Rx License-Rx

TAB-4254

Anti-CD133 Monoclonal Antibodies as Cancer Therapeutics

Most early work on CD133 was carried out using one of two monoclonal antibodies (mAbs), AC133 and AC141, which recognize an undefined glycosylated epitope of CD 133. Researchers from NCI's Pharmacodynamic Assay Development and Implementation Section generated novel anti-human CD133 monoclonal antibodies from large extracellular domain loops of CD133 using peptide residues selected from the native extracellular domains of CD133 protein as an immunogen. They selected sequences for immunization that do not overlap with known glycosylation sites. Peptide antigens comprising the amino acids in the extracellular domain were synthesized and conjugated to carrier proteins as the immunogen. A key step was screening for specificity using peptides and expressed recombinant extracellular domains of CD133. The resulting antibodies recognize both glycosylated and non-glycosylated regions of the cognate antigen. The inventors have demonstrated the...

Intelligence Memo

Owner: National Institutes of Health

Core category: Therapeutics

Therapeutic area: Oncology

Indication: Oncology

Modality: Biologic

Focus tags: Oncology, Immunology

Technology tags: Biologic, Drug Delivery, Diagnostic / Biomarker

Mechanism:

Development stage: Early / Discovery

Patent status: Expired; Abandoned; Issued

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 early / discovery and is associated with National Institutes of Health. The practical first use case is Oncology. Public description: Most early work on CD133 was carried out using one of two monoclonal antibodies (mAbs), AC133 and AC141, which recognize an undefined glycosylated epitope of CD 133. Researchers from NCI's Pharmacodynamic Assay Development and.

What is exciting

Early enough to shape the whole strategy: Because the asset is still early, a licensee can choose the best indication, data package, CRO path, and partnering story before heavy spend.

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.

Delivery can refresh known biology: A better route, depot, local exposure profile, or targeted formulation can create new IP and reduce systemic risk around existing mechanisms.

Negatives / diligence concerns

Very early technical risk: The asset likely still needs independent replication, translational validation, and a clear go/no-go experiment before a serious license fee is justified.

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

Exposure advantage must be real: Delivery stories fail when biodistribution, local tolerability, stability, or payload compatibility does not beat simpler alternatives.

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.