Use Case · Drug Repurposing

Build a complete mechanism + clinical evidence chain for a new therapeutic indication

PubMed-grounded biomedical AI that drills from broad mechanism overview to specific patient stratification. Built for translational researchers, pharma R&D scientists, and biotech teams constructing repositioning evidence chains for a known drug or drug class in a new therapeutic area.

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Pre-loaded BioSkepsis session showing a complete drug-repurposing workflow from broad mechanism overview through to patient stratification.

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Built for translational researchers and pharma R&D

Pharma R&D scientists: map the literature foundation for life-cycle management, label-expansion, and Phase IIb/III repurposing decisions.
Biotech research teams: build mechanism + clinical evidence chains for licensing discussions, pipeline pivots, and new indication strategies.
Academic translational researchers: ground hypothesis-driven repurposing work in verified mechanistic literature with traceable citations.

The 7-step workflow — drug-indication pair to patient stratification

Each step refines the evidence chain. Start broad with the mechanistic rationale, then progressively drill into mechanism nuance, clinical outcomes, comparative effectiveness, and which patient subgroups respond.

Enter a focused research question about a specific drug-indication pairing

Frame the question around what published evidence supports a known drug or drug class being effective for a new therapeutic use. Specify the drug (or drug class) and the candidate indication clearly.

Example query "What is the published mechanistic and clinical evidence that aspirin has therapeutic potential in colorectal cancer prevention?"

Review the initial AI synthesis

BioSkepsis returns a structured answer covering the molecular rationale for the repurposed indication and the clinical evidence organised by sub-indication or disease stage. Pay attention to the unverified citations panel — this is where the system catches misquoted effect sizes or misattributed findings from the source literature.

Drill into the core mechanism of action in the new indication

Use a suggested follow-up or type your own question to explore how the drug's known pharmacology engages the pathways relevant to the new disease. Ask how the mechanism behaves under different biological states (e.g., healthy vs. diseased tissue, naive vs. chronic exposure).

Example query "What molecular mechanisms distinguish aspirin's effects on COX-2 inhibition in colorectal epithelium versus its systemic anti-platelet activity?"

Link the mechanism to a measurable clinical or behavioural outcome

Ask how the mechanistic findings from Step 3 correlate with observable endpoints. This tests whether the biology translates into something that a clinical trial could measure.

Example query "How do these mechanistic findings on prostaglandin signaling correlate with observed reductions in colorectal adenoma incidence in clinical trials?"

Explore a nuanced distinction that affects therapeutic design

Identify a conceptual split that matters for how the drug would be developed for the new indication (e.g., motivation vs. pleasure, acute vs. chronic pathology, upstream vs. downstream targets) and ask BioSkepsis to dissect which side the evidence supports.

Example query "Does aspirin's chemopreventive effect selectively target sporadic versus Lynch syndrome-associated colorectal cancer?"

Benchmark against the current standard of care for the new indication

Ask how the repurposed drug compares to existing approved treatments on specific metrics — effect sizes, hazard ratios, hospitalisation rates, or adverse event profiles.

Example query "How does aspirin's chemopreventive effect on colorectal cancer compare to established screening strategies in real-world cohorts?"

Identify a patient stratification factor for the new indication

Ask whether a specific patient characteristic (e.g., BMI, genotype, comorbidity, disease severity) predicts who responds to the repurposed therapy. This surfaces both positive predictors and any paradoxical or adverse findings in subgroups that would shape trial design.

Example query "What role does baseline body mass index or PIK3CA mutation status play in predicting aspirin's chemopreventive efficacy?"

What you walk away with

Mechanistic rationale chain

From known pharmacology to disease-relevant pathways, with traceable PubMed citations.

Clinical evidence organised by sub-indication

Sub-population, dose, comparator, primary and secondary endpoints — extracted with sources.

Comparative effectiveness benchmarks

Effect sizes vs. standard of care across hazard ratios, adverse event profiles, real-world outcomes.

Patient stratification factors

Positive predictors and paradoxical subgroups identified across published trials.

Walk through the live workflow

Pre-loaded BioSkepsis session demonstrating the full drug-repurposing workflow with verified mechanistic and clinical evidence at every step.

See it in the app →

Honest limits — what AI will not do for drug repurposing

AI cannot propose unpublished candidates. BioSkepsis surfaces mechanistic links across the published literature; it will not invent novel drug-target pairings that have no precedent.
AI does not replace clinical judgement. Translation from preclinical mechanism to clinical trial design requires regulatory, statistical, and ethics expertise. BioSkepsis builds the literature foundation; humans design the trial.
AI does not guarantee a clean evidence chain. Where studies disagree, BioSkepsis surfaces the disagreement rather than smoothing it into false consensus. You decide which evidence to weight.
Final review remains human. Every claim should be verified against the cited source before inclusion in a regulatory filing, grant, or publication.

Frequently asked questions

What kinds of drug repurposing questions does BioSkepsis support?

Any biomedical drug-indication pairing supported by published literature. Common workflows include CNS-active drugs repurposed for addiction or neurodegeneration, metabolic drugs repurposed for inflammation or cardiovascular disease, oncology drugs repurposed across cancer types, and antivirals repurposed for autoimmune disease. BioSkepsis pulls from a 40M+ paper biomedical corpus including PubMed, biorxiv, and medrxiv.

Can BioSkepsis identify novel drug repurposing candidates I have not considered?

BioSkepsis surfaces mechanistically related drugs and pathways across its biomedical knowledge graph. It will not propose unpublished candidates but will reveal published mechanistic links you may not have known about — including cross-indication evidence buried in single-publication preclinical studies.

How does BioSkepsis distinguish baseline predictors from post-treatment correlates?

The Step 5 nuance probe is built for exactly this. Ask whether a biomarker was measured pre-treatment as a predictor or post-treatment as a correlate, and BioSkepsis distinguishes the two and flags any study design that conflates them. This matters for clinical translation and trial design.

Can the output be used for an IND or grant submission?

BioSkepsis output is structured to map into preclinical rationale sections, mechanism of action narratives, and patient stratification justifications. Final IND or grant submission requires regulatory and PI review — the verified PMIDs and mechanism table give your team a head start.

Does BioSkepsis cover pharmacology and clinical trial literature?

Yes. The corpus includes pharmacology, pharmacokinetics, clinical trial reports, real-world evidence, comparative effectiveness studies, and preclinical mechanistic literature across all major biomedical therapeutic areas.

Build your next drug repurposing evidence chain

PubMed-grounded biomedical AI. Verified citations. Mechanism-to-stratification in one workflow. Free tier — no credit card.

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