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Antibiotic Selection00:57

Antibiotic Selection

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Overview
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Microorganisms in Medicine and Therapeutics01:29

Microorganisms in Medicine and Therapeutics

Microorganisms play a fundamental role in vaccine development, gene therapy, and therapeutic production. Their biological properties are harnessed to advance medicine and public health. Beyond immunization, microorganisms contribute to gut health, antibiotic synthesis, and genetic disease treatment.Live Attenuated and Inactivated VaccinesLive attenuated vaccines, such as the measles, mumps, and rubella (MMR) vaccine, utilize weakened forms of pathogens to closely resemble natural infections.
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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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The central dogma explains the flow of genetic information from DNA nucleotides to the amino acid sequence of proteins.
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Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection
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一种用于优化抗生素的生成人工智能方法.

Marcelo D T Torres, Yimeng Zeng, Fangping Wan

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    此摘要是机器生成的。

    一个新的人工智能 (AI) 框架APEX生成优化 (APEX GO) 设计了新型抗菌来对抗抗菌素耐药性 (AMR). 这种人工智能方法加速了基于的有效抗生素的发现.

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    科学领域:

    • 计算化学和药物发现
    • 医学中的人工智能.
    • 抗微生物的设计

    背景情况:

    • 抗菌素耐药性 (AMR) 是一个关键的全球健康威胁,需要新的抗生素发现策略.
    • 抗微生物的实验优化是具有挑战性的,因为难以预测和资源密集的试错方法.
    • 现有的方法通常依赖于选固定的数据库,限制发现真正的新型药物.

    研究的目的:

    • 引入APEX生成优化 (APEX GO),这是一个用于设计和优化抗菌的生成AI框架.
    • 通过产生超越现有数据库的新奇序列来证明设计的范式转变.
    • 提供第一个体外和体内实验验证,用于酸设计的生成贝叶斯优化.

    主要方法:

    • 开发了APEX GO,集成基于变压器的变化自编码器与贝叶斯优化.
    • 将设计和多样性限制纳入可控序列创新的变异自编码器中.
    • 利用十种已灭绝的类作为生成新型抗微生物衍生物的模板.

    主要成果:

    • 在APEX GO的实验中,实验成功率达到了85%,而在增强抗微生物活性对抗格兰氏阴性病原体方面,成功率达到了72%.
    • 合成和表征了100个AI优化的,评估了抗菌活性,作用机制,二级结构和细胞毒性.
    • 几种AI优化的,特别是mammuthusin-3和mylodonin-2的衍生物,在小鼠模型中显示出强大的体内抗感染活性,对抗Acinetobacter baumannii,与polymyxin B.相比.

    结论:

    • APEX GO代表了一种新的生成性AI方法,用于加速体设计和抗生素优化.
    • 该框架成功生成了优化的抗微生物,提高了有效性,并验证了它们在体外和体内的性能.
    • 通过发现新的基于的抗生素,APEX GO提供了一种强大的工具,以应对抗菌素耐药性日益严重的挑战.