Microbiome intelligence for health innovations across industries
What is Synaptiflora?
Microbiome intellegence platform to transform product innovation across pharmaceuticals, nutrition, cosmetics and agriculture
Use cases
Pharma & Biotech – Target discovery, preclinical modeling, patient stratification, responder analysis, and post-market insights.
Precision Health & Providers – Clinical decision support and differential diagnosis.
Food & Nutrition – Ingredient R&D, product development, and diet-linked health monitoring.
Cosmetics & Personal Care – Skin product innovation and post-market safety monitoring.
Agriculture – Crop health, soil microbiome optimization, and sustainable farming solutions
Our long-term vision is to enable multiple industries with microbiome-driven insights, while our immediate focus is on the most urgent challenge – redefining drug development for safer, more effective therapies
The Clinical Trial Problem: Broken and Inefficient
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Phase I–III attrition: ~90% of drugs fail before approval
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Each trial costs $50–100M+ and runs 5–7 years
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Even “successful” drugs help only ~40% of patients
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Non-responder recruitment = delays, wasted budgets, failed approvals
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Microbiome impact on response is ignored in almost all trials
Response Prediction and Stratification Tool Demo
Microbiome index
Proprietary pipeline that profiles the microbiome down to the strain level across bacteria, fungi, viruses, and phages in a single run. Each finding is enriched with a knowledge graph that links microbes and genes to biochemical pathways, drug interactions, and clinical relevance.
We recreate in ex-vivo how diverse compounds behave within real, living microbiome systems.
The methodology allows to discover and describe
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Strain-level shifts
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Functional pathway changes
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Compound metabolism
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Response markers
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Metabolite profiles
Drug-Microbiome Modeling
Synapticore ML
A machine learning model purpose-built for multi-omic microbiome data. SynaptiCore ingests microbial profiles, clinical variables, genomic and drug data, and converts them into unified feature representations. This allows the model to learn non-linear relationships between microbial composition, host physiology, and therapeutic outcomes.
Our
Mission
Raise drug efficacy rates from 40% to
60–80%
Cut drug trial timelines by the factor of
2x
Be the foundational technology for microbiome-based clinical development
globally
