by Mark Uehling
April 15, 2005 | WITH MORE THAN $50 billion in revenue, Pfizer remains the industry's behemoth, overseeing the most ambitious research agenda of any company in the world. The company's spending on R&D supports 12,000 scientists. Pfizer's research budget increased just 3 percent in 2004, to $7.7 billion, after a 2003 increase of 44 percent -- attributable to the acquisition of Pharmacia.
At the end of 2004, Pfizer had 225 projects in 18 therapeutic areas under active development. (The company is studying 145 new molecular entities and 80 extensions of existing drugs.) An additional 400 compounds are in the discovery phase of testing. In 2004, 43 discovery projects advanced into preclinical testing; 23 projects moved from preclinical into human trials. Wall Street remains unimpressed, having cut the company's stock in half since 2000.
Against that backdrop, a year ago, Nicholas Saccomano was named senior VP of research technology at Pfizer. He was trained at Columbia University as a chemist. At Pfizer, he worked on diseases of the central nervous system for more than a decade before coordinating the technology for discovery research at the company's Groton, Connecticut research facility.
Saccomano's current jurisdiction includes scientific technology at Pfizer from discovery-stage research through development. The goal: to eliminate duplicative technologies and find global synergies that transcend project teams and therapeutic areas.
In a wide-ranging interview, Saccomano discussed the challenges affecting technology selection and consolidation at Pfizer.
Q. Why was your position created? Doesn't each therapeutic area need to pick its own tools?
A. Often, to solve a problem, it doesn't take one skill set or one technology skill set, it might take three. And if all three things are separated and distributed and scattered, then people never find each other to solve the particular problem. Maybe I've got a problem on a particular program in the obesity group and I need to do a variety of things to increase my confidence in the target or find new lead material. You might need to bring together a cross-functional technology group to take a look at this problem and get it on solid footing.
Technology in drug discovery seems accretive. New technologies get layered on top of the old, which are never unplugged. Has Pfizer ever said, "We don't need X because we just bought Y"?
IT is probably the worst-case scenario of that. But we've made some great progress in moving things forward and coming up with minimum set of tools, which actually sit on top of our data infrastructure. [That is] not necessarily our fault, simply because when you acquire several companies, all with disparate platforms, there is some clean up work that has to be done. One of the things we have done is we try to minimize the number of IT-intensive platforms that we're carrying. For example, I don't want to carry five expression profiling platforms, which maintain separate IT platforms to function.
Is that an actual example?
We retired a number of expression profiling platforms and went with a single system, which is being used consistently across the organization. We retired several data-handling systems and replaced them with a single one that in essence came from Pharmacia. We had three modeling platforms used in structure-based drug design; the eventual solution was to get rid of one platform and consolidate the other two into a single, more proprietary tool.
When won't it work to consolidate platforms?
When the technology is new. For example, I don't necessarily want to limit people when they are looking at new ways to automate a genotoxicity assay. Because I don't know which is the best way quite yet. But what I do want to do is coordinate the research effort that gets us to the best platform. If I see three or four people all on one site trying to solve the same problem, I say, "Let's stop for a second. Does everybody need to be solving this problem at the same time?"
Does scientific quality ever suffer by applying one technology to too many situations?
It is a sort of a fan dance. Here's my problem, what are the possible solutions? The slogan is: Bring the disciplines to the problem, not the other way around. So I don't have to shop my problem around, I should have a full view of all the types of diverse technologies. Pfizer pretty much has every skill set that you can imagine, other than a theologian-and we probably have one of those.
What is the overall objective?
We'd like to form a cohesive technology unit that spans the organization and can work in conjunction with a therapeutic area to help them solve their problems and move their program forward.
Can you supply a specific example?
Imaging. There is no research line or development line, for that matter, that doesn't use imaging. You can make investments in platforms and infrastructure that support each of them in a holistic fashion.
Have you ripped out any imaging systems?
We try to minimize the disparate IT or image analysis platforms that are necessary, but not to the point where we are undermining the quality of the research. I don't want to do three deals in image analysis, one with Virtual Scopics and two at two other places, with the same darn thing. When you have an organization the size of ours, you can get an enormous of leverage and advantage by saying, "This is the image analysis strategy, these are the small number of investments we need to make that will meet 99 percent of the needs of all the lines." That is enormously powerful, not just for efficiency but data quality and collaboration across the organizations.
Can data quality be improved by picking better technology?
There are many elements of quality. Quality is not only a function of the platform but is also dependent on the quality of the experimentation. So, there has to be a specification of what you are doing that will actually beget quality data. This important function is overseen at Pfizer by Scientific Discipline Councils. It's the discipline councils in
Biology, Chemistry, SafetySciences, Pharmacodynamics, Metabolism and Pharmaceutical sciences that ensure that the lines, sites and project teams have the technology, best practices and infrastructure to ensure data quality and consistency.
Just one, for the whole company?
They are discipline-based councils. And they look after how we are doing our research, what is the quality of the data that we get from this assay, and they try to have a standard, which all assays must meet.
If a scientist wants a new assay, it goes before the council?
We try not to make it that bureaucratic. But there is sort of a local curation of these assays. In harmonizing these assays and bringing them up to a level of data consistency and a data quality, then we can consider them global data. These data are something of lasting value to the organization. It's not sort of, "I use it for this particular project and then it's no longer relevant." We are finding that the curation and the means of generating these data sets provide a lot of value. Data quality really comes in two forms. It's the quality of the experiment, and the quality of the execution. And then it's the consistency of the data across the organization.
Are individual scientists starting to appreciate data quality?
They are the ones crying for it more than anyone. Compliance to global data standards used to be a stick and carrot thing, but it's becoming more carrot than stick. Once people and product teams get a taste, they became inordinately more excited about it and begin to utilize broader and more informative datasets when they try to solve problems in a particular program.
What if a scientist insists on a technology that only serves a very small niche?
We listen. Often their arguments hold. But what we find is that they will say, "I need this" or, "I want to do this collaboration." We'll say, "Are you aware of these four other collaborations that we have?" And for any one of them that we are invested in, we could re-write the scientific plan, and that could meet your needs. Often they say, "Sure, that will meet my needs." As a result, we don't have to spend any additional money -- the capability is already there.
Do you worry about IT, or is that someone else's problem?
Clearly not! I worry about the simplicity and the robustness of our data stores and architecture and the flexibility with which we put the analysis tools on top of them. And there is a lot of effort going into that, as we've become a much larger organization, to clean that up. We are to the stage where we are layering on more sophisticated data acquisition, data formatting, data recall, data analysis tools that can be used against these data warehouses.
So the design of these systems is something that you do spend time on?
Whenever you bring a new technology or evolve the system, IT has to be out in front.
What can you say about Pfizer's use of unstructured data?
We've done some work with BioWisdom. They built some ontologies for us. But we will also be looking for additional collaborators in the area. We are looking for the best groups that are going to help us with this problem, not necessarily are the ones who say, "Ontologies R Us." We are looking a little bit more creatively for people who can solve some of the basic technical issues that continue to plague this area. They may be people who are working in computer modeling for NASA or are working in electrical or civil engineering.
Do you worry about scientific data being stored in Excel spreadsheets, where colleagues can't see it or use it?
Not so much for us. Most of it is dumped right into standard applications. You don't mail out an Excel spreadsheet on what happened in the clinical trial or a given experimental run -- that's gone.
In terms of translational medicine, does technology really help across multiple therapeutic areas?
There are certain things which therapeutic area specific and then there are those that are not. So for instance, pharmacokinetic and pharmacodynamic models are therapeutic-area-independent. Adverse event and safety pharmacology are therapeutic area independent -- you've got to get your multiple toleration studies. Serum-based protein markers for efficacy and adverse events -- that is going to be important in every therapeutic area, but it is also going to cut across all therapeutic areas.
Are you optimistic about advances in translational medicine happening soon, either at Pfizer or in the industry at large?
I can't say that the success rate is going to change. But the percentage of time we have a definitive answer, as opposed to something that is equivocal, will go up.
That sounds like good news...
Yes, that is terrific news. You bring down the cost basis of getting to proof of concept. Then the percentage of time you get the proof of concept, you could say with hand on heart, that you made the right choice, which we certainly in this industry cannot say right now. We can only say it one third of the time.
Your attrition rates are going down?
Where we have seen some significant improvements would be in pre-clinical studies on compounds with regard to safety, physical chemical properties, drug metabolism and so forth. We have a better sense of how to engineer molecules against these parameters. The investments we've made in molecular diversity and file enrichment have provided us a more healthy cohort of new chemical leads to work on, to add the appropriate properties that will get over the attrition barriers.
Can you be specific about how much attrition rates are dropping?
We've quantified them in the preclinical setting to a point. It's going in the right direction. We are also beginning to see trends that things are getting better in the early human phase of testing.
Do you worry about getting a return on investment when spending on a new technology?
Yes, of course. But you need to get back to where you can easily make a statement with some certainty. For example, "If I do this, it's going to save me this many full-time employees, who then can go off and work on various other things." But you have to be very careful about the questions that you ask. If I try to have something that looks at seven different parameters simultaneously, then it becomes a mess.
Are you hiring more people in any specific field?
There are particular skill sets, which are under-represented in the industry at large. The company that grabs onto those folks will have a powerful advantage. You can't just say, "Well, I'll just get more medicinal chemists or fewer of this." You have to look at where the pinch points are and where you can actually add a lot of value. If you think that you are going to use the same discipline-based demographics to solve a problem that is evolving and becoming more complex with time, then you are missing the point. New skill sets are needed. Look at a university. Over time, certain departments get bigger, certain departments decline, new departments spring up.
How are collaborations with academia going?
We partner with academia a lot. Does the academic community really get it? Are they really willing to own your problem statement and work on it? More and more, they are. I was just at a conference and this was the first of this sort that I had been to in a while, where people were actually coming up and showing me things that they had done and pretty relevant to biomedical research. Whereas I think several years ago, that wouldn't have been the case.
Do scientists really enjoy having some discovery technologies chosen centrally?
I'd love to be able to give you examples where programs stalled and where we kind of put the full court press of, "Let's see how far we can transform this problem by applying pretty much all the technologies that we have." That program just moves to an entirely different place: new lead matter, added structural data, new pharmacogenomic data, new insights into how they're going to run clinical trials. It's just remarkable what you can do when you actually bring all the disciplines to the problem.
Are there cultural challenges in getting people from different disciplines to cooperate?
You have to nurture that kind of attitude. But most of it is fed by the hunger of people to get their problems solved. They don't like the fact that they can't get the right crystals to form. It's the desperation and passion of scientists that drives the interaction. I just have to get the right people in the room. What happens at that point is shared.
photo credits: Kathleen Dooher