How Biotech and IT are Redefining the Healthcare Industry

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How Biotech and IT are Redefining the Healthcare Industry

p_bioTechnology is essentially transforming how healthcare providers prevent, diagnose and treat disease, how information is shared (and how quickly) and how patient records are analyzed, stored and managed. Utilizing software applications to automate scientific protocols, and to manage data in the healthcare and life sciences arena has especially been beneficial in modern times. From imaging in medical science to storage and digital curation of data, IT applications have been the backbone of several breakthroughs without which these scientific efforts would have stagnated. We take a glance at the broad areas that Information Technology continues to support in the human’s quest for a healthier and fast-paced life.

 Genomics and “Big Data”

Simply said, genes are the functional units of an organism that determine the way it looks, functions and lives. Generally, the higher the organism is in the evolutionary tree, the more complex its genetic data tends to get. Genetic data is not just a simple code that drives a living being; with increasing knowledge of genetics it is now understood that there are intricate and elaborate mechanisms through which these genes interact with each other as well as with the environment. Although members of any complex species are sufficiently similar, they are yet completely different from one another. Science today seeks to uncover these differences between individuals not only for new understanding of beings, but to make any medical treatment more personalized and customized. In fact, we are in an age where diseases can be predicted before there are obvious signs. Treatment options are made to fit each individual for the best outcomes. These are possible with the help of generation of large amount of genetic data from whole populations.

This is made possible today in genomics and genetics labs that generate gigabytes of data in a single experiment. It is difficult to analyze such data and have a plausible interpretation unless it is done on a platform that can process this “big data”. Big Data is also being used to model disease progression and treatment response and outcomes, apart from being predictors of disease susceptibility. To process, analyze and access this data, highly efficient and reliable computer programs are needed. Several emerging businesses are now utilizing this opportunity of gathering and analyzing data and providing it to the general public for consumption. Efficient and well integrated computing tools are needed to deal with unstructured data and deal with issues of data access, curation, transfer and storage.

Diagnostic Imaging

Science has always depended on imaging to understand the basic processes of life. From the era of Leeuwenhoek and Hooke, scientists that visualized cells for the first time several centuries ago, to the present-day visualization of disease states, imaging has come  a long way in understanding complex living systems and managing their states. Automation and digital integration has made these leaps more rapid and easier. Organs are not only visualized in great detail to track the disease states utilizing algorithm guided image reconstructions, such as with MRI and CT scans, it is also possible now to image cells and its components such as proteins and its complexes. Computational methods are now being used to generate high resolution, artifact-free 3D images with a broader field of view that utilize lens-free, on-chip microscopes that can do away with manual focus adjustments. These chip-based microscopes tend to be easier to use and economically advantageous too (1) .

 Drug Discovery and Pharmaceutical Development

Although medical knowledge has grown by leaps and bounds in the present century, there are still many disorders and diseases that need effective cure and treatments. Even for conditions that already have drug treatments in the market, newer more-effective alternatives are constantly being discovered or tested. However, the costs in terms of time, money and trials on human subjects hinder the discovery process.

Identifying promising compounds even before phase I clinical trials can take up to 6 years for any pharmaceutical lab. Effective computing and simulation tools can cut this time significantly, for example by predicting ligand-target binding affinities and saving time spent on generating iterations of the desired compound. (2,3). The power of computing can be tapped further by designing algorithms that can account for more variables while predicting leads for a molecular target, significantly speeding the discovery process and cutting down costs for pharmaceutical companies.

Healthcare and Clinical Trial Data Management

Hospitals and healthcare firms generate large amounts of patient data that needs to be accessible by healthcare providers, insurance agents and patients with the added layer of security and privacy. With the increasing population and expanding healthcare institutes and governmental policies for privacy and accessibility, specialized applications are needed to store and manage such data in paperless format. With the complex workflow system of the modern hospitals, accessing data from different locations needs digitization as well as integration that can only be brought about by modern information technology applications. Apart from regular patient data from hospital visits, organizations involved with clinical trials of new drugs and disease treatment protocols also need techniques for monitoring and accumulation of new data, that seems burgeoning these days. The number of trials that a typical drug had to undergo has doubled between 1980s-1990s, while the number of human subjects steadily tripled (4). With the push for newer alternative drugs with higher efficacy and fewer side-effects as well as drugs for previously untreatable conditions, the amount of data that will be generated can only be imagined. Integrated IT applications will therefore continue to be the platform to organize and handle such data.

Opportunities for Biotechnology Industry 

Biotech companies need efficient management of resources, equipment and data to be viable in the business world. Protocols and products may also fall under regulations for quality, making it imperative to automate monitoring of experimental conditions to ensure consistency in product quality. Further, most procedures integrate the functions of a variety of tools and machines into a final product. For example, a simple flow process for antibiotic production needs a series of variable-controlling instruments for microbial growth as well as a series of at least two to three varying biochemical purification systems that can remove the biomass as well as residual contaminants, before final processing and quality checks. To make this process faster, economical, convenient and reliable every time the product is made, there rises a need of an automated and integrated system that can check, track and maintain optimal conditions throughout the process. Systems integrated with maintenance software is therefore an indispensable tool for any productive biotechnological industry.

Information Technology systems are an integral part of the developing biotech and pharma sectors globally. Automation, Digitization, and Integration of systems and data would continue to be a necessary part of the development and real-world application of scientific knowledge as well as for expanding businesses and increasing revenues. Convergence of Biotechnology and Information Technology is the best tool at our disposal for superlative outcomes in the healthcare and cutting-edge technology sector.

References:

  1. A. Greenbaum, Y. Zhang, A. Feizi, P.-L. Chung, W. Luo, S. R. Kandukuri, and A. Ozcan. (2014), Wide-field computational imaging of pathology slides using lens-free on-chip microscopy. Sci. Transl. Med. 6, 267ra175.

  2. Singh J, Chuaqui CE, Boriack-Sjodin PA et al. (2003), Successful shape-based virtual screening: the discovery of a potent inhibitor of the type I TGFbeta receptor kinase (TbetaRI). Bioorg. Med. Chem. Lett. 13 (24): 4355–9.

  3. Becker OM, Dhanoa DS, Marantz Y et al. (2006), An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression. J. Med. Chem. 49 (11): 3116–35.

  4. DiMasi, Joseph A., Ronald W. Hansen, and Henry G. Grabowski. (2003). The Price of Innovation: New Estimates of Drug Development Costs. Journal of Health Economics 22: 151–85.

By | 2017-09-05T14:44:57+00:00 June 3rd, 2015|Mobile, Software, Technology, Web|0 Comments

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