available databases. Most product development does not start from scratch. Researchers often begin with a product
“chassis”—perhaps a basic skin cream formulation—and then add active attributes
such as anti-aging or skin lightening capabilities that might appeal to the target market. Innovation usually results from a
combination of experimentation and building on what came before.
oriented architecture and collaborative
technologies are enabling a more unified
approach to managing complex scientific
data. For example, a web services-based
foundation for scientific informatics can
bring together multiple sources of data in a
“plug and play” environment. This allows
project stakeholders to create automated
workflows that streamline experimental
progress and conduct modelling, analysis
and reporting across different data sets. Additionally, when information is all in one
place, it can more easily be searched.
Through this“shared knowledge base,” organizations can enable richer collaboration,
faster innovation and more effectively capitalize on all their information resources.
Consider the example of a company
working to introduce a sunscreen formula
originally developed for European consumers to Asian markets. While chemists
can likely build on the existing formulation,
they also need to ensure that that the Asian
version is optimized to meet the unique
tastes and requirements of the target consumer. As a result, there are a number of
variables that must be considered.
A Huge Knowledge Base
The good news is that the available
knowledge base that researchers can tap
into when developing compelling new
products is huge—and that’s also the bad
news. A typical personal care product development project may span many scientific disciplines and include information
related to thousands of possible compounds and formation ingredients, high
throughput experimental results, historical project information and more. This information is also often spread across a
diverse array of formats and proprietary
systems, such as text documents saved in
an electronic lab notebook or images generated by a microscope. Disciplinary silos
create further complications, locking information within a particular department
or research group (biology experiments
may be conducted independently of
chemistry experiments or toxicology testing, for example). As a result, stakeholders
may spend countless hours tracking down
what they need or they may simply miss
critical knowledge entirely, burning resources on redundant experiments.
Extracting maximum value from massive quantities of scientific data requires
both the ability to integrate disparate information sources as well as quickly find the
content most relevant to the research problem at hand. Being able to analyze data as
a cohesive whole, especially across different areas of specialization, allows researchers to make important connections
that otherwise would have been missed.
They also must be able to access specific information—a skin cell image or an existing
formulation recipe—without a lot of hassle.
Fortunately, new advances in service-
Variables to Consider
First, there’s the original formulation: How
did it test under different temperature and
environmental conditions? What are the
solubility profiles? Can this mixture be
modified easily in order to change its color
or texture in order to address the preferences and realities of the new market?
There are also local considerations:
How does the climate and geography impact the efficacy of the product’s UV
blocker? What about its shelf life? Will
Asian skin (due to differences in genetics or
diet) absorb the product differently? Can
additional ingredients (such as an active
that will enhance the product’s ability to
penetrate the skin) be mixed safely and effectively with the original formulation?
With a system in place that enables de-
velopment teams to easily find, access and
use any type of existing R&D knowledge
related to the sunscreen formulation (as
well as information on suppliers, process-
ing data, test results and more), the cos-
metics organization can build on existing
innovation to reach new markets.
About the Author
Michael Doyle, Ph.D., is principal application scientist at Accelrys, a provider of scientific business
intelligence software and services for the life sciences, energy, chemicals, aerospace and consumer products industries. His blog can be found