Reaching Net Zero Profitably in Biomanufacturing:
Aligning Teams and Making Decisions with Performance, Cost and Sustainability in One View
Product: Recombinant bovine β-lactoglobulin at 100,000 L scale | Host: Pichia pastoris Relevant for: R&D Directors · Heads of Process Development · Operations Directors · Sustainability Directors · Net Zero Teams
At a glance
Biomanufacturers committed to net zero face a recurring problem: performance, cost and sustainability are assessed separately – so the decisions that matter most are made before all three dimensions are visible together. The cost of this disconnect shows up later: capital committed to a route that turns out to be commercially unviable, sustainability gains quietly cancelled by an unrelated process decision, or an assumption about where to focus sustainability investment that turned out to be wrong once the full picture was visible.
This application note shows how connecting the three views can change which decisions get made and when, using an illustrative β-lactoglobulin production scenario at 100,000L scale. In this case, three downstream configurations are compared on yield, unit cost and 16 LCA impact categories under consistent assumptions, followed by an evaluation of operational and longer-term levers including CIP optimisation, energy procurement, and sourcing location.
The Challenge in Biomanufacturing
Biomanufacturers with net zero commitments face the difficulty of hitting performance and cost targets, especially when raw material and energy prices are fluctuating. The practical difficulty is that these three dimensions are usually assessed separately and sequentially — experimental campaigns, techno-economic analyses, and life cycle assessments each run in isolation, often by different teams, at different points in the development cycle.
General LCA software can calculate the environmental impact of an existing process inventory, but it cannot simulate the process itself: change a parameter — the molecular weight cut-off of a membrane (MWCO) , a buffer strategy, a carbon source — and the inventory has to be rebuilt manually before any environmental answer comes back, with no view of what the change does to yield or unit cost. So even when an LCA catches a problem (typically $50,000–$100,000 and several months after commissioning), the underlying process decisions that drove it may already be locked in — in equipment choices, supplier contracts and facility commitments. Revisiting them means revalidation, new capital, and sometimes regulatory resubmission.
Bioprocess Foresight is built for dynamic decision making. The questions that matter in practice - which membrane, which buffer strategy, which sourcing route — can be answered across yield, unit cost and environmental impact at the same time. What used to take three separate studies and several months can be explored dynamically on our platform— change an assumption and instantly see the full consequence. The platform achieves this by simulating the process itself, with a hybrid approach that combines mechanistic models of mass balances and unit operation physics with machine learning that adapts to a specific process — the equipment, the materials, the dynamics that generic models miss. A change in any process configuration — membrane choice, buffer volume, CIP regime, unit operations — is propagated through the full process in a single operation, with consequences across all three dimensions visible in the same view.
Selecting between three process configurations
Three downstream process variants were modelled, all producing bovine β-LG from a 100,000 L Pichia pastoris fermentation with glucose as the carbon source. Before optimising any single process, the first decision is which downstream architecture to take forward — and that choice locks in cost, yield and sustainability outcomes for years. Fermentation performance and production scale were held constant, so the comparison isolates the impact of downstream architecture choices on commercial outcomes. The shared downstream sequence is disc-stack centrifugation, ultrafiltration/diafiltration, and spray drying. The processes differ in the UF membrane MWCO and the corresponding buffer volume of sodium phosphate used during diafiltration.
Membrane MWCO affects product recovery, purity, and the total volume of process fluid handled - which in turn affects cleaning requirements, energy consumption, and waste generation. When modelled through the full process chain, these parameter choices cascade into measurable cost and sustainability differences.
These three configurations were evaluated simultaneously in Bioprocess Foresight under consistent assumptions — yield, unit cost, and 16 environmental indicators (13 ISO 14040/44 impact categories plus energy, water and waste) in a single modelling session, rather than requiring separate experimental campaigns, TEA studies and LCA assessments for each route.
How the optimisation works
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Model: The baseline process is modelled on our platform with target optimisation goals defined.
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Calibrate: Existing experimental data is uploaded to tune the model to the specific equipment & operating conditions.
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Optimise: The platform simulates thousands of scenarios per cycle, recommending optimal parameter sets with clear trade-offs between yield, cost, and purity.
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Experiment: The team runs targeted experiments based on the platform’s recommendations.
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Refine: New experimental data is uploaded, the model improves, and the next cycle narrows in further.
Results
Over 12 months and four optimisation cycles, Multus transformed the economics of their growth factor purification:
8.6x
increase in product recovery
55%
reduction in production costs
37.4%
reduction in processing time
2.1x
fewer experiments vs DoE
Production costs reached commercially competitive levels. Product concentration more than doubled. The 92% reduction in experiments compared to a traditional Design of Experiments (DoE) approach meant the full optimisation was achieved with just eight datasets.
“With New Wave we have been able to create an 8X increase in yield and speed up R&D within a year, which is impressive. As a young company, it was really important for us to have strong support and New Wave were always on hand to answer questions and tackle challenges. Together we were really able to test the capability of the platform and learned a lot in the process.”
— Brandon Ma, Senior Scientist, Multus
What this enabled
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Commercially viable unit economics: costs reduced to levels that support scaled production and competitive pricing
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Early elimination of unviable routes: the platform identified which processes could and couldn’t reach target performance before committing resources to scale-up
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Batch-adaptive DSP: parameters tuned to each batch’s specific upstream output, not just optimised for the process in general
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Accelerated R&D timeline: four optimisation cycles within a year, with only eight datasets required
Relevance beyond growth factors
The downstream challenges in this project — membrane fouling, batch variation, process route lock-in — are not unique to growth factor purification. They apply wherever large molecules are purified through filtration, centrifugation, or related unit operations.
The Bioprocess Foresight platform now covers 18 unit operations across the full downstream processing train, with integrated Techno-Economic Analysis and ISO 14040/44-aligned Life Cycle Assessment. Companies producing recombinant proteins, industrial enzymes, yeast-derived proteins, fermentation-derived food ingredients, or lipid products face structurally similar DSP decisions — and the same optimisation approach applies.
"Combining AI and real-world data collection to produce cheaper and more scalable growth media ingredients helps us put our customers one step closer to making widely available and affordable cultivated meat a reality.”
— Cai Linton, CEO and Co-Founder, Multus
