Abuja: A Nigerian researcher, Dr. Gideon Gyebi, has emphasized the potential of emerging computational technologies to accelerate the discovery of new antibiotics, addressing the pressing global issue of antimicrobial resistance (AMR). Gyebi, a scientist specializing in Computational and Systems Biology, shared insights from his recent study with the News Agency of Nigeria (NAN).
According to News Agency of Nigeria, the study, titled 'Computational profiling of terpenoids for putative dual-target leads against Staphylococcus Aureus Penicillin-Binding Protein 2a and Beta-Lactamase,' highlights how Artificial Intelligence (AI), machine learning, and molecular modeling can expedite drug discovery. Presented at the Durban University of Technology in South Africa, the research focuses on Staphylococcus aureus (S. aureus), a bacteria causing numerous hospital-acquired infections and a primary example of antimicrobial resistance.
Gyebi noted that the emergence of Methicillin-Resistant Staphylococcus Aureus (MRSA) poses a significant global public health challenge by limiting the efficacy of commonly used antibiotics. He stressed that by concentrating on S. aureus, the study targets an urgent need for innovative strategies to combat AMR.
He elaborated on the transformative role of computational biology in medicine, explaining that simulating drug interactions with bacterial proteins can guide experiments more intelligently and accelerate discoveries. Computational studies offer speed, cost-efficiency, and precision that traditional lab experiments may lack, allowing thousands of compounds to be virtually screened in hours compared to months or years in laboratory testing.
Gyebi explained that computational modeling enables researchers to observe drug interactions with bacterial proteins at the molecular level, a task difficult to achieve directly in the lab. He emphasized that while computational tools do not replace experiments, they complement them by providing a roadmap to make laboratory studies more focused and efficient.
The study utilized computational tools to identify natural compounds, known as terpenoids, that could block two major bacterial defense systems: Penicillin-Binding Protein 2a (PBP2a) and B-lactamase. The synthesis of B-lactamase degrades B-lactam antibiotics before they can act, a key resistance mechanism of S. aureus. MRSA carries a modified penicillin-binding protein called PBP2a, which has a low affinity for most B-lactam antibiotics, rendering them ineffective.
Gyebi highlighted the importance of a dual-target approach to restore the effectiveness of common antibiotics that S. aureus has developed resistance against. By targeting both B-lactamase and PBP2a simultaneously, antibiotics have a higher chance of effectively combating S. aureus.
NAN reports that the World Health Organization (WHO) has listed antimicrobial resistance as one of the top ten global public health threats, with projections indicating it could cause ten million deaths annually by 2050 if uncontrolled. With over 70 publications indexed in Scopus and Web of Science and more than a thousand citations, Gyebi is among Africa's emerging scientists utilizing technology to tackle urgent global health challenges.
Gyebi concluded that integrating computational studies, artificial intelligence, and biotechnology could redefine the global antibiotic discovery pipeline, offering faster solutions to the growing threat of drug-resistant infections.