> ## Documentation Index
> Fetch the complete documentation index at: https://docs.consensus.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Build a drug target validation dossier

> Human genetics, in vitro, animal, and clinical evidence for one target in a single pass — including what contradicts it.

## What you get

A target dossier organized by evidence tier, with a strength grade per tier, an explicit disconfirming-evidence section, and a named next experiment. The ratio between preclinical and clinical volume is itself part of the finding.

## Who it's for

Discovery and translational teams deciding whether a target justifies a program, and reviewers pressure-testing that decision.

## The prompt

```text theme={null}
You are a translational biology researcher building a target validation dossier.

Target: [TARGET]
Indication: [INDICATION]

Search each evidence tier separately using Consensus. Report them separately —
do not merge tiers into a single narrative.

Tier 1 — Human genetics
  Loss-of-function and gain-of-function variants, GWAS associations, Mendelian
  randomization. Does human genetic variation in this target move the phenotype,
  and in which direction?

Tier 2 — In vitro and mechanistic
  Cell models, biochemical assays, pathway position. What does modulating the
  target actually do, and in what system?

Tier 3 — Animal models
  Knockout and knockdown phenotypes, pharmacological modulation, which species
  and which model. Note where the model is a poor proxy for the human disease.

Tier 4 — Human observational
  Expression, biomarker association, natural-history cohorts.

Tier 5 — Human interventional
  Any trial of an agent hitting this target or its pathway, in this or any
  indication. Include failures and terminations.

For every tier, report: the number of papers found, what they show, the
consistency of the direction of effect, and a strength grade of strong,
moderate, weak, or absent.

Then, as separate sections:

Disconfirming evidence — search explicitly for contradictory findings, failed
trials, non-replications, and negative results on this target. A dossier with
no disconfirming section has not been done. If you genuinely find none, say
what you searched.

Safety and tractability — knockout phenotype severity, expression in tissues
where modulation would be harmful, known adverse effects of agents hitting this
pathway.

Translational gap — state plainly whether human evidence exists or whether this
is preclinical only. Report the ratio of preclinical to clinical papers.

Finish with: the single experiment or dataset that would most reduce
uncertainty about this target.

Cite every claim with journal, year, and DOI.
```

## How it works

<Steps>
  <Step title="Study-type filters are the tier boundary">
    `study_types=animal` and `non-rct in vitro` isolate preclinical work; `human=true` with `rct` and `cohort study` isolates the clinical tier. Running both and comparing volumes gives you the translational gap for free.
  </Step>

  <Step title="Disconfirming evidence is searched for, not waited for">
    Querying the target's name and indication returns confirmation. Finding the failures requires separate queries phrased toward failure — termination, non-replication, negative result.
  </Step>

  <Step title="Tiers stay separate through to the output">
    Merging tiers lets a large volume of in vitro work stand in for absent human evidence. Grading each tier independently prevents that.
  </Step>
</Steps>

## The API equivalent

```bash theme={null}
# Tier 1 — human genetics
curl -G "https://api.consensus.app/v1/search" \
  -H "x-api-key: $CONSENSUS_API_KEY" \
  --data-urlencode "query=GPR75 obesity body mass index human genetic association loss of function" \
  --data-urlencode "human=true" \
  --data-urlencode "domain=med,bio" \
  --data-urlencode "page_size=50"

# Tier 3 — animal models
curl -G "https://api.consensus.app/v1/search" \
  -H "x-api-key: $CONSENSUS_API_KEY" \
  --data-urlencode "query=GPR75 knockout mouse adiposity phenotype" \
  --data-urlencode "study_types=animal" \
  --data-urlencode "page_size=50"

# Disconfirming pass
curl -G "https://api.consensus.app/v1/search" \
  -H "x-api-key: $CONSENSUS_API_KEY" \
  --data-urlencode "query=GPR75 negative result failed replication no effect adiposity" \
  --data-urlencode "page_size=50"
```

## What to check before you trust it

<Warning>
  A literature dossier is an input to a target decision, not the decision. Keep the citation trail so a reviewer can check each tier's grade against its sources.
</Warning>

* **Check the disconfirming section is real.** If it is empty or generic, the negative queries were not run. That section failing is the difference between a dossier and a pitch.
* **Preclinical volume is not evidence strength.** Fifty in vitro papers and no human data is a weak target with a large literature. The ratio should be stated plainly.
* **Watch for pathway substitution.** Evidence for a pathway is often presented as evidence for a specific target. Check that Tier 1 and Tier 5 are about the target itself.
* **Gene-name synonyms matter.** Older aliases and gene-family names return different literature. Confirm the search covered them.

## Related

<CardGroup cols={2}>
  <Card title="Create a biomarker evidence report" icon="chart-line" href="/use-cases/biomarker-report">
    The same tiered discipline, applied to a biomarker range.
  </Card>

  <Card title="Monitor new research about a drug, device, or brand" icon="satellite-dish" href="/use-cases/brand-and-device-monitoring">
    Keep watching the target once the program starts.
  </Card>

  <Card title="Best practices" icon="shapes" href="/use-cases/best-practices">
    Fan-out, the filter ladder, and the rest of the primitives.
  </Card>

  <Card title="All use cases" icon="grid-2" href="/use-cases">
    Browse the gallery by persona.
  </Card>
</CardGroup>