Helen K. Donnelly

Cefepime is used in critically ill pneumonia patients, but dosing in renal replacement therapy (RRT) is complicated by altered renal function, extracorporeal removal, and variability in pulmonary epithelial lining fluid (ELF) exposure. We sought to develop an integrated model linking systemic, extracorporeal, and pulmonary cefepime pharmacokinetics in RRT. Pharmacokinetic (PK) data from critically ill adults receiving cefepime and requiring RRT were included. In a dedicated continuous RRT (CRRT) cohort ( n = 8), pre- and post-filter plasma and effluent PK data were collected. Additional patients ( n = 43) requiring RRT also contributed plasma and ELF samples. A non-parametric population PK model was developed using an 80/20 subject-level development-validation split. Covariate models assessed the impact of residual renal function, effluent flow rate, body size, and fixed intradialytic hemodialysis (HD) clearance. Monte Carlo simulations evaluated plasma and ELF target attainment across dosing strategies, weight strata, and minimum inhibitory concentrations (MICs). The final model jointly described systemic, extracorporeal, and pulmonary cefepime disposition using piecewise central volume, native clearance, linear effluent-flow-dependent CRRT clearance, fixed HD clearance, and ELF distribution. Simulations showed adequate plasma target attainment, whereas ELF attainment was sensitive to MIC, target, regimen, and body weight. Continuous infusions of 4 g/day provided adequate ELF attainment at MICs ≤4 mg/L. Plasma-based cefepime target attainment may overestimate pulmonary exposure during RRT, particularly at higher MICs, body weights, and f T>MIC targets. External validation and prospective studies are needed to refine pulmonary target estimates and determine whether plasma-guided Bayesian dosing can reliably ensure site-of-infection exposure.

Population pharmacokinetic (PK) models may not generalize across critically ill populations. We evaluated the transportability of published parametric and nonparametric meropenem models as Bayesian priors. Seven published meropenem models were implemented in Monolix 2024R1. Nonparametric models were converted to log-normal priors from reported medians and CV%. An independent cohort was used for evaluation. A priori and a posteriori predictions were summarized across eligible observations and separately for observations obtained during continuous renal replacement therapy (CRRT) and in the absence of CRRT. Predictive performance was assessed using relative mean prediction error (rMPE) and relative median absolute prediction error (rMdAPE). An rMPE within ±20% and an rMdAPE ≤30% were defined as acceptable. Seventeen patients, including six receiving CRRT, contributed 74 plasma concentrations. In the pooled cohort, only the Rohani and Shekar models met predefined performance thresholds at both population and individual levels ( a priori rMdAPE 24.6%–26.4%; a posteriori rMdAPE 6.7%–11.1%). In CRRT patients, no model achieved acceptable a priori performance; after Bayesian updating, all CRRT-specified models met accuracy and precision criteria, with the nonparametric models (Reed and Rohani) demonstrating low imprecision. In non-CRRT patients, several models showed population-level bias, but all met predefined accuracy and precision thresholds after Bayesian updating. Among evaluated models, Rohani, Reed, and Shekar demonstrated the most consistent predictive performance across the CRRT and non-CRRT subgroups. Nonparametric model summaries can be translated into parametric priors, but model selection remains critical when applying Bayesian dosing in critically ill patients.

Viral pneumonia is perpetuated by inflammatory circuits between activated T cells and monocyte-derived alveolar macrophages (MoAM). T cells and macrophages express ORAI1 and STIM1, which form calcium release-activated calcium (CRAC) channels that allow extracellular calcium entry in response to endoplasmic reticulum calcium store depletion. In a randomized, placebo-controlled, multicenter phase 2 trial (CARDEA), Auxora, a CRAC channel inhibitor, reduced all-cause 30-day mortality by 56% in patients with severe SARS-CoV-2 pneumonia. Here, we report a multi-omics analysis of serially collected alveolar samples from unvaccinated patients with severe SARS-CoV-2 pneumonia treated with Auxora versus placebo. We found reductions in plasma levels of the monocyte– and T cell-chemokines, CCL8 and PDGF-AA. Using peripheral blood mononuclear cells (PBMC) from healthy volunteers, we show that Auxora directly targets T cells to inhibit the transcription of CCL8 and PDGFA in monocyte-derived macrophages, supporting a mechanism for its effects and a potential intermediate biomarker of efficacy.

Pneumonia is the leading cause of death from infectious disease worldwide. The diagnosis and treatment of patients with pneumonia lag behind other major conditions, relying on syndromic definitions that lack molecular resolution and ignore underlying endotypes. We sought to test the hypothesis that dynamic pathogen-specific host responses in the alveolar space represent distinct pneumonia endotypes linked to different clinical features and outcomes. We prospectively enrolled a cohort of 690 patients (including immunocompromised patients) with known or suspected pneumonia receiving mechanical ventilation in whom the etiology of pneumonia was determined by gold-standard analysis of distal lung fluid obtained by bronchoalveolar lavage (BAL) combined with clinical adjudication. From these patients, we analyzed 792 BAL fluid samples, including 310 serial samples, using flow cytometry (482 patients) and single-cell RNA-sequencing (170 patients; 263 samples, complemented by 9 healthy controls and 25 post-COVID-19 patients, yielding ~2.4 million single cells across 28 cell types), and extracted daily clinical data from the electronic health record (>15,000 patient-days). We used machine learning models to identify pathogen-specific host responses in the transcriptome of alveolar immune cells that were associated with changes in alveolar cell abundance and clinical features. Our results suggest that therapeutic strategies for pneumonia should be individualized to specific host-pathogen interactions.

Background: Meropenem is widely used to treat hospital‐acquired pneumonia in critically ill patients, with efficacy dependent on the time that free concentrations exceed the minimum inhibitory concentration ( f T > MIC). In practice, single‐sample therapeutic drug monitoring (TDM) may not ensure target attainment, particularly in patients requiring continuous renal replacement (CRRT). Model‐informed precision dosing (MIPD) enables individualized, real‐time adjustments, but implementation is limited. Herein, we developed a Shiny application for Bayesian meropenem dose optimization. Methods: A previously published nonparametric model was translated into a parametric Bayesian framework with maximum a posteriori (MAP) updating implemented through a Shiny interface. Plasma pharmacokinetic (PK) data from critically ill patients served for external validation. Posterior predictions were benchmarked against population and individual (MAP) outputs from Pmetrics 3.09 and population median profiles from 1000 Monte Carlo simulations per patient. Predictive performance was assessed using relative median prediction error ( r MPE) and relative median absolute prediction error ( r MAPE), for the combined cohort overall and stratified by CRRT status. Sparse sampling scenarios (one, two, and three observations per patient) were also evaluated. Results: Eighteen patients were evaluated (non‐CRRT n = 13; CRRT n = 5). In Pmetrics, r MPE ranged from −7.7% to 9.4% and r MAPE from 18.5% to 25.5% across renal replacement strata and prediction types. Simulation‐based population predictions yielded greater error, with r MPE/ r MAPE of −25%/34% in CRRT patients and −21%/30% in non‐CRRT patients. Across Bayesian Dosing Simulator (BDS) sampling scenarios, prediction error remained within prespecified limits ( r MPE ±20%, r MAPE ≤ 30%), with F20 ranging from 80% to 94% and F30 from 87% to 94%. Conclusion: A validated nonparametric meropenem model was successfully implemented within an open‐source Bayesian framework yielding predictive accuracy comparable to the reference model with robust performance under sparse sampling, supporting its feasibility for individualized meropenem dosing. Prospective evaluation of its clinical safety and effectiveness is needed.

Many analyses of critically ill patients focus on admission features and discharge outcomes, overlooking daily events and intercurrent complications. Pneumonia is a syndromic entity that is difficult to adjudicate outcomes without clinical expertise. Here, we examine clinical features on a day-by-day basis, mirroring the practice of daily multidisciplinary rounds, with annotated pneumonia episodes and outcomes. The Successful Clinical Response in Pneumonia Therapy (SCRIPT) CarpeDiem Dataset includes 21,931 patient-hospital-days from 704 patients enrolled in the SCRIPT study between June 2018 and May 2023. These patients were all receiving mechanical ventilation and underwent bronchoalveolar lavage to diagnose and establish the etiology of their pneumonia or an alternative diagnosis. The dataset captures patient demographics, daily clinical parameters, including vital signs, laboratory results, and mechanical support data, detailed pneumonia episode results adjudicated by critical care physicians, and outcomes. All data are de-identified per HIPAA Safe Harbor guidelines. This dataset offers a unique resource for analyzing the clinical trajectories of patients with severe pneumonia.

Optimizing antibiotic exposures in hospital-acquired pneumonia (HAP) is crucial; however, dosing decisions often overlook target levels for the beta-lactamase inhibitor tazobactam (TAZ), and the impact of renal dysfunction and renal replacement therapy on TAZ pharmacokinetics (PK) remains poorly described. We developed a population PK model of TAZ using 162 plasma samples from 35 ICU patients with HAP, including those receiving continuous renal replacement therapy (CRRT). TAZ concentrations were quantified using validated LC-MS/MS methods. A one-compartment model was fit using Monolix, with clearance modeled as a function of creatinine clearance, CRRT effluent flow rate, and intermittent hemodialysis. Monte Carlo simulations assessed the probability of unbound TAZ concentrations exceeding 1, 2, or 4 mg/L for 100% of the interval between 24 and 48 h across intermittent, extended, and continuous infusion (CI) regimens, stratified by renal function and CRRT. TAZ clearance was driven by renal function and CRRT flow rate. All regimens achieved ≥90% probability of target attainment (PTA) at thresholds of 1–2 mg/L. At 4 mg/L, PTA fell below 90% in patients with creatinine clearance ≥150 mL/min for all regimens, with low-dose CI performing the worst. High-dose CI improved PTA at this higher threshold but may increase the risk of piperacillin overexposure. TAZ exposures sufficient for enzymatic beta-lactamase inhibition are generally achieved with standard dosing for lower thresholds but not for more aggressive targets (e.g., high extended-spectrum β-lactamase expression), particularly in patients with preserved or augmented renal function, who may require therapeutic drug monitoring or alternative therapy.

Optimizing β-lactam antibiotic exposure in critically ill patients with hospital-acquired pneumonia (HAP) remains a challenge due to significant pharmacokinetic variability, particularly in the setting of renal dysfunction and replacement therapies. Continuous infusion (CI) of piperacillin/tazobactam aims to improve pharmacodynamic target attainment, though both subtherapeutic and potentially toxic concentrations have been reported in practice. We developed a population pharmacokinetic model of piperacillin using 162 plasma samples from 35 intensive care unit (ICU) patients with HAP, including those receiving continuous renal replacement therapy (CRRT). Piperacillin concentrations were quantified using a validated LC-MS method. A one-compartment model parameterized with renal and non-renal clearance was implemented in Monolix, incorporating creatinine clearance (CrCL), CRRT effluent flow rate, and intermittent hemodialysis as key covariates. Monte Carlo simulations in Simulx evaluated steady-state drug exposures following renal dose-adjusted CI regimens. Simulation showed that renally adjusted lower doses administered via CI (3–9 g/day) achieved target concentrations in 74–82% of patients with CrCL ≤75 mL/min. Higher doses (6–12 g/day) resulted in >20% of patients exceeding 96 mg/L across all renal strata. Among CRRT patients, lower doses provided a 100% probability of maintaining targeted piperacillin concentrations. In patients with supra-normal renal function (i.e., CrCL = 150 mL/min), low-dose CI regimens yielded a 6.1% probability of underexposure, compared to 2.7% with high-dose. CI PIP dosing based on CrCL results in variable exposures among ICU patients. Individualized dosing of PIP may be required to optimize efficacy and minimize toxicity in ICU patients treated with CI dosing.

Background: Despite sophisticated standard of care (SOC) testing (including bronchoalveolar lavage (BAL) fluid culture and multiplex PCR), the etiology of pneumonia in immunocompromised patients is frequently unknown. Microbial cell-free DNA (mcfDNA) sequencing increases diagnostic yield in plasma but remains understudied in BAL fluid. Methods: This was a single center, retrospective, observational cohort study. Residual BAL fluid collected from immunocompromised, mechanically ventilated patients with suspected pneumonia was sent to Karius® for mcfDNA sequencing. Given the retrospective design, mcfDNA sequencing results were unavailable to clinicians. SOC testing results were compared to Karius® BAL (KT-BAL) test reports that classified identified microorganisms as Category One (always pathogenic), Category Two (usually pathogenic), or Category Three (rarely pathogenic). Findings: 228 BAL fluid samples from 155 patients were analyzed. In pneumonia patients, KT-BAL yielded 18 additional Category One organisms, 138 additional Category Two organisms, and 313 additional Category Three organisms compared to SOC. Organisms missed by SOC and identified by KT-BAL included bacterial pathogens (99 patients), clinically relevant DNA viruses (34 patients), non- Candida fungi (11 patients), and parasites (1 patient). Compared to patients with Category One or Two organisms identified by both SOC and KT-BAL (n = 68), patients with organisms identified by KT-BAL alone (n = 87) had significantly more cumulative intubation days (11 [5, 20] days vs. 15 [8, 32] days, p = 0.035). Interpretation: KT-BAL increased diagnostic yield for immunocompromised patients with suspected pneumonia with suggestion of prolonged respiratory failure in patients with pathogens missed by SOC.