AMPQuest - Journeying to new horizons in treating drug-resistant infections.
Lead Research Organisation:
St George's University of London
Department Name: Institute of Infection & Immunity
Abstract
Context and Significance
The World Health Organization (WHO) has identified antibiotic resistance as a major global health threat, impacting not only health but also food security. This issue has led to increased medical costs, longer hospital stays, and a rise in mortality rates, with 1.2 million deaths in 2019 attributed to antibiotic resistance. We are approaching a critical point where common infections and minor injuries might become life-threatening due to drug resistance. This scenario could make routine surgeries highly risky, potentially undermining the achievements of modern medicine.
The Potential of Antimicrobial Peptides (AMPs)
Antimicrobial peptides (AMPs) are emerging as one of many promising solutions to this crisis. These naturally occurring substances are effective against multi-drug resistant bacteria. The diversity in their modes of action allows for the development of various AMPs into new drugs, potentially bypassing existing bacterial resistance mechanisms.
Our Team's Approach and Expertise
Our team, with over 60 years of combined experience in AMP research and 50 years in AI and data analysis, is at the forefront of this field demonstrated by more than 100,000 citations. Our team possesses the unique capability to synthesize and evaluate thousands of AMPs. We also utilize an advanced AI tool to predict new AMPs from extensive genomic data.
Challenges and Opportunities in AI Application
While AI has advanced in predicting AMP activity, challenges remain, particularly due to variability in existing datasets. These datasets often lack comprehensive information, such as toxicity and effectiveness in human conditions, which are critical for identifying peptides suitable for advanced drug development.
Vision and Objective
To overcome these challenges, we are compiling an extensive dataset of thousands of peptides. These will be tested against a multi-drug resistant bacterium under various conditions, generating a rich dataset of 105,000 data points. This initiative will enable our AI system to identify high-value peptide sequences more efficiently, reducing the time and cost of early drug development phases. The savings can then be reallocated to later development stages, enhancing the likelihood of success in clinical trials. Our long-term goal is to extend this research to include all WHO-priority organisms, further refining our AI-driven approach to expedite the development of effective drugs against multi-drug resistant pathogens.
Conclusion
Our mission is to harness AI to revolutionize antimicrobial drug development. With a skilled team, innovative technology, and a strategic plan, we are well-positioned to make significant contributions to combating drug-resistant infections. This project represents a vital addition to the UK's research landscape.
The World Health Organization (WHO) has identified antibiotic resistance as a major global health threat, impacting not only health but also food security. This issue has led to increased medical costs, longer hospital stays, and a rise in mortality rates, with 1.2 million deaths in 2019 attributed to antibiotic resistance. We are approaching a critical point where common infections and minor injuries might become life-threatening due to drug resistance. This scenario could make routine surgeries highly risky, potentially undermining the achievements of modern medicine.
The Potential of Antimicrobial Peptides (AMPs)
Antimicrobial peptides (AMPs) are emerging as one of many promising solutions to this crisis. These naturally occurring substances are effective against multi-drug resistant bacteria. The diversity in their modes of action allows for the development of various AMPs into new drugs, potentially bypassing existing bacterial resistance mechanisms.
Our Team's Approach and Expertise
Our team, with over 60 years of combined experience in AMP research and 50 years in AI and data analysis, is at the forefront of this field demonstrated by more than 100,000 citations. Our team possesses the unique capability to synthesize and evaluate thousands of AMPs. We also utilize an advanced AI tool to predict new AMPs from extensive genomic data.
Challenges and Opportunities in AI Application
While AI has advanced in predicting AMP activity, challenges remain, particularly due to variability in existing datasets. These datasets often lack comprehensive information, such as toxicity and effectiveness in human conditions, which are critical for identifying peptides suitable for advanced drug development.
Vision and Objective
To overcome these challenges, we are compiling an extensive dataset of thousands of peptides. These will be tested against a multi-drug resistant bacterium under various conditions, generating a rich dataset of 105,000 data points. This initiative will enable our AI system to identify high-value peptide sequences more efficiently, reducing the time and cost of early drug development phases. The savings can then be reallocated to later development stages, enhancing the likelihood of success in clinical trials. Our long-term goal is to extend this research to include all WHO-priority organisms, further refining our AI-driven approach to expedite the development of effective drugs against multi-drug resistant pathogens.
Conclusion
Our mission is to harness AI to revolutionize antimicrobial drug development. With a skilled team, innovative technology, and a strategic plan, we are well-positioned to make significant contributions to combating drug-resistant infections. This project represents a vital addition to the UK's research landscape.
| Description | Antibiotic resistance is a growing global crisis, with some bacteria no longer responding to existing treatments. The World Health Organization (WHO) has identified a list of priority pathogens that urgently require new antibiotics. One promising solution is antimicrobial peptides (AMPs)-naturally occurring molecules that can kill even multi-drug-resistant (MDR) bacteria. However, a major challenge is that many AMPs lose their effectiveness once inside the human body. This happens because enzymes break them down, or they bind to proteins like albumin, preventing them from attacking bacteria. Until now, no one had systematically studied how widespread or severe this issue is. Through this research, we tested 1,700 peptides against a WHO-priority pathogen and found that about 700 were effective under standard lab conditions. However, when we introduced human serum (which mimics real-life conditions), only 70 peptides remained active. This finding is significant because it shows: Most peptides lose their activity in human serum, meaning this challenge is common and needs to be considered when developing new drugs. Some peptides remain active despite serum exposure, making them promising candidates for new antibiotics. From the 70 peptides that retained activity, three stood out with exceptional therapeutic potential. These could form the basis for new drugs to combat MDR infections, and we will seek further funding to develop them further. Our approach allows us to rapidly identify promising new drug candidates, accelerating the fight against antibiotic resistance. |
| Exploitation Route | Drug Developers & Researchers - Our findings help design more stable antimicrobial peptides (AMPs) resistant to inactivation in human serum. AI & Data Scientists - The data generated can train AI models to predict high-value therapeutic peptides, optimizing drug discovery. Healthcare & Global Health Organizations - WHO and policymakers can prioritize investment in effective AMPs for antibiotic resistance. Pharmaceutical Industry - Companies can use our approach to accelerate peptide-based drug development. Patients & Clinicians - Our identified lead peptides offer potential new treatments for multi-drug-resistant infections. |
| Sectors | Chemicals Healthcare Manufacturing including Industrial Biotechology Pharmaceuticals and Medical Biotechnology |