Towards the sustainable discovery and development of new antibiotics

Abstract

An ever-increasing demand for novel antimicrobials to treat life-threatening infections caused by the global spread of multidrug-resistant bacterial pathogens stands in stark contrast to the current level of investment in their development, particularly in the fields of natural-product-derived and synthetic small molecules. New agents displaying innovative chemistry and modes of action are desperately needed worldwide to tackle the public health menace posed by antimicrobial resistance. Here, our consortium presents a strategic blueprint to substantially improve our ability to discover and develop new antibiotics. We propose both short-term and long-term solutions to overcome the most urgent limitations in the various sectors of research and funding, aiming to bridge the gap between academic, industrial and political stakeholders, and to unite interdisciplinary expertise in order to efficiently fuel the translational pipeline for the benefit of future generations.

Artificial Intelligence and Antibiotic Discovery

“…Consequently, antimicrobial resistance becomes a more accelerated phenomenon, with additional negative consequences for the healthcare systems. Therefore, artificial intelligence methods, such as machine learning or deep learning, may help to achieve a faster delivery of promising antibiotic candidates [ 20 ].…”

“…A Strength, Weaknesses, Opportunities and Threats (SWOT) analysis by Miethke et al offers an in-depth perspective on the development of novel antibiotic drugs, mentioning the emerging artificial intelligence technologies as opportunities and future solutions for their ability to make hit discovery more efficient and reliable [ 20 ]. The extensive use of antibiotics in animals and the development of multi-drug-resistant bacteria render the process of antibiotic development useless, while fueling a never-ending race for finding novel antibiotics as current antibiotics become obsolete.…”

“…The latest improvements in AI technologies are seen as an opportunity to discover novel antibiotics, develop and further improve existing ones, and better handle the current antibiotic crisis, due to their increased efficiency during the “hit discovery” phase of drug development [ 20 ]. Current algorithms and machine learning programs make it possible for large databases to be scanned and analyzed in order to select the best suitable “hit compounds” that possess antimicrobial activity.…”

“…It could significantly reduce both the duration needed to select the candidate molecules as well as the cost per molecule. The costs of the early stages of candidate discovery (screening and hit generation, hit-to-lead and lead optimization) are estimated to be around 5–10 million euros, even without taking into consideration the so-called “attrition-molecules” that are abandoned later in the development process [ 20 ]. Additionally, this would mean a faster, more feasible process of drug development that would limit the existing threats, such as the rising death toll per year due to antimicrobial resistance, and the high socio-economic cost over future decades due to the increased hospitalization and treatment of multi-drug-resistant bacteria [ 20 , 54 ].…”

Application of Polymeric Nanocarriers for Enhancing the Bioavailability of Antibiotics at the Target Site and Overcoming Antimicrobial Resistance

“…Through mutation and selection, most pathogenic microorganisms can be adapted and develop defenses against antibiotic attacks. As the threat of antibiotic resistance spreads, the need for more advanced approaches and overcoming the mechanisms behind antibiotic resistance become essential [1].…”