Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Parasitology Research

DOI: 10.1007/s00436-026-08674-6

Authors: Ruqaiyyah Siddiqui, Sutherland K Maciver, David Lloyd, Naveed Ahmed Khan

Institutions: Heriot-Watt University, Istinye University, University of Edinburgh, Cardiff University, University of Derby

Language: English

Topic: Legionella and Acanthamoeba research

Subfield: Endocrinology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,590