From nabraham's post , it seems that this is actually the result of completely transparent salary information. The firms are all in lockstep because they can all see what each other are paying and adjust to the same number. I'm not sure collusion really applies to that type of scenario, especially when the net result seems to be that employees get paid more, not less. (A raise in salaries at one firm will cause an equivalent raise in others, because they're afraid of missing out on talent.)
Understandable. The seed environment in Boston is sub-optimal. But hopefully it will continue to change as more people like me band together with friends to start seed stage VC firms. The market is just too good to not take advantage while there's no competition and no crazy valuations.
It seems I want the opposite. I don't want to have the files on my local machine. I want storage that is bigger than what my local machine has. Hopefully at least one of us will get what we want from this.
- What's your approach on how to eventually apply the process to cells that don't normally grow in suspension?
- How do you guard against harmful drug interactions in vivo, given that most of these drug combinations probably haven't been seen in a person at the same time? Does this fall under compassionate use?
- Are you sequencing tumors and looking for predictive markers for future applications?
- This is one of the reasons we have started with Glioblastoma Multiforme (grade 4 brain cancer), as it has been grown in serum free suspension cultures (the neurosphere assay) by many labs for the past decade or so. When we eventually look to move into other cancers we will need to re-engineer the process.
-Since all of the drugs are FDA approved we don't have to apply to get compassionate use as you would if you were obtaining an experimental therapy outside of a clinical trial. The approved drugs can be used at a doctor's discretion as an off label prescription within our current system. As for safety please see my response to the first comment.
-Yes we are going to be sequencing all of our tumors. The difference between our approach to this versus existing personalized medicine sequencing is that we're more interested in using biomarkers in the future once we've identified a patient population that responds to a certain drug combination.
This is our long-term goal, to use a patient's biomarkers to predict drug response that we can then validate in vitro and in vivo rather than just relying on empirically physically testing drugs on a patient's cells.
This is a really great point srunni thank you for bringing it up. We agree that the current approach to identifying predictive cancer biomarkers has many challenges. That's why we believe in a different approach.
There have been four subtypes found in the disease, but all four are still treated ineffectively with the same standard of care chemotherapy (Temozolomide). To date none of these predictive biomarkers have led to a successful targeted therapy.
This was a personal frustration for me, as I had my dad's tumor sequenced for genetic mutations, but there were no targeted therapies that reach the brain in high enough concentrations to be effective.
This is an important distinction in personalized medicine specific to brain cancer, as many drugs do not cross the blood brain barrier. For example the most prevalent mutation in Glioblastoma is a mutation in the Epidermal Growth Factor Receptor (EGFR) pathway. This is also a driver mutation in lung cancer that has approved targeted therapies like Afatinib.
Unfortunately none of these drugs have worked in trials for brain cancer, in large part because they do not cross the blood brain barrier well. For example here is a study recently published on the poor results with Afatinib in brain tumor patients.
There are currently very few targeted therapies in development for brain cancer as it is a small disease for pharma companies to market drugs.
This is why our approach is to correlate tumor response to existing therapies with empirically identified biomarkers. In contrast using tumors alone to predict cancer biomarkers is challenging because it requires a hypothesis or target to be tested one at a time with a novel therapy in a trial.
For underserved diseases like glioblastoma, this could result in many years passing by as each hypothesis driven therapy fails in clinical trials.
Definitely not a stupid question it is really important!
There are several novel ways to deliver drugs to tumors in the brain in trials. Some use specially designed pumps, nanotechnology, and intra-arterial delivery (where a catheter is snaked to the tumor through blood vessels).
The problem is that all of these methods are experimental so they can't be used in combination. We're very hopeful that they are successful though!