By John Russell
December 18, 2008 | To a considerable extent, the acceptance of modeling and simulation (M&S) approaches (beyond traditional PD/PK) inside biopharma remains stuck at a low level and something is needed to get it unstuck—especially among senior management.
One idea might be to compile a list of successful projects, regularly updated, in which modeling and simulation had an important or decisive effect, and to characterize the approach used and quantify the M&S benefits to each project. Such a listing might be something Predictive Biomedicine and Bio-IT World could help coordinate, administer, and disseminate. Researchers could leverage the information in guiding their own efforts or use it to persuade internal stakeholders of the value of pursuing modeling and simulation.
This idea occurred to me after attending IBM’s recent, invitation-only Modeling & Simulation Summit at the company’s Executive Conference Center in Palisades, NY. Roughly 85 delegates from around the world gathered for two days of presentations and workshops. Most of the presentations demonstrated solid technical progress and touched on virtually every aspect of modeling and every phase of drug discovery and development. IBM, of course, provides both high performance computing technology and has its own ongoing computational biology research efforts.
Some projects discussed aren’t ready for prime time but soon will be. In one session on heart models, distinguished cardiologist and researcher Arthur Moss from the University of Rochester reviewed the field to date. Researcher Natalia Trayanova from the Institute for Computational Medicine at Johns Hopkins presented visually stunning models exploring cardiac electromechanical functioning. Alejandro Frangi of Fabra University reviewed EU Heart Project progress. (Conference organizers asked me not to describe presentations in great detail at this time).
In the Q&A following presentations, Moss commented on one NIH-funded effort, “This modeling process may allow early identification medications that prolong the QT interval and mimic the long QT syndrome… to my knowledge this is the first validation that’s been done in this.” There are of course many companies looking at modeling QT prolongation to speed identification of this too-frequent drug killer. Clearly this is leading edge work whose payoff is still a bit in the future.
Conversely, Donald Stanski, global head of modeling and simulation at Novartis, discussed the impact his department is having today. You may already know of Stanski, whose distinguished career includes stints at Stanford University; at PD/PK technology provider, Pharsight; and with FDA working on model-based drug development work as part of the Critical Path Initiative. His Novartis modeling group is growing, he said, and its modeling activities encompass a wide variety of projects (preclinical and clinical). Stanski made passing reference to having saved $60 million in one area and added pointedly that his group has helped move good projects forward, not just kill bad ones. That gets management’s attention and is no doubt a driver of Stanski’s ongoing success.
There is a right way and a wrong way to organize and implement modeling and simulation efforts, Stanski observed, and Predictive Biomedicine hopes to profile the Novartis approach soon. Stanski is also scheduled to speak at the 2009 Bio-IT World Conference & Expo in Boston this spring (April 27-29).
The conference also contained doses of reality—– SAR tools, for example, received decidedly mixed grades from one presenter who assessed a fair number of them. It should be said that many showed accuracy when matched against a specific class of problems.
Perhaps the most worrisome note was struck during a workshop on modeling in clinical development. Attendees complained that getting executive attention and buy-in was difficult. Top execs are generally not mathematicians and their eyes tend to glaze over when presented too many curves. This conversation later spilled over into the general session where it struck a similar chord.
Another issue raised was that that M&S often delivers bad news in the sense of killing projects in which egos have already been invested. Killing projects is extremely valuable and saves money but as Barney Frank, the longtime congressman from Massachusetts said about politics on 60 Minutes this week, “You don’t get any credit for disasters averted.” Then again, that’s a dicey proposition for politicians given the current disaster that wasn’t avoided.
Measuring modeling and simulation’s contribution to a project’s success is also a challenge. It’s often not easy to demonstrate that modeling was decisive versus incremental to a project’s overall success. Even when modeling is successful, other researchers on the project may feel they would have come to the correct answer soon enough without modeling.
Yet modeling and simulation is working and for many kinds of problems the kinks have been worked out. It’s working at Novartis. It’s being employed on critical projects at Vertex. Biosimulation specialist Entelos says it has been delivering concrete results to clients for years, and while many of those results were project-killers, some were not, and co-founder Alex Bangs says there are one or two compounds moving through the pipeline whose advance was substantially helped by Entelos. Merrimack Pharmaceuticals has a compound in phase three and has hung its hat on a discovery-development approach that relies heavily on mechanistic modeling.
The best way to overcome these barriers to M&S acceptance is to showcase successes. While concern over competitive issues has long kept biopharma tight-lipped about internal projects, the industry as a whole is under such pressure that there is now more openness to different kinds of collaboration and data sharing. It should be possible to define the elements of a repository of successful modeling and simulations projects in such a way that real competitive issues are not compromised.
One of the speakers at the IBM Summit was John Howell, president of Portfolio Decisions. His presentation was during the “Lessons from Other Industries” session. An expert on the energy industry, he noted the turmoil oil and gas underwent a couple of decades ago, when the prices of oil plunged, and the industry shed about 750,000 workers over the course of a few years. (That seems so unreal now, perhaps, but was terrifyingly true for oil and gas at the time.)
Many things have changed in the energy business. Sophisticated technology replaced more primitive discovery tools – modeling and simulation and visualization were big parts of that. Business models too had to morph, and they did, causing a disaggregation in much of the business. Such change is coming to the pharmaceutical industry, he warned, and there was no loud nay-saying from the audience.
It’s an imperfect analogy, for sure. Crude oil and gas are, well, crude oil and gas. Drugs come in huge variety. Yet it’s too easy to hide behind this complexity. Yes, drug finding and making—– it seems to me—is still a mostly discovery-informed process rather than engineering-driven; there are many reasonable questions about how fast this process can evolve. But modeling and simulation can play a bigger role today. Showcasing successes can help not only speed M&S adoption, but also stimulate wider creative thinking around the improvement to M&S tools.
Write to me, firstname.lastname@example.org with your ideas.
This article first appeared in Bio-IT World’s Predictive Biomedicine newsletter. Click here for a free subscription.