Accelerate your bioprocess development.
Demonstration of Inline Particle Analysis PAT for Bioprocess Harvesting

Webinar: Demonstration of Inline Particle Analysis PAT for Bioprocess Harvesting

In this webinar, we discuss how inline image analysis in real-time gives insights into process mecha...

Green Solvent Swap Distillation

Webinar: Green Solvent Swap Distillation

Solvent swaps are common operations within a synthetic step to setup the following reaction, extract...

Scale-up of Batch Crystallization From Lab to Plant

Batch Crystallizer Scale-Up and Design

Scale-up of crystallization is notoriously complicated and companies are under pressure to develop s...

Effective Design of Experiment Studies

Effective Design of Experiment Studies

This paper describes the Design of Experiments (DoE) approach and how it is used to identify the rel...

What does Dynochem Biologics software do?

Dynochem Biologics captures decades of fluid mixing knowledge and contains an integrated database of your equipment to perform rapid and accurate mixing assessments, including mass transfer, mixing time, power input, and shear. Users can also characterize equipment using their data.

Users can characterize and optimize cell growth, nutrient uptake, titer, and metabolite concentration for upstream applications, including oxygen transfer, carbon dioxide stripping, and pH level.

For downstream applications, users can develop custom models or choose from the library covering harvest, depth filtration, chromatography, viral inactivation, TFF, UF/DF (including gel polarization), and lyophilization.

Similar tools are included for ancillary operations such as media and buffer preparation operations. 

Why is this bioprocessing software valuable?

Predict, optimize, and scale critical unit operations using digital twins that leverage your process and analytical data and combine it with institutional knowledge. For example:

  • Ensure correct selection of agitation conditions and gas flow rates to achieve the required oxygen transfer rate (OTR) at any scale
  • Predict glucose concentrations and feeding strategies to optimize viable cell count; use models predictively to develop control strategy and at-line to ensure process robustness
  • Predict cycle times/diavolumes needed in UF, DF, and up-concentration operations, including buffer fraction; anticipate changes due to membrane module vendor and scale; ensure adequate agitation in the product collection vessel 
  • Predict appropriate addition rates and mixing conditions to ensure correct pH during virus inactivation to ensure successful inactivation while limiting protein denaturation and aggregation risk