Toward personalized and longitudinal antibiograms—a proof-of-concept for in vivo assessments of immunological-antibiotic-bacterial interactions
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Introduction
In vivo , temporal, and personalized data on immunological-antibiotic interactions may provide richer, earlier information that improves infection treatment. To test this hypothesis, we applied a proof-of-concept approach to evaluate a non-reductionist methodology.
Methods
A non-reductionist, data-driven, pattern recognition-based combinatorial method that captures relationships was evaluated with blood leukocyte data collected from 401 individuals from Greece [n=331] and the US [n=70] who experienced sepsis, pneumonia, endocarditis, tuberculosis, syphilis, skin and soft tissue, intra-abdominal, or urinary tract infections associated with meningitis.
Results
While ambiguity was observed when variables were measured in isolation by a reductionist alternative, ambiguity was prevented and hidden information was uncovered when complex dynamics were assessed by the non-reductionist method. The combinatorial approach grouped together observations that displayed similar immune profiles, distinguished those with different mortality risks, identified antibiotics that modulated specific leukocytes, differentiated two types of non-responsiveness (associated or not associated with antibiotics), and facilitated earlier and personalized evaluation of therapies. For instance, in tuberculosis, blood monocytes were modulated by isoniazid-related antimicrobials.
Discussion
Across numerous syndromes, the non-reductionist method extracted more information from the same data than the reductionist alternative. If corroborated, non-reductionist methodologies may promote research and support personalized medicine.
