Biopharma process simulation

Biopharma process simulation from upstream to facility fit.

Evaluate mAbs, recombinant proteins, vaccines, and advanced biologics with an explicit seed train, production mode, harvest, purification, viral safety, formulation, filling, utilities, cleaning, release, and evidence model.

Model a biopharma process See facility scheduling

Biology, equipment, clean utilities, and regulated operations share one design basis.

Media + bufferSeed trainProduction BRHarvestCaptureViral safetyPolishingUF/DF + fill

Upstream

Scale and mode

Fed-batch versus perfusion, viable-cell density, titer, OUR, kLa, feeds, metabolites, bleed, and harvest logic.

Downstream

Capacity and yield

Clarification, filters, chromatography cycles, pool tanks, buffers, hold times, viral clearance, UF/DF, and formulation.

Facility

Fit and transfer

Campaign scheduling, reusable assets, CIP/SIP, single-use changeover, rooms, WFI, clean steam, QC, and release.

Acatian biopharma flowsheet with connected unit operations — **Editable production architecture**Open every equipment item, stream, reaction, assumption, and data gap.

Acatian biopharma plant readiness overview — **Review readiness**Separate implemented logic, screening assumptions, missing evidence, and validation work.

Engineering model basis

What a decision-useful biopharma simulation must resolve.

A credible model links biological performance to downstream cycles, shared assets, release constraints, cost, and evidence quality. A titer assumption alone cannot establish annual throughput or facility fit.

Inputs and equations

Define viable and total cell density, growth and death rates, specific productivity, glucose and glutamine uptake, lactate and ammonium formation, oxygen demand, gas transfer, osmolality, volume additions, harvest, bleed, product degradation, and quality-critical trajectories. For each pool, reconcile component accumulation, reaction, transfer, hold-up, sampling, and loss.

Downstream and facility data

Provide harvest-titer distributions, filter capacities, column diameter and bed height, dynamic binding capacity, cycle yield, clean and dirty hold times, buffer recipes, viral-inactivation window, membrane area, flux, diafiltration volumes, formulation demand, filling rate, room occupancy, labor, WFI, clean steam, and CIP/SIP recipes.

Outputs for a technical decision

Review batch trajectories, per-step and overall yield, chromatography cycles, resin lifetime, buffer and single-use demand, equipment occupancy, queue and hold-time violations, achievable batches per year, COGS intervals, utility peaks, waste, LCA inventory, sensitivities, and every criterion that prevents the model from being decision-ready.