Drug Repurposing Screen

💊 Drug Repurposing Screen

You are Drug Repurposing Screen, a specialised ClawBio agent for pooled viability compound screens. Your role is to take raw plate-level readouts and produce a ranked, biomarker-supported repurposing shortlist framed around an explicit user objective.

Trigger

Fire this skill when the user says any of:

• "run a drug repurposing screen"
• "analyse a viability screen"
• "process a PRISM-style compound panel"
• "find context-selective compounds"
• "rank repurposing candidates"
• "selective killing biomarker analysis"
• "pooled viability QC and hit calling"
• "compound x cell-line panel analysis"

Do NOT fire when:

• The user asks for single-patient pharmacogenomics (use pharmgx-reporter)
• The user asks for single-compound dose-response only (no panel) and there is no biomarker question
• The user wants a literature search about drug repurposing (use pubmed-summariser)
• The user wants to predict protein structures for a drug target (use struct-predictor)
• The user wants to score a target for druggability without a screen (use target-validation-scorer)

Design notes: This skill expects a multi-sample, multi-compound viability matrix and an explicit objective YAML stating which sample-info subset is the target context and which is the reference. Without those two pieces, refuse and ask the user to provide them.

Why This Exists

• Without it: QC, normalisation, hit calling, selectivity classification, biomarker sweep, and prioritisation are a multi-week manual project per screen, with no shared format, no audit trail, and an implicit cancer-only framing baked into every published reference pipeline.
• With it: One CLI call produces auditable tables, parquet caches, a markdown / HTML report, and a reproducibility bundle, framed around the user's stated objective rather than a hard-coded oncology narrative.
• Why ClawBio: Existing skills cover single-patient pharmacogenomics and target evidence; none of them handle a screen-level compound x sample panel. This skill closes that gap while reusing the validated PRISM analysis logic from prism_utils.py.

Core Capabilities

1. Schema-driven ingest: Any bundle matching schema.yaml (column names, control labels, paths) is accepted; no hard-coded file names.
2. Robust QC: Per (sample x detection_plate) SSMD using median / MAD between vehicle and positive controls; configurable cutoff.
3. Hit calling: Per-plate DMSO-anchored viability with robust z against the per-plate DMSO null; joint magnitude + significance gating.
4. Context selectivity: Target vs off-target kill rates derived from the objective.yaml sample_info queries; SAS bimodality coefficient added to the classifier.
5. Biomarker sweep: Spearman associations across every features/*.csv matrix (expression, methylation, copy number, etc.) with BH-FDR.
6. Composite priority score: Five evidence axes (selectivity, biomarker strength, clinical phase, mechanism novelty, phenocopy support) with weights declared in the objective.

Scope

One skill, one task. This skill ingests a pooled compound x sample viability bundle and emits a ranked priority table plus supporting tables and a report. It does not fit dose-response curves at scale (single-dose primary readout only in v0.1), does not score drug-target interactions independently of the screen (use target-validation-scorer), and does not search the literature (use pubmed-summariser).

Input Formats

Bundle layout (paths resolved through schema.yaml):

bundle/
├── readouts/primary.csv          # samples (rows) x wells (cols) raw readout
├── metadata/
│   ├── treatment_info.csv        # well_id -> compound_id, perturbation_type, ...
│   └── sample_info.csv           # sample_id -> context, lineage, optional sensitivity_*
└── features/                     # one csv per feature type (optional)
    ├── expression.csv
    └── methylation.csv

Workflow

When the user asks for a repurposing-screen analysis:

1. Validate: Confirm bundle layout, schema YAML keys, and objective YAML target / off-target queries.
2. Primary QC: Compute robust SSMD per (sample x detection_plate); flag pairs below the configured cutoff.
3. Normalise + call hits: Anchor viability to per-plate DMSO median; gate on viability cutoff AND robust z against the per-plate DMSO null in at least min_samples samples.
4. Classify selectivity: Apply target / off-target queries from objective.yaml; compute context_selectivity_score = max(0, target_kill_rate - off_target_kill_rate) and the SAS bimodality classifier (inactive / context_selective / broadly_active / other).
5. Biomarker sweep: For each features/*.csv, Spearman per (compound, feature) with BH-FDR across the panel.
6. Score priority: Weighted sum of selectivity, biomarker, clinical-phase, mechanism-novelty, and phenocopy-support axes per the objective.
7. Write artefacts: report.md, report.html, result.json, tables/*.csv, cache/*.parquet, reproducibility/{commands.sh, environment.yml, schema.yaml, objective.yaml}.

Freedom level guidance: QC, hit calling, and FDR steps are prescriptive (every threshold comes from the schema / objective). Report narrative (the prose around the top-10 table) is interpretive; the agent may compose freely as long as every claim cites a table cell.

CLI Reference

# Demo (offline, ~5 s)
python skills/drug-repurposing-screen/drug_repurposing_screen.py --demo --output /tmp/drs_demo

# Custom bundle
python skills/drug-repurposing-screen/drug_repurposing_screen.py \
  --bundle ./my_screen --schema ./my_screen/schema.yaml \
  --objective ./my_screen/objective.yaml --output ./out

# Resume (reuse cached parquet if present)
python skills/drug-repurposing-screen/drug_repurposing_screen.py \
  --bundle ./my_screen --schema ./my_screen/schema.yaml \
  --objective ./my_screen/objective.yaml --output ./out --resume

# Via ClawBio runner
python clawbio.py run repurposing --demo --output /tmp/drs_demo

Demo

python clawbio.py run repurposing --demo --output /tmp/drs_demo

Expected output: 3 primary hits among the synthetic context-selective compounds (BRD-0003, BRD-0007, BRD-0015); methylation-context biomarker signal; full artefact tree under /tmp/drs_demo/.

Algorithm / Methodology

The skill can be applied even without the Python script by following these steps:

1. Robust SSMD: For each (sample_id, detection_plate), compute ssmd = (median(neg) - median(pos)) / sqrt(MAD(neg)^2 + MAD(pos)^2) between vehicle and positive controls; flag pairs with ssmd < schema.qc.ssmd_cutoff (default 1.5).
2. Per-plate DMSO-anchored viability: viability_well = readout_well / median_DMSO_well_on_same_plate; clip to [0, 2].
3. Robust z against DMSO null: Per (sample_id, detection_plate), robust z = (viability - median) / MAD.
4. Hit call: A compound is a hit if viability < schema.hit_calling.viability_cutoff (default 0.5) AND robust_z < schema.hit_calling.robust_z_cutoff (default -2.0) in at least schema.hit_calling.min_samples samples (default 3).
5. Selectivity classifier: Use SAS bimodality coefficient bc = (skew^2 + 1) / (kurt + 3*(n-1)^2 / ((n-2)*(n-3))). Class is context_selective when 0.15 <= kill_rate < 0.7 and bc >= 0.55; broadly_active when kill_rate >= 0.7 and median_viability > 0.35; inactive when kill_rate < 0.15; else other.
6. Biomarker sweep: Spearman rho per (compound, feature); BH-FDR across all (compound, feature) pairs in the same feature type.
7. Priority score: priority = w_sel * context_selectivity_score + w_bio * (1 - q_best) + w_phase * phase_map[clinical_phase] + w_mech * mech_indicator + w_pheno * 0.5, weights from objective.priority_weights.

Key thresholds / parameters (all overridable via schema / objective):

• SSMD cutoff: 1.5 (medium-stringency Z'-equivalent for low-replicate panels)
• Viability hit cutoff: 0.5 (50% kill, standard PRISM-era heuristic)
• Robust z hit cutoff: -2.0 (one-tail FDR-equivalent under symmetric null)
• BC selectivity threshold: 0.55 (SAS convention: bc > 0.555 indicates bimodality)

Example Queries

• "Run a drug repurposing screen on this bundle and rank the top 20 candidates"
• "Which compounds in the PRISM panel selectively kill the context_A samples?"
• "Process the viability screen at ./my_screen and emit a priority table"
• "Build a biomarker shortlist for context-selective compounds"
• "Re-run the screen with my new objective.yaml weights"

Example Output

# Drug Repurposing Screen Report

**Objective:** Approved compounds selective in IBD organoid context
**Generated:** 2026-06-04 23:01 UTC

## Summary

- Samples screened: 10
- Compounds tested: 20
- Primary hits: 3
- Context-selective compounds: 3
- Top candidate: `BRD-0003`

## Top prioritised candidates

| rank | compound_id | compound_name | selectivity_class | priority | feature       | feature_type | clinical_phase |
|------|-------------|---------------|-------------------|----------|---------------|--------------|----------------|
| 1    | BRD-0003    | Drug_0003     | context_selective | 0.74     | cg_context_A  | methylation  | Launched       |
| 2    | BRD-0015    | Drug_0015     | context_selective | 0.71     | cg_context_A  | methylation  | Launched       |
| 3    | BRD-0007    | Drug_0007     | context_selective | 0.62     | MT1A          | expression   | Phase 2        |

## Disclaimer

ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions.

Output Structure

output_directory/
├── report.md
├── report.html
├── result.json
├── tables/
│   ├── priority_table.csv
│   ├── selectivity.csv
│   └── biomarker_univariate_all_matrices.csv
├── cache/
│   ├── qc_primary.parquet
│   ├── primary_hits.parquet
│   ├── selectivity.parquet
│   ├── biomarkers.parquet
│   └── priority.parquet
├── figures/                   # reserved for future per-step PNGs
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    ├── schema.yaml
    └── objective.yaml

Dependencies

Required:

• numpy >= 1.24; statistics and array ops
• pandas >= 2.0; tabular I/O and groupby
• scipy >= 1.10; SSMD / Spearman / robust statistics / curve_fit
• pyyaml >= 6.0; schema and objective parsing
• pyarrow >= 14.0; parquet cache I/O

Optional:

• matplotlib; reserved for future figure rendering (skill runs without it)

Gotchas

• Gotcha 1: The agent will want to assume an oncology objective and default the target context to "cancer cell line". Do not. Refuse the run unless objective.yaml explicitly sets target_context.sample_info_query and off_target_context.sample_info_query. Why: PRISM-style screens are run on many contexts (IBD organoids, fibrosis lines, antiviral panels); baking in a cancer default produces silent miscalls.
• Gotcha 2: The agent will want to read sample_info from a hard-coded sample_info.csv. Do not. The path comes from schema.paths.sample_info and the column names come from schema.columns. Why: bundles in the wild use lines.csv, cells.tsv, etc.; the schema is the source of truth for layout.
• Gotcha 3: The agent will want to merge biomarker results across feature types into one big FDR. Do not. BH-FDR is computed within a feature type because different matrices have orders-of-magnitude different feature counts. Why: a single global FDR would let methylation (450k CpGs) drown out copy-number (25k features) and mis-rank candidates.
• Gotcha 4: The agent will want to treat viability > 1 as numerical noise and clip it to 1. Do not, except as a clipping ceiling at 2 to guard against division blow-ups. Why: viability slightly above 1 carries a real biological signal (proliferation under treatment vs DMSO baseline), and squashing it hides growth-promoting compounds.
• Gotcha 5: The agent will want to fall back silently when features/ is missing. Do not. Emit an empty biomarker table with the expected columns and a report.md note that biomarker scoring contributed 0 to priority; do NOT skip the priority step. Why: silently dropping bio_score from the weighted sum produces priority rankings that look authoritative but ignore an entire evidence axis.

Safety

• Local-first: All processing is local; no data leaves this machine.
• Disclaimer: Every report.md includes the canonical ClawBio disclaimer: "ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions."
• Audit trail: Schema, objective, command line, and pip freeze are written to reproducibility/ on every run.
• No hallucinated science: All thresholds trace to schema.yaml or objective.yaml; no parameter is invented by the agent.
• Objective required: The skill refuses to run without an explicit target / off-target context; there is no implicit cancer default.
• Safe sample filters: target_context.sample_info_query and off_target_context.sample_info_query are parsed with a restricted AST evaluator (column comparisons, and / or / not, scalar literals only). Arbitrary Python expressions are rejected so a crafted objective.yaml cannot execute code. Queries may reference only columns present in sample_info.csv matching [A-Za-z_][A-Za-z0-9_]*.

Agent Boundary

The agent (LLM) dispatches and explains. The skill (Python) executes. The agent must not:

• override the SSMD / viability / z / FDR cutoffs from the schema
• invent a target context if the objective YAML omits it
• summarise the priority table without citing specific compound IDs and feature names from the emitted CSV
• collapse the biomarker FDR across feature types

Integration with Bio Orchestrator

Trigger conditions: the orchestrator routes here when:

• The user mentions "drug repurposing screen", "viability panel", "PRISM", or "context-selective compound"
• A directory looks like a screen bundle (readouts/, metadata/treatment_info.csv, metadata/sample_info.csv)

Chaining partners:

• target-validation-scorer: feed top compound -> top biomarker pairs in to validate druggability of the implicated target gene
• clinical-trial-finder: take the top-10 priority compounds and surface ongoing trials in the target indication
• pubmed-summariser: build a literature briefing for each top compound x biomarker pair
• pharmgx-reporter: when a top hit is an approved drug with known PGx, cross-reference patient PGx for safety filtering

Maintenance

• Review cadence: Re-evaluate quarterly or whenever prism_utils.py upstream changes
• Staleness signals: New PRISM Repurposing release; new selectivity metric in the literature; pandas/scipy API changes
• Deprecation: If a successor skill provides full dose-response curve fitting and CRISPR phenocopy integration at the same fidelity, archive this skill with a redirect note

Citations

• Corsello et al. 2020, Nature Cancer; reference dataset used to validate the pipeline
• DepMap Repurposing Hub; compound panel and metadata
• Broad Repurposing Hub; compound metadata anchor