I read every paper twice. Once as the scientist building the models. Once
as the operator who used to decide which programs lived or died. Those two
readings rarely agree, and the disagreement is usually the interesting part.
Short entries, one paper at a time, with the part most summaries skip:
what it would take to matter outside the lab.
New entries most weeks. Follow along via
RSS or
LinkedIn.
#003 ·
Regulatory
The FDA just rewrote “animal” out of its rulebook
U.S. Food and Drug Administration · Direct final rule, Sep 22, 2026 ·
FDA NAMs page
In one line: The FDA replaced “animal” with
“nonclinical” across its drug safety regulations, so organ chips
and computer models now count as legitimate safety testing before
first-in-human trials.
Takeaways
It is a terminology rule, not a new requirement: parts 312, 314, 315,
361, and 601 of 21 CFR now say “nonclinical” where they said
“animal.” Effective Feb 4, 2027, unless significant adverse
comments arrive by Dec 7, 2026.
The bar did not move. Sponsors must still show a drug is reasonably safe
before human trials. What changed is that a validated chip or model can
now be part of that showing, and sponsors can request feedback on a
specific NAM through a Type D meeting.
The quieter story: the FDA published a database of 25 real cases where
non-animal data was already accepted in past reviews. Precedent, in
writing, from the agency itself.
The bridge
I have sat in the sponsor’s seat. Policy does not move pipelines;
precedent and de-risked validation do. The 25-example database is the real
story here. It is the beginning of a qualification playbook. Anyone
building organ-on-chip platforms should be mapping their validation data
against those 25 cases right now, because that is exactly where the BD
conversations will happen next year.
NAMs
regulatory
#002 ·
Paper
Eighteen ways immune cells misbehave in sepsis, counted one cell at a time
In one line: They turned microscope movies of immune cells
into behavioral data, found 18 distinct behavior clusters during sepsis,
and showed an existing asthma drug can break up the dangerous ones.
Takeaways
The method is the headline: “cellular behavioromics.” Shape,
speed, and direction per cell, quantified and run through a
bioinformatics pipeline. Imaging treated as a data modality, not a
picture.
Heterogeneity is the story again. Eighteen behavior clusters in immune
swarms during sepsis, some protective and some pathogenic. Phenotype
first, molecule second.
In mice, a well-known asthma medication prevented the pathogenic
clustering. Same repurposing logic as my 2025 paper: match the
mechanism to the subgroup, then pick from the approved shelf.
The bridge
For industry, the drug hint is not the interesting part. The assay logic
is. If behavior is quantifiable, it is screenable. A microfluidic platform
that reads out 18 behavioral clusters instead of one viability number is
a far stronger validation tool for AI-predicted compounds. That is the
direction NAMs validation has to go: richer readouts, not just
animal-free ones.
sepsis
drug repurposing
AI
#001 ·
Note
Why I’m keeping field notes
In one line: Fourteen years deciding which drug programs
deserved funding, now building the assays those decisions depend on. This
log is where the two perspectives meet.
The bridge
Most paper summaries stop at what the authors found. I want to write down
the next question: what would it take for this to survive contact with a
real development program? Throughput, validation, cost, regulatory
precedent. If an entry here ever helps someone build that case, it did
its job.