Design of Experiments (DOE) Techniques

If I had to give one tip to biotech startups, it would be to use Design of Experiments (DOE). It helps you save time and get more reliable results. I first heard about DOE during my Master’s in Industrial Biotechnology. It was introduced as a way to speed up experimental design. At the time, I was still convinced that optimizing a process meant changing one variable at a time. Temperature, then pH, then nutrients. I had the chance to apply DOE in my first job. That’s when I saw the real difference. The sequential approach was slow, often misleading, and blind to how variables actually interact. With DOE, I could: -Test multiple factors at once -Detect hidden interactions -Build predictive models without running every single experiment. That changes everything, especially in fermentation, where parameters are tightly interconnected. I’ll give you a concrete example. A team was optimizing enzyme production using 3 variables: temperature, nutrient concentration, and agitation speed. Sequential method: 27 experiments.DOE method: 9 well-designed tests. Not only did they save time, but they also discovered a key insight: agitation speed strongly influenced nutrient availability. That single piece of information drove faster, smarter decisions. Obviously, when I founded Cultiply, I made sure DOE would be part of our DNA. It allows us (and our clients) to reduce uncertainty and make solid technical choices from the start.

DoE, QbD and PAT 1. Introduction Evolution of pharmaceutical development: from empirical trial-and-error → risk-based scientific approaches. Regulatory drivers: ICH guidelines (Q8–Q14), FDA PAT initiative (2004). Importance of integrating design, knowledge, and real-time control. Positioning DoE, QbD, and PAT as a “triad” for robust, efficient, compliant development. 2. Historical Context and Regulatory Push Past reliance on end-product testing and its limitations. Shift to lifecycle management approaches. Role of FDA’s Critical Path Initiative. QbD introduced into regulatory lexicon in 2004; PAT guidance published. Global adoption: EMA, MHRA, WHO. 3. Understanding the Three Pillars 3.1 Quality by Design (QbD) – The Framework Definition & Philosophy: Proactive design vs reactive testing. Key Concepts: QTPP – Quality Target Product Profile. CQA – Critical Quality Attributes. CPP – Critical Process Parameters. CMA – Critical Material Attributes. Stages of Application: Early development → Technology transfer → Lifecycle management. Regulatory Basis: ICH Q8(R2), Q9, Q10, Q11, Q12, Q13, Q14. Tools: Risk assessments (FMEA, Ishikawa, Fault Tree Analysis), control strategy design. Case Study Example: QbD applied to controlled-release tablet development. 3.2 Design of Experiments (DoE) – The Optimizer Definition: Statistical framework for systematic factor–response exploration. Role in QbD: Tool to identify design space. Types of DoE: Screening designs (Plackett-Burman, Fractional Factorial). Optimization designs (Central Composite, Box-Behnken). Robustness studies. Benefits: Identifies interactions, reduces experiments, builds knowledge quantitatively. Case Example: Optimizing binder level, granulation time, and impeller speed. 3.3 Process Analytical Technology (PAT) – The Real-Time Guardian Definition: Real-time monitoring and control toolkit. Role: Ensures processes remain within validated design space. Techniques: NIR, Raman, FTIR, Particle size analyzers, Focused Beam Reflectance Measurement (FBRM). Applications: Blend uniformity. Moisture control. Coating thickness. Continuous manufacturing. Regulatory Context: FDA PAT Guidance (2004). Case Example: Inline NIR monitoring for RTRT (Real-Time Release Testing). 4. Interrelationship of the Three Pillars DoE as the engine of knowledge → defines design space. QbD as the overarching framework → integrates knowledge, risks, and control strategy. PAT as the execution safeguard → ensures adherence in manufacturing. Lifecycle integration (development → validation → continuous verification). 5. Benefits of Integrated Use Regulatory alignment & faster approvals. Cost savings through fewer failed batches. Increased robustness and reproducibility. Knowledge management & data-driven decision-making. Example: Continuous manufacturing systems where DoE defines design space, QbD integrates it, and PAT ensures execution.

Multi-objective formulation optimization, too few samples, dismal model performance. Where to go? If you've worked on industrial formulations, you've seen this before: a handful of experiments, properties that fight each other, and models that look great on training data… only to be seriously overfit on noise. A new paper in Chemical Science offers a surprisingly practical account, using multi-objective optimization of self-healing polyurethanes as a concrete case. Rather than hiding the failures, the authors walk through them and turn them into a playbook that you can adapt: Step 1. Start with a random baseline A small, randomly sampled dataset is used to train standard models and then naively expanded with more random experiments. Overfitting dominates, making it clear that random sampling doesn't solve the problem. Step 2. Diagnose failure instead of tuning harder Feature-importance analysis shows that chemically important variables contribute little to predictions, confirming that the models are learning spurious correlations rather than structure–property relationships. Step 3. Redefine the inputs using chemistry-informed descriptors Raw formulation ratios are replaced by a small set of descriptors encoding stoichiometric balance, chain-extender balance, and hard/soft segment ratio. This reduces the experimental design space while encoding known chemical mechanisms. Step 4. Design the dataset instead of sampling blindly A gradient-designed dataset is constructed in descriptor space. With just 9 designed samples, model generalization improves substantially, showing that data quality and coverage matter more than sample count. Step 5. Use Pareto optimization and expand the design space Multi-objective optimization makes trade-offs visible. When progress stalls, key descriptor ranges are widened to explore new regions. Step 6. Consolidate datasets and validate predictions Complementary designed datasets are merged to predict candidates beyond the current Pareto front. But initial experimental validation fails dramatically, signaling extrapolation beyond the covered chemical space. Step 7. Fill gaps, re-optimize, and validate successfully Failures are traced to missing regions of descriptor space. Targeted experiments fill these gaps, after which re-optimization yields predictions that closely match experiments. In total, ~20 samples prove sufficient for this system. Step 8. Confirm physical consistency, convergence, and generalization Structure–property analysis aligns with established polymer physics, further data no longer improves the Pareto front, and the same workflow generalizes on a different polyurethane system. If you're stuck in complex formulation modeling challenges, this paper is worth a careful read. 📄 Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes, Chemical Science, December 23, 2025 🔗 https://lnkd.in/eTAg7QkW

One of the central value-drivers of #DesignOfExperiments is avoiding unnecessary work, but show-casing that is not so easy. Any decent experimental plan made with DOE will still have lots of individual tests (or runs as we call them) and that makes it hard to see how much work was saved. I mean, I can’t just point to the missing experiments and say: “Look how productive we were!”, now can I? 😉 But the power of examples is strong, so as part of my preparation for an upcoming talk on how DOE has benefitted me in my work, I figured I might as well share examples here. The first the case where DOE “clicked” for me, my first success story. This was a project where we helped a company that makes wood coatings develop water-based products with a better environmental profile. For reasons I won’t get into, our main task was the choice of potential candidates for the four main components of a formulation: A film-former (4 options), a surfactant (5 options), a filler (4 options) and a binder (4 options). You can combine these in 320 unique ways. Essentially, we wanted to answer: “Which combination of ingredients (among the 320) should we choose?”. To answer this, we used DOE to generate a multi-level categoric design with 30 total experiments, that you can see applied to oak boards below. The second figure shows their distribution in “ingredient space”. The coatings clearly behave very differently (they're not meant to change the color of the wood)! This experiment allowed us to predict a combination of ingredients (that wasn’t among the initial 30) that met the quality requirements! 👏 And this even without messing with the mixture ratios. But is 30 mixtures a small amount of work in this kind of project? Well, before we decided to try this “new” DOE thing I had stumbled upon, we had spent a full year of the project using the one-factor-at-a-time approach and tested more than 150 different formulations 😓 These 30 runs took us two months to complete, from first plan to final validation. I assume you can see why this really got me hooked on DOE… 😝

When #xylanases were first introduced to #flour, they were hailed as a miracle #enzyme, revolutionizing bread-making by improving -> dough handling, -> volume, and -> crumb structure. Their impact was so significant that they quickly became a standard ingredient in flour treatment and bread improvers. ▪️ But soon, the industry realized a key challenge -> not all xylanases perform the same way. ✔ A xylanase that works perfectly in one recipe may fail in another. ✔ The same enzyme at the same dosage can produce wildly different results depending on - flour type, - water absorption, - mixing conditions, and - proofing times. ✔ There is no universal xylanase solution—each application requires careful selection and fine-tuning. ▪️ Traditionally, bakers and mills rely on trial and error, but there’s a smarter way: Design of Experiments #DOE. So, how can bakeries and mills move beyond trial and error to optimize xylanase performance? 🔹 DOE: A Smarter Way to Optimize Xylanases Instead of testing one factor at a time—which is slow and overlooks key interactions—Design of Experiments (DOE) allows us to: ✅ Compare multiple xylanases and conditions at once ✅ Identify the best enzyme (or blend) for a specific application ✅ Map how xylanase type, dosage, water absorption, and mixing interact ✅ Reduce costly trial-and-error while achieving consistent results For example, a DOE study could compare: 🔹 Aspergillus vs. Bacillus vs. Trichoderma xylanases 🔹 Different enzyme dosages (e.g., 10, 20, 30 ppm) 🔹 Varying water absorption and mixing times -> By analyzing the data, bakeries can pinpoint the optimal xylanase and process conditions—without endless guesswork. ▪️ At GRAINAR -> We have seen firsthand that DOE is the most effective approach to optimizing xylanase performance. -> Rather than relying on traditional trial and error, we work together with our customers to systematically fine-tune their enzyme strategies—ensuring better dough performance, volume, and consistency every time. ▪️ Xylanases are a huge topic in milling and baking✨, and from time to time, I come back to them because the more we understand them, the better our flour and bakery products become. Stay tuned! 👀🚀 Thanks for reading✨📚 #bioscience #rheology ##breadmaking #Grainar

💪🏻 𝐖𝐡𝐲 𝐒𝐞𝐪𝐮𝐞𝐧𝐭𝐢𝐚𝐥 𝐃𝐨𝐄 𝐁𝐞𝐚𝐭𝐬 "𝐁𝐢𝐠" 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐬 ��️ Traditional experimental design may often follow a "big DoE" approach: plan everything upfront, run all experiments at once, then analyze. But there's a smarter way. Sequential Design of Experiments builds knowledge iteratively: • 𝐋𝐞𝐚𝐫𝐧 𝐚𝐬 𝐲𝐨𝐮 𝐠𝐨: Use early results to refine later experiments. • 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐰𝐚𝐬𝐭𝐞: Stop when you have enough information, or pivot when assumptions prove wrong. • 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Focus experimental effort where uncertainty or benefit is highest. • 𝐋𝐨𝐰𝐞𝐫 𝐫𝐢𝐬𝐤: Catch problems early rather than after completing hundreds of runs, smaller batches are easier to complete even when resources shift or priorities change. • 𝐅𝐚𝐬𝐭𝐞𝐫 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Get preliminary answers sooner, refine as needed. 🤓 In one of the use cases I have contributed to, the advantages of sequential DoE become clearly visible: at each stage, we are able to maximize the response as well as quickly reduce the variation of the response, while adapting the experimental space to new findings: modifying factors ranges, adding new factors, etc... 🤝🏻 This situation also help increasing discussions and collaboration between domain experts and design creators, where new knowledge can quickly be incorporated in the next augmentation phase. 🔄 Sequential DoE embraces uncertainty and turns it into an advantage. Why commit all resources upfront when you can learn, adapt, and optimize along the way ? ⏯️ If you are interested about this topic, I also highly recommend watching the recorded presentation 𝑩𝒊𝒈 𝑫𝑶𝑬: 𝑺𝒆𝒒𝒖𝒆𝒏𝒕𝒊𝒂𝒍 𝒂𝒏𝒅 𝑺𝒕𝒆𝒂𝒅𝒚 𝑾𝒊𝒏𝒔 𝒕𝒉𝒆 𝑹𝒂𝒄𝒆? with David Wong-Pascua, Phil Kay, Ryan Lekivetz and Ben Francis, which highlights how and why sequential DoE make the study of large experimental space (and high number of possible combinations) possible and efficient. 🔗 Link: https://lnkd.in/ercR9AQV #DoE #Learning