A growing number of researchers are looking to nanotechnology to find ways of delivering drugs directly to cancer cells or creating fast, inexpensive diagnostic tools – such as over-the-counter tests for avian flu. But the expertise in materials science needed to create such nano devices often doesn’t overlap with an in-depth knowledge of biology, which could help guide researchers toward materials likely to be safe and effective.

Now Accelrys, a San Diego-based company with experience developing modeling software used for designing drugs and materials, is building software that will bring together life science and materials science expertise into one system. The goal: bridging the gap between these two fields, and thereby saving researchers time and money by quickly identifying designs that will work in the body.

For example, the software would help researchers sort through many combinations of drugs and polymers, to predict which ones will be compatible with each other and which can be made into nanoparticles that safely deliver the drug to target tissues, such a cancer tumor.

Similarly, using the software to match materials with protein-detecting molecules could help in designing diagnostic nanoparticles that latch onto molecular biomarkers in the body and reveal their location with various imaging technologies, according to Leroy Hood, a biochemist, founder of Seattle’s Institute for Systems Biology, and scientific advisor for the software project.

One example of a problem the modeling might solve, says Hood, is measuring multiple biomarkers in order to monitor the effects of disease treatments. He says that models, by quickly sorting through a large number of possible materials and detector molecules, might help researchers “go beyond just our intuitive feeling about these things” and discover more possibilities.

Accelrys will build on existing modeling software that allows researcher to predict, for example, the physical properties of new materials, or how drugs will connect to target molecules. An important early project is predicting how molecules will interact with surfaces such as a cell membrane or a polymer, or even a carbon nanotube. This effort will attempt to answer several questions: How does a single molecule stick to a surface? What happens when you add more molecules or different types of molecules? Or, given several different possible materials, which will show the strongest binding for a drug?

Modeling software already plays an important part in nanotechnology. James Baker, director of biologic nanotechnology at the University of Michigan, whose nanoparticle-based cancer imaging and treatment technology is now in animal tests, was able to use modeling early in the design process to reveal how a version of the particles he used damaged the cell membrane. Knowing the mechanism allowed him to design a safer version.

“Being an immunologist and an allergist, I knew a lot about toxicology of drugs and adverse reactions to materials,” he says. “So very early on we looked at those parameters in the materials side of what we were doing. That was sort of a unique thing that allowed us to move quickly into animals and get success – we were aware of some of the toxicological barriers that we’d have to address.”

Easy-to-use modeling software could help materials scientists do the same thing. The director of the Accelrys project, Deepak Singh, says his goal is for people – not experts in biology or materials science – to be able to simply say, “Here’s my material, here’s my drug. Give me a report.”

Another scientific advisor on the project, MIT chemical engineer Robert Langer, says that the software could potentially speed up the development of new materials for biological applications, by helping researchers make more intelligent decisions early in the design process. Langer says the software itself could help researchers identify experiments that would better predict the toxicity of nanomaterials, for example.

But the software won’t be a panacea, caution some experts. One limiting factor could be the speed of computers. Researchers already know quite a lot about chemical interactions in the body, and Michigan’s Baker is concerned that Accelrys has overestimated the power of algorithms to simplify these interactions and still turn out meaningful results. Baker says the bottleneck now is the hardware, not the software.