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Editor’s Note: A Year of Tumult


By John Russell, Editor

June 25, 2009 | If you think of the ‘business year’ as an annual race extending from Labor Day to roughly July 4 - followed by a brief summer pause to reset for the next race – this year the economy trampled all other contenders. Budgets shrank. Clients retrenched. Untouchables weren’t. Whatever your plan of attack was on September 1, by January 1 it wasn’t.

This made for an interesting first year for Predictive Biomedicine (PB). It served as a brutal reminder of the power of the economy over technology. Much of PB’s coverage was caught in the maelstrom. Several stories first presented with rosy expectations, later took unexpected darker turns. Yet the steady advance in predictive approaches to drug discovery and development was also captured in PB coverage.

As Predictive Biomedicine begins its planning pause, it is worth reviewing notable points raised by coverage throughout the year:

1. Economics Drive (Almost) Everything  

2. (Selective) Modeling Works Now!

3. New Business Models Needed?

Rewind to the launch issue of Predictive Biomedicine last September. It featured Eric Schadt’s pioneering work in network biology. The work was expensive, he conceded, but it was also the best way to bring truly predictive power to drug research. He noted his group was responsible for identifying roughly a third of Merck’s new targets in diabetes and obesity. Such work, he emphasized, was only possible at a company which had both size and enlightened management (see Merck's Eric Schadt Discusses Integrative Genomics, Sept. 11, 2008).

Schadt said, “[None of this work we’re doing could have happened anywhere else. It was really the support of Merck, having leadership like Peter Kim and Stephen Friend say, ‘We have got to better understand biology and we’ve got to understand it earlier in the process of drug discovery to increase our probability of success downstream.’ So they were hugely backing the very large-scale experiments you needed to carry out that cost real money to profile thousands of individuals, and thousands of experimental crosses and multiple tissues and genotyping at a genome-wide scale and clinically characterizing. That scale of work was undoable anywhere else.”

PB jumped onboard. “Given the tough times technology advocates have encountered inside biopharma – largely because tech bets haven’t produced as expected – Schadt’s work is seen by many as the bow of an advancing icebreaker starting to shatter attitudes long frozen against predictive technologies,” noted the enthusiastic writer (me).

Instead, the world economy shattered. The downdraft of recession and long-avoided structural problems forced most of biopharma into cost-cutting and reorganization modes.

In October, Merck announced plans to close its Seattle operations where Schadt was based at Merck’s Rosetta Inpharmatics division. In March, Schadt and his Merck/Rosetta colleague Steven Friend announced plans to found, Sage, an open source repository of network biology information (see Merck Execs See "Sage" as Key Ingredient for Disease Biology). By late May, Schadt switched gears again, this time named CSO for Pacific Biosciences, a next generation sequencing company, (see Pacific Biosciences Pacific Biosciences Nets Eric Schadt as Chief Scientific Officer).  Merck later sold the remaining assets of Rosetta, (Schadt and Friend were Rosetta co-founders) to Microsoft.

This is not a slam of Merck or Schadt. Change and displacement were in the wind. In January PB published an interview with David Cox (see Pfizer's BBC Brings Fresh Perspective to Targets: Jan 8, 2009) in which he discussed Pfizer’s nascent Biotherapeutics and Bioinnovation Center’s (BBC) initiative to bring predictive technologies to bear on biotherapeutics development. Not long afterward, the Pfizer-Wyeth deal was announced bringing reorganization, consolidation plans, and the resignation of Cory Goodman who headed the BBC. Of course the BBC continues and Merck hasn’t abandon network biology, but the tumult at both companies seems hardly done.

It's been an exhausting year for everyone! The power of the economy to quickly and savagely reshape biopharma priorities, with technology as an attractive target for de-emphasis, was on startling display this year and in a concentrated fashion that we may not (hopefully) see again for years.  

2. Modest Modeling is Making a Difference
Despite the economic hurricane, modeling’s modest foothold in drug discovery and development continued growing. PB had two articles describing modeling’s advance and those efforts are persisting because of the clear value they bring and, it’s true, much of the investment has already been done. The first piece (see Novartis Savors Early Modeling Success -- Mar 19) examined Novartis’s solid efforts.

Led by Don Stanski, Novartis seems to have found the right formula for organizing and deploying modeling and simulation within its development organization. The scope of the M&S group’s activities is impressive (disease, pathway, PK/PD, more generalized decision support). Perhaps most important is the close alignment of M&S with Novartis’s therapeutics franchises and its direct participation on development project teams. Confidence has been built with other constituencies. 

Excerpt from the article: “I’ve always seen modeling and simulation as a key technology that really hasn’t been as widely deployed or as effectively deployed as it could be,” says Trevor Mundel, global head of drug development at Novartis. “We’re now having the chance to work with Don’s group and to give it its proper place in the context of development. We are starting to see some real traction.”

Not surprisingly, Mundel is focused on “the phase two problem (attrition, duration, and cost) we talk about in industry and how one de-risks the process with proof of principle efforts. Given that proof of principle gives you a pretty small data set, how can you play that up and extract more information so you don’t have to spend excessive time in phase two. We bring in modeling and simulation as one aspect of it.”  End of excerpt. 

The FDA is likewise embracing modeling. As PB reported in the last issue (see Remodeling the FDA), FDA will soon re-start its End of Phase 2A meeting program in which it meets with sponsors to use modeling & simulation to inform key decisions about late phase trial design and go/no-go decisions. There are even plans afoot to elevate the modeling group to division status within CDER. Led by Joga Gobburu, the modeling has seen its workload soar and is actively hiring.

This is not to say modeling is ready for prime time in all areas, or that it is easy to deploy. Many needed skills remain scarce and much of the industry remains less convinced of modeling’s game-changing capacity. PB reported on the range of issues still facing modeling (see Modeling & Simulation Still Need a Push) discussed at IBM’s excellent Modeling Summit last November.

Perhaps the clearest statement of challenges facing modeling came from PB reader Frank Tobin, former head of a modeling initiative at GSK and now the chief computation officer for start-up, Strategic Medicine (see Letter to Editor: Modeling is Powerful BUT Has Far to Go). His real world discussion of the organizational and technological obstacles facing modeling and simulation is well worth reading.

Nevertheless, modeling is making steady progress and with FDA encouragement and example such as Novartis, it should expand its foothold.

3. New Business Models Needed?
Roughly a week ago, Entelos issued a press release with more details on recent management changes, its proposal to seek delisting from the AIM stock exchange, and this: “Entelos also announces that, following receipt of unsolicited expressions of interest from certain third parties investigating the possibility of acquiring or entering into another strategic transaction with Entelos, the Company has retained Seven Hills Partners LLC as its financial advisor. Seven Hills is assisting the Company’s Board of Directors and senior management in evaluating various strategic alternatives which the Company hopes will enhance shareholder value, including various financing and capital raising alternatives.”  

By very many measures, Entelos is an excellent company. Founded in 1996 (click for company history), It is a pioneer in the systems biology and biosimulation space. Its PhysioLab and Virtual Patient technology (click for technology information) is innovative and has helped clients make decisions in discovery (target and lead identification, for example) and in clinical trial design (responder/non-responder, dosing, and competitive analysis, for example). It is working with FDA on a platform to better predict DILI (drug induced liver injury) problems. It collaborated with ADA, developing a diabetes PhysioLab for researchers to use in exploring or generating new hypothesis. It has a slew of patents. These are just a few accomplishments.

While the drug industry has regularly kicked the Entelos tires, it never took the deep plunge. Entelos understandably explored additional avenues. It built a PhysioLab around skin sensitivity with Unilever, putting itself into cosmetics market. It launched a personalized medicine site, MyDigitalHealth.com, which could leverage Entelos’ formidable knowledge management and simulation technology to guide physicians or even patients. It was able to mount these latter initiatives at least in part because its core biopharma-related business ticked along. It also made a couple of acquisitions. In 2006 it went public on AIM.

What it hasn’t done is thrive, at least in traditional financial terms, and its future direction seems uncertain.

Leaving aside the Entelos particulars - it is just the most current example - the broad truth is few computational-based discovery technology providers (s/w, R&D collaboration, fee for services, etc.) have hit financial home runs. Getting started is (or was) easy enough, but scaling up the business and finding an exit strategy satisfactory to investors has proven mostly elusive. Companies seem to get stuck on a financial treadmill with a firm ceiling.

The relatively small market size, the widespread availability of open source software, the unwillingness of biopharma to share meaningful IP, and the long drug development cycle have all played a role in holding back the market. So has a certain amount of seller hype. One wonders if a different business model is needed to overcome these challenges. Some platform companies have tried morph into products companies (DX or RX) but that hasn’t proven too easy. Neither has drug repositioning. What are your thoughts on the matter?

Looking forward, Predictive Biomedicine is building editorial plans for next year. What topics or specific stories or other activities would you like to see PB tackle? Write to me at
 John_Russell@bio-itworld.com.
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This article first appeared in Bio-IT World’s Predictive Biomedicine newsletter. Click here for a free subscription.

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