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<p>Free-living amoebae such as <i>Acanthamoeba</i>, <i>Balamuthia mandrillaris</i>, and <i>Naegleria fowleri</i> cause lethal infections of the central nervous system, with mortality rates exceeding 90%, despite intensive therapy. These infections remain among the most challenging in clinical practice because therapeutic outcomes are unpredictable and there are no reliable prognostic markers. This article proposes the use of a unified, treatment-centred digital twin framework capable of integrating molecular, pharmacological, immunological, and imaging data to simulate patient-specific responses in real time. By continuously assimilating clinical and biological information, the model forecasts lesion regression, survival probability, and toxicity thresholds under different therapeutic regimens. In contrast to static empirical approaches, this adaptive system can support dose adjustment, predict failure earlier than imaging alone, and test drug combinations virtually before administration. Such a paradigm could transform management of amoebic encephalitis from empirical to predictive medicine, providing a transferable foundation for other neglected central nervous system infections.</p>

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A unified digital twin framework for predicting therapeutic response to central nervous system infections by pathogenic free-living amoebae

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月22日

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  • Ruqaiyyah Siddiqui,
  • Sutherland K Maciver,
  • David Lloyd,
  • Naveed Ahmed Khan

摘要

Free-living amoebae such as Acanthamoeba, Balamuthia mandrillaris, and Naegleria fowleri cause lethal infections of the central nervous system, with mortality rates exceeding 90%, despite intensive therapy. These infections remain among the most challenging in clinical practice because therapeutic outcomes are unpredictable and there are no reliable prognostic markers. This article proposes the use of a unified, treatment-centred digital twin framework capable of integrating molecular, pharmacological, immunological, and imaging data to simulate patient-specific responses in real time. By continuously assimilating clinical and biological information, the model forecasts lesion regression, survival probability, and toxicity thresholds under different therapeutic regimens. In contrast to static empirical approaches, this adaptive system can support dose adjustment, predict failure earlier than imaging alone, and test drug combinations virtually before administration. Such a paradigm could transform management of amoebic encephalitis from empirical to predictive medicine, providing a transferable foundation for other neglected central nervous system infections.