Rx License-Rx

TAB-2173

Mouse Anti-Mouse CXCL9 (Mig) Monoclonal Antibodies

This technology describes monoclonal antibodies against mouse chemokine (C-X-C motif) ligand 9 (CXCL9), also known as Monokine induced by gamma interferon (Mig). CXCL9 is a secreted protein that functions to attract white cells and increased expression of CXCL9 has been linked to several diseases. The inventors at the NIH generated over 100 anti-mouse CXCL9 antibodies from a CLXL9/Mig knockout mouse and further characterized several antibodies to show neutralization of CXCL9. As such, these antibodies could be used to measure concentrations of mouse CLXL9 in laboratory samples and block the activity of CXCL9 in injected mice. These antibodies are suitable for ELISA and Western blot. The antibodies have not been tested in flow cytometry or immunohistochemistry, but may also be useful for these applications. Commercial applications: ELISA assays for detection and measurement of CXCL9.. Neutralization of CXCL9 activity in mouse models...

Intelligence Memo

Owner: National Institutes of Health

Core category: Therapeutics

Therapeutic area: Oncology

Indication: Oncology

Modality: Biologic

Focus tags: Oncology, Immunology

Technology tags: Biologic, Diagnostic / Biomarker

Mechanism:

Development stage: Preclinical

Patent status: Research Material

Availability: Available for license

Plain-English Licensing Breakdown

This is a license opportunity for a therapeutic asset or drug-enabling technology in Oncology. In plain English, the buyer would be licensing science that could become a treatment program, usually after more validation. The current package appears to be preclinical and is associated with National Institutes of Health. The practical first use case is Oncology. Public description: This technology describes monoclonal antibodies against mouse chemokine (C-X-C motif) ligand 9 (CXCL9), also known as Monokine induced by gamma interferon (Mig). CXCL9 is a secreted protein that functions to attract white cells and.

What is exciting

Already past pure discovery: Preclinical validation gives a buyer something concrete to reproduce, optimize, or package into an IND-enabling plan.

Oncology remains highly partnerable: Pharma buyers still pay attention when an asset can be tied to biomarkers, combinations, resistance biology, or a defined tumor segment.

Can sell into pharma before reimbursement: A biomarker or AI tool can create value as trial enrichment, patient stratification, or translational support before becoming a regulated diagnostic.

The License-Rx pivot is the real unlock: The exciting version is not just the university pitch; it is the focused path: Monetize first as a pharma enrichment engine, not a reimbursed diagnostic

Negatives / diligence concerns

Translation still unproven: Animal or lab data may not predict human performance; tox, PK/PD, CMC, and indication selection still need diligence.

Validation can be harder than the demo: Models and biomarkers need locked datasets, external validation, clinical utility, data rights, and a regulatory/reimbursement plan.

Competitive field may be crowded: Oncology buyers will ask why this is better than existing modalities, combinations, and biomarker strategies already in the clinic.

Risk Flags

  • Human validation and clinical path require diligence.
  • Patent scope and remaining exclusivity need review with counsel.
  • Inventor readiness and licensing terms are not yet verified.

Strategic Pharma Attractiveness

Large pharma would care if this becomes more than an interesting university-originated technology: it needs a crisp Oncology wedge, a measurable value inflection, and a diligence package that makes the first deal feel like an option on upside rather than a blind research bet.

Most logical pharma targets Merck — Checkpoint-franchise adjacency and combination-trial appetite. AstraZeneca — Oncology breadth plus interest in biomarker-defined populations. Roche / Genentech — Diagnostics plus oncology translational machinery.

Development Strategy to Increase PoS

First indication: Oncology

Study design: Retrospective locked-dataset validation followed by one prospective pharma enrichment pilot.

Key experiments Validate the AI-optimized pivot: Monetize first as a pharma enrichment engine, not a reimbursed diagnostic Run independent replication of the core claim with pre-specified success criteria Generate a partner-facing risk register that separates solved, testable, and unresolved risks

Final Recommendation

Proceed: Strong enough to test buyer appetite now while validating the cheapest decisive experiment. The most investable version is: Monetize first as a pharma enrichment engine, not a reimbursed diagnostic

Best next experiment: Run the smallest independent study that validates: Package the model or assay with a locked validation dataset, CLIA/service workflow, and one sponsor-ready use case.

Best licensing timing: Begin BD conversations after the next validation package; pursue a license, option, or asset sale once the first value inflection is visible.