Novasign Studio for biopharma process development

• Historical data proof of concept

  The most common starting point is building a process model from data your team already has, such as campaign data from prior development runs, without generating a single new experiment. That lets your team evaluate what Novasign Studio produces in your specific context, with your own data, before any further commitment. The output is a working model and a clear view of which questions it can and cannot answer at this stage.

  How a study starts

• Upstream process optimization

  Model-based design of experiments for batch, fed-batch and perfusion processes, including seed trains. Rather than screening a predefined set of conditions, Novasign Studio builds a model from your existing data and identifies which untested combinations of process conditions are most likely to contain the optimum. Experimental effort goes to the runs most likely to be informative instead of being spread across a predefined grid.

• Process characterization

  Once the objectives are defined, the focus shifts to the design space: which critical process parameters drive variability in critical quality attributes, and within which ranges does the process stay in specification? Model-based characterization identifies CPP to CQA linkage with fewer runs than standard design of experiments, and gives the characterization package a stronger basis.

• Downstream development and robustness

  Filtration workflows including tangential-flow filtration in ultrafiltration and diafiltration mode, on a dedicated process unit. Chromatography workflows covering batch bind-elute and flow-through formats as well as continuous chromatography. Across all of them, model-based optimization and characterization establish robust operating windows with fewer runs.

• Scale-up and technology transfer prediction

  Scenario-based simulation lets the model predict large-scale performance before engineering or manufacturing runs happen, so those runs validate expectations rather than explore them. Where bioreactor geometry matters, CFD-derived parameters such as kLa, mixing time, shear rate and power per volume (P/V), from Novasign’s technology partner SimVantage, are integrated directly into the process model, which puts a quantified basis under the prediction. Process understanding is built to apply across scales, from lab through pilot to manufacturing.

  How Arkeon scaled up from 2 L to 150 L

• Deviation investigation support

  When a deviation occurs, the first problem is having enough traceable information to understand what happened and why. Reproducible data processing workflows mean every transformation step is documented and repeatable, so process and quality teams see not only the data but how conclusions were drawn, which process parameters were considered, and how the process was characterized at the time. Studio supports the scientific investigation behind a deviation; the CAPA record stays in your quality system.

• Cross-project workflow standardization

  Process knowledge built for one molecule does not have to stay with that molecule. Novasign Studio captures the experimental planning logic, the data processing steps and the model structure behind a process decision in a reusable workflow. The next program starts from an established baseline rather than from scratch, and the knowledge belongs to the team rather than to whoever developed it the first time.

• For VPs of process development, heads of bioprocess sciences and CMC directors

  Your team keeps redoing work it has already done.

  You have historical data, internal expertise, and development campaigns that produced real process knowledge. The problem is that the knowledge is not reusable: it lives in files, in tools, and in the heads of the people who built it. Without a structured way to capture it, it stays personal rather than shared, and the next team starts from scratch instead of building on what exists.

• For digital CMC leads, process modelling leads and data science leads

  Your team has models and scripts that only one person can use.

  Your team has invested real effort in digital capability: data processing scripts, analytical pipelines, models. The people who built them want that work to be useful to the broader team rather than locked to their own desk. Novasign gives it a structured home, so process teams apply it directly without needing the original author to explain, translate, or be in the room.

• For process scientists, research scientists and USP or DSP process engineers

  You need a modelling workflow you can run yourself.

  You know the biology and you know which questions need answering. What you need is a workflow you can drive yourself, without waiting for a data scientist to be available or asking them to rerun the analysis every time something changes. Novasign Studio gives you a no-code interface for day-to-day process work, with the models and scripts your colleagues built already integrated and ready to use.

Build on what you already have. Your organization has valuable code, models, datasets and scientific expertise. The challenge is not a lack of digital capability, it is making that capability usable, reproducible and accessible beyond the person who created it. Novasign Studio is designed to accelerate and scale what you are already building.

The right starting point if you want concrete results. We work with you on a scoped proof of concept using your existing process data, so the outcome is relevant to your specific context from the first step.

1. 1

   Scoping call

   We sign an NDA with you. Then you define one process challenge and one unit operation or workflow to focus on.

2. 2

   Data review

   Novasign reviews your existing data and defines what the proof of concept can produce, at no cost up to this point.

3. 3

   Proof of concept

   We build and run the workflow in Novasign Studio together with you.

4. 4

   Results

   Clear outputs, a defined scope, and your decision on how to proceed.

5. 5

   Handover

   The project is handed over to you in Novasign Studio, so your team continues from where the proof of concept ended.

What you need to get started, how many experiments it takes, whether a data science team is required, whether to begin with a proof of concept or go straight to a license, and how deployment, transparency and model ownership work, are all answered in the frequently asked questions.