#clinical-ops

A helpful biotech starter pack needs one regulatory process guide, one translational science lens, and one open computational toolkit people can actually learn from. That mix reminds the team that evidence, operations, and computation all need owners.

The kinds of materials worth saving in this space:
- primary literature with clear translational implications
- trial operations checklists and startup timelines
- regulatory guidance mapped to actual development choices

Read:
- FDA drug development and approval process: fda.gov/drugs/development-approval-process-drugs
A grounding document for the path from development to review and approval.
- NCATS translational science spectrum: ncats.nih.gov/translation/spectrum
Useful for keeping research work tied to concrete translational stages.
- scverse: scverse.org/
A strong starting point for open computational work in modern omics analysis.

Documents and downloadable guides:
- Addgene protocols: addgene.org/protocols/
Practical wet-lab documentation that is genuinely useful for day-to-day work.
- NCBI Bookshelf: ncbi.nlm.nih.gov/books/
A deep public archive for primers, reference texts, and method overviews.

Watch:
- NIH video archive: youtube.com/@NIH/videos
Webinars and talks that help keep the science connected to real public research practice.
- Addgene video archive: youtube.com/@addgene/videos
Clear explainers and protocols from a source readers already trust for plasmid work.

Build or inspect:
- scvi-tools: github.com/scverse/scvi-tools
Open source probabilistic tooling for single-cell and spatial omics work.
- Biopython: github.com/biopython/biopython
Still useful as a practical reminder that a lot of bio tooling is public and inspectable.

Image references:
- Addgene protocol visuals: addgene.org/protocols/
Bench-ready diagrams and step images that make the written protocols more legible.

The classic mistake is overselling platform breadth before the lead program has earned it. Another is treating regulatory strategy like a writing exercise that happens after the science is done.

Common traps to watch:
- overstating platform breadth before lead programs mature
- underestimating the operational complexity of trials
- treating regulatory strategy as a downstream writing exercise

References that help correct the drift:
- NCATS translational science spectrum: ncats.nih.gov/translation/spectrum
Useful for keeping research work tied to concrete translational stages.
- Addgene protocol visuals: addgene.org/protocols/
Bench-ready diagrams and step images that make the written protocols more legible.

This folio post is meant to be saved and revised. Add examples from your own work whenever one of these mistakes keeps resurfacing.

A healthy workflow names the program hypothesis, maps preclinical and translational milestones to the next financing or partnering decision, and then builds clinical and regulatory readiness in parallel. The work gets expensive when those streams only meet at the deadline.

A sequence I would actually hand to a teammate:
1. Clarify whether the value is in the platform, the lead program, or the operating model.
2. Map preclinical and translational milestones to the next financing or partnering decision.
3. Build trial operations and regulatory preparation in parallel with scientific execution.

Useful operating references:
- NCATS translational science spectrum: ncats.nih.gov/translation/spectrum
Useful for keeping research work tied to concrete translational stages.
- scvi-tools: github.com/scverse/scvi-tools
Open source probabilistic tooling for single-cell and spatial omics work.

If your team has a better workflow, post it with the context around team size, constraints, and exactly where the process tends to break.