Kinetic analysis of bacterial clearance in mice using the ESTRIPc and KINET microcomputer programs

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.

Multiplex PCR pathogen detection in two severely burned patients with suspected septicemia

The case studies reported in this article strive to illustrate the clinical value of multiplex polymerase chain reaction (PCR)-based pathogen detection in patients with burn sepsis. Adult (age ≥18 years) burn patients (≥20% TBSA) presenting with signs and symptoms of burn sepsis were enrolled into a prospective, observational trial. Patients received PCR testing in parallel to routine blood cultures. The authors report two cases in which PCR was used to rapidly detect pathogens in whole blood from burn patients with suspected septicemia. PCR identified Escherichia coli in 5.8 hours in case 1. Blood and sputum cultures required 17 hours for Gram stain results. Empiric ceftriaxone therapy was initiated. Blood cultures required an additional 18 hours to identify the same pathogen detected by PCR. Ceftriaxone was replaced with ciprofloxacin for improved coverage. Follow-up antimicrobial susceptibility results revealed intermediate ciprofloxacin resistance. Meropenam therapy was initiated. In case 2, PCR detected Pseudomonas aeruginosa in 5.45 hours while blood cultures remained negative. Respiratory cultures became positive for P. aeruginosa 2 days later. Serial PCR samples continued detecting P. aeruginosa despite negative blood cultures and appropriate antimicrobial therapy. The patient later became hypotensive and coagulopathic and expired. For both patient cases, PCR identified high-risk pathogens faster than culture methods. In the second patient case, PCR identified the presence of pathogen DNA despite negative cultures before the onset of septic shock and presumptive disseminated intravascular coagulation. These results warrant further investigation to determine the clinical significance of pathogen DNA in burn sepsis.

Dynamical system analysis of Staphylococcus epidermidis bloodstream infection

Unlike many localized infections, the development and resolution of bacteremia involves physical and immunological interactions between many anatomic sites. In an effort to better understand these interactions, we developed a computational model of bacteremia as a dynamical system fashioned after multicompartmental pharmacodynamic models, incorporating bacterial proliferation and clearance in the blood, liver, spleen, and lungs, and the transport of pathogens between these sites. A system of four first-order homogeneous ODEs was developed. Blood and organ bacterial burdens were measured at various time points from 3 to 48 h postinoculation using an LD25 murine model of Staphylococcus epidermidis bacteremia. Using these empiric data, solutions to the mathematical model were recovered. A bootstrap resampling method was used to generate 95% confidence intervals around the solved parameters. The validity of the model was examined in parallel experiments using animals acutely immunocompromised with cyclophosphamide; the model captured abnormalities in bacterial partitioning previously described with this antineoplastic agent. Lastly, the approach was used to explore possible benefits to clinically observed hyperdynamic blood flow during sepsis: in simulation, normal mice, but not those treated with cyclophosphamide, enjoyed significantly more rapid bacterial clearance from the bloodstream under hyperdynamic conditions.

Computational models in immunological methods: an historical review

The utilization of computational models in immunology dates from the birth of the science. From the description of antibody-antigen binding to the structural models of receptors, models are utilized to bring fundamental understandings of the processes together with laboratory measurements to uncover implications of these data. In this review, an historical view of the role of computational models in the immunology laboratory is presented, and short mathematical descriptions are given of fundamental assays. In addition, the range of current uses of models is explored -- especially as seen through papers which have appeared in the Journal of Immunological Methods from volume 1 (1971/1972) to volume 208 (1997). Each paper which introduced a new mathematical, statistical, or computer simulation model, or introduced an enhancement to an instrument through a model in those volumes is cited and the type of computational model noted.