New Tools for the Translational Researcher


Developing treatments that take individual variability into account (“personalized medicine”) has given rise to a new discipline in science: translational research or translational medicine. Scientists in this field work to translate biological phenomena into targeted, evidence-based medicines that improve health and treat disease by more optimally matching drugs and individuals.

Currently, the field of translational medicine research is accelerating, with 90 percent of pharmaceutical companies reported to be engaged in some translational projects as a means of reducing cost while improving outcomes. 

This translational revolution affects academic research as well. For instance, the National Institutes of Health created the National Center for Advancing Translational Science (NCATS) in 2012 to speed the translation of basic research into new treatments and cures for patients – moving more quickly from “bench to bedside.” The goal is to merge basic, preclinical, and clinical research with clinical implementation and public health data to develop new approaches and demonstrate usefulness. 

Moreover, translational medicine is the focus of many governmental initiatives around the world including:

• Genomics England, a company wholly-owned and funded by the UK Department of Health, was set up to deliver a flagship project to sequence 100,000 whole genomes from NHS patients by 2017. 

• The Obama Precision Medicine Initiative, announced at the beginning of the year. 

• And collaboration between public and private institutions, like the human genetics initiative Regeneron launched last year with Geisinger Health System of Pennsylvania and the National Human Genome Research Institute. 

Top Challenges for Translational Research

But research cost and complexity are among the top challenges for clinical research and translational projects, according to NCATS. Contributing to cost and complexity are the growing sources, types, and volumes of data stemming from newer high-content techniques in translational research, including: 

• Digital pathology

• Multiplexed flow cytometry

• Next-generation sequencing

• Proteomics

• Metabolomics

• Genomics

• Cellular assays

Translating Data Management & Analytics into Knowledge

Since Moore’s Law has propelled technical innovation, with faster and more precise systems generating vast sums of data, the challenge has become effective data management and data analytics. How do we make sense of the data and convert it into knowledge?

Current software solutions are ill-equipped to help translational researchers search, access, integrate, and analyze all the data that could help them make that next breakthrough. Therefore, as the field of translational medicine continues to grow, researchers need best-in-class solutions that lend speed and ease to their work. Self-serve access to a wide variety of data, using an informatics solution designed specifically for translational medicine workflows, will enable these researchers to more quickly and easily identify and manage the biomarkers that are essential to realizing the promise of personalized medicine.

“Unless you can start harnessing data and making sense of it, in an automated way, with systems that are engineered to solve big data problems, you’ll be overwhelmed by the data very quickly,” says Nicolas Encina, vice president of the Innovation Lab at PerkinElmer . “You can no longer effectively manage this data manually, and you certainly can’t analyze it or process it manually either.”

Introducing Signals™ for Translational

As a company dedicated to providing products and services that help researchers answers questions that improve life, PerkinElmer has built, from the ground up, a cloud-based data management and aggregation platform designed specifically to address the translational workflow. 

PerkinElmer Signals™ for Translational offers out-of-the-box support for the complete precision medicine workflow – from data acquisition to biomarker discovery to validation. The purpose-built, Software-as-a-Service (SaaS) platform easily integrates experimental and clinical data, enabling translational scientists to search for, retrieve, and analyze relevant aggregated data from across internal and external sources. PerkinElmer Signals™ has been designed with flexible and scalable data models to provide the scalability, agility, and collaborative environment required to support modern life science research.

“Too often, people think about data oriented from the informaticist’s or technologist’s point of view,” says Daniel Weaver, senior product manager for translational medicine and clinical informatics at PerkinElmer. “Signals for Translational presents the data in a way a regular scientist will be able to understand. It’s organized around concepts a scientist gets, around the subjects of clinical trials, patient visits, samples collected, etc. We view it as the next generation of how users will interact with data – by connecting instruments to a global cloud environment and serving as a bridge from the laboratory to the Internet of Things.

By connecting instruments and systems involved in translational research to the cloud, PerkinElmer offers researchers and project managers more insight into how the translational project is performing, when data is available, and what the data is telling them – in a sense, becoming a central nervous system for the connected research environment.

Get to Know PerkinElmer Signals™ for Translational

If you are interested in learning more on PerkinElmer Signals™ for Translational, we’re offering a webinar looking at examples of use in areas as varied as Translational Medicine and High Content Screening, led by Jens Hoefkens, director of strategic marketing and research at PerkinElmer. 

You will learn how PerkinElmer Signals™ will enable you to: 

• Access all your research and clinical data the way you want to, when you need to

• Develop and validate your hypothesis with integrated analysis solutions

• Enable effective collaboration within and across organizational boundaries

“Pharmaceutical companies are poised to generate very large volumes of complex datasets,” Hoefkens says. “The webinar will cover how Signals supports modern life science research with flexible and scalable data models.” 

How is cost and complexity affecting your translational research?