Build a drug target validation dossier

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

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

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.

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.

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.

The API equivalent

# 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

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.

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.

Create a biomarker evidence report

The same tiered discipline, applied to a biomarker range.

Monitor new research about a drug, device, or brand

Keep watching the target once the program starts.

Best practices

Fan-out, the filter ladder, and the rest of the primitives.

All use cases

Browse the gallery by persona.

Overview

Use cases

Build a drug target validation dossier

What you get

Who it’s for

The prompt

How it works

The API equivalent

What to check before you trust it

Create a biomarker evidence report

Monitor new research about a drug, device, or brand

Best practices

All use cases

​What you get

​Who it’s for

​The prompt

​How it works

​The API equivalent

​What to check before you trust it

​Related

Create a biomarker evidence report

Monitor new research about a drug, device, or brand

Best practices

All use cases

What you get

Who it’s for

The prompt

How it works

The API equivalent

What to check before you trust it

Related