The Potential of Arterial Pulse Wave Analysis in Burn Resuscitation: A Pilot In Vivo Study.

While urinary output (UOP) remains the primary endpoint for titration of intravenous fluid resuscitation, it is an insufficient indicator of fluid responsiveness. Although advanced hemodynamic monitoring (including arterial pulse wave analysis [PWA]) is of recent interest, the validity of PWA-derived indices in burn resuscitation extremes has not been established. The goal of this paper is to test the hypothesis that PWA-derived cardiac output (CO) and stroke volume (SV) indices as well as pulse pressure variation (PPV) and systolic pressure variation (SPV) can play a complementary role to UOP in burn resuscitation. Swine were instrumented with a Swan-Ganz catheter for reference CO and underwent a 40% TBSA burns with varying resuscitation paradigms, and were monitored for 24 hours in an ICU setting under mechanical ventilation. The longitudinal changes in PWA-derived indices were investigated, and resuscitation adequacy was compared as determined by UOP vs PWA indices. The results indicated that PWA-derived indices exhibited trends consistent with reference CO and SV measurements: CO and SV indices were proportional to reference CO and SV, respectively (CO: postcalibration limits of agreement [LoA] = ±24.7 [ml/min/kg], SV: postcalibration LoA = ±0.30 [ml/kg]) while PPV and SPV were inversely proportional to reference SV (PPV: postcalibration LoA = ±0.32 [ml/kg], SPV: postcalibration LoA = ±0.31 [ml/kg]). The results also indicated that PWA-derived indices exhibited notable discrepancies from UOP in determining adequate burn resuscitation. Hence, it was concluded that the PWA-derived indices may have complementary value to UOP in assessing and guiding burn resuscitation.

Calibration and Validation of a Mechanistic COVID-19 Model for Translational Quantitative Systems Pharmacology - A Proof-of-Concept Model Development for Remdesivir.

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an urgent need to accelerate the traditional drug development process. Many studies identified potential COVID-19 therapies based on promising nonclinical data. However, the poor translatability from nonclinical to clinical settings has led to failures of many of these drug candidates in the clinical phase. In this study, we propose a mechanism-based, quantitative framework to translate nonclinical findings to clinical outcome. Adopting a modularized approach, this framework includes an in silico disease model for COVID-19 (virus infection and human immune responses) and a pharmacological component for COVID-19 therapies. The disease model was able to reproduce important longitudinal clinical data for patients with mild and severe COVID-19, including viral titer, key immunological cytokines, antibody responses, and time courses of lymphopenia. Using remdesivir as a proof-of-concept example of model development for the pharmacological component, we developed a pharmacological model that describes the conversion of intravenously administered remdesivir as a prodrug to its active metabolite nucleoside triphosphate through intracellular metabolism and connected it to the COVID-19 disease model. After being calibrated with the placebo arm data, our model was independently and quantitatively able to predict the primary endpoint (time to recovery) of the remdesivir clinical study, Adaptive Covid-19 Clinical Trial (ACTT). Our work demonstrates the possibility of quantitatively predicting clinical outcome based on nonclinical data and mechanistic understanding of the disease and provides a modularized framework to aid in candidate drug selection and clinical trial design for COVID-19 therapeutics.

Mathematical Modeling, In-Human Evaluation and Analysis of Volume Kinetics and Kidney Function After Burn Injury and Resuscitation.

OBJECTIVE: Existing burn resuscitation protocols exhibit a large variability in treatment efficacy. Hence, they must be further optimized based on comprehensive knowledge of burn pathophysiology. A physics-based mathematical model that can replicate physiological responses in diverse burn patients can serve as an attractive basis to perform non-clinical testing of burn resuscitation protocols and to expand knowledge on burn pathophysiology. We intend to develop, optimize, validate, and analyze a mathematical model to replicate physiological responses in burn patients. METHODS: Using clinical datasets collected from 233 burn patients receiving burn resuscitation, we developed and validated a mathematical model applicable to computer-aided in-human burn resuscitation trial and knowledge expansion. Using the validated mathematical model, we examined possible physiological mechanisms responsible for the cohort-dependent differences in burn pathophysiology between younger versus older patients, female versus male patients, and patients with versus without inhalational injury. RESULTS: We demonstrated that the mathematical model can replicate physiological responses in burn patients associated with wide demographic characteristics and injury severity, and that an increased inflammatory response to injury may be a key contributing factor in increasing the mortality risk of older patients and patients with inhalation injury via an increase in the fluid retention. CONCLUSION: We developed and validated a physiologically plausible mathematical model of volume kinetic and kidney function after burn injury and resuscitation suited to in-human application. SIGNIFICANCE: The mathematical model may provide an attractive platform to conduct non-clinical testing of burn resuscitation protocols and test new hypotheses on burn pathophysiology.

Mathematical model of volume kinetics and renal function after burn injury and resuscitation.

This paper presents a mathematical model of blood volume kinetics and renal function in response to burn injury and resuscitation, which is applicable to the development and non-clinical testing of burn resuscitation protocols and algorithms. Prior mathematical models of burn injury and resuscitation are not ideally suited to such applications due to their limited credibility in predicting blood volume and urinary output observed in wide-ranging burn patients as well as in incorporating contemporary knowledge of burn pathophysiology. Our mathematical model consists of an established multi-compartmental model of blood volume kinetics, a hybrid mechanistic-phenomenological model of renal function, and novel lumped-parameter models of burn-induced perturbations in volume kinetics and renal function equipped with contemporary knowledge on burn-related physiology and pathophysiology. Using the dataset collected from 16 sheep, we showed that our mathematical model can be characterized with physiologically plausible parameter values to accurately predict blood volume kinetic and renal function responses to burn injury and resuscitation on an individual basis against a wide range of pathophysiological variability. Pending validation in humans, our mathematical model may serve as an effective basis for in-depth understanding of complex burn-induced volume kinetic and renal function responses as well as development and non-clinical testing of burn resuscitation protocols and algorithms.