The Biologics
COGS Estimator

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.

Bispecific antibodies are modeled as a sub-modality of mAb with parameter adjustments reflecting the typical reality: lower expression titer (~45% of equivalent mAb), reduced overall DSP yield (~78%, from additional homodimer/aggregate removal), and elevated QC overhead (1.35×). Other cost drivers and the rest of the workflow are inherited from the mAb engine.

mRNA therapeutics are built around in vitro transcription, with raw-material costs (cap analog, modified nucleotides, template plasmid, enzymes) typically dominating the cost stack. An optional LNP formulation layer adds ionizable lipid, helper lipids, microfluidic mixing, and an encapsulation-loss term, and typically lands at roughly similar magnitude to the DS cost.

Oligonucleotide therapeutics are modeled with two sub-modalities reflecting the same underlying solid-phase phosphoramidite chemistry but materially different manufacturing realities. ASO (Spinraza/Tegsedi-class) is a single-strand synthesis with heavy phosphorothioate backbone and 2′-MOE modifications; per-step coupling yield compounds across (length − 1) cycles. siRNA (Alnylam-class) is a duplex of two strands — both must be synthesized in parallel, purified independently, then annealed; per-step yield compounds across each strand separately, an annealing yield is applied, and material consumption (amidites, support, cycle reagents, purification) is approximately doubled per gram of finished duplex. GalNAc conjugation is an optional add-on for hepatocyte-targeting siRNAs and select ASO programs.

RNA defaults are calibrated for clinical to early-commercial scale — the audience the tool is most useful for (small biotechs pricing first programs, BD teams evaluating clinical CDMO quotes, investors sanity-checking early-stage pitch decks). Mature commercial-scale RNA therapeutics cost meaningfully less per unit than these defaults suggest. For reference: published estimates of pandemic-scale mRNA vaccine manufacturing land at roughly $0.50–$3.00 per dose (Light & Lexchin 2021; Kis et al. 2022), and Alnylam-class commercial GalNAc-siRNA is widely estimated at $200–$800 per gram of finished drug substance. To model larger commercial scenarios, increase IVT scale (mRNA) or synthesis scale (oligo), increase batch frequency, and reduce per-batch consumable assumptions to reflect bulk contracting. The default per-gram numbers will drop 5–20× depending on the magnitude of scale-up.

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, cap analog, phosphoramidites), 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