Hi everyone! – We’re Piotr, Weronika, and Adam, a team of biochemists and evolutionary biologists from the University of Oxford on a mission to make the next-generation of antibiotics.

Current antibiotics stop working because bacteria evolve resistance to them. Our approach leverages co-evolutionary protein-protein interaction datasets combined with AI to forecast bacterial mutations and create ‘future-proof’ antibiotics, addressing antibiotic resistance before it develops. This changes the game for how frequently we’ll need to make new antibiotics and how long our new antibiotics will be able to treat patients.

Let's get into more detail:

❌ What’s the problem?

Antibiotic resistance is a looming global health crisis:

• ☠ Already killing more people than Malaria and HIV
• 📈 Getting exponentially worse because of bacterial resistance
• 💸 $100 trillion economic burden undermining modern medicine

✅ We solve this by making future-proof antibiotics that:

1. 🏃‍♂ Stay ahead of bacterial mutations to prevent resistance
2. 🎯 Precisely target harmful bacteria without disrupting beneficial microbes
3. 💵 Overcome the economic challenges of antibiotic development

Our Science

Traditional trial-and-error discovery cannot compete with bacteria's ability to mutate and acquire resistance genes. Our evolutionary datasets and AI will allow us to stay one step ahead of bacteria. We don’t react, we anticipate:

You might wonder whether bacteria would eventually be able to mutate in other ways around our antibiotics. Well, yes, they could, but our approach forces all the escape mutations to be extremely costly. In fact, so costly that the bacteria wouldn’t survive. How?

Our experiments are like running a battle simulation hundreds of times to find enemies’ weak points. This means that we can create detailed maps of the co-evolutionary landscapes of bacteria and our antibiotics so that we can ultimately engineer medicines with a low propensity for resistance emergence.

Watch how that works:

We then engineer our antibiotics for stability and safety inside the human body using a suite of protein AI models (both diffusion and language model-based). This engineering means our antibiotics 1) only target pathogenic bacteria and not human cells or microbiomes and 2) have the potential to be given as a single dose – reducing the amount of monitoring that doctors have to do on patients. This is in contrast to current antibiotics, which can have human cell toxicity, disrupt microbiomes, and have to be dosed every few hours.

👩‍⚕️ Why doctors are excited

Our blueprints have the potential for:

• Low drug-drug interactions
• Low risk of C. difficile infection
• Low dosing regimes
• Low risk of resistance emergence
• Low side effect profile

📊 Evolvere Bio Factfile

• 🧪 We have already synthesized molecules that specifically kill bacteria in physiological conditions. These molecules are specific to only their target bacteria.
• 🧬 Our R&D generates valuable data on protein co-evolution.
• 🤖 We build AI models to predict and prevent resistance that arises from protein co-evolution, with potential applications in other therapeutic areas.

Team

• 🧑‍🔬 Adam Winnifrith - Oxford biochemist who developed new biochemical assays based on advanced statistical concepts and published work on the advancements in generative AI in protein design.
• 🧑‍🔬 Piotr Jedryszek – Oxford computational biologist studying the evolution of bacteria using deep learning techniques. His past work included molecular dynamics simulations, nanopore engineering, and biofuels.
• 👩‍🔬 Weronika Ślesak – evolutionary biologist who worked at renowned microbiology laboratories (at the University of Oxford and Institut Pasteur) on high-throughput experimental evolution, antibiotic resistance genes and evolutionary trade-offs.
• 👨‍💼 Oliver Waterhouse - serial biotech entrepreneur who sold his previous Oxford-based company Base Genomics for $410 million.

Our Asks

Are you as excited as we are about making future-proof antibiotics?

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• Reach out to us at founders@evolverebiosciences.com