December 14, 2009

Applications of Systems Biology in Drug Discovery

Filed under: Bioinformatics,Chemoinformatics,Systems Biology — Biointelligence: Education,Training & Consultancy Services @ 4:33 am
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Till date we have made a lot of posts on Systems Biology, its applications and it scope. Indeed, Systems Biology has brought a big revolution in cell biology and pathway analysis. When seen in combination with treatment of diseases and drug discovery, it proves even more handy. Here we discuss Systems Biology in combination with drug discovery.

The goal of modern systems biology is to understand physiology and disease from the level of molecular pathways, regulatory networks, cells, tissues, organs and ultimately the whole organism. As currently employed, the term ‘systems biology’ encompasses many different approaches and models for probing and understanding biological complexity, and studies of many organisms from bacteria to man. Much of the academic focus is on developing fundamental computational and informatics tools required to integrate large amounts of reductionist data (global gene expression, proteomic and metabolomic data) into models of regulatory networks and cell behavior. Because biological complexity is an exponential function of the number of system components and the interactions between them, and escalates at each additional level of organization.

There are basically three advances in the practical applications of systems biology to drug discovery. These are:

1. Informatic integration of ‘omics’ data sets (a bottom-up approach)

Omics approaches to systems biology focus on the building blocks of complex systems (genes, proteins and metabolites). These approaches have been adopted wholeheartedly by the drug industry to complement traditional approaches to target identification and validation, for generating hypotheses and for experimental analysis in traditional hypothesis-based methods.

2. Computer modeling of disease or organ system physiology from cell and organ response level information available in the literature (a top-down approach to target selection, clinical indication and clinical trial design).
The goal of modeling in systems biology is to provide a framework for hypothesis generation and prediction based on in silico simulation of human disease biology across the multiple distance and time scales of an organism. More detailed understanding of the systems behavior of intercellular signaling pathways, such as the identification of key nodes or regulatory points in networks or better understanding of crosstalk between pathways, can also help predict drug target effects and their translation to organ and organism level physiology.

3.  The use of complex human cell systems themselves to interpret and predict the biological activities of drugs and gene targets (a direct experimental approach to cataloguing complex disease-relevant biological responses).

Pathway modeling as yet remains too disconnected from systemic disease biology to have a significant impact on drug discovery. Top-down modeling at the cell-to-organ and organism scale shows promise, but is extremely dependent on contextual cell response data. Moreover, to bridge the gap between omics and modeling, we need to collect a different type of cell biology data—data that incorporate the complexity and emergent properties of cell regulatory systems and yet ideally are reproducible and amenable to storing in databases, sharing and quantitative analysis.

This is how Systems Biology has aided in Drug Discovery Research and paved its path to cure many vital diseases.

Read our other posts on Systems Biology –

September 18, 2009

Chemoinformatics Companies Worldwide

Here is a list of companies working in Chemoinformatics and Drug Discovery.

Also check out these:

August 11, 2009

Bioinformatics In Pharma Industry

Bioinformatics provides the computational support for functional genomics which will link the behavior of cells, organism amd population to the information encoded in the genomes, as well as structural genomics. The utility of bioinformatics lies in the identification of useful genes leading to the development of new gene products. The subject covers topics such as protein modeling and sequence alignment, expression data analysis, and comparartive genomics. It combines algorithmic, statistical and database methods for studying biological problems also.

The greatest achievement of bioinformatics methods, the Human Genome Project. Because of this the nature and priorities of bioinformatics research and applications are changing. Many experts believe that this will affect bioinformatics in several ways. For instance some scientists also believe what some people refer to as research or medical informatics, the management of all biomedical experimental data associated with particular molecules or patients – from mass spectroscopy, to in vitro assays to clinical side-effects-move from the concern of those working in drug company and hospital IT (information technology) into the mainstream of cell and molecular biology and migrate from the commercial and clinical to academic sectors.

Drug Development

Only 10% of drug molecules identified in research make it through development. This means that many potential drugs do not make it to market, and expensive time and resources are invested m molecules that will generate no revenue. Simulation and informatics can significantly increase these odds by improving the efficiency of drug development, cutting costs, and improving margins.

Formulation Design

Formulation is the process of mixing Ingredients in such a way as to produce a new or improved product. The formulation department must balance the different marketing and deliverability requirements with cost and chemical constraints to come up with the best possible drug delivery method at the best price. With laboratory results stored in legacy systems, it takes expert company knowledge and experience to know which methods and suppliers are available, let alone to locate them quickly. In many cases scientists find that it is easier to repeat an experiment than to find previous results. This situation is compounded in global R&D set-ups, and after mergers and acquisitions.

Crystallisation and Structure Determination

Determining the crystal structure of an active compound is one of the first steps in pharmaceutical development. The crystal structure of a drug affects how easy it is to formulate, its bio-avail- ability, and its shelf life. Knowledge of the different possible polymorphs of a crystal can also give better patent protection for a drug.

Polymer Modeling

Drug delivery is a complex task. The drug must be delivered in a way that transports the active component intact to the appropriate part of the body. The way the cell takes up the drug is also very important: drugs that go to parts of the body other than the intended target are wasted and may lead to unwanted side effects.

Many delivery devices are polymeric with the drug either solubilised or emulsified in the polymer. Drug delivery systems have mesoscale structures; between 10 to 1000 nm. The amount of computing power required to model these systems at an atomistic level is prohibitive, and macroscale techniques such as Finite element analysis or computational fluid dynamics do not give the required level of detail. Mesoscale modeling, focusing on the nanometer length scale, is helping scientists to develop colloidal delivery systems for drugs.

The great advances in human healthcare that are presaged by the Human Genome Project can be realized by the pharmaceutical industry. A prerequisite for this will be the successful integration of bioinformatics into most aspects of drug discovery. Although, from a scientific viewpoint, this is not a difficult problem, there are formidable technological obstacles. Once these are overcome, rapid progress can be expected.