{"id":3538,"date":"2026-10-08T21:44:02","date_gmt":"2026-10-08T13:44:02","guid":{"rendered":"http:\/\/www.mozaik-people.com\/blog\/?p=3538"},"modified":"2026-10-08T21:44:02","modified_gmt":"2026-10-08T13:44:02","slug":"what-role-does-data-play-in-process-development-4cd7-2407ec","status":"publish","type":"post","link":"http:\/\/www.mozaik-people.com\/blog\/2026\/10\/08\/what-role-does-data-play-in-process-development-4cd7-2407ec\/","title":{"rendered":"What role does data play in process development?"},"content":{"rendered":"

Hey there, let\u2019s cut to the chase \u2013 if you\u2019re in any kind of manufacturing or biotech space (and if you\u2019re here, you probably are), you\u2019ve heard \u201cdata is king\u201d a million times. But as a process development (PD) supplier, I don\u2019t just say that as a buzzword I heard at a conference. I live this stuff, every single day with the teams we partner with, and let me tell you \u2013 data isn\u2019t just a nice-to-have. It\u2019s the backbone that turns a \u201cwe think this works\u201d lab experiment into a scalable, profitable process that doesn\u2019t crash and burn at 10,000 L batches. Process Development<\/a><\/p>\n

<\/p>\n

Let me start with a real example from last year, to make this concrete. We had a biotech startup client that had a tiny, 1L lab process for their monoclonal antibody (mAb) \u2013 looked great on paper, 92% yield, super pure. But when they tried to scale it to 500L, they watched their yield drop to 68%, purity plummet, and they were missing critical quality attributes (CQAs) left and right. They called us panicking, because their investor deadline was 6 months out and they were on track to blow it. The first thing we didn\u2019t do was \u201cfix\u201d the process right away \u2013 we pulled every bit of data they had, then ran our own small-scale (10L) trials with full data logging that they couldn\u2019t capture in their old lab setup.<\/p>\n

Turns out, their original process had a 2-hour hold step post-protein A capture \u2013 something they thought was harmless because it worked in 1L. But the data showed that when the hold time stretched to even 2.5 hours at scale (due to downstream equipment bottlenecks), the target protein started aggregating. We also found that their pH adjustment rate, which they\u2019d tested at 10 mins in the lab, was causing pH gradients at scale because their mixing wasn\u2019t as fast. That\u2019s the thing about process development data \u2013 it\u2019s not just about what works in small volumes. It\u2019s about capturing the variability<\/em> that gets amplified when you scale, because the lab world is controlled, but real manufacturing is messy.<\/p>\n

Here\u2019s the first big role data plays: it closes the \u201clab to fab gap\u201d that trips up so many teams. Before we came in, this client was guessing at scaling factors \u2013 doubling mixing time, doubling hold time, just because that\u2019s what everyone does. But guess what? Generic scaling rules don\u2019t work when you\u2019re dealing with biological molecules that are super sensitive to their environment. Data from small-scale, representative<\/em> trials (not just tiny lab vials) lets you map exactly how variables like flow rate, temperature, hold time, and pH impact your CQAs, so when you move to scale, you\u2019re not flying blind. For that mAb client, we ran 12 different hold time conditions at the 10L scale, logging aggregate levels every 15 mins, and found that a 90-minute hold (with a quick stir at 30 mins to prevent settling) kept yield at 90% even at 500L. That took 3 weeks of trials, all data-driven, and we avoided a $2M delay and investor pullout.<\/p>\n

Now, let\u2019s talk about another part: reducing risk. Data isn\u2019t just for fixing existing processes \u2013 it\u2019s for not building a bad one in the first place. We have a pharma client that\u2019s developing a vaccine for a respiratory virus, and they were trying to cut corners on data collection to hit their timeline. They wanted to skip testing intermediate hold times and just run the process straight through, because \u201cit\u2019s stable right after expression.\u201d But we pushed back \u2013 not because we\u2019re annoying, but because we have the data (from 10+ similar vaccine processes we\u2019ve developed) that skipping hold time testing leads to 3x higher failure rates at commercial scale. We worked with their team to build a centralized data logging system that tracks every single variable in the fermentation, so they could test multiple hold conditions in parallel without wasting too much material. The payoff? When they did scale to 20,000L, they had zero unplanned downtime in the first run, and their CQAs were within specs on the first try. That\u2019s data mitigating risk before it becomes a problem.<\/p>\n

Wait, let\u2019s get specific about the kind of data we\u2019re talking about here \u2013 because not all data is created equal. A lot of teams think \u201cdata\u201d is just a spreadsheet of yields. No. It\u2019s real-time sensor data from bioreactors, it\u2019s analytical data from HPLC or mass spec that shows purity and aggregation, it\u2019s even data from operator notes (like \u201cmixing was slow this run because of a clogged impeller\u201d) that might seem trivial but can make or break a batch. We work with clients to set up \u201cdata pipelines\u201d that pull all this info together, so they don\u2019t have 10 different spreadsheets stored on 5 different drives \u2013 they have a single, unified dataset they can actually analyze.<\/p>\n

For example, we had a chemical manufacturing client that was struggling with batch-to-batch variability in a specialty polymer they make. Their old process was \u201cif it looks like this, it\u2019s good.\u201d But visual checks are subjective, and operators would adjust temperature or pressure based on gut feel, not data. We helped them install in-line viscosity sensors that log data every second, and correlate that with molecular weight data from lab tests. Now, their operators know that if the viscosity hits 1200 cP at hour 8 of the reaction, their molecular weight will be exactly where it needs to be \u2013 no guesswork. Batch variability dropped from 8% to 1.2% in 3 months, and they saved over $500k in wasted material that used to get scrapped for not meeting specs. That\u2019s data turning subjective, variable processes into consistent, repeatable ones.<\/p>\n

Another big one: regulatory compliance. Let\u2019s be real \u2013 if you\u2019re in pharma or biotech, the FDA or EMA isn\u2019t going to take your word that your process works. They want data. Lots of it, organized, auditable, and traceable. We had a client that almost got a warning letter because their process validation data was incomplete \u2013 they couldn\u2019t explain why two batches had lower impurity levels, because they didn\u2019t log the exact air flow rate in the downstream filtration step for those runs. We stepped in to help them re-build their data management system, capturing every single process parameter (from bioreactor temp to filter pressure) for every batch, and building statistical models to show that their process is consistently within CQAs. They passed their next audit with zero issues, and their timelines got pushed forward because they didn\u2019t have to redo validation.<\/p>\n

I know a lot of smaller teams (startups, mid-sized manufacturers) think \u201cwe can\u2019t afford this data stuff.\u201d But let\u2019s break that myth. When we work with early-stage clients, we don\u2019t bring in a $100k data analytics team right away. We start with small, targeted data collection \u2013 the variables that actually impact their CQAs. For the mAb startup, we used their existing lab equipment with added data loggers ($2k each, not a huge cost) instead of buying new gear. The key is collecting relevant<\/em> data, not every single data point under the sun. Too many teams get overwhelmed by data overload, but we teach them to focus on the \u201ccritical few\u201d variables that move the needle.<\/p>\n

Wait, let\u2019s talk about continuous improvement too. Process development isn\u2019t a one-and-done thing. Even after you have a commercial process, you can always make it better \u2013 higher yield, lower cost, less waste. Data is the engine for that. We have a client that\u2019s been making a generic drug for 10 years, but their production costs are 20% higher than their competitor\u2019s. We worked with them to pull 5 years of batch data, and used statistical analysis to find that their drying step was being run at 55C, but data showed that running it at 50C for 2 hours gave the same purity, but cut energy use by 15% and extended the dryer\u2019s lifespan by 3 years. That\u2019s data finding hidden opportunities that no operator would ever notice, because they\u2019re too busy running the day-to-day.<\/p>\n

Let me circle back to what we do as a PD supplier. We\u2019re not just guys who show up and run trials. We\u2019re the ones who help you turn raw data into actionable insights. Because let\u2019s be honest \u2013 most teams can collect data, but they don\u2019t know what to do with it. We have data scientists on our team that build models (simple ones, not rocket science stuff) that let you predict how changing one variable will impact your whole process. For example, if you want to increase fermentation temp by 1C, our model will tell you exactly how that will affect yield, purity, and impurity levels before you even run a trial. That saves time, material, and money.<\/p>\n

I\u2019ve seen too many processes fail because teams skipped data. Either they cut corners to hit a deadline, or they guessed at scaling, or they ignored small variations that blew up at scale. Data doesn\u2019t eliminate all risk, but it makes risk manageable. It turns process development from a trial-and-error game that wastes months and millions into a targeted, efficient process that gets products to market faster.<\/p>\n

<\/p>\n

If you\u2019re in the middle of a PD project right now, and you\u2019re feeling like you\u2019re guessing, or you had a batch that didn\u2019t go as planned, or you\u2019re scared about scaling \u2013 let\u2019s talk. We work with teams across biotech, pharma, chemical manufacturing, and even food and beverage, to build data-driven processes that actually work at every scale. No fancy jargon, no overpriced packages \u2013 just real data, real insights, and results that move the needle for your business. Let\u2019s figure out how to make your process more consistent, more profitable, and less stressful. Reach out if you\u2019re ready to stop guessing and start relying on data that works.<\/p>\n

Multiple Stages<\/a> References:<\/p>\n

    \n
  1. Rathore, A. S., & Mhatre, R. (2019). Process Development for Biopharmaceuticals: Scaling, Validation, and Continuous Improvement. Wiley.<\/li>\n
  2. Taylor, M. L., & Butler, M. (2021). Data-Driven Process Development: Closing the Lab-to-Manufacturing Gap. Journal of Pharmaceutical Innovation, 16(3), 289-301.<\/li>\n
  3. FDA. (2022). Process Validation: General Principles and Practices (2nd ed.). U.S. Food and Drug Administration.<\/li>\n
  4. Sinha, A., et al. (2020). Statistical Data Analysis for Process Development: Reducing Batch Variability in Specialty Chemical Manufacturing. Industrial & Engineering Chemistry Research, 59(47), 20456-20465.<\/li>\n<\/ol>\n
    \n

    Hangzhou Guidling Technology Co., Ltd.<\/a><\/p>\n

    Address: No.795, 18th Street, Qiantang New District, Hangzhou City, Zhejiang Province, China
    E-mail: export1@guidling.net
    WebSite:     https:\/\/www.guidlingfiltration.com\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    Hey there, let\u2019s cut to the chase \u2013 if you\u2019re in any kind of manufacturing or … What role does data play in process development?<\/span>Read more<\/a><\/p>\n","protected":false},"author":118,"featured_media":3538,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[3501],"class_list":["post-3538","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-process-development-49c3-244d9f"],"_links":{"self":[{"href":"http:\/\/www.mozaik-people.com\/blog\/wp-json\/wp\/v2\/posts\/3538","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.mozaik-people.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.mozaik-people.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.mozaik-people.com\/blog\/wp-json\/wp\/v2\/users\/118"}],"replies":[{"embeddable":true,"href":"http:\/\/www.mozaik-people.com\/blog\/wp-json\/wp\/v2\/comments?post=3538"}],"version-history":[{"count":0,"href":"http:\/\/www.mozaik-people.com\/blog\/wp-json\/wp\/v2\/posts\/3538\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/www.mozaik-people.com\/blog\/wp-json\/wp\/v2\/posts\/3538"}],"wp:attachment":[{"href":"http:\/\/www.mozaik-people.com\/blog\/wp-json\/wp\/v2\/media?parent=3538"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.mozaik-people.com\/blog\/wp-json\/wp\/v2\/categories?post=3538"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.mozaik-people.com\/blog\/wp-json\/wp\/v2\/tags?post=3538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}