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:

1. 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>".
2. Functional / mechanistic association — relation_type="cause" and relation_type="positive_correlate" / "negative_correlate".
3. 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.