Which is why the goal at LifeSpan isn't to find the switch that doubles stem cell populations; it's to discover gene targets on disease pathways. These are two discrete research paths, and the latter is equally important. "For many people," says Brown, "the goal is avoiding debilitating diseases of aging or at least controlling and treating them, rather than just extending the number of years they are able to live."
In order to find the common pathways implicated in disease, you have to sift through huge volumes of genetic information. Then you have to determine which of the many gene targets that you've identified are specifically disease-associated or causative, and thus possible drug targets. LifeSpan researchers accomplish this by correlating gene expression to changes in actual human tissue samples representing diseased versus normal (aging) states.
LifeSpan has an on-site human tissue bank containing 1 million normal and disease samples. The samples are anonymous and were taken from hospital pathology departments. They include more than 175 different types of tissues from most body organs, and represent all age groups. There are samples for more than 500 different disease categories at every stage of disease progression, including aging diseases, autoimmune diseases, infectious diseases, degenerative diseases, cancer and benign proliferative diseases, and genetic diseases.
Any individual tissue expresses about 20,000 to 50,000 of the approximately 100,000 genes that make up the entire human genome. Further, genes that are expressed in normal tissue may be turned off in disease states, and different genes may be turned on instead. In order to analyze hundreds of genes simultaneously and to compare patterns of gene expression in diseased and healthy tissue samples, LifeSpan researchers use proprietary "high density array" technology. A high-density array is a glass or nylon square plate that supports a filter of "DNA spots," with each spot representing a single gene (the DNA comes from cloned genes). An array can have up to 10,000 spots per filter, allowing the testing of a highly efficient 10,000 genes at a time.
To analyze the genes from a tissue sample, researchers first extract what's known as the messenger RNA, or mRNA. The mRNA is labeled so it acts as a probe when you place it on the filter. "Each gene hybridizes (or binds) to its complementary mRNA probe and you get an array of spots," explains Brown. "The darker the spot, the more gene expression in that tissue."
The process yields a river of genetic and biological information. "We have a million tissues and will be generating tens of millions of data points on gene expression," says Brown. In order to make sense of it all and put it into useful forms (a computing science called bioinformatics), LifeSpan has developed proprietary DNA sequence analysis software, which can search through a million different genes in less than 24 hours and identify novel gene-disease associations or gene-drug associations.
The flux of gene expression can be visualized as a series of lists of DNA sequences, 3-D bar graphs, or "topograms," in which the heights of the peaks represent the amount of gene expression. "You see different genes being over-expressed in young versus old tissues and in different tissue types," explains Brown. "Each tissue type gives a characteristic gene pattern, and in disease you get more subtle changes. So, we look for these subtle differences."
According to Brown, by this stage in the search for candidate genes, "You've narrowed the field from 100,000 genes to about 10,000 interesting genes for which there is some evidence they are related to the disease process, either because they're over-expressed or mutated. In effect, you've gone from deep-sea fishing to fly-fishing. But, the big question still remains: Which of these candidate genes would make a good drug target?"
LifeSpan researchers use a variety of methods to help narrow the field even further. These include functional assays in cell cultures (you specifically inhibit a gene in culture, or in vitro); functional assays in what's known as a "knockout mouse" (the gene is mutated in fruit fly or another organism and you analyze the effect of that on cells, organs, and organism); and "target validation." To validate a target, researchers make specific probes or antibodies that bind selectively to the gene or protein under study. Then they compare antibody-gene binding patterns in diseased and normal tissues. This pinpoints precisely where in the cell or organ the gene is expressed and how it's associated with the actual disease pathology.
By this stage, you've teased out 1,000 most-likely drug targets. You still don't have a final answer, says Brown, but you have enough good leads to proceed to the next step-actually picking a target to test.
By narrowing the field and validating targets, LifeSpan helps reduce some of the financial risk involved in choosing a target to test and develop-typically, a $10 million expenditure for drug companies. It's not perfect, but it's better than traditional drug discovery, which starts with a natural substance, such as a plant, and randomly screens compounds extracted from it in an attempt to find ones that have potential therapeutic activity. This usually takes years and isn't always fruitful. Once you do find something you think has therapeutic activity, the next step is extensive animal testing. Fewer than one compound in 10,000 makes it through this process, which typically takes 12 to 15 years and costs about $300 million.
With genomics, there's a better chance you'll find the right target. "By identifying every part of the pathway, a drug company can choose different parts to manipulate," says Burmer. "Once a target is chosen, it's basically traditional drug development from there, albeit with more efficient combinatorial chemistry. It's a powerful new way to construct giant libraries of potential drugs by synthesizing thousands of variations on each chemical theme." Big pharmaceutical companies aren't the only ones interested in new targets. So are cosmetics companies, most of which have aging research groups. LifeSpan is in discussion with a number of them; their goal is to investigate the normal process of skin aging in an attempt to develop new therapies called "cosmeceuticals," pharmacologic agents that act at a cellular level, stimulating actual change. Examples include retin-A for wrinkled skin, and minoxidil for hair loss.
Compounds such as these and others than may be developed can have a dramatic effect on aging, says Brown. "So there's a realization in this industry that there are compounds that can have an actual affect on aging skin and the goal is to find these. Our process can do that because we're looking specifically at gene expression in young and older skin, and finding genes that are differentially expressed and thus potential targets for cosmeceuticals," he adds.
When genomics first hit, venture capitalists, the big drug companies and Wall Street swooned over the commercial possibilities, and poured millions into startups. However, it's clear genomics is in its infancy and everything takes longer than anticipated. Genetic information is swelling databases at a prodigious rate, but there's still a lot that is unknown about the relationship between DNA sequences and helping people live longer, healthier lives.
Add to that the length of time required for gene discovery and drug development. "It takes one to two years to filter through your initial discovery and focus on the ones that are truly important to a disease. From gene discovery to drug discovery is about 10 years, even after you've found the gene. We don't anticipate a radical shortening of this process. Even if an aging-related gene were discovered, it would take a minimum of 10 years before it hit the market." One problem is that genomics emphasizes the front end of drug discovery, so drug companies may end up with a logjam of potential drugs to take into development. Currently, the tail end of drug development isn't getting a similar push from other breakthrough technologies.
That's not about to slow research at LifeSpan. "Every time we discover a gene that looks interesting, we follow the lead," says Burmer. "We do more functional genomics to see if it's really involved in aging disease or aging normal tissue or aging mouse. We're hoping for clues to treat the chronic diseases of aging, so that one can improve the quality of life during the later years."
No one is anticipating a major leap in how diseases of aging are treated in the near future. What's more likely, says Burmer, is that treatments will be developed one disease at a time. Eventually, you could end up with multiple drugs to target different steps in the pathway and multiple drugs to target the individual diseases of aging.
These probably won't be preventive agents, since diseases such as Alzheimer's only affect a small percentage of the population. "So, in many cases, it will be precise diagnosis and very early treatment," explains Brown.
"We can't be taking 25 different pills to cover all the possible diseases of aging. You're much better off if you can control or treat the ones you do end up getting."