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A Tool for Process Economics

The Biologics
COGS Estimator

Transparent, source-anchored manufacturing cost modeling for monoclonal antibodies, recombinant proteins, antibody–drug conjugates, and cell & gene therapy — from clinical to commercial scale.

Version v1.2 · 2026
Process & facility assumptions
0Manufacturing grade
GMP baseline. Switch grade to model preclinical/tox or research-grade economics using category-specific multipliers — savings concentrate in QC/QA and facility allocation, not raw materials.
1Program
Industry median ~3–5 g/L; best-in-class >8 g/L
2Upstream
Bag, tubing, filter sets per batch
3Downstream
Capture → Polish → VI → UF/DF
Typical lifetime 100–200 cycles
4Facility & Labor
Depreciation + utilities + maintenance
DPDrug Product (Fill/Finish)
Estimated cost of goods
$— per gram GMP
—
—
Batches / Year
—
Capacity (kg/yr)
—
Yield / Batch
Cost Composition
Sensitivity · Titer × Scale
$/g at varying titer (rows) and bioreactor scale (cols). Current configuration highlighted.

How this model works

The estimator allocates manufacturing costs across four categories — upstream (media, feed, consumables, inoculum), downstream (chromatography, filtration, buffers, single-use), facility & labor (depreciation, utilities, FTEs, QC/QA), and raw materials. For ADCs, a fifth category captures linker–payload cost and conjugation processing, which typically dominates the cost stack.

Total COGS per gram is derived from batches per year, yield per batch (scale × titer × DSP yield × success rate), and annual fixed-cost allocation. The sensitivity matrix recomputes the model across titer and scale at constant downstream and facility assumptions, exposing the two largest leverage points in mAb economics.

Drug product (fill/finish) is modeled as an additive layer: vials per kg derived from concentration and fill volume, with vial cost and DP yield loss applied to the DS COGS.

Cell & gene therapy is modeled separately on a per-dose basis. AAV economics center on plasmid cost, USP yield (vg/L), and overall DSP yield. Autologous CAR-T is modeled as per-patient batch economics with explicit failure-burden accounting — failed manufacturing runs still incur the material and labor cost they consumed.

Manufacturing grade (GMP, Preclinical/Tox, Research) is modeled with category-specific multipliers rather than a flat factor. Savings concentrate in QC/QA (8–30% of GMP), facility allocation (30–55%), and modality-specific high-cost inputs (plasmid, payload, vector), while raw materials see more modest reductions (40–65%). Net total reductions land roughly at 0.45–0.55× GMP for Tox grade and 0.15–0.25× for Research grade, varying by modality.

Input fields remain GMP-anchored regardless of grade selection. When the grade toggle is switched to Tox or RG, the displayed input values (media cost, payload cost, plasmid cost, etc.) do not change — they represent the GMP reference, and the grade multipliers are applied internally to produce the lower-grade output. This is deliberate: it keeps inputs comparable across grades and lets the sensitivity matrix remain interpretable. Treat displayed inputs as the GMP baseline and the headline COGS as the grade-adjusted result.

Default assumptions sourced from

Farid, S. S. — Process economics of industrial monoclonal antibody manufacture (J Chromatogr B, 2007, 848:8–18. doi:10.1016/j.jchromb.2006.07.037)
Kelley, B. — Industrialization of mAb production technology (MAbs, 2009; Biotechnol Prog updates)
Pollock, J., Coffman, J., Ho, S. V. & Farid, S. S. — Integrated continuous bioprocessing: economic, operational, and environmental feasibility for clinical and commercial antibody manufacture (Biotechnol Prog, 2017, 33(4):854–866. doi:10.1002/btpr.2492)
Sinclair, A. & Monge, M. — How to evaluate the cost impact of using disposables in biomanufacturing (BioPharm Int, 2008, 21(6):26–30)
Walsh, G. — Biopharmaceutical benchmarks (Nat Biotechnol, 2018, 2022 surveys)
v1.2 — illustrative defaults. This tool produces order-of-magnitude estimates intended for early-stage planning, BD scenario analysis, and comparative discussion. Default parameters reflect published industry ranges but have not been calibrated against any specific facility or program. Validate against your own process data and CDMO quotes before relying on outputs for material decisions.
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