reach-gap

Is the target physically reachable?

An evidence-aware spatial modelling tool that asks whether target-positive tumour cells are accessible to an antibody, and refuses to answer when the required biology has not been measured.

v0.8.0 · Research preview View repository

The problem

A tumour target can be highly expressed and still be difficult for an antibody to reach. Accessibility depends on far more than expression level:

  • which vessels are structurally present, and which are actually perfused;
  • how far target-positive cells lie from those vessels;
  • how quickly an antibody moves through the extracellular matrix;
  • how strongly and how densely it binds;
  • whether an administered antibody reaches and engages the tissue at all.

These quantities are rarely measured together in the same sample. The common failure mode is quiet substitution: combining RNA, fluorescence, literature diffusion constants and vessel distances as though they were interchangeable.

The idea

reach-gap has two linked layers.

flowchart TB
    subgraph M[Mechanistic layer]
        A[Vessel source geometry] --> B[Diffusion through matrix]
        B --> C[Binding and internalisation]
    end
    subgraph E[Evidence layer]
        D[Same-tissue measurement]
        F[External validation]
        G[Literature prior]
        H[Missing / blocked]
    end
    M --> I{Evidence sufficient?}
    E --> I
    I -->|Yes| J[Relative target ranking]
    I -->|No| K[NOT_COMPUTED]

The mechanistic layer represents vascular geometry, diffusion, binding and internalisation in a simplified 2D reaction–diffusion model. The evidence layer records, for every required input, whether it was measured in the same tissue, externally validated, borrowed as a prior, missing, or explicitly blocked from transfer.

The second layer is the point. It makes questionable substitutions visible instead of letting them disappear into a single number.

What it returns

Depending on the evidence available:

  • mechanistic simulation outputs with propagated uncertainty;
  • relative target-rank probabilities and pairwise comparisons;
  • evidence-readiness and measurement-priority reports;
  • or an explicit abstention such as reachable_fraction = NOT_COMPUTED.

The last of these is a feature. An absolute reachable fraction requires biology that usually has not been measured, so the tool declines to produce one rather than fabricating false precision.

Design choices

Choice Reason
Relative ranking rather than absolute fractions The absolute quantity depends on unmeasured inputs; the relative comparison is defensible
Six vessel definitions instead of one Vessel identity is itself a modelling decision, and the ranking should be shown to survive it
20,000 uncertainty draws Point estimates hide how wide the plausible range actually is
Explicit NOT_COMPUTED outputs A missing answer is more useful than a confident wrong one
Auditable evidence graph Every transfer between data types can be inspected and challenged

Verified release

Item Current state
Automated tests 117
Coverage 88.3%
Uncertainty draws 20,000 per analysis
Vessel definitions compared 6
Containerised Yes (Docker)
Licence MIT

Honest limitations

  • The reaction–diffusion model is a simplified 2D representation, not a full pharmacokinetic simulation.
  • Relative rankings are comparisons between targets in the same tissue context; they do not transfer to other tumours unchanged.
  • Literature priors remain priors, however carefully labelled.
  • Research use only: no prediction of patient response, treatment recommendation, or estimate of clinical efficacy.