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Recycling Clinical Trial Data with Causal Inference
Variacle integrates heterogeneous, archived and real‑world clinical datasets using cutting‑edge causal inference technology to design faster, leaner and more ethical drug development pathways.
90%
Trials Shelved
↓ Costs
Capital Efficiency
↑ Ethics
Fewer Patients Needed
AI → Causal
Next Paradigm
Why Causal Inference Now?
Prediction alone is not enough. The real question: What happens if we intervene?
- The boundaries of prediction have been reached. Machine learning excels at forecasting outcomes, but falls short when it comes to guiding actionable decisions.
- Causality is the scientific pursuit. While traditional research warned that “correlation isn’t causation,” modern methods now rigorously formalize and address confounding—turning theory into robust, practical tools.
- Policy and health interventions are being revolutionized. Quasi-experimental techniques, driven by advances in economics, epidemiology, and the social sciences, allow us to understand the real effects of interventions.
- And more innovations are on the horizon…
Why this matters now:
- AI can achieve near-perfect predictive accuracy, yet have minimal impact if it does not guide interventions.
- Most key decisions—across business, science, and policy—are fundamentally causal (“If we do X, will Y result?”).
- Regulators in pharma and biotech are open to observational evidence, provided causal assumptions are clear.
- Scientific progress accelerates when rigorous causal reasoning informs every stage, from discovery to application.
Drug development does not suffer from a data shortage – only from data underuse.
Global pharma invests billions in clinical trials whose rich datasets are archived after headline failure. Each dataset encodes biological signals and population heterogeneity that could de‑risk future programs. Today, up to 90% of these assets remain silent, forcing redundant trials, escalating costs and delaying life‑saving therapies.
Strategic Knowledge Recovery
Failed or inconclusive trials conceal actionable invariances. We reactivate them to eliminate blind spend on redundant study designs & outdated consulting loops.
Faster Ethical Decisions
Optimised protocols lower patient enrollment requirements while preserving statistical power—accelerating timelines and reducing exposure.
Causal Over Correlation
Where conventional AI pattern‑matches, Variacle isolates stable causal structure to generalize across populations, indications and geographies.
Madrid Hub
Enabling the Spanish ecosystem to extract maximal value from existing R&D data footprints.
A unified causal layer transforming scattered clinical evidence into confident design choices.
Variacle ingests proprietary failed trials, public repositories and real‑world data. Advanced invariance & causal discovery models identify biological patterns that remain stable under shifts in cohort composition or protocol. These robust signals feed scenario simulators that propose optimized arms, inclusion criteria and endpoints.
Core Capabilities
Data Fusion Engine: Secure integration of heterogeneous historical and real‑world datasets.
Causal Discovery Suite: Identifies invariant mechanistic pathways & effect modifiers across sources.
Trial Design Optimizer: Recommends adaptive protocols reducing required sample sizes.
Regulatory Insight Layer: Transparently quantifies assumptions & transportability for reviewer trust.
Outcome Advantages
Higher Success Probability: Prior biological signal reuse reduces uncertainty in new indications.
Cost Compression: Fewer redundant studies and patients; shorter enrollment windows.
Ethical Acceleration: Minimizes patient exposure to inferior comparators.
Causal Auditability: Justifiable evidence chains enabling acceptance of non‑randomized data.
Serving pharma leaders, emerging biotechs, CROs and forward‑looking regulators.
Our technology scales across therapeutic areas—Alzheimer's, oncology, rare diseases—where failure data is abundant yet under‑leveraged. We activate Spain & Madrid as a strategic proving ground: competitive operating costs, hybrid trial momentum and a growing real‑world data focus.
- Pharma Enterprises (Roche, Pfizer, Novartis…): Recover sunk R&D value & derisk pipeline sequencing.
- Emerging Biotechs: Increase late‑stage confidence with limited capital via historical effect modifiers.
- CROs: Offer leaner, adaptive, causally‑guided trial packages to sponsors.
- Regulators (EMA, FDA): Assess structured use of non‑randomized evidence with transparent causal assumptions.
- Global Expansion: Causal inference as the evolutionary layer beyond conventional AI analytics.
Scientific depth meets translational urgency.
Variacle originates from causal invariance theory, matured through mathematical sciences research in Madrid—an epicenter of machine learning & digital health innovation. We fuse frontier research with real pharma pain points.
Foundations
Development of principles & causal methods establishing methodological core.
Mathematical Refinement
Scaling algorithms to heterogeneous clinical datasets.
Translation
Integration with ML & healthtech ecosystems to validate industry use‑cases.
Independent Spin‑Out
Evolving into a focused platform to operationalize causal inference across therapeutic areas.
Ready to unlock the silent 90% of clinical evidence?
We are engaging with select partners for pilot programs and joint methodological validation. If you steward latent trial data or seek to design lean adaptive studies, let's talk.
Get in Touch
Email: soon@variacle.com
We respond within 2 business days.
Research‑based project (current status).
Partner Criteria
• Archived phase II/III trial datasets
• Interest in adaptive / hybrid designs
• Commitment to transparency & patient ethics
• Multi‑therapeutic portfolio or high‑value niche
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