Acclerys Joins Microsoft at ACHEMA

May 24th, 2009 by George Fitzgerald, PhD
Microsoft booth at ACHEMA 2009

Microsoft booth at ACHEMA 2009

I just spent a week working in the Microsoft booth at ACHEMA 2009 in Frankfurt. Microsoft offered several of its software partners the chance to participate in their booth. Accelrys was there along with OSIsoft, Sycor, AspenTech, and others. Accelrys was demonstrating the integration between Pipeline Pilot and Microsoft Sharepoint as a solution for data integration and reporting. Pipeline Pilot provides Sharepoint customers with the means to access large amounts of scientific data, improving the discovery process. At the same time, embedding this within Sharepoint means that Accelrys solutions can be executed in a web environment, which is more familiar to most users than the specialized, “thick clients” normally employed for these applications. Read more about the integration here.

Accelrys station in the Microsoft booth

Accelrys station in the Microsoft booth

More about the conference: Held only every 3 years, the “Ausstellungskongress für Chemische Technik, Umweltschutz und Biotechnologie” is similar to the ACS or AIChE national meetings in the US in that it brings together scientists, engineers, and exhibitors from a broad range of chemical industries and universities, but the similarities end there. According to the ACHEMA web site the congress has some very impressive statistics:

  • 4,000 exhibitors
  • 180,000 participants
  • 900 lectures

Many of you will be familiar with national conventions like ACS, AIChE, and MRS so you have an idea what a typical expo looks like. This show was amazing. There were 3 floors of exhibits for pumps, fittings, and valves alone. There’s a total of 140,000 m² of exhibition space (that’s 1.5 million ft² to Americans). Keep in mind that the Germans only get to do this every 3 years, so they really need to make the most of it. I hope to do this again in 2012. See you there!

 

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Informatics lessons from the MRS

May 19th, 2009 by George Fitzgerald, PhD

The Materials Research Society (MRS) “encourage communication and technical information exchange across the various fields of science affecting materials.” It sponsors a spring meeting held in San Francisco and a fall meeting in Boston. This year’s spring meeting covered topics ranging from amorphous materials to methods for environmental stability to multiple topics in nanotechnology. (See all symposium titles here.)

Most interesting to me was Symposium Z: “Computational Nano science — How to Exploit Synergy between Predictive Simulations and Experiment, which fits with the comments I made in my previous posting, and shows just how much active interest there is in this topic. Prof. Krishna Rajan, who heads the Combinatorial Sciences and Materials Informatics Collaboratory, demonstrated how he uses data mining as a tool to understand the formation of apatites (minerals of the form A10­(BO4) 8X2) based on data mining and statistical analysis. How do you get your head around and N-dimensional space? How do you grasp trends when there are dozens of variables to consider? Use methods like recursive partitioning and Principal Component Analysis (PCA). 

Simpler than the modeling approaches I mentioned in my earlier posting, these require only a statistical analysis of the data (some experimental results, some modeling output). The results reduce N-dimensional datasets to 2 or 3 dimensions that are “grasp-able” by mere humans. Applying these approaches to the apatite data clearly shows how the choices of cation and anion influence the stability of the crystal.

Just think how many other research problems we could understand if we had the tools to look at the data in the right way.

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High Throughput – What’s a Researcher to do?

May 12th, 2009 by George Fitzgerald, PhD

High-throughput experimentation has been a mainstay in pharmaceutical discovery since the mid-1990’s. In a 1999 C&E News article (C&EN, vol. 77, pp 33-48 March 8, 1999) this approach was hailed as the next great thing. Unfortunately, we chemists soon realized that quantity is no replacement for quality; a notable article in the WSJ “Drug Industry’s Big Push into Technology Falls Short,” was critical of this approach.

 

At the time, I was working on a DOE-funded project (DE-FC26-02NT41218) for high-throughput catalyst discovery for NOx catalysis in lean diesel engines, together with GM and Engelhard (now BASF). In practice, our method was not to generate 1000’s of samples and hope for the best but to screen fewer carefully selected samples quickly, and subject the “winners” to more sophisticated testing.

 

The approach employed in our NOx project was based on analysis of experimental data, design of experiment, and fitting response surfaces – and it worked. As pointed out in a recent BIOIT World article, however, experimental data alone are usually too noisy to build reliable statistical models. What’s a researcher to do? Molecular modeling, of course – hey I’m a modeller: you knew I was going to suggest that.

 

The key for success, it seems, is to employ a plurality of methods, both experimental and computational. Given even a modest amount of experimental data, you’ll need a database with decent search & query tools and basic statistical approaches like principle component analysis. But atomistic modeling is also important. Work by a number of research groups has shown that you can generate good predictive models from quantum mechanical methods (QM) for lots of different kinds of materials. (Keep in mind that these examples barely scratch the surface of the available literature).

 

But how do get to the point that anybody can make use of QM-based results? Doing these calculations typically takes a log time.

 

QSAR (Quantitative Structure Activity Relationship) is a terrific way to leverage QM results for complex research topics. These research groups followed the same basic procedure:

  • Start with some experimental data
  • Generate a statistical model
  • Grind through a lot of calculations
  • Forward the “winners” for experimental testing

 

You can see in the examples above that the approach can actually work. But how do you figure out what QM calculations to perform, and how do you create good statistical models? Well, that’s a story for next month.

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More Personalized Medicine Surprises at ASCO Meeting?

May 12th, 2009 by Accelrys Team

am09-general1The abstracts for the semi-annual meeting of the American Society of Clinical Oncologists (ASCO) go up on their website this Thursday, May 14 at 6PM EST. This is a scientific conference that consistently reverberates on Wall Street, because so much of pharmaceutical sales is driven by cancer indications. Many of the largest mergers over the last six months had oncology as the subtext, such as Roche & Genentech and Eli Lilly & ImClone.  

 

kras1At a recent ASCO conference in January, 2009, “personalized medicine” went from a theoretical concept from science to a genuine business reality. At that conference, ASCO recommended a genetic test for a mutation in a crucial gene on human chromosome 12, called “KRAS,” that regulates cell division via signal transduction. If a colorectal cancer patient had the mutation, entire groups of therapies, called “anti-EFGRs,” were no longer recommended. This is not a rare mutation, as an estimated 40% of patients have the mutation. 

The result? Profound, immediate changes in market share potential for pharma companies offering the anti-EFGR monoclonal antibody therapies cetuximab and panitumumab.

In terms of pharmaceutical revenue, this is a very big deal. Each therapy carries a price tag that reflects the critical nature of the cancer indication, generally over $2000 per month. There are 150,000 new cases diagnosed in the US every year (resulting in 50,000 deaths). So just a simple calculation of the market shows that about $1.5 billion annual new business disappeared for makers of anti-EGFRs (40% of patients x $2,000 per month per patient x 12 months x 150,000 new patients per year), which of course excludes revenues from the estimated 400,000 current colorectal cancer patients.

And in terms of fighting this nasty scourge, the ASCO announcement was a very big deal. It personalized the treatment for 60,000 new patients annually (40% x 150,000) who would have otherwise placed their hopes for survival on a therapy that simply wouldn’t work for them.

The trade name for Cetuximab is Erbitux(FDA approval 2004), which is marketed in the US by ImClone & BMS, and in the rest of the world by Merck. Panitumumab is marketed by Amgen globally as Vectibix (FDA approval 2006).

Based on the stock market dip over the last 18 months (called the “Global Financial Crisis”), it is hard to discern definitively what effect the ASCO recommendation had on the stock prices of the companies involved.

But the bigger change will likely be observed internally at these companies, as they continue to commit resources to biomarker discovery and qualification. Biomarker activities around KRAS might have identified the lack of efficacy in the mutant form, which corresponds to not being effective in 40% of patients.

When it comes to post-hoc personalized medicine for blockbuster drugs, the pharma industry clearly doesn’t like surprises. Biomarker activities and translating those activities into clinical trials push those surprises into the research phase, where they belong.

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