How Design of Experiments Accelerates Drug Development

In pharmaceutical R&D, time isn’t just money. It’s patient outcomes, regulatory approvals, and the difference between a promising therapy and a missed opportunity. The challenge? Making smarter decisions, faster.

Design of experiments (DOE) brings structure to experimentation. It helps scientists test multiple variables at once, understand how they interact, and identify what truly drives results. The payoff? More reliable insights, faster development, and fewer costly detours.

A mindset for development, not just a tool

Design of experiments (DOE) offers more than a statistical technique. It provides a framework for structured learning. Rather than testing one variable at a time, teams can evaluate multiple factors together, identify which ones matter most, and gain deeper insight from fewer runs. In doing so, reacting to uncertainty becomes working with intent.

This approach transforms how pharmaceutical scientists plan and interpret experiments. Whether in early formulation or late-stage process validation, DOE helps bring clarity to complex systems – faster and with greater confidence.

Progress begins with planning

According to Julia O’Neill, founder of Direxa Consulting and former CMC Statistics Lead at Moderna, one of the most effective ways to accelerate results is to pause before starting. Taking time to define the constraints, identify key variables, and consider the timing of each run allows teams to work more efficiently. What might seem like an extra step often saves time, effort, and resources later.

This principle proved essential during the development and scale-up of Spikevax, Moderna’s COVID-19 vaccine, where both speed and confidence were essential. 🎥 Watch the interview

From idea to scale-up, with fewer surprises

DOE supports the full life cycle of pharmaceutical development. During early stages, it helps refine formulations, reduce batch variability, and avoid stability issues. Later, as products move from bench to plant, DOE becomes even more valuable.

Scale-up brings new complexity. Variables that were stable in the lab may behave unpredictably at production scale. DOE helps teams anticipate those changes, reducing the chance of delays or costly rework. The process becomes smoother, more predictable, and easier to validate.

 Explore how DOE supports pharma and biotech

Making complex studies more manageable

In highly regulated environments, DOE simplifies decision making. At Cerba Research, for example, teams use it to develop robust bioanalytical methods while managing variability and standardizing procedures. The result is clearer data, more reliable outcomes, and easier collaboration across teams.

Similar benefits are seen at Pharmaron, where DOE is fully integrated into process development. Scientists use JMP software to organize experiments, explore trends, and communicate findings clearly, thus improving both the pace and the quality of decision making.

Watch this on-demand webinar on scaling-up processes with DOE

Tools that support better decisions

DOE has become more accessible thanks to modern software. Tools like Easy DOE in JMP allow teams to design high-quality experiments without needing to be statisticians. It opens the door for more scientists and engineers to bring structured experimentation into their daily work.

Automation is expanding those possibilities even further. At Synthace, teams use digital platforms to plan, execute, and analyze experiments in a continuous loop. That integration reduces manual steps and generates cleaner, more consistent data. The gains are not just in time, but in clarity.

Learning doesn’t stop at analysis

DOE also changes how teams interpret their data. Advanced methods like definitive screening designs make it easier to identify important factors. Predictive modeling helps researchers go one step further, showing what is likely to happen, not just what already has.

For time-based data, JMP Functional Data Explorer allows teams to work with curves like dissolution profiles or chromatographic traces using smooth, understandable models to improve both interpretation and communication.

Why the old way costs more than you think

One-factor-at-a-time testing may feel safe, but it’s rarely smart. It wastes time, burns through materials, and often delivers results that don’t hold up. Worse, it leaves key questions unanswered.

DOE replaces guesswork with structure. By setting clear learning goals and building on existing knowledge, teams can move faster and make decisions with confidence.

What comes next

As pharmaceutical development continues to accelerate, having scalable, high-performance tools to support DOE workflows is more important than ever.

The upcoming release of JMP introduces enhancements designed with pharmaceutical teams in mind: expanded designs for multilevel categorical factors, streamlined workflows for high-throughput experiments, and built-in simulation to evaluate experimental plans before they’re put into action. These improvements help teams tailor designs to industry constraints, extract more insight from each run, and reduce development cycles even further.

Ready to see what’s next? Discover what’s coming in JMP 19.

 How is your team using DOE to move faster and smarter?

The pace of drug development is only getting faster. With DOE, your team doesn’t just keep pace – it leads with confidence.

Have a story or strategy to share? Leave a comment, share this article with a colleague, or tell us how your team is using DOE to go faster and learn more.👇🏽