious microorganisms, or empirically target a single step of a multi-step complex disease process. Therapeutic intervention, and therefore drug discovery efforts, should be aimed at the molecular events of the disease process itself. Currently, there are a number of technological limitations: 1 slow rate of cDNA sequencing; 2 high cost of sequencing; 3 poor quantification and incomplete representation of cellular mRNA, among others.
While many companies and research centers are developing high throughput, cost-effective technologies, the focus downstream should be on data, and information and knowledge derived therefrom, rather than on guesses. Thus, from a more technical point of view, drugs of tomorrow are somewhere in the vast and growing sets of data available. The market for drug discovery informatics presents an unprecedented opportunity to create value in the management and extraction of data and its conversion to information and knowledge.
While the computer can never completely substitute for laboratory work, it can however minimize bench-work and thus making drug discovery more cost-effective. The ultimate goal is to hasten the coming of age of "desk-top drug discovery" by developing the operating system of choice for drug discovery and development. In this sense, many software companies are functioning as labless pharmaceutical companies.
These integrated elements forge a connection between the drugs of tomorrow, and the vast amounts of proprietary and published data available to researchers today. The "linguae francae" is also flexible enough to accommodate all commonly used database engines Sybase, Oracle and Illustra and all versions of Unix.
It also loads annotations from external databases such as Pfam and homology models information from the Protein Model Portal. Annotations visualizing predicted regions of protein disorder and hydrophobic regions are displayed. Illustrates the correspondences between the human genome and 3D structure. Special features include support for both rigid-body and flexible alignments and detection of circular permutations. The JSmol symmetry display mode select the Symmetry button highlights global, local, and helical symmetry among subunits.
The view displays the symmetry axes, a polyhedron that reflects the symmetry, and a color scheme that emphasizes the symmetry. The slider graphic compares important global quality indicators for a given structure with the PDB archive. Global percentile ranks black vertical boxes are calculated with respect to all X-ray structures available prior to Resolution-specific percentile ranks white vertical boxes are calculated considering entries with similar resolution. Mutations in a gene can have profound effects on the function of a protein. This analysis tool highlights the location of a gene location i.
The new mapping tool can be used to locate this position on the UniProt sequence and 3D structure. This web server classifies interfaces present in protein crystals to distinguish biological interfaces from crystal contacts. EPPIC Version 3 enumerates all possible symmetric assemblies with a prediction of the most likely assembly based on probabilistic scores from pairwise evolutionary scoring.
Enter ligand IDs separated by comma or white space. PDB is an online portal for teachers, students, and the general public to promote exploration in the world of proteins and nucleic acids. View iconic illustrations by the gifted artist Irving Geis in context with PDB structures and educational information. Warning You are using a web browser that we do not support.
Our website will not work properly. Please update to a newer version or download a new web browser, such as Chrome or Firefox. Welcome A Structural View of Biology This resource is powered by the Protein Data Bank archive-information about the 3D shapes of proteins, nucleic acids, and complex assemblies that helps students and researchers understand all aspects of biomedicine and agriculture, from protein synthesis to health and disease. Job Opportunities for Biocurators and Developers. Bioinformatics may also be the only way drug companies can deal with the gigabytes of data they produce and receive every day.
The pharmaceutical trade organization Pharmaceutical Research and Manufacturers of America predicts that by , scientists will have discovered more than 10, potential targets for drug development, resulting in what some call "target glut.
The Role of Bioinformatics in Diabetes Drug Development-and Precision Medicine
At the same time, combinatorial chemistry allows companies to synthesize more than compounds per chemist per year. In the past five years, most big drug companies have created official informatics departments, either by integrating their research and IT departments or by creating close ties between the two. But the unofficial origins go back further. More people need access to this information, and the scale of information we have to disseminate to our clients -- the researchers -- is growing drastically.
So our ties to IT, which originally were almost nonexistent, have become stronger and stronger. Today, there are informatics technologies popping up to help at nearly every stage of the drug development process. Early on in the process, bioinformatics technology allows researchers to analyze the terabytes of data being produced by the Human Genome Project. Gene sequence databases, gene expression databases which track how genes react to various stimuli , protein sequence databases and related analysis tools all help scientists determine whether and how a particular molecule is directly involved in a disease process.
That, in turn, helps them find new and better drug targets. Using IT analysis tools and genomic databases, for example, Merck researchers were able to compare the entire genome sequence for mice and humans.
Applications of bioinformatics in biotechnology
Currently Merck is working with genetically altered, or "knock-out," mice, in which certain genes are altered to create a specific mutation schizophrenia, in this case to see how the animals react to drug candidates. This research, still in the very early stages, could eventually lead to a target for a new schizophrenia drug, Blevins says. Similarly, Bristol-Myers Squibb has discovered a novel method for treating epilepsy using gene-sequencing mining tools.
The particular drug candidate isolated for this research has since shown strong efficacy in knock-out mice and is nearing clinical trials, according to Siemers of Bristol-Myers Squibb. Drug companies also employ a variety of cheminformatics software -- tools that can predict the activity of a particular compound by studying its molecular structure.
For instance, scientists can use molecular modeling software tools that rely on interactive 3-D visualization or mathematical algorithms to discover and design safe and effective compounds. Chemical databases allow researchers to store and retrieve compounds and related data.
Robotics makes it possible for chemists to synthesize hundreds of thousands of chemical compound variations from a library of simpler molecules in a short amount of time. High-throughput screening technology see "The Definitions of Life," Page allows researchers to screen thousands of compounds at once, rather than just 10 or Technology may help at the clinical testing stage too, though it has been a bit slower to catch on. Virtual patient simulation software, like the Asthma PhysioLab program that showcased virtual patients Alan and Bill, can simulate patients, targets and therapies in order to predict experimental outcomes before companies commit major resources to lab research and clinical trials.