| name | target-evidence-dossier |
| description | Build a target-validation dossier for a gene / protein / pathway — biology, disease association, druggability, existing programs, key publications, trial pipeline, safety signals. Use for early-stage discovery target review, portfolio decisions, or when the user asks "what do we know about target X". |
Target Evidence Dossier
You are building the evidence package a pharma R&D team uses to decide
whether to advance, deprioritize, or further-validate a target. Audience
is a biology or computational-bio team lead — they want compactness and
citations, not narrative fluff.
Workflow
1. Identify the target canonically
Call lookup_entity_id with concept="gene" to get the canonical
PubTator3 ID (e.g. @GENE_BRCA1). Note any synonyms / aliases / paralogs
the user should be aware of (PubTator3 returns these; surface them
prominently because alias drift causes evidence to be missed).
2. Biology section
Use search_pubmed with publication_types=["review"] on the gene name
to surface the canonical reviews. Distill:
- Protein family + domain architecture.
- Cellular localization and expression pattern (which tissues highly
express it; which cell types).
- Known biological function and pathway membership.
- Knockout / loss-of-function phenotype (mouse and, where available,
human LoF).
3. Disease association
Three angles, in order:
- Genetic association — call
find_related_entities with the gene
ID, relation_type="associate", target_type="disease". Cross-check
against search_pubmed for "<gene> AND GWAS" and
"<gene> AND mutation AND <disease>".
- Functional / mechanistic association —
relation_type="cause" and
relation_type="positive_correlate" / "negative_correlate".
- Expression-based association — note if the literature flags
over- / under-expression in disease tissue.
Tag each association with strength of evidence (genetic > mechanistic >
correlation).
4. Druggability + existing programs
- Existing drugs / probes:
find_related_entities with the gene ID,
relation_type="inhibit" and relation_type="stimulate",
target_type="chemical".
- Trials targeting it:
search_clinical_trials with intervention=
the gene name and / or condition= the leading associated indication.
Group results by sponsor and phase.
- Modality landscape: small molecule vs. biologic vs. PROTAC vs. genetic
medicine. The trial table usually answers this implicitly.
5. Translatability + safety signals
- Animal-model evidence: include reviews that cite KO/CKO mouse phenotypes.
- Human genetic evidence: surface known LoF tolerance — if humans with
natural LoF are healthy, that's a positive translatability signal; if
LoF is associated with severe disease, flag the on-target safety risk.
- Literature on pathway-level toxicity (e.g. inhibiting target X disrupts
pathway Y which controls Z).
6. Output
Final structure:
# Target dossier — <GENE_SYMBOL>
## Snapshot
- Family / domain / localization
- Strongest disease association (1 sentence + PMID)
- Druggability verdict (Tractable / Challenging / Undruggable + 1 sentence)
- Pipeline status (count of trials by phase, lead sponsors)
## Biology
... cited bullets ...
## Disease association
| Disease | Evidence type | Strength | Key refs |
## Existing programs
| Asset / probe | Modality | Sponsor | Phase | NCT |
## Translatability + safety
... cited bullets ...
## Open questions / next experiments
... 3-5 bullets framed as testable hypotheses ...
## References
PMIDs grouped by section.
Optionally render a one-panel target-context diagram via
visualize_concept (figure_type="diagram") — protein in its pathway,
disease tissue overlay, existing drugs as inhibitor arrows. Useful for
slide use.
Guardrails
- Distinguish "X is associated with disease Y" from "X causes disease Y" —
use the strength-of-evidence tag.
- Do not invent KO phenotypes or LoF data — if the literature does not
cover it, write "no published mouse KO data found" rather than
speculating.
- Aliases matter: if PubTator3 returns multiple canonical IDs for the
query, run the dossier on each and note the alias mapping.
