Selling Systems Biology

Selling Systems Biology Can this still-unproven (and much-hyped) field revolutionize drug discovery? By Brendan Borrell ARTICLE EXTRAS 1,2 suggested that the drug works primarily in patients with mutations in the ErbB1 epidermal growth factor receptor. The inner workings of the ErbB receptor family, with its sprawling pathways and multiple phosphatases, had long been a headache for drug makers. That complexity showed itself in this instance, too - sometimes the drug wa

By | August 1, 2007

Selling Systems Biology

Can this still-unproven (and much-hyped) field revolutionize drug discovery?

By Brendan Borrell


1,2 suggested that the drug works primarily in patients with mutations in the ErbB1 epidermal growth factor receptor. The inner workings of the ErbB receptor family, with its sprawling pathways and multiple phosphatases, had long been a headache for drug makers. That complexity showed itself in this instance, too - sometimes the drug was effective in patients lacking the mutation, or didn't work in people who carried the mutated receptor. The studies presented more questions than they answered. In December 2004, a trial showed that Iressa failed to increase survival in a 1,700-patient clinical trial that AstraZeneca sponsored.

To solve the mystery of why Iressa failed, AstraZeneca hired Bart Hendriks, a biological engineer. Hendriks was a graduate of Doug Lauffenburger's systems biology lab at Massachusetts Institute of Technology, and he had worked on a handful of EGF receptor types for his thesis. The company asked him to expand his work, modeling in toto the phosphorylation dynamics of the ErbB receptor family.3 He integrated that model with other published models of two other pathways (ERK and AKT), which feed back into the system.

The crucial feature of the mutant ErbB1 receptors, Hendriks found, was that they couldn't shut down like normal receptors. Normally, cells downregulate the replication pathway by yanking their receptors inside the cell wall, but the ErbB1 mutants appeared to have trouble internalizing. To test this theory, the team labeled normal and mutant receptors in vitro after saturating them with the epidermal growth factor. The mutants glowed,4 and the mystery was solved: The diagnostic feature of responders wasn't the mutation, it was the internalization rates. Iressa was only good at disabling those reluctant receptors hanging outside of cells. A computational systems biology program was up and running at AstraZeneca.

The company's success at finding out why Iressa didn't work did not go unnoticed at other companies, many of which are gradually building their multidisciplinary departments to achieve the pharmaceutical Holy Grail: Rational drug design using a full systems-based knowledge of biology. AstraZeneca's approach to Iressa "helped systems biology get accepted in the whole industry, not just [at] AstraZeneca," says computational biologist Bruce Gomes, head of systems biology at Pfizer.

The industry now claims to spend $55 billion on research and development each year, so even a slight increase in its success rate could have a major impact on the bottom line. But what does Wall Street think? This spring, I e-mailed Eric Schmidt, the managing director of biotechnology at the investment firm Cowen and Company, to get his perspective on the systems biology landscape. Schmidt has a PhD in biochemistry from Massachusetts Institute of Technology and has written investment advice for Nature Biotechnology. His first response was fired off from his Blackberry: "Sorry, Brendan, I don't have any interest in systems biology." I quickly replied and asked him why not. Two minutes later he replied: "I have yet to come across an investable idea in the sector."

The hunt for compounds is a needle-in-a-haystack problem. Systems biology, supporters say, could be our magnet.

Lately, the drug industry's track record is decidedly troubled. Investment in research and development has gone up steadily in the last 25 years, yet the overall rate of new drug approvals has leveled off. The FDA approved only 18 new molecular entities in 2006, less than half the number approved in 1996. "We spent resources on the wrong projects 49 out of 50 times," says Gomes. The pharmaceutical industry is not only making mistakes, it also has no means to figure out why a drug didn't work, and therefore cannot learn from its mistakes.

This trend has led many to question the philosophy underlying target-based drug discovery, now standard in the industry. Initially, this type of screening was used simply to hone the selectivity and efficacy of known pharmacologically active compounds. Now, the method is used more broadly, and it has not been successful. From early discovery to market, the vast majority of promising pharmaceutical compounds end in failure. Some of these stories end with a whimper or, in the case of something like Vioxx, with a bang.

Enter systems biology. The field has as many definitions as it has practitioners, but Current Genomics puts it simply: "The goal of systems biology is to describe and quantitatively model complete biological systems."

"I don't think you can invest in 'systems biology,'" says Anthony Butler at Lehman Brothers. "How are we supposed to translate that to dollars?"

Ideally, systems biology incorporates what Lauffenburger calls the four M's: measurement, mining, modeling, and manipulation. Firstly, scientists catalogue de novo response pathways in order to connect cell-surface receptors with gene expression, or piece together a network from published literature. Next, they pick apart the interaction and cross-regulation of the pathways. All this is captured electronically and integrated into a computer model, which is validated in the lab. If successful, this approach could form the foundation for a rational, hypothesis-based method of identifying new drug targets and biomarkers, while minimizing side effects. The hunt for compounds is a needle-in-a-haystack problem. Systems biology, supporters say, could be our magnet.

"If you look at any Big Pharma or any big biotech, there are aspects of a systems biology approach that are being incorporated into every aspect of their research," says Carl Weissman, president of Accelerator, a venture-backed incubator with ties to the Institute for Systems Biology in Seattle.

Academics have also caught the systems biology bug. In 2000, Leroy Hood, one of the inventors of the first DNA sequencer, founded the Institute for Systems Biology, arguing that universities could not foster the type of collaboration and integration the field needed in order to flourish. Others followed: Harvard Medical School launched its Department of Systems Biology in 2003, its first completely new department in more than two decades.

Although the largest efforts in the industry have been limited to tightly focused projects and isolated subsystems, many companies are hinting at "exciting" developments just around the bend, while declining to provide data or descriptions of the experiments. It's hard to tell whether this is corporate spin or honest optimism.

For AstraZeneca, it took the ErbB1 receptor project to convince senior management that systems biology was worth pursuing. Other developments soon followed. A computational model demonstrated that an antibody therapeutic in discovery phase would never work clinically, and the company halted the project, avoiding an expense of as much as $20 million dollars, says Adriano Henney, head of the company's Pathways department, where most of systems biology has been taking place. "The data that the model predicted would not have been available until Phase II." (He declines to provide further information on the therapeutic agent in question.)

"If you look at any big pharma or any big biotech, there are aspects of a systems biology approach that are being incorporated into every aspect of their research."
-Carl Weissman.

Stewart Tilger

Researchers at Pfizer have also used mathematical models to investigate biological therapies. This is the "sweet spot" for systems biology today, Gomes argues, because biological therapies require only a few hundred parameters and can therefore be developed and analyzed in a month's time. For example, one of the biggest problems with antibody therapies is that they bind to small, soluble targets, which slows down kidney clearance. At Pfizer, he's been able to run through a number of possible solutions to this problem in silico, including truncated antibodies, single-chain variable fragments (ScFv), and pegylated ScFv's. Ultimately, the modeling suggested that antibody fusion proteins, where protein functional domains are fused with variable regions of antibodies, might solve the problem. Gomes would not say whether Pfizer is currently pursuing this project.

Modeling of larger pathways would allow researchers to rank multiple targets and understand effects from off-target pathways, but it simply takes too long to facilitate drug discovery. "I've built models that have a thousand parameters, and that took me - working real hard - a year or two," Gomes said during a talk in June at the Cambridge Healthtech Institute's Beyond Genome Conference in San Francisco. "Most drug discovery projects are on a one-month timeline."

During a roundtable discussion at the conference, Gomes admitted that he doesn't feel that systems biology has gained acceptance in the industry. Nonetheless, the company is giving it a try: In the last two years, Pfizer's systems biology program has grown from a core of five people to 30, and Gomes expects to hire 10 more people by the end of the next year. Importantly, the company doesn't rely solely on in silico models, Gomes points out. Two-thirds of the group does no modeling at all, instead running lab tests to validate what the models suggest.

In 2001, Merck acquired Rosetta InPharmatics, a gene-expression company that Hood founded in 1996. Rosetta scientists have incorporated systems approaches by twice crossing two highly inbred lines of mice that are homozygous for every gene, producing 500 grandchildren that exhibit a great deal of phenotypic diversity. They measure clinical traits and assemble a genetic map, providing clues to the genetic basis for such problems as obesity, glucose tolerance, and aortic lesions. This approach allowed them to identify the gene, Zfp90, that drives a crucial liver-specific gene network regulating obesity.5 Although a Merck spokesperson would not reveal the pathway involved, one compound developed via this method has shown promise as an anti-obesity drug, and they expect it to move into Phase II trials by 2008.

Rosetta scientists have also put two other compounds into early discovery, but with two to five years between target nomination and clinical trials, it's still the early days, says Alan Sachs, vice president at RNA Therapeutics, a department at Rosetta. "We're sitting at the front end of seeing the impact of [systems biology] in the clinic."

Novartis hasn't figured out exactly how systems biology fits into its pipeline. In March, after two years in operation, it shuttered the systems biology program and integrated some of the researchers into different departments. Gabriel Helmlinger, a computational biologist who works in Novartis' modeling and simulation department headed by Donald Stanski, says systems biology still has a place at the company.

For instance, in cardiac research, they've been able to integrate what happens at the smallest level (cardiac channels and cells) with what happens to cardiac tissue, predicting how new compounds interfere with cardiac function.6 In one case, Helmlinger says, preliminary research suggested that a drug candidate might increase the risk of arrhythmia and death, but a systems approach, which examined the other pathways for an overall picture, determined that the drug was safe after all. He declines to identify the drug, though he says the company is no longer pursuing it, for reasons unrelated to cardiac safety.

Biotechs have also been trying to get into the action, often by partnering with major pharmaceutical companies to provide cutting-edge tools. The main players include Cambridge-based biotechs such as Gene Network Sciences and Genstruct. Mostly the work is based on a fee-for-service model; Genstruct, for instance, spent four months working with Pfizer on drug-induced vascular injuries. Other companies boast similar partnerships, but they have yet to obtain royalties through comarketing and codeveloping future drugs. Biotech companies that offer platform technologies are not doing well in today's market, whatever their areas of expertise. "Platform technology has been a dirty word in the investment community since the bubble," says Eugene Butcher of BioSeek, a California-based company that uses modeling to facilitate drug discovery (see "Systems biology is... ").

Weissman's advice: For profits, think small, not big. "The companies we invest in may walk in the door talking about a technology question or platform that could be called a systems biology platform, but before we make the investment we refocus the company tightly on making a focused product from day one."

This strategy seems to have paid off with VLST, a Seattle biotech that uses viral evolution to guide the target validation process. Weissman says the company went through $5 million while at Accelerator and has since raised $55 million in a Series B venture financing round, one of the largest venture capital deals across all industries last year.

It's 8:20 a.m. and the podium is still empty five minutes after we were slated to begin the 2007 Beyond Genome Conference in San Francisco. "He's in the hotel," Mary Ann Brown of the Cambridge Healthtech Institute tells Kevin Davies, editor-in-chief of Bio-IT World.

"Who are we missing?" I ask Davies.

"Just the opening keynote," he says dryly.

It's clear that the postgenome revolution is off to a tardy start. Not so long ago, many of us believed - in spite of a few kinks in the paradigm - that the three billion base pairs that make up our genome were one long cipher containing the story of human biology and the answer to human disease.

We're still very far from that prediction. Even the keynote lecture is not about systems biology. Once he arrives, David Shaw of Shaw Research strides on stage to tell us about his "computational microscope," a specialized supercomputer that would be able to simulate the folding of a single protein 10,000 times faster than any current system. But during the panel that followed, Shaw is blunt about how far this approach could take us in terms of understanding the workings of an entire cell: "That's not ever going to be possible."

Computation biologist Stephen Naylor, president of Predictive Physiology and Medicine, a personalized-medicine startup based in Bloomington, Ind., is no less pessimistic during the meeting's closing panel. He says he feels like he is trapped in the film "Groundhog Day," in which one day constantly repeats itself. "Things we were discussing four to five years ago, we're still discussing today." He turns to Marti Jett, a molecular pathologist at Walter Reed Army Institute of Research who is part of the panel discussion, and asks, "Wouldn't you agree that systems biology, as a discipline, is lost in terms of demonstrating a 'home run'?"

"I can't possibly say that," Jett replies. She doesn't provide an example, and the conversation quickly shifts.

"I've built models that have a thousand parameters, and that took me - working real hard - a year or two."
-Bruce Gomes

Soon after, Bruce Gomes stands up and slips out of the banquet hall. Radel Ben-Av of Optimata, an Israeli modeling-based biopharmaceutical company, follows. As Naylor proceeds with his goading, a slow trickle of systems biologists slip through the side doors. It's getting late, and they likely have a lot of work to do.

Around the world, high-performance computing facilities are crunching reams of data from experiments to assemble a grand view of life. But can we ever really simulate biology? "Five of the top 10 supercomputers are dedicated to weather prediction," notes Keith Elliston at Genstruct, "and we still cannot accurately predict weather out to seven days." He pauses and adds, "I'll posit that the weather is a lot less complicated than biology."

Is systems biology a technique, or just a general philosophy to understanding living organisms? "There are many things that we do not know that we do not know," says computational biologist Gustavo Stolovitsky at IBM's Watson Research Center. "There's a huge a gap of ignorance we don't know how to cross, from molecule to cell, cell to tissue, tissue to organ, organ to organism," he says.

Pathologist Ellen Berg of BioSeek literally throws her hands up in the air when I ask for her own assessment of the in silico world. "The reality is biology is so complicated," she says. "There are so many unbelievable elegant and detailed mechanisms that you'll never be able to model this mathematically."

Even if the greatest ambition of systems biology - a comprehensive approach to drug discovery - is not realized, the field may still have something to offer industry. The most successful projects appear to take a narrow focus, such as when Novartis focuses its systems-wide approach to a well-known cardiac channel, or AstraZeneca uses system biology to understand the failure of a specific drug.

As for Iressa, it is currently used only as a last resort cancer treatment, but Hendriks says the company has filed a patent on a diagnostic based on internalization rates of ErbB1 receptors, which could identify patients likely to respond to the treatment. If all goes well, Iressa may get a second chance to save lives, and systems biology will have paid off.

And this is, essentially, the only systems biology question that matters to industry: Will it, either alone or in combination with other strategies, make companies money? For now, Wall Street remains skeptical. "I don't think you can invest in 'systems biology,'" says Anthony Butler at Lehman Brothers in New York. "How are we supposed to translate that to dollars?"

1. J.G. Paez et al., "EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy," Science, 304:1497-1500, 2004. [PUBMED]
2. T.J. Lynch et al., "Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib," N Engl J Med, 350:2129-39, 2004. [PUBMED]
3. B.S. Hendriks et al. "Computational modeling of ErbB family phosphorylation dynamics in response to transforming growth factor alpha and heregulin indicates spatial compartmentation of phosphatase activity," IEE Proc Sys Biol, 153:22-33, 2006. [PUBMED]
4. B.S. Hendriks et al., "Decreased internalization of ErbB1 mutants in lung cancer is linked with mechanism conferring sensitivity to gefitinib," IEE Proc Sys Biol, 153:457-66, 2006. [PUBMED]
5. E.E. Schadt et al., "An integrative genomics approach to infer causal associations between gene expression and disease," Nat Genet, 37:710-7, 2005. [PUBMED]
6. D. Bottino et al., "Preclinical cardiac safety assessment of pharmaceutical compounds using an integrated systems-based computer model of the heart," Prog Biophys Molec Biol, 90:414-43, 2006. [PUBMED]


August 1, 2007

Mr. Gallagher,\n\nI enjoyed reading your commentary on Systems Biology and the accompanying article by Brendan Borell. The articles were of particular interest to me since they exposed a philosophical battle within the industry. While the editorial and article point to substantive issues around the science, I believe that an underlying change in the prevailing culture of the industry must occur as well. \n\nHow did the industry arrive at this point and what is the driver to ask whether a new paradigm for drug discovery is needed and if so, how does one identify a new paradigm for drug discovery? Identifying the driver is easily suumarized: attrition is very high and is largely driven by a failure to show therapeutic efficacy for most new chemical entities (NCE).\n\nThe current paradigm was ordained by the industry as a result of the successes obtained during the development of the first real molecular medicines: e.g captopril, lovastatin, cimetidine. The successes of the development of these drugs had ramifications not only for patients but for drug companies themselves. These efforts exemplified how a deep and integrated knowledge of physiology, biochemistry, cell and structural biology coupled with technical and methodological advances could lead to spectacularly successful drugs. They represented a paradigm shift in the process of drug discovery. They lead to a view of target validation and disease therapy at a molecular level. \n\nOver the ensuing years the methodological and molecular toolkit of the modern paradigm has been refined, made less costly, simpler to apply and more robust ? industrialized. But somewhere along the way the development and application of that toolkit and the development and application of the integrated knowledge base, both of which define the modern paradigm, seem to have parted ways. The separation has had a negative impact on drug discovery and can be summed up thusly: target validation as currently practiced is largely worthless as a way of limiting attrition. \n\nTarget validation is the essential starting point for every program. But, the application of the orthodox toolkit to any particular molecular target is not necessarily a useful test of the null hypothesis: that the target has no inherent utility in treating a disease. For purposes of project initiation, target validation is used to justify an organizational rationale that, on the whole, does a poor job at highlighting biological or pharmacological relevance. As a result, the profusion of new molecular targets going into the system with very little real integration to underlying physiology and pharmacology has radically altered the success ratio in the clinic. \n \nGiven the cost of clinical trials, the benefit of pharmacologically modulating any particular target should be defined in as broad a scope as possible at the earliest possible time. The preferred method would be to test as one would treat: use a well defined tool compound ? a potential clinical candidate - in predictive models to prove the point. But, the rub is truly predictive models are few and far between. In some therapeutic areas there are none (i.e septicemia). In other cases the models are ill-defined as to whether and why false positive or false negatives can occur. Under these circumstances, the organizational impetus is to defer a comprehensive test of the null hypothesis. Models are chosen to highlight and exemplify a compounds pharmacological value ? and secondarily a targets true function - in what can be called contrived settings. A positive test in even an inferior animal or cellular model is sufficient a test of the null hypothesis. Any additional testing is deemed gratuitous. From the point of view of a traditional, target-directed medicinal chemistry program, results outside of the dogma of knowledge around a particular target may be construed as lessening the confidence in and value of a target or candidate compound. As a result, constraint is the operational mindset for a program team. This is particularly true as candidate compound progresses to the IND preparation phase. \n\nIn the absence of pre-clinical models that can accurately predict efficacy, the orthodox target/compound centric mantra is the same: take the safest compound into the clinic and finally into the market and that is where the many and varied aspects of a drugs use and utility ? or toxicity ? can be exposed. The upside is this kind of human pharmacology can be profoundly productive: the standard example of ACE inhibitors being used to connect the RAS system and oxidative stress with fibrotic responses in the kidney and heart is an excellent example. The downside of this approach is that the industry must live with high attrition rates. Likewise, for every success in extending the labeling use of a marketed compound, there seem to be equally expensive and ignominious defeats post-marketing? see Vioxx, Zelnorm, Fen-Phen. \n\nThe successful (re)implementation of an approach to drug discovery that is capable of limiting attrition and values an integrated ? some might call it systems - approach will require a deep rethinking of the organization and reward structure of the drug discovery process. Organizations must, first and foremost, scrutinize the value metrics for the managers and lab level scientists who actually lead drug discovery projects. Advancement of a company asset through the drug discovery system leads to awards and recognition: target discovery and validation, lead discovery, declaration of clinical candidate etc. However most small molecule drug discovery organizations, through the natural generation of project teams, wrongly conflate what are really two independent and equally valuable assets: there is a target asset (the biology) and there is a chemical asset. The latter is considered the most important as it is the actual deliverable to the patient. \n\nBut knowledge of the underlying biology and how the target functions within a physiological context in animal models and patients is crucial to estimate and maximize that value. For most researchers, if a drug has been approved for marketing, the model(s) used to highlight the drugs activity become de facto standards. That the model(s) may have failed to predict clinical efficacy time and again with other compound classes is almost irrelevant. Again, the organizational reasoning is that pre-clinical work is largely that of a filtration exercise. If a compound does not work in a particular standard animal model or cellular assay the compound is excluded from further development on that basis. If it does work in a model, it passes. The extent of Type I and Type II errors associated with these models can be largely unknown. On the whole, in cellula assays and animal models are considered only approximations ? and poor ones at that - of the worth of a target or compound. The mantra is that ultimate value will be discovered in the clinic\n\nWhy have drug companies largely retracted from doing basic, biological and translational research in disease areas? Animal models are widely considered the weak link in the drug discovery chain, but there is little pure work at translating results from human clinical studies (successes or failures) back to animals so as to understand and improve the models. In the neo-industrial world of drug discovery, almost no biological discovery of an integrated nature will go on since first, it will be considered wasteful of time and money and second, it may be construed as wrongly devaluing a target or chemical asset deemed validated enough. In many ways, systems biology, as defined here, is a way to initiate and progress a project by using and creating much more integrated and sophisticated biological models for decision making. In this case then, systems biology is defined as the consolidation and use of an integrated set of biological and pharmacological information around a physiological pathway. Systems Biology defines how a particular target and the pharmacological mechanisms that can be used to modulate that target, apply to the physiology in question. How that information is developed and deployed is the crucial organizational and methodological question needing answered.\n\nWhile your editorial and article point to substantive issues around the science, I believe that, in the final analysis, the prevailing culture in the industry must change. Brendan Borell?s article exposes two constitutionally opposed factions: the unbiased experimentalists - Ellen Berg and Eugene Butcher who believe that creating ever more complicated cellular models can lead to ever more sophisticated methods of classifying compound activity ? and the target centric theoreticians ? Keith Elliston et al ? who believe that ever more sophisticated computer models of a target and associated cellular processes will lead to ever more sophisticated methods of classifying compound activity. Neither, it seems, can see the value in the others efforts. Yet these two groups, along with a group to evaluate and translate the results between human clinical trials and animal pharmacology, are the three necessary legs of the systems biology stool. Instead, it seems that the groups are pitted against one another in a corporate competition that may kill off any chance of successfully inserting a true systems approach into the drug discovery process. \n\nThanks again to you and members of The Scientist for what have been a series of compelling reads about the state of the industry.\n\n\nSincerely,\n\nMatthew Kostura, Ph.D.\nTranstech Pharma\nHigh Point, NC 27265\\n336-841-0300 x148\n
Avatar of: John Torday

John Torday

Posts: 12

September 4, 2007

Dear Mr.Gallagher, I found the article by Brendan Borell regarding the application of Systems Biology to the pharmaceutical industry of great interest. The central problem in biomedical research is the integration of genes and phenotypes, for which we have no effective algorithm. Systems Biology is supposed to remedy this problem, but is predicated on the integration of genes out of biologic context. This has led to a malaise poignantly expressed by Mark Kirschner, Chair of the Systems Biology Department at Harvard Medical School(The meaning of systems biology.Cell. 2005 May 20;121(4):503-4) asking for someone to define what Systems Biology. Biology has been artifically reduced to genes, and the molecularists are now suggesting that we can put 'Humpty Dumpty' back together again by cutting and pasting genes together- this is reductio ad absurdum. Genes do not function in isolation of cells, and cells interact through cell-cell signaling mechanisms that have evolved metazoa over evolutionary time (Torday JS, Rehan VK.The evolutionary continuum from lung development to homeostasis and repair.\nAm J Physiol Lung Cell Mol Physiol. 2007 Mar;292(3):L608-11). The sooner we acknowledge that the whole of biology is greater than the sum of its parts, the sooner we'll be able to develop good pharmaceuticals based on biologic principles.\n\nJohn S. Torday, MSc,PhD\nProfessor,\nDepartment of Pediatrics and\nObstetrics and Gynecology\nDirector, \nThe Center for Evolutionary Preventive Medicine \nHarbor-UCLA Medical Center\nPhone: (310)222-8186\nFAX: (310)222-3887\n\n
Avatar of: Li Chen

Li Chen

Posts: 1

September 4, 2007

Systems biology is an exciting trend with a great potential. However, both drug industrial and Wall Street do not see its value yet. Three sectors may be essential for systems biology: molecular interactions, target evaluations, and clinical feedbacks. Unfortunately, these segments are not integrated well at the current stage. In most cases, real clinical feedbacks about the candidate compounds/targets are almost zero on public domains. \n\nPersonally, I think oral cavity might be a real practicing field for evaluating systems biology based drug discovery, as this is a place we all could feel and tell. For example, knowledge about hard tissue (bone/teeth), soft tissue (wound /wound healing/inflammation/tissue regeneration and/or reattachment), body fluids (blood/saliva/cytokines), microbiology (pathogens for gum, caries and soft tissues), pre-surgery planning (anatomy, CBCT), patient?s behaviors (smoking, sleeping, and nutrition), and surgical skill are all essential for a success dental implant. In addition, a new drug for oral health will be used by patients with different medical backgrounds and health conditions. \n\nLi Chen, Ph. D\nSenior Scientist\nGC Corporation\nTokyo 174-8585\nJapan\n03-3965-1233\\n

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