Dynamic Computational Model of Symptomatic Bacteremia to Inform Bacterial Separation Treatment Requirements

The rise of multi-drug resistance has decreased the effectiveness of antibiotics, which has led to increased mortality rates associated with symptomatic bacteremia, or bacterial sepsis. To combat decreasing antibiotic effectiveness, extracorporeal bacterial separation approaches have been proposed to capture and separate bacteria from blood. However, bacteremia is dynamic and involves host-pathogen interactions across various anatomical sites. We developed a mathematical model that quantitatively describes the kinetics of pathogenesis and progression of symptomatic bacteremia under various conditions, including bacterial separation therapy, to better understand disease mechanisms and quantitatively assess the biological impact of bacterial separation therapy. Model validity was tested against experimental data from published studies. This is the first multi-compartment model of symptomatic bacteremia in mammals that includes extracorporeal bacterial separation and antibiotic treatment, separately and in combination. The addition of an extracorporeal bacterial separation circuit reduced the predicted time of total bacteria clearance from the blood of an immunocompromised rodent by 49%, compared to antibiotic treatment alone. Implementation of bacterial separation therapy resulted in predicted multi-drug resistant bacterial clearance from the blood of a human in 97% less time than antibiotic treatment alone. The model also proposes a quantitative correlation between time-dependent bacterial load among tissues and bacteremia severity, analogous to the well-known ‘area under the curve’ for characterization of drug efficacy. The engineering-based mathematical model developed may be useful for informing the design of extracorporeal bacterial separation devices. This work enables the quantitative identification of the characteristics required of an extracorporeal bacteria separation device to provide biological benefit. These devices will potentially decrease the bacterial load in blood. Additionally, the devices may achieve bacterial separation rates that allow consequent acceleration of bacterial clearance in other tissues, inhibiting the progression of symptomatic bacteremia, including multi-drug resistant variations.

多重耐药（multi-drug resistance）的出现降低了抗生素（antibiotics）的临床疗效，进而导致有症状菌血症（symptomatic bacteremia，又称细菌性脓毒症（bacterial sepsis））相关的死亡率上升。为应对抗生素疗效下降的困境，研究人员已提出体外细菌分离（extracorporeal bacterial separation）策略，用于从血液中捕获并分离病原菌。不过菌血症是动态过程，涉及多个解剖部位的宿主-病原体相互作用。本研究开发了一款数学模型（mathematical model），可定量描述包括细菌分离治疗在内的多种条件下有症状菌血症的发病动力学与疾病进展过程，以更好地解析疾病机制，并定量评估细菌分离治疗的生物学影响。模型的有效性通过已发表研究的实验数据进行了验证。本研究首次构建了哺乳动物有症状菌血症的多室模型，可分别或联合应用体外细菌分离与抗生素治疗方案。相较于单一抗生素治疗，添加体外细菌分离回路可使免疫功能低下啮齿类动物（immunocompromised rodent）血液内细菌的总清除时间预计缩短49%。实施细菌分离治疗后，预计人体血液内多重耐药细菌的清除时间较单一抗生素治疗减少97%。该模型还提出了组织内细菌载量随时间变化与菌血症严重程度之间的定量相关性，这类似于用于表征药物疗效的经典“曲线下面积（area under the curve）”。本研究开发的基于工程学的数学模型，可为体外细菌分离装置的设计提供理论参考。本研究可定量明确体外细菌分离装置需具备的、可产生生物学益处的核心特性参数。此类装置有望降低血液内的细菌载量；此外，这些装置可实现的细菌分离速率，可进一步加速其他组织内的细菌清除，从而抑制有症状菌血症（包括多重耐药菌株引发的亚型）的病情进展。