Run better experiments, keep answers close at hand, and support new drugs from discovery through regulatory submission and commercialization.
Statistical Clarity for Confident CMC Decisions. A Safer Path from Discovery to Market.
When Analytics Stay Consistent, Your Program Stays on Course
Drug and process development are complex by nature, but many programs underperform because experimentation is not treated as a cumulative, long-term capability. When insights fail to carry forward as programs evolve, teams change, or new questions arise, organizations slow down, repeat work, and lose ground under technical, regulatory, and commercial pressure. Consistent performance requires treating experimentation as a program—one designed to build knowledge over time, account for uncertainty, and support decisions across the full development lifecycle.
What We Do
Predictum helps teams quickly develop the structure, clarity, and analytical discipline to operate with confidence across the entire development journey.
From cleaner data, to stronger experimental strategies, and a foundation that holds up from early research through regulatory preparation. With Predictum as a trusted partner, teams gain the analytical consistency and program support needed to stay on course and deliver results that matter.
Explore how we can help.
Support Areas
CMC-Focused Analytical Strategy & Experimental Design
We help teams design experiments that build on prior work, address the right questions at the right time, and generate results that remain useful as programs advance.
Data Integrity & Lifecycle Traceability
We ensure analytical data, decisions, and changes are traceable across development stages so results can be trusted, defended, and reused under regulatory scrutiny.
Modeling, Analytics & Uncertainty Assessment
We apply rigorous statistical modeling and uncertainty analysis to support confident decision-making and reduce risk as technical and regulatory pressure increases.
Knowledge Management & Program Continuity
We preserve analytical knowledge—including context, rationale, and history—so programs maintain continuity despite team changes, evolving questions, and long development timelines.
Support for Contract Research Organizations (CROs)
We help CROs deliver analytical and experimental work that is clear, traceable, and defensible across clients and studies. By structuring analyses for reuse and regulatory readiness, we reduce rework, improve transparency for sponsors, and strengthen long-term delivery quality.
CDMO Support
Preserve and transfer analytical knowledge across programs and partners, reducing risk during tech transfer, strengthening process understanding, and enabling faster, more confident responses to regulatory and sponsor inquiries.
Explore areas Predictum can support
There are many things that can kill a product, but if we can develop in the right time and the right space using analytical tools, we can reduce the amount of resources and the cost to the company.
Patricia McNeil
Associate Director Cell Culture
Lundbeck
Contact us for a no-obligation talk.
Preserving Analytical Knowledge for Regulatory Readiness
When preparing regulatory submissions for new drugs and therapies, questions will arise that require teams to revisit past analytical work.
Strong development programs treat analytical work as connected knowledge, not isolated outputs. Knowledge Relationship Management (KRM) ensures analytical studies remain findable, interpretable, and defensible when regulatory questions arise, by capturing complete analytical history—data, models, decisions, and change.
KRM was created by Predictum to address a gap no other analytical or CMC practice covers: preserving analytical knowledge as part of routine experimental work so it remains usable over time.
It builds directly on how teams already design and analyze studies, ensuring experimental results can be reused, defended, and extended as programs advance.
Phil is a Senior Data Scientist and Statistical Consultant at Predictum. He provides consulting services in data science, statistics, and machine learning for integrated analytical systems, custom projects, and training. He specializes in modern experimental design and analysis strategies and the use of statistics, data science, and machine learning in engineering and science.
In addition, Phil is a Professor in the Department of Mathematics and Statistics at the University of New Hampshire (UNH) where he teaches courses at the undergraduate and graduate levels in design of experiments, machine learning, and statistical methods for quality improvement. He has held the following relevant industrial positions: Senior Engineer for Materials and Processes Development, McDonnell Douglas, St. Louis, MO; Staff Scientist/Statistician, Alcoa Technical Center, Pittsburgh, PA; and Statistician/Senior Engineer, Rohm & Haas Electronic Materials (now Dow), Marlboro, MA.
In addition, Phil is a Professor in the Department of Mathematics and Statistics at the University of New Hampshire (UNH) where he teaches courses at the undergraduate and graduate levels in design of experiments, machine learning, and statistical methods for quality improvement. He has held the following relevant industrial positions: Senior Engineer for Materials and Processes Development, McDonnell Douglas, St. Louis, MO; Staff Scientist/Statistician, Alcoa Technical Center, Pittsburgh, PA; and Statistician/Senior Engineer, Rohm & Haas Electronic Materials (now Dow), Marlboro, MA.
Phil holds a Ph.D. in statistics from Virginia Polytechnic Institute and State University.
Marie is a Senior Data Scientist at Predictum. She specializes in predictive modeling, design of experiments, statistics, and machine learning. She has extensive experience in consulting and statistical training in a wide variety of industries.
Marie is Professor Emerita of Statistics at the University of New Hampshire (UNH), where she has worked extensively with students and companies on the practical application of statistics. She is also a co-author of two books, one of which is about the use of JMP software and statistical methods to improve quality and the other is about the partial least squares technique. She was also a statistical writer for several years as a member of the JMP documentation team at SAS Institute.
Marie is Professor Emerita of Statistics at the University of New Hampshire (UNH), where she has worked extensively with students and companies on the practical application of statistics. She is also a co-author of two books, one of which is about the use of JMP software and statistical methods to improve quality and the other is about the partial least squares technique. She was also a statistical writer for several years as a member of the JMP documentation team at SAS Institute.
Marie holds a Ph.D. in Statistics from the University of Massachusetts at Amherst.
Cy Wegman is a Senior Analyst at Predictum. He specializes in data analytics and empirical modeling and optimization. Before joining Predictum, he worked for 38 years at Procter & Gamble, where his last assignment was the Empirical Modeling leader for the company. Cy is a member of the P&G Prism Society, which represents the top 1% of P&G engineers, for profitably applying his technical mastery. His contributions have resulted in hundreds of millions of dollars in annual savings. His cross-industry work in manufacturing, engineering, and R&D has spanned molecular-scale to full-scale technologies, as well as material, formulation, process, packaging, and consumer models. Cy holds a B.S. in Civil Engineering from Rose-Hulman Institute of Technology.
We work alongside leaders in life science development
Our experts bring decades of hands-on work with life science, allowing us to engage quickly and deliver results.
Getting started
Getting started with Predictum is easy, and built upon close understanding and partnership
Schedule a Call
This allows us to understand your challenges, practices and current technology.
Build an Appropriate Solution Plan
We will design a roll-out plan that suits your budget and needs
Begin Stronger Experimentation and Knowledge Redeployment
Begin plan, measuring success with use of new methods, trainings, and/or tools.
