History of Antibody Discovery

Since the 70s, scientists have progressively attempted to recreate the human immune system’s most powerful disease fighting mechanism: first through animal and chimeric hybridization and then humanized and recombinant forms.

All of these methods had shortcomings through immune system rejection, toxicity, prohibitive costs, or limited antibody diversity.

1978

Previous Limitations to Natural Human Antibody Discovery

1. Limited Lifespan of Antibody-Producing Cells Outside Body

2. Memory B Cell Rarity (0.001% of blood cells)

3. Signal among the noise - finding the most effective, relevant antibodies

Requires High Speed

Requires High Throughput

Requires High Resolution

How Our CellSpot Technology Works

The proprietary CellSpot discovery platform overcomes previous limitations to discovery and identifies the most effective antibodies naturally expressed in healthy donors and turns them into therapeutics.

Trellis monoclonal antibodies (mAbs) have demonstrated efficacy and safety in their original, healthy donors.

Trellis infectious disease mAbs have demonstrated greater potency (in vitro and in vivo) than those from competitors.

1. Human Donors Naturally Produce Antigen-Binding B-Cells

2. CellSpot Identifies Memory B-Cells with Most Effective Antibodies

Multiple US Patents Issued


Capable of Discovering Rare, Broad-spectrum Immunities


4 therapies in preclinical/clinical manufacturing stages

High Throughput

via drastically miniaturized assays

Ability to identify extremely rare antibodies

High Speed

via digital microscopy and custom software

Ability to screen millions of B-cells in weeks

High Resolution

via nanoparticle reagent technology

Ability to rapidly characterize antibody affinity and specificity

3. Target Antibody is Cloned and Therapies Generated

Target Memory B-Cell expressing most effective antibody

Single-cell cDNA Cloning


Efficacy Studies


Drug Development

Lead Candidate

Clinical Testing

Scaled Manufacturing

Patient-Ready Therapeutic

Non-Immune Patient

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