Quaternary ammonium compounds (QACs) serve as mainstays in the formulation of disinfectants and antiseptics. However, an over-reliance and misuse of our limited QAC arsenal has driven the development and spread of resistance to these compounds, as well as co-resistance to common antibiotics. Extensive use of these compounds throughout the COVID-19 pandemic thus raises concern for the accelerated proliferation of antimicrobial resistance and demands for next-generation antimicrobials with divergent architectures that may evade resistance. To this end, we endeavored to expand beyond canonical ammonium scaffolds and examine quaternary phosphonium compounds (QPCs). Accordingly, a synthetic and biological investigation into a library of novel QPCs unveiled biscationic QPCs to be effective antimicrobial scaffolds with improved broad-spectrum activities compared to commercial QACs. Notably, a subset of these compounds was found to be less effective against a known QAC-resistant strain of MRSA. Bioinformatic analysis revealed the unique presence of a family of small multiresistant transporter proteins, hypothesized to enable efflux-mediated resistance to QACs and QPCs. Further investigation of this resistance mechanism through efflux-pump inhibition and membrane depolarization assays illustrated the superior ability of P6P-10,10 to perturb the cell membrane and exert the observed broad-spectrum potency compared to its commercial counterparts. Collectively, this work highlights the promise of biscationic phosphonium compounds as next-generation disinfectant molecules with potent bioactivities, thereby laying the foundation for future studies into the synthesis and biological investigation of this nascent antimicrobial class.
Quaternary ammonium compounds (QACs) are vital disinfectants for the neutralization of pathogenic bacteria in clinical, domestic, and commercial settings. After decades of dependence on QACs, the emergence of antimicrobial resistance to this class of compounds threatens the ability of existing QAC products to effectively manage rising bacterial threats. The need for new disinfectants is therefore urgent, with quaternary phosphonium compounds (QPCs) emerging as a new class of promising antimicrobials that boast significant activity against highly resistant bacteria. Reported here is a series of twenty-one novel QPCs that replace phenyl substituents on the phosphorus center with alkyl groups yet allow for rapid synthetic routes in high yields. Within this series are structures containing methyl, ethyl, or cyclohexyl phosphonium substituents on bisphosphane scaffolds bearing ethyl linkers, affording atom economical structures and ones that represent exact analogs to nitrogenous amphiphiles. The resultant bisQPC structures display high antibacterial efficacy enjoyed by comparably constructed QACs, with three structures in the singledigit micromolar activity range despite structural simplification.
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design. However, the advancement of deep generative models is limited by challenges to generate objects that possess multiple desired properties: 1) the existence of complex correlation among real-world properties is common but hard to identify; 2) controlling individual property enforces an implicit partially control of its correlated properties, which is difficult to model; 3) controlling multiple properties under various manners simultaneously is hard and under-explored. We address these challenges by proposing a novel deep generative framework, CorrVAE, that recovers semantics and the correlation of properties through disentangled latent vectors. The correlation is handled via an explainable mask pooling layer, and properties are precisely retained by generated objects via the mutual dependence between latent vectors and properties. Our generative model preserves properties of interest while handling correlation and conflicts of properties under a multi-objective optimization framework. The experiments demonstrate our model's superior performance in generating data with desired properties. The code of CorrVAE is available at https://github.com/shi-yu-wang/CorrVAE.
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