Built for a pre-AI world
Traditional CDMOs are trusted to scale biologics for the clinic, but their workflows are slow, costly and experiment-heavy by design.
AI-native CMC development · Bioprocess scale-up
Eliminating the manufacturing bottleneck in drug development
Bioqore pairs proprietary Bayesian AI with an automated wet lab to find robust, scalable process conditions for biologics. Predicted by models, proven by experiment, and delivered as data your team can act on.
- US-built
- Peer-reviewed method
- Your data stays yours
7‑14 days
TO ACTIONABLE INSIGHTvs 6–12 weeks at legacy CDMOs
3–30×
FEWER EXPERIMENTS REQUIREDvs traditional DoE approach
3mL - 80,000L
MODELED SCALE RANGEbench to commercial scale-up
IND‑ready
CMC DATA PACKAGEFDA-compatible deliverables
Backed by
- MaC Venture Capital
- Adverb
- OV/V
- Epsilon
- a16z speedrun
- Z Fellows
- Pareto
The bottleneck
The science is ready. The manufacturing isn't.
Drug development doesn't stall in discovery or in the clinic. It stalls in between, when a promising biologic has to become a manufacturable one. Cell lines, process conditions and scale-up still depend on slow, broad experimental campaigns, and the lessons from one program rarely carry to the next.
Stage
Discovery
The bottleneck
Manufacturing
Stage
Clinical Trials
Prediction without proof
Digital-only platforms offer models without physical validation. That's half a solution, and your team still has to run the experiments.
How it works
A frictionless handoff from sample to actionable data
No changes to how your team works. You send the candidate and we close the loop.
You provide
Your drug candidate, shipped under MTA, with your manufacturing goals, operating boundaries and any prior experimental data.
We cycle
- HHarmonize: our AI unifies your prior data and maps the full design space.
- PPredict: Bayesian models identify the most promising, unexplored conditions, each with 95% confidence intervals.
- VValidate: our automated lab tests those predictions physically, and results feed the next cycle.
We deliver
A structured CMC data package with standardized protocols and robust operating ranges, built to support your IND-enabling work and scale-up. You own every result.
Timelines scale with program complexity, from micro-scale screens in days to full validation runs in weeks.
A staged path to scale with no surprises at handoff
Six stages, each a defined 2–4-week cycle ending in a report and a go/no-go decision.
- Stage 1
High-throughput media testing
HT screen - Stage 2
HT microbioreactor runs
100 µL – 3 mL - Stage 3
Flask bioreactors
50–250 mL - Stage 4
Standard bioreactors
500 mL – 1 L - Stage 5
Bioreactor runs
2 L · 10 L · 50 L - Stage 6
Data package and strain handoff
Handoff
If a clone's performance diverges at intermediate scale, the model flags it before significant capital is committed, not after.
Platform validation
Validated end-to-end, with active drug candidates
Case study · Mammalian cell line developmentLeadgene Biosolutions
From single cell to 1 L bioreactor readiness in 3 months
Before a biologic can enter the clinic, a team has to find the one clone that will produce it reliably at scale. That typically takes 12–15+ months and 3+ rounds of trial and error, because the clones that look best in early screening often fail at production volumes. Bioqore's models surfaced a counterintuitive pattern: moderate early performers scale better. Applying that prediction at single-cell selection collapsed months of trial and error into a single run.
3 monthsCHO cell line development through 1 L bioreactor readiness
vs 9–16+ monthsthrough a comparable CDMO engagement
Fixed pricebelow the comparable CDMO cost range
Time to 1 L bioreactor readinessCHO cell line development, months
“Bioqore is delivering biomanufacturing-ready therapies in weeks versus years, so lifesaving medicines reach patients that much sooner.”
Leadgene Biosolutions, via LinkedIn (March 2026)
Three therapeutic programs from pre-clinical to Phase 3
Bioqore's workflow has already generated CMC data for three therapeutic programs under active engagements with major therapeutics companies.
Program 1Phase 3
Clinical trial start targeted January 2027.
Program 2Pre-clinical complete
IND filing in process.
Program 3Pre-clinical complete
IND filing in process.
Partner and program identities withheld under confidentiality obligations.
Nature Communications (2025)
Bayesian optimization for bioprocess development.
Why Bioqore
Two half-solutions. Bioqore closes the loop.
| Capability | Digital-only software | Traditional CDMOs | Bioqore |
|---|---|---|---|
| AI-guided experiment selection | Often black-box ML | Limited | Bayesian, with 95% CIs |
| Physical wet-lab validation | No | Yes | Yes, in-house automation |
| Learning across programs | Siloed | Rarely | Closed-loop, compounding |
| Experimental efficiency | No physical runs | Broad DoE campaigns | 3–30× fewer experiments (peer-reviewed) |
| Recommendations | Point predictions | Empirical | Robust parameter ranges |
| Operations | Varies | Often offshore | US-built |
A compounding data moat
Every program adds validated data no software-only company can buy.
Physical proof others lack
Software-only players can't add a validated wet lab overnight, and CDMOs can't bolt on the model.
Domestic by construction
US operations for teams second-sourcing away from offshore suppliers.
Core offerings
One predictive engine. Endless possibilities.
Strain and cell-line engineering across microbial and mammalian systems. Our staged framework is active across CHO, HEK, Pichia pastoris and human immune-cell programs (PBMCs, CAR-T, NK cells).
CHOHEKPichia pastorisPBMCsCAR-TNK cells
Cell Line Development
Predictive clone selection and scale-up validation to identify stable, high-producing cell lines with greater manufacturing potential.
Protein-Protein Interactions
Expression and validation for AI-designed binders, with binding and developability data in a single pass.
Enzyme Development
Contract strain development and stability/half-life optimization for enzyme-replacement candidates.
Toxicology + Drug Safety
Variant panels (codon, glyco, PTM) and high-throughput validation to surface safety risk before human dosing.
Glycosylated Molecules
Chassis development and yield optimization for glycosylation-dependent biologics, including antibodies.
Advanced Food Science
Strain development and bench-to-pilot scale-up for alternative-protein and fermentation companies.
Our approach
Start with one molecule
- 01
Start
One program, one fixed scope, and results you can put to work (and, with your permission, a case study you can publish).
- 02
Expand
Parallel and repeat programs, with models that already know your process space.
- 03
License
Validated production strains and process packages, licensed or transferred on a per-asset basis.
- Coming next
Platform access
Predictive models deployed against your own environment.
Leadership
Built on deep science. Guided by decades of experience.
Josh Hinckley
CEO & Co-Founder
Computational biology and bioprocess development. Cornell PhD; former MIT and Broad Institute postdoc; World Economic Forum Davos honoree.
Janette Phi, MBA
Chief Business Officer & Co-Founder
30+ years commercializing life-science platforms; former CCO/CBO at Deepcell, ThinkCyte, IntelliCyt and ForteBio.
Our story
Bioqore was founded by Josh Hinckley to develop a therapeutic that could save his wife’s life after she was diagnosed with a rare cancer. That mission grew into a company aimed at fixing the manufacturing bottleneck that stands between promising drugs and patients.
Get started
Start with one molecule
One program, one fixed scope, and data you can act on. Tell us what you're developing.
Prefer email? janette@bioqore.ai
Investors: select Investor inquiry in the form.