Xconomy’s Biotech Predictions for 2010-2020

In January, Xconomy posted three articles predicting the next ten years of biotech. Here are the summaries (with my comments in italics):

Robert Nelsen, co-founder and Managing Director at ARCH Venture Partners, suggested we watch the following:

1. Nanotech – This sector has been getting a lot of interest lately and Nelsen mentions applications in solar energy that will blow away current names in photovoltaics.
2. Industrial applications of synthetic biology (!) – I’m a huge fan of non-medical biotechnology and Nelsen credits decreases in sequencing costs and the creation of synthetic genomes as the driving forces.
3. Personalized, predictive, preventive and participatory medicine – This is largely being driven by plummeting sequencing prices (I’d bet $100 in 3 years? Any takers?) and more effective, targeted medicines. It is also being driven by Patient 2.0 companies like Patientslikeme.com, acor.org and armyofwomen.org, among others. Participatory medicine, however, could become a privacy nightmare very quickly.
4. The merger of cloud computing and mobile devices – This one is already underway and will revolutionize wearable healthcare devices.
5. In-vivo potentiation – Basically, this is about using drugs to trigger the body’s own ability to cure itself using our own cells. This one may be a toss up with stem cell research.
6. China – Nelsen adds China as his bonus, saying “they will make strides in their own internal innovation” and we ignore them at our own risk. That said, the U.S. will continue to be the biggest driver of innovation in the world (I hope so). My prediction: 5 will happen in China before it happens in the U.S.

Stephen Friend, president and co-founder of Sage Bionetworks, a global genomic collaborative took a different tact and predicted the following five biotechnologies will be history by 2010:

1. Genome-wide assocation studies (GWAS) based on single nucleotide polymorphisms (SNPs) – This approach scans for markers across a complete genome, among many individuals to find variations that might be associated with a particular disease. This type of study will go away because dropping sequencing costs will make it easier to analyze the entire genome of hundreds, even thousands of people, and will probably be replaced by studies that find rare variants linked to diseases. The question is: Will this happen before or after we hit the $100 genome?
2. Proteomic approaches as an end solution to understanding diseases. Right now, “many people believe 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 biological insights.” Friend believes the next wave of insights will belong to those who can build network models of what goes wrong with DNA, RNA and proteins in disease states. In other words, a combination of high speed computing and the analysis skills to take part interactions in cells real time.
3. Biomarker signatures a commercially viable robust markers akin to cholesterol or estrogen receptor positivity for breast cancer. Friend points out that these biosignatures will likely be replaced. Think about the debate over what constitutes healthy cholesterol. This is akin to that but on a genome-wide level. You might have a predisposition for a disease as demonstrated by a biomarker but that doesn’t mean you’ll get it.
4. Indications for drugs will be determined by clinical trials performed by the biotech/pharma company developing the drug. Friend predicts we will very soon move into a world where who gets the drug will be modified continuously by large trials organized by payers and patients. The data will be updated real-time. “This is something to look forward to, as it will take much of the trial-and-error nature out of prescription medicine. It will be a world of real evidence based therapies.”
5. Hunter-gather approaches like the Framingham Heart Study. Large studies, like this 60 year old study, assume a small group of collectors of samples and data would analyse the data as well. Given that information is more easily distributed and knowledge is evolving faster, having a distributed group of scientists work on data and come to conclusions can only benefit us as results will only accelerate.

David Walt, professor of Chemistry at Tufts, co-founder of Illumina and subject of Luke Timmerman’s How to Build a Billion Dollar Company and Keep an Academic Day Job, offered the following 5 disruptive biotech ideas to watch:

1. Moore’s Law won’t be able to keep up. Sequencing costs will continue to plummet thanks to a number of new technologies being developed, but data analysis and bioinformatics won’t happen fast enough because the computing power won’t be there.

2. As Friend predicts above, Walt notes that GWAS have identified only a small percentage of the underlying DNA markers linked to hereditary disease. Identifying rarer, and perhaps more meaningful single nucleotide polymorphisms (SNPs), will happen thanks to the decrease in genome sequencing costs.

3. In some cases, cancer will be cured by sequencing genomes of tissues from cancer patients and comparing them to non-cancerous tissues from the same individual. Personalized treatments will then be employed and personalized medicine will arrive for some cancers and certain other diseases.

4. Developments in bioanalytical science will impact on clinical diagnostics. Imaging technologies that provide high-res pictures of cells will be applied to monitor living cells real-time in the clinic.

5. Single molecule measurements will lead to more sensitive measurements of biomarkers that have not even been detected. The relevance of these markers to disease and wellness will start to be uncovered but don’t expect immediate clinical applications. These things take time.

What do you think of these predictions? Drop me a line and let me know.