Wolfram Digital in Health

About a breathtaking future of healthcare
2. Auflage 2019
ISBN: 978-3-7504-4508-6
Verlag: BoD - Books on Demand
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 416 Seiten

Indicating ways to improve patient-outcome was the main driver writing this book. Two years of research, structuring, collecting, and writing around the central subject of our days: digital. Similar to other spheres of our lives, digital has the power to change many aspects of healthcare and improve patient's lives. Not only tells the book about the obvious, but also points to many use-cases and innovative examples of digital in health. By definition, digital interconnects and integrates people and things. Maybe some of the players in healthcare find the idea of working across sectors harmful to their own business model? Digital in health in any case means leaving traditional comfort zones. Besides telling about the digital past and present, the book Digital in Health defines, conveys ideas, triggers concepts, and indicates digital business models and cases already existing. In contrast to what we encounter every day, digital concepts in healthcare are lagging far behind. The rest of the world has already advanced a lot further. After technology is available, the most important prerequisite for the development of digital in health evidently is the mindset of protagonists. Digital today is far more than a buzzword, after every one of us is regularly confronted with it. Today many are already depending on digital, reaching into every corner of our life. None of our today's lives could continue to function as it is, without leveraging and using all these digital tools and applications. Digital in Health wants to help pave the way for digital ideas, supporting the constant ambition to improve patient-outcome. Joining forces for the common goal of better care, the coauthors added most relevant and important views. Each from his own professional background. Many thanks to Salil Kallianpur, Gerd Luippold, Christian Milaster, and Anup Soans who contributed so greatly to the book, while living in India, Germany, and the United States of America.

More than 40 years ago, Hanno Wolfram started his professional life in the healthcare arena as a medical representative working for a research based pharmaceutical company. After 22 years in various managerial positions he stepped out of this industry after a number of international assignments being Area Manager Europe. In the more than 20 years following, he worked for the pharmaceutical industry in consulting, educational and change projects in more than 25 countries on all continents. This created a steep learning curve. Having met numerous C-suite and line managers it became clear that in the pharmaceutical industry many terms and buzzwords are used, without ever having been defined. Not a problem as such, but not having the identical understanding, meetings and discussions cannot bear fruits. This was the trigger to start writing. In 2016 his first textbook about Key Account Management in Pharma was published in English. Being familiar with digital tools himself was the basis for starting to investigate, collect, synthesize and write this book showing pathways into a different, yet breathtakingly different future, designed to create better healthcare putting the patient in the center.

Autoren/Hrsg.

Wolfram, Hanno

Leseproben

BIG DATA IN BIOLOGICAL RESEARCH

On every step of the drug discovery process digitization and the handling and analysis of large datasets help to improve the development of drugs. The completion of the human genome project in the early 2000s (Venter & al, 2001), for example, substantially increased the number of potential drug targets. The human genome is comprised of ~3 billion base pairs with an individual genome representing ~100 gigabytes of data. Sequencing multiple genomes and tracking gene interactions multiplies that number many times - hundreds of petabytes in some cases. As an outcome of the human genome project, the total number of protein-encoding genes was estimated to around 30,000. Analysis of sequence data led to the identification of 3,000-5,000 genes encoding proteins that are useful drug targets.

Validation of the target is important in the process of drug discovery. This means that the target is an effector of a therapeutic drug, leading to benefits in clinical outcome when modulated in humans. Therefore, biological information on the role of the putative target in the disease process is needed. This requirement was the driver for the large datasets gathered with high-throughput methodologies bearing the –omics37 era.

The use of micro array technology, for example, paves the way to assess RNA levels of thousands of genes in one single experiment (transcriptomics), comparing expression profiles in different tissues or between individuals with and without disease. Other omics-disciplines refer to proteins (proteomics), lipids (lipidomics) or as an outcome of the Human Genome Project the genes itself (genomics).

These disciplines of science are characterized by large data pools describing the interactions and functions of biological information entities under various conditions. Scientists with IT-background develop methods and software tools for understanding these biological data.

WITH THE HELP OF BIOINFORMATICS

The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the Human Genome Project and by rapid advances in DNA sequencing technology.

In research and development (R&D), the strong increase in biomedical data, generated a hype to use these “big data” to improve productivity of the drug discovery process. The term “big data” was first used in an article by the NASA researchers Michael Cox and David Ellsworth in 1997.

They published their work in Proceedings of the IEEE 8th conference on Visualization dealing with simulations of airflow around aircraft that could not be processed and visualized. They stated: “Visualization provides an interesting challenge for computer systems: data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk. We call this the problem of . When data sets do not fit in main memory (in core), or when they do not fit even on local disk, the most common solution is to acquire more resources” (Cox & Ellsworth, 1997).

Big data could be seen under two aspects: the one already mentioned above, meaning a fast-growing flood of data that are dependent on the digitization process and the storage in more or less structured data bases. While the capacities to store the data were limited at the beginning of the digitization era (see the first use of the term big data), storage capacity is nowadays unlimited. On the other hand, big data is almost always related to IT-solutions and analytical tools, which help to extract value from the flood of information.

TEXT AND DATA MINING TOOLS

Interestingly, the pharmaceutical industry lately joined the big data era, although it is an industry which clearly depends on data-driven decisions. Text mining, for example, was an early tool to get high-quality information from a text. At the beginning, text-based searches were used, which only found documents containing specific user-defined words or phrases (e.g. PubMed38, Scopus39 searches).

Publications are one way to share the results of the lab work to the scientific community in journals as a print medium or online. Over the years the search for publications in the WWW, via the internet is displacing print media. Open Access platforms offer an alternative publishing model, allowing anyone to visualize published work for free. In addition to becoming more open, articles are bound to become more interactive. Text mining tools are available to help to choose from the thousands of journals. For example, eLife40 is providing open access to the most promising advances in the medical sciences. GigaScience41, another open-access journal is focusing on big-data studies across the entire spectrum of life and biomedical sciences. GigaScience has been selected as the 2018 Prose Awards Winner for "Innovation in Journal Publishing”.

Related to text mining is the more general process of data mining. Data mining describes the search process of discovering patterns in large data sets involving methods at the intersection of learning, statistics, and database systems. The data which are available come from a variety of sources but include publications and patent information, gene expression data, proteomics data, transgenic phenotyping, compound profiling data and more.

Data mining of available biomedical data has led to a significant increase in target identification. In the context of drug discovery, data mining refers to the use of a bioinformatics approach (see above) to not only help in identifying but also selecting and prioritizing potential disease targets (Yang & al, 2009).

Nowadays it is almost unavoidable for most researchers to manage large data sets and programing code. Therefore, tools have been generated to efficiently store and share data and code.

Code Ocean42, for example, is a cloud-based computational platform which provides a way to share, discover and run published code in academic journals and conferences. Delvehealth43, a data collection of global clinical trials, clinical trial investigator profiles, publications and drug development pipelines helps to identify ways for faster discoveries and resolving data silos across multiple research segments.

The team behind consists of data experts having years of technology and science experience working together to identify better ways to save patient lives. GenBank44 is the National Institute of Health (NIH) genetic sequence database. The database is designed to provide and encourage access within the scientific community to the most up-to-date and comprehensive DNA sequence information.

Today’s research also needs to reach out to other researcher and services and find expertise for new collaborations. Therefore, networking platforms like LifeScience.net45, which is an online platform for professional networking and sharing of knowledge in life sciences, LinkedIn46, a networking site for all or ResearchGate47, a social network for researchers, exist.

Research outsourcing has already become a vital component of pharmaceutical drug discovery. Many thousands of Contract Research Organizations (CROs) located throughout the world are providing different research services. To find, compare and order outsourced research services would be very cumbersome, if multiple CROs needed to be approached individually. Thus, an online marketplace (Scientist.com48, former Assay Depot) has been founded in 2007 by a software developer, a chemist and a cell biologist for life science research services. They already work together with some pharma companies to provide tailor-made solutions.

BIG DATA AND DIGITIZATION IN CHEMISTRY

Digitization and big data are not only buzzwords in the field of biological research, also chemistry must develop new methods and approaches for the targeted drug synthesis and the analysis of the increasing volume of data. Initially, natural products or extracts (e.g. teas, tinctures) for medicinal purposes were used thousands of years. Early drug discovery started by identifying the active ingredient from traditional remedies (e.g. digitalis alkaloids from foxgloves). Since the natural products often bear high structural complexity, the chemists entered the R&D arena in pharma. Why? Because chemists make molecules and the basis of the pharmaceutical industry is to test synthetic small molecules in intact cells or whole organisms. This process is known as classical pharmacology and has the goal to identify substances with a desirable therapeutic effect.

A classical chemical synthesis begins by mixing selected compounds that are known as reagents or reactants in a reaction vessel such as a chemical reactor or a simple round-bottom flask. Dependent on the reaction type a product or an intermediate product will be synthetized. Normally, a multistep approach, meaning a series of individual chemical reactions, is needed to synthesize a small molecule. This process is time consuming. Therefore, chemical synthesis machines would be highly welcome to automatically synthesize small organic molecules that could revolutionize drug discovery. For instance, Martin Burke, a chemist at the University of Illinois, invented a machine that welds molecular building blocks into a vast array of drug-like compounds (Service, 2015).

Widely used in the design of new drugs is another branch of chemistry, the computational chemistry. Here “going digital” means that computer simulation helps in solving chemical problems by using methods of theoretical...