Tuesday, December 30, 2008
Do patients have a moral right to know their treatment group at the end of the study?
Do patients (in particular those from a placebo arm) have a moral right to access the experimental therapy when the study ends?
If such a moral obligation exists, does an informed consent absolve a sponsor?
In each of the stories that have come to the mainstream media over the past few months (including the case of Fred Baron seeking off-label Tysabri), there is seems the assumption that the experimental therapy is not only known to be safe but assumed to be effective.
As argued in the WSJ, the constitution should be interpreted to allow "a crtitically ill patient...access to a potentially lifesaving drug that has been deemed safe for human consumption, if the patient agrees to bear the risks involved."
But an IND to initiate human trials is based only on preclinical/animal testing -- a barometer few would interpret as a stamp of being deemed safe for human consumption. In fact, it's the very clinical trial being conducted that is meant to demonstrate safety during the drug's life as an experimental medicine.
And is a patient's signature on a consent going to be adequate to demonstrate that the "patient agrees to bear the risks involved"? In fact, didn't the patient sign a very similar consent agreeing to risks involved when signing on to the clinical trial (including those of placebo use)?
Patient rights are a moral obligation. But so too is patient safety, which much be the priority. Finding a common ground in the coming year will be important for ensuring patients feel both safe and not-abandoned in clinical trials.
Now Washington is stepping forward with the "Access, Compassion, Care and Ethics for Seriously Ill Patients Act." Is Washington best suited to solve matters of morality and ethics?
Thursday, December 18, 2008
The Latest Industry Being Outsourced to India: Clinical Drug Trials -- From the St. Petersburg Times
Wednesday, December 17, 2008
I recently hosted a meeting of several international key opinion leaders representing a number of innovative technologies with applications for drug development. One spoke to a clever computer algorithm – an artificial neural network – that was being used for clinical purposes (as an example, identifying patients likely to develop Alzheimer’s Disease).
If you are not familiar with neural networks, then you may want to turn elsewhere for a primer (as I am hardly an expert). What I will say is that these are computer programs designed to act like the human brain and adapt – or learn – as they are shown more data. In the example above, show the algorithm more patient cases and it will be increasingly likely to predict who would get Alzheimer’s.
The speaker then showed a slide entitled “Over-Training”. In over-training, if one shows the computer too many cases they actually can start to see error rates increase. To be honest, I tuned out much of the conversation after this slide. I became fixated on the concept that over-training does not cause a plateau in the error, but that too much training actually causes more mistakes. The system becomes overly focused on individual cases and can no longer see the big picture.
Artificial neural networks were designed to mimic the construction of the human brain and our neural networks. But what can we learn from the lessons of over-training the artificial system?
Can we be over-trained? If we see too many cases or focus too sharply on one task, do we lose the ability to see the big picture? If so, how can we innovate and advance drug development? Are those on the “inside” too narrowly focused – and over-trained?
Expertise can be of value – but perhaps all knowledge being with the beginner’s mind.
Perhaps the grandparents of this space are groups such as the Biomarkers Consortium and the SAE Consortium. The C-Path Institute was created, in-part, to create consortia to act upon the FDA's Critical Path Initiative. Where there has been a lack of sound business models for creating new tools for drug development, consortia have been a good solution.
The ability for otherwise competitors to suddenly collaborate is based upon what some have called the “pre-competitive space”. At least one group has defined this as “technologies that aren’t really the basis on which they are competing but helps them do their jobs.”
But there is an inherent conflict. By their very design, consortia are meant to be inclusive and bring representatives from various related areas around the table. But what is pre-competitive to one stakeholder is likely a key revenue source, business opportunity, or competitive differentiator to another stakeholder. In most cases, “pre-competitive” is in the eye of the beholder.
Today it seems not a week goes by without another new industry-wide initiative being launched. Each requires an investment of resources and a commitment to see value ultimately generated.
Impact on drug development? Consortia have emerged as important mechanism for improving the clinical development toolbox (biomarkers may be a good example). But each initiative must start with an honest discussion among stakeholders around the table about what is truly pre-competitive, and whether everyone sees the same opportunity in the same light.
Meanwhile, a similar story from the
Impact on drug development – Increasing the potential for companies and trials to survive the economic chaos...especially as proposals in the US and UK stipulate applying funds toward R&D.
Friday, December 5, 2008
Media attention around the potential for Research 2.0 continues to swell...
Wednesday, December 3, 2008
Whether or not the scientific and clinical communities accept the trend, Web 2.0 will continue to enable and empower patients to share information and initiate their own research. (Once upon a time the medical community also dismissed the role of the Internet for patients to find health information.) Can we take measures to strengthen the tools available to patients, to make their efforts in generating research data a source of value (rather than fear) to scientists and clinicians?
Interesting and creative new model proposed for managing both cost and risk in drug development. Will it work?