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Fifteen Years: Where Biotech Has Been And Where It Is Going


By Bio-IT World Staff 

April 4, 2017 | In 2002 we held the first Bio-IT World Conference & Expo in Boston; 2017 marks the 15th anniversary of our event and our community.

Much has changed since then. In March 2002, Blue Gene, IBM's new supercomputing effort, was not yet completed, and Eric Lander, our first plenary speaker, was still leading the international Human Genome Project (HGP).

But not everything is new. In his 2002 keynote, Lander observed: "We indeed have the keys to this remarkable library" of genomic information, but "we still don't know how to use it fully." We've made amazing progress since then, but his words still ring true.

I’m so grateful that the Bio-IT World Conference remains the community home to industry veterans and newcomers alike who gather in Boston each year for expert commentary, industry insights, and the chance to grab a beer with the people driving the bio-IT industry forward.

It’s fun to look back, but we—and the industry—are moving full speed ahead. We asked several friends of Bio-IT World to weigh in on the biggest opportunities and biggest roadblocks to bio-IT, research computing, drug discovery, and precision medicine in the next 15 years.

Responses are still coming in, but here’s a sneak peek. Enjoy the vision and wisdom of just a few of the people who have built this community. (Some answers have been edited for length.)

 

Kevin Davies, Founding Editor, Bio-IT World

When we launched Bio-IT World magazine and Expo in 2002, the term “bio-IT” wasn’t an entirely comfortable fit, even among industry aficionados. Despite a slow start, what has been most gratifying is to observe the sustained expansion and importance of this community, anchored and fueled by the annual Bio-IT World conference. I remember CHI president Phillips Kuhl predicting as much when he acquired the event in 2006, which raised a few eyebrows. But he was right: the following year we reached a tipping point, thanks in large part to the excitement surrounding next-gen sequencing and cloud computing. It’s exciting now to see the titans of the IT industry rubbing shoulders with more and more audacious start-ups. Over the next 10-15 years, we can expect many more dramatic advances on the scientific and technology front; changing the culture to enhance data access and sharing may prove to be a thornier problem. 

Chris Dwan, Broad Institute (long-time Bio-IT World Best Practices Judge)

I'm starting to see a real change in the level of competence that young scientists bring surrounding software / data / infrastructure. Most of the informatics, computational biology, and even chemistry folks that I work with these days are fluent in some combination of statistical (R, SAS) and programming (Python, Java, Scala) tools. Many of them have worked with public clouds, and bring journeyman level infrastructure skills. Asking for access to the AWS console is the new version of buying a high powered workstation to sit under a lab bench. Over the next 15 years, these people are going to rise in their organizations, and it's going to change a lot about how leadership perceives the value and challenges of data and infrastructure.

I'm also seeing the emergence of notebook technologies like iPython and Jupyter - coupled with the "markdown" combination of active code and documentation. This is important because it is the first really viable shared digital workbench -other- than the command line. While there have certainly been tools in this space before (Pipeline Pilot is a big one), these are free, open, and rapidly becoming the standard way that computational scientists interact.

From the tool-builder / software side, I see the "devops" style of product development maturing. Combined with "agile" development, we're seeing integrated teams that include software engineers, infrastructure engineers, data scientists, scientific domain experts. In the old mode, we would throw badly written, incomplete requirements back and forth between teams that were defined by their technical specialties. Then we would get all self righteous when the result was late and didn't do exactly what we needed.

That means that we're making products faster with smaller teams. This threatens the existing power structure in a big way.

Taken together, along with the maturity of multiple public megaclouds (Amazon, Google, and Microsoft), I have no idea what "IT" will mean in 15 years. I don't think that anybody does. While there is a -lot- of work to be done, and certainly a lot of it centered on data, infrastructural technology, and software ... we're going to see a re-drawing of some of the most fundamental organizational command and control lines. Whole departments and companies will meld and merge into each other, with all of the stress and uncertainty that always comes with reorganization.

That's my scary message. Here's my hopeful one: It's still all about human beings working together to reduce disease and improve quality of life. I'm a big believer in people, and in networks of people.

I think that we're going to see regional affiliations that include many of the same players that have learned to collaborate and innovate together over the last 15 years. This is a community that is always moving on to the next special thing. We're an industry of innovation, where technologists partner with life scientists for the greater good. That's not going to change. In 15 years we won't be doing the same things we're doing now, but I expect we'll be doing them with many of the same partners and for the exact same fundamental reasons. 

*Chris will be speaking at the 15th Anniversary Bio-IT World Conference & Expo on Wednesday, May 24, at 11:00 am. He'll be speaking on IT Design Patterns to Support Genomic Science in the Age of the Cloud: Challenges and Possibilities. He'll also be moderating the BioTeam Micro-Symposium on 2017 Bio-IT Trends on Thursday, May 25, at 2:00 pm. 

Peter Elkin, Mayo Clinic (Health-IT World Plenary Speaker, 2004)

Much has changed since [I gave a keynote address at Health-IT World in] 2004. I am gratified to see how correct the predictions that we made were as we look at the world today and it is my pleasure to speculate with you where we will be in the future. The future is exciting. New predications include [In coming years,] doctors will no longer bill for their care; the notes they produce will be analyzed and appropriate payment rules will be applied. Drug discovery increasingly is utilizing in silica protein structure predication and docking techniques to speed drug development and testing including both efficacy and toxicity testing. Digitization of the practice will continue to increase to include diagnostic decision support, workup management decision support and therapeutic decision support.  Multidisciplinary team practice will be the rule rather than the exception. Telemedicine will grow as an industry. We will systematize the practice of medicine. The patient doctor relationship will actually increase in importance. 

Martin Leach, Biogen Idec (Bio-IT World Plenary Speaker, 2012 & 2013)

Biggest opportunities

  • In 15 years, whole genome data will be a commodity with more genome data available than can be consumed, from single cell through whole organism genome representation.
  • There will be a growing resurgence of semantic representation of life sciences data enabling us to ask complex questions and not just perform data queries.
  • We will have micro-services for everything, replacing virtual machines with an ecosystem of micro-services as a service.

 Biggest roadblocks

  • Data privacy will become an increasing concern with the wealth and insights being gleaned from data.
  • High-profile cloud breaches [are coming, and] will cause a rethink of cloud architectures and we may see a drift back to the internalization of technology within organizations to protect data assets and intellectual property.

John Quackenbush, Dana-Farber Cancer Institute (Bio-IT World Plenary Speaker 2012 & 2014)

When I consider where we are in health and biomedical research today, I find myself very excited by the scope and scale of the data available to us. Whether it is the exponentially growing number of genome sequences and increasing number of multi-omic studies, the expansion of electronic health records, the population studies that follow thousands or even hundreds of thousands of individuals, or the large pharmacogenomics screens available, the size, complexity, and diversity of the individual data sets that are being generated is astounding. 

[Recent work modeling gene networks, and looking at tumor mutations] have all been possible thanks to access to multiple sources of independent data on large numbers of individuals, and we are indebted to those who have made them available. But unfortunately, our ability to make advances is often limited by incorrect, incomplete, or inadequate data… Incomplete or inadequate data can present barriers to progress. Despite the massive quantity of data collected in cancer, my colleagues and I have struggled to find a data set that includes gene-expression data, tumor grade and stage, outcome, and drug treatment. While incomplete data sets may seem like a minor annoyance, they can have important ramifications. For instance, not knowing what drugs patients were treated with could easily confound a survival analysis. Incompleteness may also factor into the poor reproducibility of many published studies.

Nevertheless, I believe that we should be optimistic about the future. More biologists and bioinformatics scientists are quickly learning how to deal with messy data, including mastering the art of data wrangling, to create large and increasingly useful data resources. I believe that these data sources will drive innovation and that will advance our understanding of basic biology, help us to identify new drug targets, let us evaluate those drugs more thoroughly and more quickly, and open up opportunities to intelligently repurpose existing therapies. Ultimately, it is data that will help us move from the somewhat anecdotal way in which medicine is currently practiced to a new era in which information allows us to make more-informed decisions about patient care.

* John will be speaking at the 15th Anniversary Bio-IT World Conference & Expo on Wednesday, May 24, at 5:00 pm. He'll be speaking on Using Networks to Link Genotype to Phenotype

Heidi L. Rehm, Partners Healthcare (Bio-IT World Plenary Speaker 2016)

The biggest roadblock to advancing precision medicine is not enough open data sharing and open source software. If we all share our data and can contribute collectively to an open source genomics and healthcare platform, we will reduce duplication of effort and advance medicine more quickly!

Jerald S. Schindler, Merck Research Laboratories (long-time Bio-IT World Best Practices Judge)

Biggest opportunities:

  • Companies that are able to rapidly deliver actionable information to the decision makers in a useful format will have a strategic advantage over those that can’t. Companies that can provide tools to the decision makers that allow them to review live information in real time will be leaner and more nimble and will be able to adapt more rapidly.
  • Artificial Intelligence, deep learning and machine learning will move to clinical and pharmaceutical research. The current process for drug development will evolve into a constant learning environment as more information becomes available. This will challenge the regulatory process and may lead to earlier “partial” approvals.
  • The ability to create large integrated databases opens the door for collaboration across companies, universities and government. The could create (solvable) issues concerning access and ownership of data but will also enable better, faster research and knowledge creation.
  • Wearables will provide more information than time and heart rate. As they become more sophisticated and connected to the user’s own personal medical record, real time health tracking will become possible. Early warning for acute events such as Heart attacks and strokes will be possible. The wearable could even call 911 if necessary.
  • Observational data and randomized controlled trial data will be combined for regulatory and reimbursement decision making.

Biggest roadblocks:

  • Lack of openness and cooperation. Some groups remain closed and competitive.
  • Data standards have not been adopted broadly. Integration of disparate databases is a challenge.
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