Exclusive Interview with David de Graff, Pfizer's First Director of Systems Biology

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Exclusive Interview with David de Graff, Pfizer's First Director of Systems Biology

David de Graaf joined Pfizer in March 2005 to spearhead efforts to understand and judiciously spread systems biology (SB) throughout the world's largest pharmaceutical company. Working from Pfizer's Research Technology Center (RTC) in Cambridge, Mass., he quickly built a small interdisciplinary team to tackle some of Pfizer's thorniest problems and biggest opportunities. One and a half years later, de Graaf reports enthusiastic support from top management, recognition inside Pfizer that it must change, and – most importantly – promising results from showcase projects have stirred demand beyond his expectations and his small group's capacity. Challenges remain, but de Graaf is optimistic SB methodologies will have a growing place in Pfizer's drug discovery and development arsenal. He spoke with Bio-IT World's executive editor and SBNL editor, John Russell, about Pfizer's growing comfort with systems biology. A brief biography of de Graaf is included at the end of the interview.


By John Russell



JR: I was hoping you could provide a retrospective on the past year as Pfizer's first director of systems biology and talk about what's been accomplished and what remains to be done.


de Graaf: So the past year has been a bit of a whirlwind. I came on board in March of 2005, and we reached eight people by the end of the year so we've only been staffed at the initially appropriate levels since the beginning of this year. There are always issues with getting people with the right crossover skills. One of the bigger challenges was putting an appropriate team together, getting people who are comfortable using mathematical models, as well as being in the lab and generating some of the data that underlie these models.


That hiring challenge is somewhat abated [because] we're in the Boston area. We've recruited very heavily from local universities, and that has helped us out. Obviously, there's a disadvantage there as well. It's hard to get people with a deep knowledge of industry and who also have the appropriate background. We have a physicist, chemical engineer, biomedical engineer, physiologists on the team right now, as well as toxicologists. It's a fairly diverse team for just eight people. We've taken on a number of projects, and I think that what we can say is that we're seeing the first glimmers of hopes of delivery.
 

We decided we needed to make clear what the RTC could contribute, what our unique niche was. Very quickly we decided that where we wanted to work was in quantitative measurements on biological systems, rather than, for example, on global omics measurements which are often not quantitative, or are usually not quantitative. We decided we would work on modeling approaches that were able to simulate biological systems, so these were not inferential models, but forward-simulating models. That limits us somewhat in terms of the biological space we can work in because generally we need to be in areas that are relatively knowledge-rich compared to some of the areas where you can go if you decide to go with an unsupervised approach.


We picked three projects, one that dealt with enlarged signal transduction pathways playing to the strengths of the RTC in terms of being a central focus for kinases [at Pfizer]. We picked one project focused on a new development area for the company, which is moving into biologics, and where we wanted to understand some of the pharmacokinetics of biologics in order to differentiate them from competitor products. [The third] project took a large global look at hepatic-toxicity, trying to optimize both our predictive ability, as well as the number of assays that we ran in order to get to a particular outcome. The ultimate goal there was front-loading hepatic-toxicity [determination] early rather than where we're catching it right now. We've done [all of] this in partnership with Doug Lauffenburger at MIT and Peter Sorger at Harvard [Medical School].





JR: Were the models themselves were built up internally or mostly with Doug or with Peter?


de Graaf: Actually the model building has been done virtually completely internally. The other group we worked with, in terms of infrastructure, was Teranode. One of the interesting things that happened with Teranode is we're starting to see aspects of knowledge management around these larger systems biology projects, and together with Teranode, we're pushing the envelope there in terms of what we can do. So we're thinking about common shared spaces for data that can be accessed within Pfizer or by partners external to Pfizer, or even by competitors, with appropriate restrictions.


JR: How are the projects progressing?


de Graaf: Out of those three projects, we have delivered on the biologics project. We have been able to help steer that program in a slightly different direction than where they thought they were going, and given them some alternatives to a strategy they were considering. That has been impactful. We are now looking at broader opportunities to interact with the biologics portfolio. One of the interesting things, of course, is that biologics come with a couple things. There's much less pre-candidate research that needs to be done. It's usually about picking the right target in the right context. And making the definitive agent is not the difficult thing; it's really scaling that up. That's exactly the space where we're helping out. It is understanding potential efficacy and safety issues, and also PK issues. All of this has given us a platform on which we hope to build a stronger collaboration with people working in biologics.


Then there are early glimpses around hepatic injury. We have made significant strides predicting liver tox for compounds that fell into a class that previously nobody was able to predict. So these are DILI2-type compounds – DILI stands for drug-induced liver injury. DILI2 compounds are essentially compounds that previously fell through the cracks as they went through internal safety screening – not only at Pfizer, but across the industry – and ended up causing liver damage in patients.


JR: Bruce Gomes (head of modeling in de Graaf's group) said a couple of the projects had really excited interest inside Pfizer. Are they exciting other researchers to take advantage of the approach?


de Graaf: Correct. We are starting to see more and more people come to us and say, 'Gee, we want to do more of this. Can you do this in a different area of tox?' Can we apply this in selecting better candidates in this particular targeting family? I must say, [this is] one of my very pleasant surprises over the last year. We, as a group of eight, are way overstretched, and that's good. We know that there's a lot of interest and buy-in across the company for these types of approaches.


One of the problems, because we are still relatively small, has been influencing effectively in such a large organization. So although we see major interest, it's still very spotty, and I know that we haven't covered the Pfizer universe. That's one of the challenges for the year to come: How do you now spread the word effectively and make sure that we cross systems biology across the company to be appropriate for those levels of interest.


JR: How do you raise awareness? Is it something you do with peer-reviewed literature, or are the projects so proprietary that there must be another vehicle to communicate?


de Graaf:  If I had an answer, I would be much better off than I am. The practical tactic we're taking, which may not be optimal but is what we're doing, is to do a little bit of everything. I encourage, and am supported in making sure my team can publish. This is one of the reasons for having this very open collaboration with MIT, because there's nothing like peer-reviewed publications to communicate success within the organization. The other thing – rather than to wait for systems biology to deliver and then [articulate a] strategy – is to say what are the potential strategic benefits now? How could you apply systems biology across a broad portfolio? Rather than try to bite off everything, we've decided to, at least in the first order, focus on biologics, and in the second order, definitely other projects as well.


JR: Does evangelizing get you on an airplane traveling to other Pfizer facilities to make presentations?


de Graaf:  [Somewhat]. One of the nice things is I can count on virtually everybody on the team to give that presentation. So the RTC is well connected, and my management has been very proactive in getting the message out as well. I've done far less travel than I've done in other jobs. And I've been very pleasantly surprised that a lot of people have come to us, come to the RTC, or that through our current project attractions, people have started to know about what we're doing. So our collaborators at other sites talk about the work they're doing with us, and that is how the news spreads. Obviously one of the things that is a real challenge for Pfizer is leveraging scale appropriately, and what I've been amazed at is that despite the scale, things have gone pretty far, and lots of people do know about us and interact with us.


JR: I've been curious about what kinds of researchers get excited by systems biology approaches? Are there attitudes they tend to share that you just have noticed?


de Graaf: Yes, and I think this may also explain some of the receptive environment we're seeing at Pfizer, and this may be true to some degree across the industry. There's a general feeling we need to do things differently, that we can't squeeze out more by just doing twice as much work. So everybody knows there are going to be real changes in how we do science. Nobody knows what those changes look like. Some people are more receptive to that idea than others. There are people who will hunker down and do twice as much work. There are other people who want to do the same amount of work that just want to do it more smartly. Those are the people who look at systems biology as a possibility. Just like in any organization, you have the traditionalists within Pfizer, and you have people who are going to push the envelope and look at doing things differently. That level of bravery is often restricted to people at the bench. One of the great things about Pfizer is that we've seen that senior management is willing to really consider doing things differently, and that has been a great boon for me, and a great support.


JR: What's your sense of how the current changes at the top of Pfizer and the pressure to be more productive will affect the RTC and your group?


de Graaf: In a large organization, it's very easy to actually not feel that pressure at all and to say, look, I've got to go on with my life. I'm going to deliver in my small space and whatever happens, happens. One of the interesting things for the RTC is that I'm getting the feeling that we've been put front and center in this need for Pfizer to change how it does science. As a center for innovation, we are a logical place to start building on that. Systems biology is playing a large role in how not only the discovery organization but also how the other research lines want to see their biological process, their way of doing biology, change.


One of the hard things has always been communicating a clear vision of what systems biology is, what it can do for people. I think we have been able to put a fairly simple and straightforward message together around integrated capabilities between people, designing experiments, people measuring that output, people who then data mine that, and then people who provide models to interpret that [result], and those models are usually mathematical in nature. So the vision of these integrated teams is there, and now the question is, how can you implement that across an organization? So the pressure is there. The understanding of what a potential solution is, is there as well. The willingness to potentially invest in that area could be there as well. What that organization would look like in the end is still something to be seen.


JR: Do you have goals and milestones you're looking to reach in the coming year?


de Graaf: I do. For my team, the projects that we've initiated need to fully deliver. We need to go beyond glimpses of hope, clearly. But I have lots of confidence that will happen. My goal for this coming year is not to do systems biology for Pfizer, but to teach systems biology to Pfizer. That's a dangerous thing to say because lots of these people have been taught lots of things and they're very well educated. We are talking not about owning systems biology for the company, but trying to implement the process in different places where that's appropriate.


One of the roles that I see growing for my group at the RTC is where we work more closely together with project teams that own more of the problem than in the first iteration, when a lot of the scientific problem definition was by my group at the RTC. We will really start to bring people on board with the integrated approach rather than having some people in informatics sitting off in some corner, somebody doing some modeling and mathematics in clinical, and somebody in a proteomics group measuring something. Get all of those people together in one place and start to provide an integrated capability throughout the company in appropriate areas.


JR: Will you use other systems biology vendors in your particular efforts besides Teranode?


de Graaf: Absolutely. One of the nice things about Pfizer is, because of its scale, we have engaged a lot of people in this space. So whether it is BG Medicine or Genstruct or Entelos, or whether it is a company like GNS; all of those companies have been engaged, and sometimes we've done projects with them. One of the nice things about systems biology is that, as a process, the tools that these companies provide fit into very particular niches and spaces. We're not here to say, 'Oh, we're working with Genstruct or we're working with Entelos, we've solved all our problems, we're done with systems biology.' Actually we want the companies in this space to keep exploring and innovating and moving, and we want to stay close to them and move with them we implement some of their technologies across our portfolio.


JR: Will you look at doing work with so-called unsupervised learning and inference engine technology to develop hypotheses, which it sounds like you're not doing at present?


de Graaf:  In this case when you say you, it will be Pfizer. Yes, I could definitely see that that's a place where we want to grow. We have a pilot with Cellcom, for example, in that particular space, and we are in late-phase negotiations with a number of other companies. One of the pushes I'm making right now is to set up an investment strategy that takes into consideration that systems biology is a cycle. It's something that needs to keep on going. So you can't just make a solitary investment, look at the output, and say, 'Gosh, does this deliver value or not?' We will need to do another iteration of the same process in order to make sure that we understand what was delivered to us.


JR: When you encounter a knotty problem or roadblock in terms of your work in systems biology, who do you call among your peers in the industry?


de Graaf: Well, it's interesting. First of all, I think my line with sort of the roadblocks – the political roadblocks within the organization – my line management has been incredibly supportive. I couldn't ask for a more supportive set of bosses. And that goes up all the way throughout the chain. So that part, luckily, I can deal with internally. When it comes to technical problems, there are a few people I stay close to, and a couple of people who I would love to stay close to but who I don't. So Carolyn Cho and I talk on a fairly regular basis. Carolyn is at Novartis (head of computational systems biology). And this ends up being useful, whether it's around hiring or around a particular technology or particular company, and we've actually looked at opportunities to explicitly work together.


Everybody keeps running into the same toxicity and we can't solve it. Actually putting our heads together and, more importantly, putting our data together may be something that's worthwhile, and we're exploring that together with the folks at Science Commons right now, as well as the folks at Teranode. I also speak to my old boss at AstraZeneca, Dr. Adriano Henney (director of pathways capability), and to Dave Cook (associate director), who I was very close to there as well. Anything that comes up in text mining or informatics or data mining, I tend to speak to William Hayes at Biogen Idec, who used to be on my team at AstraZeneca as well. These are all outstanding people. There are some people who are more on my periphery. And a small company that we love to work with is Numerica Technologies. They helped us out – this is an MIT spin-off – with some very specific issues around large ODE models and how you interpret those.


JR:  Thanks for your time, David.


Please write to SBNL editor John Russell at john_russell@bio-itworld.com with comments about this article or on other systems biology topics.

 

Pfizer's Director of Systems Biology


David de Graaf did his undergraduate work at the University of Utrecht, the Netherlands, where he obtained a Master's degree in Evolutionary Genetics. His doctoral work, under supervision of Professor Igor Roninson at the University of Illinois at Chicago, focused on the multi-drug resistance pump p-glycoprotein and its transport mechanisms. At this time, David got involved in some of the earliest functional genomics approaches using gene fragment libraries in retroviral vectors.


His postdoctoral work focused on the pharmacogenomics of olfaction. His work with Prof. Doron Lancet at the Weizmann Institute of Science in Israel, used computational and wet biology approaches to try to deconvolute odorant binding to olfactory receptors.


At Prof. Eric Lander's instigation, David then relocated to the Center for Genome Research at the Whitehead/MIT, where he set up a group working on target validation, and worked in close collaboration with colleagues at Millennium, Bristol-Myers Squibb, and Affymetrix.


David joined AstraZeneca, where he built the first systems biology team, focusing on modeling and simulation in both the UK and the US. He recently joined Pfizer as its first director of systems biology. David has a global strategic role in determining how systems biology can transform drug research for Pfizer, and a local role, heading a team at the Pfizer Research and Technology Center, which works on applying systems biology approaches to issues across the Pfizer pipeline.



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News in Brief

GeneGo Goes Wet

Pathway tool pioneer GeneGo started offering wet lab services to clients about a month ago, says Julie Bryant, VP, business development. Speculation that software-centric systems biology toolmakers might jump into the wet lab game has bubbled for some time.


Bryant says the move is a natural next step for GeneGo. "We first used our own tools (mining literature) on internal projects and moved to the stage of proving the results in the wet lab. Customer asked if we could do it for them."


She says GeneGo currently offers gene expression and proteomic lab work but not metabolomic work. She was a little fuzzy about whether GeneGo actually owned web lab equipment and facilities, saying the company has access to wet lab capacity. Asked if GeneGo would consider purchasing a company to add wet capacity by buying a company, she offered "no comment at the moment." GeneGo has already begun wet lab work for clients on a few projects.


GeneGo has long claimed to be profitable and to have more "users" than any competitor. Just this month, it received a Phase II SBIR grant from the National Cancer Institute for discovery of new biomarkers implicated in breast cancer based on network analysis. The research program includes a novel large-scale gene expression and genotyping study to be run at Mayo Clinic and data analysis in MetaCore, GeneGo's flagship data mining platform. GeneGo owns IP surrounding these potential biomarkers and any successful commercialization of them – still very far off – would expand its business model even further.


"We are very happy to receive this Phase II award from NCI in the current highly competitive environment as it validates our commitment to scientific research and our novel, innovative ideas in pathway analysis", said Dr. Tatiana Nikolskaya, CSO and president of GeneGo in a prepared release. "In Phase I, we made some exciting new findings in sub-categorization of breast cancers based on network analysis of previously published microarray expression data.


"However, the biomarkers found in such "meta-analysis" have to be validated in a new design study in a stringent clinical environment. We are pleased to collaborate with Mayo Clinic, a world-leading institution in clinical and research oncology. The microarray and genotyping assays will be conducted at Mayo labs, and the analysis will be performed with state-of-the art translational medicine and systems biology."


Optimata Released Promising Results

Also earlier this month, at the NCRI Cancer Conference in Birmingham, U.K., model and biosimulation specialist Optimata reported its Virtual Cancer Patient platform can predict with 70 percent accuracy how patients with advanced breast cancer respond to treatment. Here are the highlights from Optimata's announcement.


The team from Nottingham City Hospital, in collaboration with researchers at the Institute for Medical Biomathematics in Israel, undertook a pilot study on 33 patients with advanced breast cancer that had spread to the liver, lymph nodes, or lungs.  They used the cyber-patient, based on advanced mathematical models, to find out which drug out of two would work best in each patient, based on certain characteristics of their cancer, such as the size of their tumours and how fast they were growing. 


In this retrospective study, partially funded by Cancer Research UK, the Optimata "virtual cancer patient" (VCP) model accurately predicted how around 70 percent of the patients responded to their treatment.  In the future, technology like this could help doctors tailor treatment more accurately to ensure every patient receives the most appropriate therapy to treat their particular disease.


The two chemotherapy drugs compared were called docetaxel and doxorubicin – these can be used on their own to treat a number of cancers but can have different effects in different people. Patients with advanced cancer were chosen for this pilot study because they are most likely to suffer serious side effects, such as fatigue and sickness, due to the volume of anti-cancer drugs they receive.     


Abhik Mukherjee from Nottingham City Hospital, who worked on the study, said, "Every cancer is slightly different, and every patient will respond to treatment differently.  We wanted to find a way to predict how patients would respond to a particular drug in order to limit their side effects and give them the best chance of beating their disease."


The VCP was "trained" using clinical data from real patients.  The team programmed the model to look at how the drugs affected the growth of the cancer, how the drugs behaved in the body and how the cancer cells responded to the drugs.  Once the model had been fully "trained," they compared the predictions of the VCP program with the actual response of patients to the treatment, to test the effectiveness of the technology.


Dr. Stephen Chan, who also worked on the study, added: "We found the computer program accurately predicted how the patients responded to treatment in around 70 percent of cases.  However, this was a pilot study in a small number of patients, so now we want to fine-tune the model to improve its accuracy and test it in a larger study.  We also want to see how it works when we use combinations of drugs, and whether the model can predict if a patient will suffer other side effects in response to the treatment."


Systems Biology Dream Project Update

If you still have Monday (October 23) open, you might consider attending Cambridge Healthtech Institute's Regulomics Symposium, which is part of Discovery on Target being held at the World Trade Center in Boston The last session, The DREAM Project: Assessing the Accuracy of Reverse Engineering Methods, features an update from Gustavo Stolovitzky, Ph.D., Manager, IBM Functional Genomics and Systems Biology Group, IBM Research.


Here's the program description: Many of the efforts in the field of systems biology focus on the inference of networks of interactions of molecular species from biological data. A logical place to start is to produce a coarse diagram or map of the connections (physical, chemical or statistical) between molecular species of the network, without worrying, initially, about the detailed kinetics. A number of methods have been developed, and continue to be developed, to disentangle the connectivity maps within the cell. To organize the sea of methods and help the community understand the merits and pitfalls of one method versus the other, a group of systems biologists have started what we call the DREAM (Dialogue on Reverse Engineering Assessment Methods) project. In this presentation we will discuss the different thrusts of this project and the results of the first DREAM conference, held on September 7 and 8.


Last Word: Conversation with Novartis' Manuel Pietsch

I've just returned from a journalists' junket to Switzerland, where among other things, I was able to meet with Manuel Peitsch, director of system biology for Novartis, and also to hear Rudi Abersold talk about SystemsX, a Swiss systems biology initiative. Coverage of both will appear in upcoming issues of SBNL and Bio-IT World.



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