Redefining Bacterial Mechanics

Fighting Resistance Through
Mechanical Adaptation

A discovery platform identifying modifiers of bacterial cell mechanics to treat drug-tolerant infections and overcome antimicrobial resistance.

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The Silent Crisis:
Drug-Tolerant Infections

Antimicrobial resistance is a global health emergency projected to cause 10 million deaths annually by 2050.

Current antibiotics often fail not because bacteria are genetically resistant, but because they physically adapt. In chronic conditions like Cystic Fibrosis and COPD, pathogens like M. abscessus and P. aeruginosa enter "tolerant" states that conventional drugs cannot breach.

  • Targeting a $50Bn global anti-infectives market
  • Focus on untreatable chronic lung infections
  • Addressing the root cause of phenotypic resistance
Bacterial Infection Rendering

Mechanical Morphotype Switching

Bacteria are not static; they mechanically adapt to survive. We have discovered that pathogens switch between "Soft" and "Hard" cell states to evade the immune system and resist antibiotics.

Based on research published in Science Advances (Eskandarian et al. 2024)

Genetic Control

Specific bacterial genes regulate these mechanical transitions. By identifying these genes, we uncover new therapeutic targets that traditional screens miss.

Chemical Modulation

We screen for small molecules that force bacteria into a "drug-sensitive" mechanical state, effectively stripping away their physical defenses.

Potentiation

Our compounds don't just kill bacteria; they potentiate existing antibiotics, restoring efficacy to drugs that have become obsolete due to resistance.

Abstract AI and Biological Network

AI Discovery Engine

We combine high-throughput biophysical screening with state-of-the-art generative AI to predict and validate mechanical modulators.

Generative AI Models

Leveraging Chemical and Genomic Transformers, fine-tuned on proprietary biophysical datasets.

Biological Validation

Predictions are validated via macrophage infection assays, creating a feedback loop that constantly improves model accuracy.

High-Throughput Sorting

Automated sorting of bacteria based on density and stiffness to identify hit compounds rapidly.

Lead Program: Anti-Persistence

Our lead candidate demonstrates the power of mechanical modulation. By driving pathogenic mycobacteria into a specific mechanical state, we have observed significant attenuation of infection and a dramatic increase in sensitivity to standard-of-care antibiotics.

10x
Potentiation
Novel
Non-Antibiotic MOA
Broad
Spectrum Potential

Leadership Team

Combining expertise in microbiology, computational neuroscience, and business operations.

Vahan Manukyan

Vahan Manukyan

Business Lead | CFA, MBA

Co-Founder of multiple tech startups. Extensive experience leading finance & operations for VC-backed companies.

Haig A. Eskandarian

Haig A. Eskandarian

Science Lead | PhD

Microbiologist & Host-Pathogen specialist. Institut Pasteur, EPFL, UCSF, Harvard University.

Mehran Spitmann

Mehran Spitmann

AI Lead | PhD

Computational Neuroscientist. Expert in AI models for biomarker discovery and computational modeling