Malik Momna Arooj, Manzoor Sobia, Ashraf Javed
National University of Sciences and Technology (NUST), Atta-Ur-Rehman School of Applied Biosciences (ASAB), Islamabad, Pakistan.
University of Eastern Finland, Institute of Dentistry, Kuopio, Finland; Department of Oral Public Health, Riphah International University, Islamabad, Pakistan.
Int J Antimicrob Agents. 2025 Aug;66(2):107508. doi: 10.1016/j.ijantimicag.2025.107508. Epub 2025 Apr 9.
The objective of this study is to examine the efficacy of bacteriophage therapy in combating Acinetobacter baumannii, a pathogen known for its multidrug resistance, through the application of Bayesian statistical models. The research focuses on measuring survival outcomes in preclinical animal models that have been treated with bacteriophages, highlighting the promise of Bayesian methods in tackling the uncertainties present in biological data.
We carried out a systematic review, focusing on identifying pertinent studies regarding phage therapy in animal models. Bayesian exploratory data analysis was utilized to evaluate survival rates among three species: rodents, Galleria mellonella, and zebrafish. Various prior distributions were utilized in sensitivity analyses to assess the reliability of the results.
The study showed that groups that were treated with phage therapy had much higher survival rates across all experimental models. In untreated groups, survival rates for rodents ranged from 20% to 40%, while treated groups saw an increase to between 60% and 80%. Survival rates went up in the G. mellonella and zebrafish models. They went from 30% to 50% in the untreated groups to 70%-90% and 70%-80%, respectively, in the treated groups. The analysis demonstrated the reliability of these findings, showing consistent survival advantages across different prior assumptions.
The results support the clinical development of phage therapy as a possible way to treat infections that are resistant to multiple drugs. The use of Bayesian methods provides a strong foundation for assessing therapeutic effectiveness, especially in situations where conventional statistical approaches might fall short.
ORIGINALITY/VALUE: In this study, Bayesian methods are used in a new way to figure out how well phage therapy works. This shows that they can handle variation and uncertainty in preclinical studies. This research adds to the increasing body of evidence that highlights phage therapy as a viable alternative to traditional antibiotics.
本研究的目的是通过应用贝叶斯统计模型，检验噬菌体疗法对抗鲍曼不动杆菌（一种以多重耐药性著称的病原体）的疗效。该研究聚焦于测量经噬菌体治疗的临床前动物模型中的生存结果，凸显了贝叶斯方法在应对生物数据中存在的不确定性方面的前景。
我们进行了一项系统综述，重点是识别动物模型中有关噬菌体疗法的相关研究。利用贝叶斯探索性数据分析来评估啮齿动物、大蜡螟和斑马鱼这三个物种的存活率。在敏感性分析中使用了各种先验分布，以评估结果的可靠性。
研究表明，在所有实验模型中，接受噬菌体治疗的组存活率要高得多。在未治疗组中，啮齿动物的存活率在20%至40%之间，而治疗组的存活率提高到了60%至80%之间。大蜡螟和斑马鱼模型中的存活率也有所上升。未治疗组中它们的存活率从30%至50%分别提高到了治疗组中的70% - 90%和70% - 80%。分析证明了这些发现的可靠性，表明在不同的先验假设下都存在一致的生存优势。
研究结果支持将噬菌体疗法作为治疗多重耐药感染的一种可能方法进行临床开发。贝叶斯方法的使用为评估治疗效果提供了坚实基础，尤其是在传统统计方法可能不足的情况下。
原创性/价值：在本研究中，贝叶斯方法以一种新的方式被用于确定噬菌体疗法的效果。这表明它们能够处理临床前研究中的变异性和不确定性。这项研究增加了越来越多的证据，突出了噬菌体疗法作为传统抗生素可行替代方案的地位。
