MS drug beneficial in an underrepresented group
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Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review
Predicting drug-target interactions (DTI) is a complex task. With the introduction of artificial intelligence (AI) methods such as machine learning and deep learning, AI-based DTI prediction can significantly enhance speed, reduce costs, and screen potential drug design options before conducting actual experiments. However, the application of AI methods also faces several challenges that need to be addressed. This article reviews various AI-based approaches and suggests possible future directions.
Flash optimization of drug combinations for Acinetobacter baumannii with IDentif.AI-AMR
Antimicrobial resistance (AMR) is an emerging threat to global public health. Specifically, Acinetobacter baumannii (A. baumannii), one of the main pathogens driving the rise of nosocomial infections, is a Gram-negative bacillus that displays intrinsic resistance mechanisms and can also develop resistance by acquiring AMR genes from other bacteria. More importantly, it is resistant to nearly 90% of standard of care (SOC) antimicrobial treatments, resulting in unsatisfactory clinical outcomes and a high infection-associated mortality rate of over 30%. Currently, there is a growing challenge to sustainably develop novel antimicrobials in this ever-expanding arms race against AMR. Therefore, a sustainable workflow that properly manages healthcare resources to ultra-rapidly design optimal drug combinations for effective treatment is needed. In this study, the IDentif.AI-AMR platform was harnessed to pinpoint effective regimens against four A. baumannii clinical isolates from a pool of nine US FDA-approved drugs. Notably, IDentif.AI-pinpointed ampicillin-sulbactam/cefiderocol and cefiderocol/polymyxin B/rifampicin combinations were able to achieve 93.89 ± 5.95% and 92.23 ± 11.89% inhibition against the bacteria, respectively, and they may diversify the reservoir of treatment options for the indication. In addition, polymyxin B in combination with rifampicin exhibited broadly applicable efficacy and strong synergy across all tested clinical isolates, representing a potential treatment strategy for A. baumannii. IDentif.AI-pinpointed combinations may potentially serve as alternative treatment strategies for A. baumannii.
Pharmacodynamics of interspecies interactions in polymicrobial infections
The pharmacodynamic response of bacterial pathogens to antibiotics can be influenced by interactions with other bacterial species in polymicrobial infections (PMIs). Understanding the complex eco-evolutionary dynamics of PMIs and their impact on antimicrobial treatment response represents a step towards developing improved treatment strategies for PMIs. Here, we investigated how interspecies interactions in a multi-species bacterial community affect the pharmacodynamic response to antimicrobial treatment. To this end, we developed an in silico model which combined agent-based modeling with ordinary differential equations. Our analyses suggest that both interspecies interactions, modifying either drug sensitivity or bacterial growth rate, and drug-specific pharmacological properties drive the bacterial pharmacodynamic response. Furthermore, lifestyle of the bacterial population and the range of interactions can influence the impact of species interactions. In conclusion, this study provides a foundation for the design of antimicrobial treatment strategies for PMIs which leverage the effects of interspecies interactions.
Free mobility across group boundaries promotes intergroup cooperation
Group cooperation is a cornerstone of human society, enabling achievements that surpass individual capabilities. However, groups also define and restrict who benefits from cooperative actions and who does not, raising the question of how to foster cooperation across group boundaries. This study investigates the impact of voluntary mobility across group boundaries on intergroup cooperation. Participants, organized into two groups, decided whether to create benefits for themselves, group members, or everyone. In each round, they were paired with another participant and could reward the other’s actions during an ‘enforcement stage’, allowing for indirect reciprocity. In line with our preregistered hypothesis, when participants interacted only with in-group members, indirect reciprocity enforced group cooperation, while intergroup cooperation declined. Conversely, higher intergroup cooperation emerged when participants were forced to interact solely with out-group members. Crucially, in the free-mobility treatment – where participants could choose whether to meet an in-group or an out-group member in the enforcement stage – intergroup cooperation was significantly higher than when participants were forced to interact only with in-group members, even though most participants endogenously chose to interact with in-group members. A few ‘mobile individuals’ were sufficient to enforce intergroup cooperation by selectively choosing out-group members, enabling indirect reciprocity to transcend group boundaries. These findings highlight the importance of free intergroup mobility for overcoming the limitations of group cooperation.
Unlocking the potential of experimental evolution to study drug resistance in pathogenic fungi
Exploring the dynamics and molecular mechanisms of antimicrobial drug resistance provides critical insights for developing effective strategies to combat it. This review highlights the potential of experimental evolution methods to study resistance in pathogenic fungi, drawing on insights from bacteriology and innovative approaches in mycology. We emphasize the versatility of experimental evolution in replicating clinical and environmental scenarios and propose that incorporating evolutionary modelling can enhance our understanding of antifungal resistance evolution. We advocate for a broader application of experimental evolution in medical mycology to improve our still limited understanding of drug resistance in fungi.
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