Five Biotechnologies That Will Fade Away This Decade


Xconomy National — 

[Editor’s Note: This is part of a series of posts from Xconomists and other technology leaders from around the country who are weighing in with the Top 5 innovations they’ve seen in their respective fields the past 10 years, or the Top 5 disruptive technologies that will impact the next decade.]

We look at amazement at strange technologies from the past. How did people function in worlds with quill pens or connect with each other by Morse code and telegrams? Within biotechnology’s short history, we have already seen approaches from the ’90s such as Southern blots that look at sizes and amounts of DNA, and antisense therapies, are being replaced. I guarantee you at least 50 percent of what we think of as the enabling technologies and approaches to biological knowledge will be relegated to museum displays in the next five (OK, maybe 10) years.

Here are five that are ready to be replaced:

1. Genome-Wide Association Studies (GWAS) studies based on single nucleotide polymorphisms (SNPs). This is the approach that does high-speed scanning for markers across the complete sets of DNA, or genomes, among many individuals to spot small variations that might be associated with a particular disease. Single nucleotide polymorphisms (SNP) analysis isn’t going to last long as a major driver of biologic insight. Within the next one to two years, people will wake up to “ITEGS”—“It’s the entire genome, stupid.” Technologies are poised to allow analysis of variations in thousands to even hundreds of thousands of people. Do not be surprised when all the people with a disease such as Huntington’s are analyzed for DNA alterations across their entire genome. Groups such as Cure Huntington’s Disease Initiative are already preparing for this world.

2. Proteomic Approaches as an end solution to understanding diseases: Many people believe that following quantitative proteomic analysis which looks at a wide array of proteins that carry out the functional instructions from DNA, will be the key to the next wave of biologic insights. Many today yearn for a world where we could know the levels of all the proteins in a cell to finally functionate the cell—as if knowing all the elements allows one to understand all chemical structures—NOT. It’s unlikely the levels of protein components are the sufficient keys to the puzzle. It’s more likely they will become yet another layer of key information along with readouts on metabolites and RNA. The real decoding of diseases will be driven by those that know what to do with the component lists—be they DNA, RNA, or proteins. The next wave of insights will be in the hands of those that can build network models of what went wrong in the disease states.

3. Biomarker signatures as commercially viable robust markers akin to cholesterol or estrogen receptor positivity for breast cancer. Identifying signatures of certain genes or proteins is currently all the rage among those finding the right drug for the right patient. For the most part, these signatures are done on populations of hundreds to thousands of patients. Many hope to turn these into definitive markers that will guide treatment over decades. But hey VCs, you might want to try … Next Page »

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6 responses to “Five Biotechnologies That Will Fade Away This Decade”

  1. Mark Minie says:

    Perfect timing–I teach an advanced course in Molecular & Cellular Biology for the UW BioE Department/UWEO this year and I use articles like this to point out how fast things are changing and what direction things may be changing towards…this was great and generated a lot of discussion in my class last week. The class is made-up of people working at all levels in the regional Biotech/Pharma industries and organizations…in addition to the science, I emphasize new tools such as social networking and video, which as you point out with Sage Bionetworks will be important tools for the future of bioscience…this was great, and I hope you say more in the future!

  2. Samuel Pitamah says:

    “Hunter-gatherer approaches where large groups collect massive clinical and genomic information and expect that they as the data generator will be the data analyzer.” – Dumb and quite self-serving dont you think Dr. Friend?