last1holding recorded dr tony's presentation and transcribed the entire text. i recomend that you take the time to read the presentation. there were slides that acompanied his talk but aren't available, and some of this is technical in nature...it's rather long...edit...i had to break it up...it was too long for posting...LOL...
good fortune...
pops
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DNA PRINT GENOMICS OPEN HOUSE TEXT
December 2. 2000
(Craig Hall intro) Thanks everybody for coming out today. My name is Craig Hall. I am director of corporate analysis for TBF. So I have been fortunate enough to be a part of DNA Print this year and we feel like the company has really come along over the last 4 months since it’s been public, which just over a short period of time, we feel like the gangs come a long way. I just want to thank DNA print specifically and the people they’re for all their hard work. They’ve really put their hearts and souls into this and I think it’s going to be a very successful endeavor, and right now I’d like to introduce the visionary behind this who I think has some very interesting things to tell you today, and I’ll go ahead and do that. Let’s go ahead without further ado, Chief Scientific Officer Dr. Tony Frudakis. (clapping)
(Dr. Frudakis presentation) Actually the visionaries here aren’t just me, there are people like TBF who we sincerely thank for the opportunity to actually do our business model, but also people such as yourselves. I mean to a certain extent were all risk takers. I was a risk taker when I started this company, when I wrote the business plan. To fund the company TBF is a risk taker. In funding a company that operates in a space with which they have had no prior experience or exposure. These people are mostly risk takers. Bill Gates was a risk taker; People that funded him were risk takers, People that funded Cisco systems (etc). Were all aware of what the potential rewards are for sticking your neck out like we all have—So I want to make an announcement here that at 2:00 today the company actually is going to be releasing a press release concerning the following topic. We have filed our very first provisional patent application for analytical software. It’s a major company milestone. This is the type of development that others in the investment community and scientific community look to when evaluating the company, after more important scientific prepared review publications. They are the core and essence of what small start up companies do, and we are very happy to announce that we have filed our first one and you will be learning a little bit about what that program does here from my talk. What I’m going to do here in terms of the talk is try to keep things as simple as possible (inaudible). But yet there are some scientifically fine people in the audience. We are a technology company, a very special technology, and so it’s impossible to eliminate all the technical details. If I begin to bore you or confuse you, you might want to check out and check back within a few months (laughing) because I’ll be going in and out of the technical details. Obviously this is our new place. Were happy that you could come today. We anticipate being moved in by the Christmas holidays.
You see only a portion of the equipment in our building. The rest of our equipment is in our current location on Independence Blvd., where we are doing our sequencing right now. We have a few people there, employees of the company that are happy to meet you. If you want a map to see that facility, I have several of them up here.
I want to just briefly describe the people, the scientific people that are behind DNA Print Genomics, There is myself, I’m the Chief Scientific officer and acting CEO at the moment. My extended background is molecular genetics from U.C. Berkeley, with about 15 years experience in the field. Professor Ponnuswamy is the person we recently announced the hiring of. He’s a world renowned statistician with around thirty-two year’s experience in the field. He will be our Vice-president of bio-statistics and he’ll be joining the company on February 1st Mung Ho Kim is our director of informatics. He’s an experienced mathematician. Professor Sowaro (inaudible) A very special guy, Were very lucky to have him. He comes to us as a laboratory professor (inaudible) one of the world’s foremost statistical geneticists with regards to the analysis of STRD. Which is commonly referred to as DNA testing used by the (inaudible) here. (inaudible) This statistical ideology, which is similar to ours. Zachery Gaston who is at our Independence Blvd. location, is our lead technician. We’ll be announcing some more hires in the near future.
The company was really founded to study human traits using genomic data. The Human Genome project wrapping up, the technology that’s come through that, has created an environment for the first time in the history of mankind where questions about these and other physical human traits can be asked on massive scales. Massive genetic scales. We can sequence hundreds of positions along the chromosomes of any one of you, and learn a lot about you. This is impossible without the Human Genome Project, and really the closing of the Human Genome Project marked the beginning of real genomic research in our population.
Our founding hypothesis is that most traits, whether they be a drug reaction trait, propensity to develop an adverse reaction to a drug or whether it be a disease trait, a complex disease trait. Their not caused by rare mutations. They’re caused by specific, very definitive combinations of otherwise frequently found variations in the population. That’s the big idea. In the past, genetics was the study of a rare event. A mutant so to speak. That’s all changing. Those diseases account for less than 1% of all afflictions on the face of our planet. The remainder, people have no explanations for.
We know that many of them are inheritable. They can be passed from generation to generation. We don’t know what the genes are because their complex diseases and they are difficult to study. Genomics changes all that. Genomics enables us to look at the other 99% of diseases and drug reaction traits. And we’re founded to develop genetic solutions by applying genomic research to that field. We work backwards, we start with a genetic trait such as a propacity to develop an adverse (something topatocellular) reaction to a drug or such as the presence of a certain complex disease to collect people that exhibit these traits similar (inaudible) and we screen each one of them as various genetic positions. The idea being, to find what makes those people similar and as a group. What distinguishes them from people who don’t have the problems. These are called population studies. Most companies that are in this field, I’ll mention that were 1 of 20 companies that has this type of technology that you see inside here. This automated technology. Were one of twenty! Were the only one that is almost exclusively focused on the analytical space within the field. Most of the companies that you might call our competitors are pumping out massive amounts of data. They don’t know what to do with it yet. Were focused on the mathematics of the statistics on how to understand that data. And we will pump out our own massive amount as well. But our goal has been to draw results from these population potential responder,non-responder or somebody who is not compatible with the drug studies that are complex in terms of genotype and environmental variables unlike our competitors which will focus on a single mutation almost as if they are harkening back to the old days of genetics leading to new genomics technology.
How are we going to make money. Well this is how. And I apologize for these slides. I didn’t account for the sunlight. So their a little harder to see. This is the point of care. The physicians office or hospital. The physician of the future will use our genetic solutions in order to classify a patient as a potential responder non-responder or somebody who is not compatible with the drug. A drug like lipitor or provacal, when you get prescribed that drug to help you with your Cholesterol level, you typically have to go back and have your blood taken from one or two applications of the drug, from the time you take the drug, and they look to see if there is evidence of liver damage. That’s the only way that they can tell the drug is hurting you is to let it hurt you and then tell you later that its hurting you. The whole goal of DNA Print is going to be to eliminate that type of ridiculous risk that people are subjected to. To not prescribe medications to people based on population averages but to each individuals genetic profile to help applications of drugs. The physician of the future is going to have to rely on the research and development of companies such as ours, to be able to do that. They’re going to access the solutions that we produce with our equipment and from our brains, from our mathematical ideas, from our applications of math to the data. A solution for a problem might involve ten or more environmental variables or other physical characteristics of people that aren’t obstensibly related to the trait. What they will be is complex. Each patient that is going to be screened for compatibility with the drug, will have to have their genetic profile taken. That profile will have to be compared against the solution for that drug. That solution determined by companies like us. The solution that describes how people in our population respond to drugs based on their genetic profiles. These are going to be very complex solutions. Accessing of the data is going to be accomplished through the internet most likely. First of all the hospital is going to need to know where to genotype a patient, where to look in their chromosome. We will tell them from our results. Our software will be a sort of an operating system. They want to produce a test for provacal for patients, we’ll look up provacal and see what markers they need to screen. They’ll order the test kits from one of our partners, commercialization partners. They’ll perform the test at their hospital and they’ll have raw genetic data from the patient. Which they won’t know what to do with. What their going to do with it is, use our operating system to push it through the internet Compare it against our population level solutions and to basically classify the patient based on our results. Every time a patients profile comes through our servers or goes through our partners, will be paid. Every time a physician orders a diagnostic test that is based on our genomic research we’ll be paid. O.K. So that’s the model. The model is to get physicians and even patients independently, to access our information in order to classify themselves with the patients that are responders or non-responders with drugs. Well sell the hardware and software involved in that system. Or I should say our partners will. What were going to do here at DNA Print Genomics is rely on our skill in developing these solutions and were going to serve as a sort of a node on a tree. Were going to pump out solution after solution after solution. Were going to partner each one of them so that other partners worry about commercializing them, worry about liability issues, basically become our money machines. We are going to be allowed to focus on the research and development, and as a node on the tree, we can grow our revenues exponentially rather than linearly as opposed to a marketing firm that’s going to grow their revenues strictly as a linear function of how many products they sign up. Since were branching out, maybe ten branches for every solution we come up with we can grow exponentially. Were working on proprietary software intended for those things here in red (diagram on screen) to indicate things that are proprietary to DNA Print that none of our competitors or any other companies in the world have. Our software that we are working on is of course proprietary. The data warehouse that we are creating is of course proprietary and that is why were here. Something that we will be developing for the next many years. One of the main attractions for this field is the creation of new markets, basically a pharmaceutical product enjoys a certain type of a market. If we can tap into that market by offering a diagnostic test that must be used prior to administration of that drug . This whole field is called consumer genomics, personalized medicine. The patient of the future is going to demand these tests be done prior to taking any drug. Its not really up to the drug companies, its not up to the insurance companies, its up to the patients and where they spend their medical dollar. So it’s going to be a consumer driven market That is why it’s called consumer genomics. Well we are going to be able to take you back on each one of the pharmaceutical companies markets for drugs that we address, and I’ll show you here briefly (showing Physicians desktop reference book to audience) this book is called the Physicians Desk Reference. It contains information about thousands of drugs. Many of these drugs cause adverse events to patients who take them. Cause real damage! The long term effects of which aren’t known. So there are a lot of targets out there. Way more targets than there are companies that can handle the number. Another hundred companies couldn’t handle this number of targets. And we are not even talking about experimental drugs. Were just talking about drugs on the market. Cause I’ll show you where we are one of a very small group of companies that are looking at all of these drugs. We are going to take them one by one.
Doing that, making a solution that can help target a pharmaceutical companies drug for a certain segment of the population is a nice little business model, you could make a lot of profit. Probably not enough to cause a company to be a fortune 500 company, but none the less, a company that could be very successful. Why do we think we are going to be a fortune 500 company. Well we think we are going to be able to open up new markets. Drugs such as Staten’s, and by the way, I have to mention that Bob White, our key collaborator on the Staten project is here today if you want to meet with him, and he’s actually walking up behind us here, but drugs such as Staten’s are typically used only by people that exhibited abnormal levels of cholesterol, are therefore at risk of developing arthorsclerodic (not sure of spelling) or other cardiac disease. Their used only by sick people and not by healthy people, for only one reason . Because their risk associated. Healthy people prevent Staten’s, and from many other drugs. They could be used prophylacticly to help prevent disease rather than to just treat disease. Staten’s are known to be functional in that area.
Their able to help people. People still don’t take them. Their a risk. Staten’s can damage the liver to a high percentage of the people taking the drug. And as I mentioned your Physician has to monitor your blood in order to see if your liver is being damaged. If we could develop a diagnomics test that defines who is compatible and who isn’t, that really eliminates a lot of the reasons why the drug isn’t used by ninety million people in our country. And in our country, it is only used by ten million or so people that are afflicted with the disease. We could take a margin of ten million people strong or two million people strong and turn it into a margin that’s a hundred million people strong. And then reproduce the results of each major ethnic background in the world. (inaudible) People were talking about obscene revenues. That’s why I feel that in a decade we are going to be a fortune 500 company.
I’m going to tell you a little bit about our internal research and development. The talk is going to get slightly technical here. As I mentioned , we take normal and afflicted specimens from randomly selected people in the population. We acquire proprietary biochemical reagents, traciate the proprietary gene maps that we harvest from the Human genome Project which is all available online. To turn these specimens into real genetic information, We call it a resequencing data base. Where we are basically establishing the positions within genes that are variants within the population. Places in a gene where I may be different from you or from you or from you. Even though 99.9 % of our DNA is identical . Its that .1 % that makes us unique. Nobody knows what’s even close to that .1% is. We currently have in our data bases probably 1 x 10 to the - 4 of that .1 %. A lot of work needs to be done. There are not enough databases to understand this information. Databases that come from companies like Celera or horizontal databases. They look at many genes in small numbers of people. In order to understand what makes us all different you have to look at many, many, many people. And best and most economically done with you folks on a small number of genes. Fortunately for Pharmico Genomics, the number of genes that are involved in drug metabolism is a couple of hundred
So were taking those couple of hundred genes and we’re sequencing, actually sequencing these genes all the way through from beginning to end, of a couple of hundred different randomly selected people. We’re going to end up with a database that’s similar to Celera’s. Instead of being horizontal, many genes in a small number of people, it’s going to be vertical. It’s going to be a small number of genes, many people. And in our opinion, it’s going to be just as valuable. This resequencing database serves as a foundation for all the research that we do. It tells us where to use our automated platform to look at people’s DNA. Where to target people. You have to know without getting to technical, you have to know the genetic address of the site of variation, what your looking for. Our software determines that address, enables us to go to intelligent experiments, to produce data that nobody else has. We use a procedure that’s termed inferential haplotyping. Probably the second company that I know of in the world that considers data in terms that can infer haplotypes. And for reasons that I won’t get into, this supports a power of the magnitude of about 10. 10 to 20 over conventional genotyping. These machines that you see in here produce raw genotype data. Converting them to haplotypes is an arcane field. It’s not well documented nor understood. We’re working on algorithms that will enable us to do that. Those algorithms will create a tremendous amount of volume. And give us a really fine advantage over all the other companies out there . Basically the inferential haplotyping tool takes high density genotypes that we can produce from this type of machine, convert them to high density haplotypes and then were going to apply proprietary algorithms and analyze the applied genetic pattern in these high density haplotype data sets. A number of these steps are going to be proprietary to DNA Print Genomics. We were supposed to (inaudible) our databases, our specimen collections, our haplotype databases, which is SNPdata that’s a haplotype database . Of course , the genetic patterns in the haplotypes, and the tools that go into the process. Most of these other companies really focus on SNP’s. Not haplotypes. So they would have their own version of a resequencing database and they would go from high density genotypes and they would try to find patterns. The problem is that most of these statistical analytical tools used by our competitors are simple in nature. Very little work is being done in the analytical space. It’s a brand new field. We know that genetics is far more complicated than what our research has told us in the past and simple analytical tools aren’t going to get the job done. Small companies like us are going to have to develop next generation analytical tools in order to understand this information.
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