Parkinson Pipeline Project-treatment development database, research, FDA drug approval, PD clinical trials and sponsors, patient involvement

Page 2

Page 1 - 3 - 4 - 5 - 6 - 7 - 8 - 9 - 10 - 11 - 12 - 13

At the same time, because of the cost effectiveness of this technology, the production volume and the market for these products skyrockets and computer shipments are heading upward. And not only are these computers shipped in larger and larger numbers, they are more and more capable of doing things from word processors back in the '70s and all the way to MP3 players and cell phones and all the implements of a collection of social networking applications that Web 2.0 signifies. And with all this, it becomes so pervasive that one can actually talk about creating computers that can be shipped to underdeveloped countries, and can operate even without electric power by substituting human labor for electric power.

It’s a remarkable record. It was hard work. A lot of capital went into it. A lot of human capital also went into it, a lot of development and evolutionary and revolutionary changes.

So let's switch to a field that's a little closer to us here and look at the history of Levodopa. Levodopa was found to be effective in animal models in the '50s, and it was given to patients around the same time Gordon Moore formulated Moore's Law. It became the mainstay of Parkinson's disease treatment, and my impression is that there is no comparable neuroactive drug in any other disease. Fast-forward 40, 45 years and it's still the mainstay of Parkinson's disease treatment. To be sure there have been modifications to it and adjuncts were developed. The number of drugs, drug types in the arsenal of neurologist basically delivering the same active ingredient grew. The modifications have had benefit to patients and benefits to companies, as measured by the cost of treatment which, unlike microprocessors, has gone upward in that period of time.

Dr. Kordower’s assignment to me is to spell out and give some possible explanations, from my vantage point, of the differences between the two industries. They fall into three categories: speed, failure and success. That's the construct that I want to follow the rest of the talk.

We're talking about speed, the speed of experimentation, the speed of data gathering, the speed of digesting all of those and turning those into new experiments. That is the engine that changes the rate of discovery. I have a sort of an information wheel in mind, that’s shown on the next slide.

We do an experiment, we evaluate the experiment, from this we plan the next experiment. We all do this consciously or unconsciously. The difference is that any given intelligence and the state of science, the faster we turn the experiments, the faster we're going to get the desired result.

This is a very important tenet of experimentation and infrastructure development in the high-tech industry. We know that information turns determine success. We can't make our engineers and scientists smarter. We cannot make – well, we try to make them work harder but it doesn’t work . .

[Laughter]

...beyond a certain point. But if we can turn this information crank faster. I'll give you an example of the heroics we undertake to do this.

We take a microprocessor, one of those billion-transistor contraptions that are being produced in millions of unit quantities in very expensive production facilities. The technologists steal a little bit of the real estate at the corner of this chip and put in a test pattern that travels part way along with the wafer. This was designed to give us measures of the technology and the performance of the chip in a production setup. Why in a production setup? Because it's realistic, it is populated with wafers that move day and night. So if you piggyback on those wafers, information about experiments, they travel like a continuous stream of FedEx trucks.

We are using manufacturing, actual profit-driven, customer-driven manufacturing, to be the host for these experiments. It's a little bit like using clinical practice to run neuroscience experiments. There's a lot of patients being measured every day, a lot of patients examined by physicians every day, with a disease. Piggybacking the clinical practice with appropriately designed experiments would increase the flow of information and allow you to turn much faster. It’s an idea that is practiced day in and day out in information technology, an idea that is occasionally discussed in the bio industry.

Continued:
Page 1 - 3 - 4 - 5 - 6 - 7 - 8 - 9 - 10 - 11 - 12 - 13