A-muto suggests many drug programs fail due to toxicity from hitting the wrong epitope, not a flawed biological concept. By identifying and targeting a structural epitope unique to the diseased state of the same protein, these previously abandoned but promising therapies could be salvaged.
The company focuses on disease-specific 3D protein conformations, which exposes new binding sites (epitopes) not present on the same protein in healthy cells. This allows for highly selective drugs that avoid the toxicity common with targets defined by genetic sequence alone.
To overcome on-target, off-tumor toxicity, LabGenius designs antibodies that act like biological computers. These molecules "sample" the density of target receptors on a cell's surface and are engineered to activate and kill only when a specific threshold is met, distinguishing high-expression cancer cells from low-expression healthy cells.
Instead of patenting a specific molecule, Alt-Pep underwent a decade-long process to patent the novel alpha-sheet protein structure itself. This unconventional IP strategy gives them a powerful, defensible platform applicable across numerous amyloid diseases, not just a single target composition.
A-muto's CEO argues that shaving months off discovery isn't the real prize. The massive cost in drug development comes from late-stage clinical failures. By selecting highly disease-specific targets upfront, their platform aims to reduce the high attrition rate in clinical trials, which is the true driver of cost and delay.
Traditional methods like crystallography are slow and analyze purified proteins outside their native environment. A-muto's platform uses proteomics and AI to analyze thousands of protein conformations in living disease models, capturing a more accurate picture of disease biology and identifying novel targets.
ProPhet's strategy is to focus on 'hard-to-drug' proteins, which are often avoided because they lack the structural data required for traditional discovery. Because ProPhet's AI model needs very little protein information to predict interactions, this data scarcity becomes a competitive advantage.
Targeting the MYC cancer protein presents a dual challenge. Biologically, it's vital for healthy cells, creating a high risk of toxicity. Biophysically, its disordered, 'floppy' structure lacks the defined pockets that traditional drugs need to bind to, making it a 'holy grail' target.
A significant, often overlooked, hurdle in drug development is that therapeutic antibodies bind differently to animal targets than human ones. This discrepancy can force excessively high doses in animal studies, leading to toxicity issues and causing promising drugs to fail before ever reaching human trials.
Antibodies bind to specific amino acid sequences, making them unable to distinguish between a protein's healthy and toxic structural forms. Alt-Pep's synthetic peptides use a complementary structure (alpha-sheet) to selectively bind only the toxic oligomers, enabling both targeted therapy and highly specific diagnostics.
ProPhet uses its AI not just for efficacy (finding a molecule for a target protein) but also for safety. By reversing the query—taking a promising molecule and asking which other proteins it might bind to—they can identify potential off-target interactions, a primary source of toxicity.