Novasign Studio for CDMOs

The bottleneck is rarely expertise. It is the overhead built into how process development has always worked.

• Faster platform fitting and fewer runs for optimization and characterization

  Build a workflow around your platform process to reduce the experimental effort early development needs. Process optimization and characterization can easily consume more runs than necessary when experiments are planned without a model to guide them. Novasign replaces that with model-guided experimental planning, tailored to your specific process question and refined as data comes in. Model-guided planning has reduced experimental effort by 35–70% in our projects.

• Scale-up and technology transfer prediction

  Scale-up is where a CDMO can demonstrate the strength of its process development strategy. Novasign integrates CFD-derived parameters such as kLa, mixing time, shear rate and power per volume (P/V), from its technology partner SimVantage, directly into process models, so process performance at the target scale can be predicted in silico before a single engineering run. This adds a quantified, model-based layer on top of existing engineering rules and experience rather than replacing them.

  How Arkeon scaled up from 2 L to 150 L

• Single unit operations or the full bioprocess chain

  Novasign Studio works at the scale you need. Where several unit operations are in scope, upstream process parameters feed directly into downstream modelling for chromatography and TFF. That makes the influence of earlier steps on downstream performance visible and quantifiable, which is something often assumed and rarely shown.

  What Studio models

• Process characterization

  Once the objectives are defined, the question becomes which critical process parameters drive variability in critical quality attributes, and within which ranges the process stays in specification. Model-based characterization establishes CPP to CQA linkage with fewer runs than a standard design of experiments, and gives the characterization package you hand to a sponsor a stronger basis.

  Design of experiments in Studio

• Process knowledge that travels with the team

  Valuable process knowledge sits in spreadsheets, local scripts and individual expertise. Novasign Studio gives every team member a structured way to build on what a colleague has already developed, so expert knowledge is shared, reused and extended across programs instead of staying with one person.

• 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. 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 of the deviation. Studio supports the scientific investigation behind a deviation; the CAPA record stays in your quality system.

• For directors of MSATEvery client program starts from scratch, even when you have done something similar before.You have run something close to this before. The data exists, the decisions were made, and somewhere there is a person who remembers why. What does not exist is a way to pick that up and apply it to the program that started this month, so the work gets done again instead of reused. Novasign Studio captures the data processing, the model and the reasoning behind a process decision as a workflow, so the next engagement begins from what the last one produced.

• Build efficiency and reuse across every program

  Reduce manual data wrangling and inconsistent interpretation across upstream and downstream programs. Give process teams reusable workflow support without adding modelling overhead or specialist resource requirements. Each workflow built today becomes a template for the next program. The workflow is what carries over; each sponsor’s data and models stay in its own program.

• Stand out as a digitally capable partner

  Differentiate from classic asset-driven CDMOs. Novasign Studio gives you a structured digital layer that makes process decisions faster, more consistent and easier to justify, without overhauling your existing infrastructure. The result is a differentiator you can show a sponsor rather than describe.

Novasign Studio is built for teams that need a practical, end-to-end workflow, from experimental data through modelling and simulation to better process decisions.

• Against internal scripts and local models

  Move from person-dependent analyses to reusable, structured workflows your whole team can review, apply and build on.

• Against cloud-based bioprocess modelling platforms

  Keep full flexibility in deployment, workflows and model integration, including fully on-premises setups where data governance requires it.

• Against standalone solutions

  Use Novasign Studio as an integrated workflow platform and as a client-facing tool in sponsor interactions.

• Against pure mechanistic or pure ML modelling tools

  Hybrid models are supported across multiple unit operations: the mechanistic structure you give, and the kinetics the network learns from the data you have. Connect unit operations so a change propagates through the whole workflow.

The right starting point if you want concrete results. We work with you on a scoped proof of concept using your own process data, so you see what Novasign Studio does in your context rather than on a generic example.

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 to begin with a proof of concept or go straight to a license, deployment and data ownership, and how results and confidentiality work when several client programs run side by side, are all answered in the frequently asked questions.