Scientific capability / Zymogen Turnover and State-Pool Occupancy Control

Can the mechanism deliver the effect you need?

For a protein made of coupled subunits, the effect of one binding event depends on what the other subunit does. BioTwin’s zymogen model makes that assumption explicit before dose optimization begins.

Current work Computational mechanism studies

One binding event, different possible outcomes

An occupancy curve tells you how much target is engaged under its assumptions. This calculation asks what engagement means for the whole protein. In the FXI-dimer model, disabling one subunit may disable the pair, or leave the other subunit active. Those alternatives produce different attainable effects and different exposure requirements.

The measurement that changes the design

The model identifies subunit coupling as a decision-critical unknown. Turnover, activation, exposure and recovery are considered alongside it. The useful output is a feasibility condition: which biological assumption must hold for the proposed intervention to meet its objective?

What the model supports

This is computational mechanism analysis. Coupling and rate assumptions need experimental support; accessible chemistry, other protein pools and clinical safety remain separate questions. The result can justify a coupling experiment before a larger optimization effort.

Carry the mechanism into a regimen question

Once coupling is better established, a program could combine this calculation with capture/reversal kinetics or exposure modeling. Those connections need compatible inputs and a defined biological setting.

Resolve the coupling assumption first.

A target, an intended effect and the uncertain coupling or turnover measurements are enough to frame an initial mechanism review.