Achievement of therapeutic antibiotic exposures using Bayesian dosing software in critically unwell children and adults with sepsis.

PURPOSE: Early recognition and effective treatment of sepsis improves outcomes in critically ill patients. However, antibiotic exposures are frequently suboptimal in the intensive care unit (ICU) setting. We describe the feasibility of the Bayesian dosing software Individually Designed Optimum Dosing Strategies (ID-ODS™), to reduce time to effective antibiotic exposure in children and adults with sepsis in ICU. METHODS: A multi-centre prospective, non-randomised interventional trial in three adult ICUs and one paediatric ICU. In a pre-intervention Phase 1, we measured the time to target antibiotic exposure in participants. In Phase 2, antibiotic dosing recommendations were made using ID-ODS™, and time to target antibiotic concentrations were compared to patients in Phase 1 (a pre-post-design). RESULTS: 175 antibiotic courses (Phase 1 = 123, Phase 2 = 52) were analysed from 156 participants. Across all patients, there was no difference in the time to achieve target exposures (8.7 h vs 14.3 h in Phase 1 and Phase 2, respectively, p = 0.45). Sixty-one courses in 54 participants failed to achieve target exposures within 24 h of antibiotic commencement (n = 36 in Phase 1, n = 18 in Phase 2). In these participants, ID-ODS™ was associated with a reduction in time to target antibiotic exposure (96 vs 36.4 h in Phase 1 and Phase 2, respectively, p < 0.01). These patients were less likely to exhibit subtherapeutic antibiotic exposures at 96 h (hazard ratio (HR) 0.02, 95% confidence interval (CI) 0.01-0.05, p < 0.01). There was no difference observed in in-hospital mortality. CONCLUSIONS: Dosing software may reduce the time to achieve target antibiotic exposures. It should be evaluated further in trials to establish its impact on clinical outcomes.

Efficiency of dosing software using Bayesian forecasting in achieving target antibiotic exposures in critically ill patients, a prospective cohort study.

INTRODUCTION: Broad-spectrum antibiotics such as beta-lactams and vancomycin are frequently used to treat critically ill patients, however, a significant number do not achieve target exposures. Therapeutic drug monitoring (TDM) combined with Bayesian forecasting dosing software may improve target attainment in these patients. This study aims to describe the efficiency of dosing software for achieving target exposures of selected beta-lactam antibiotics and vancomycin in critically ill patients. METHODS: A prospective cohort study was undertaken in an adult intensive care unit (ICU). Patients prescribed vancomycin, piperacillin-tazobactam and meropenem were included if they exhibited a subtherapeutic or supratherapeutic exposure informed by TDM. The dosing software, ID-ODS™, was used to generate dosing recommendations which could be either accepted or rejected by the treating team. Repeat antibiotic TDM were requested to determine if target exposures were achieved. RESULTS: Between March 2020 and December 2021, 70 were included in the analysis. Software recommendations were accepted for 56 patients (80%) with 50 having repeated antibiotic measurements. Forty-three of the 50 patients (86%) achieved target exposures after one software recommendation, with 3 of the remaining 7 patients achieving target exposures after 2. Forty-seven patients out of the 50 patients (94%) achieved the secondary outcome of clinical cure. There were no antibiotic exposure-related adverse events reported. CONCLUSION: The use of TDM combined with Bayesian forecasting dosing software increases the efficiency for achieving target antibiotic exposures in the ICU. Clinical trials comparing this approach with other dosing strategies are required to further validate these findings.

Beta-Lactam Dose Optimisation in the Intensive Care Unit: Targets, Therapeutic Drug Monitoring and Toxicity.

Beta-lactams are an important family of antibiotics used to treat infections and are commonly used in critically ill patients. Optimal use of these drugs in the intensive care unit (ICU) is important because of the serious complications from sepsis. Target beta-lactam antibiotic exposures may be chosen using fundamental principles of beta-lactam activity derived from pre-clinical and clinical studies, although the debate regarding optimal beta-lactam exposure targets is ongoing. Attainment of target exposures in the ICU requires overcoming significant pharmacokinetic (PK) and pharmacodynamic (PD) challenges. For beta-lactam drugs, the use of therapeutic drug monitoring (TDM) to confirm if the desired exposure targets are achieved has shown promise, but further data are required to determine if improvement in infection-related outcomes can be achieved. Additionally, beta-lactam TDM may be useful where a relationship exists between supratherapeutic antibiotic exposure and drug adverse effects. An ideal beta-lactam TDM service should endeavor to efficiently sample and report results in identified at-risk patients in a timely manner. Consensus beta-lactam PK/PD targets associated with optimal patient outcomes are lacking and should be a focus for future research.

Accuracy of a precision dosing software program for predicting antibiotic concentrations in critically ill patients.

BACKGROUND: Critically ill patients with sepsis are predisposed to physiological changes that can reduce the probability of achieving target antibiotic exposures. Precision dosing software programs may be used to improve probability of obtaining these target exposures. OBJECTIVE: To quantify the accuracy of a precision dosing software program for predicting antibiotic concentrations as well as to assess the impact of using software predictions on actual dosing adjustments. PATIENTS AND METHODS: The software program ID-ODS was used to predict concentrations for piperacillin, meropenem and vancomycin using patient covariate data with and without the use of therapeutic drug monitoring (TDM) data. The impact of these predictions on actual dosage adjustments was determined by using software predicted concentrations versus measured concentrations. RESULTS: Software predictions for piperacillin and meropenem exhibited large bias that improved with the addition of TDM data (bias improved from -28.8 to -2.0 mg/L for piperacillin and -3.0 to -0.1 mg/L for meropenem). Dosing changes using predicted concentrations of piperacillin and meropenem with TDM data versus measured concentrations were matched on 89.2% (107/120) and 71% (9/69) occasions, respectively. Although vancomycin predictions demonstrated good accuracy with and without TDM, these findings were limited by our small sample size. CONCLUSION: These data demonstrate that precision dosing software programs may have scope to reasonably predict antibiotic concentrations in critically ill patients with sepsis. The addition of TDM data improves the predictive performance of the software for all three antibiotics and the ability to anticipate the correct dose change required.

Precision dosing software to optimize antimicrobial dosing: a systematic search and follow-up survey of available programs.

BACKGROUND: Precision dosing programs are promising tools for optimising antimicrobial dosing. Selecting the ideal program for local application may be challenging due to the large variety of available programs with differing characteristics. OBJECTIVES: The objectives of this study were to systematically identify available precision dosing software programs to optimize antimicrobial dosing and describe the characteristics of each program. Details on the ability of programs to provide beta-lactam dosing support was also gathered. SOURCES: A systematic review search strategy was used to identify candidate software programs described in the literature in Embase and PubMed. A detailed survey was then developed to identify characteristics of programs, including details on the underlying methodology driving dosing software recommendations, interface characteristics, costs and regulatory affairs. Software developers from all identified programs were invited to participate in the survey. CONTENT: The systematic search results identified 18 programs. Fifteen developers responded to the survey (83%) and 11 programs provide dosing support for at least one beta-lactam. Fourteen programs can utilize measured drug concentrations to generate dosing recommendations, with 13 able to generate empiric dosing recommendations. Six programs integrate with local electronic health records and four are registered with at least one regulatory agency. Pharmacokinetic models in combination with Bayesian statistics is the most common methodology used to generate dosing recommendations, with 14 programs utilizing this method. IMPLICATIONS: There was significant variability in the available antimicrobial profiles and characteristics among dosing software programs. As healthcare providers will differ in their requirements within their local settings, clinicians should use these findings to identify potential candidate programs and, if feasible, trial these to ensure they meet their specific requirements.

What Are the Current Approaches to Optimising Antimicrobial Dosing in the Intensive Care Unit?

Antimicrobial dosing in the intensive care unit (ICU) can be problematic due to various challenges including unique physiological changes observed in critically ill patients and the presence of pathogens with reduced susceptibility. These challenges result in reduced likelihood of standard antimicrobial dosing regimens achieving target exposures associated with optimal patient outcomes. Therefore, the aim of this review is to explore the various methods for optimisation of antimicrobial dosing in ICU patients. Dosing nomograms developed from pharmacokinetic/statistical models and therapeutic drug monitoring are commonly used. However, recent advances in mathematical and statistical modelling have resulted in the development of novel dosing software that utilise Bayesian forecasting and/or artificial intelligence. These programs utilise therapeutic drug monitoring results to further personalise antimicrobial therapy based on each patient's clinical characteristics. Studies quantifying the clinical and cost benefits associated with dosing software are required before widespread use as a point-of-care system can be justified.

Chronic stress in mice remodels lymph vasculature to promote tumour cell dissemination.

Chronic stress induces signalling from the sympathetic nervous system (SNS) and drives cancer progression, although the pathways of tumour cell dissemination are unclear. Here we show that chronic stress restructures lymphatic networks within and around tumours to provide pathways for tumour cell escape. We show that VEGFC derived from tumour cells is required for stress to induce lymphatic remodelling and that this depends on COX2 inflammatory signalling from macrophages. Pharmacological inhibition of SNS signalling blocks the effect of chronic stress on lymphatic remodelling in vivo and reduces lymphatic metastasis in preclinical cancer models and in patients with breast cancer. These findings reveal unanticipated communication between stress-induced neural signalling and inflammation, which regulates tumour lymphatic architecture and lymphogenous tumour cell dissemination. These findings suggest that limiting the effects of SNS signalling to prevent tumour cell dissemination through lymphatic routes may provide a strategy to improve cancer outcomes.

Bioluminescent orthotopic model of pancreatic cancer progression.

Pancreatic cancer has an extremely poor five-year survival rate of 4-6%. New therapeutic options are critically needed and depend on improved understanding of pancreatic cancer biology. To better understand the interaction of cancer cells with the pancreatic microenvironment, we demonstrate an orthotopic model of pancreatic cancer that permits non-invasive monitoring of cancer progression. Luciferase-tagged pancreatic cancer cells are resuspended in Matrigel and delivered into the pancreatic tail during laparotomy. Matrigel solidifies at body temperature to prevent leakage of cancer cells during injection. Primary tumor growth and metastasis to distant organs are monitored following injection of the luciferase substrate luciferin, using in vivo imaging of bioluminescence emission from the cancer cells. In vivo imaging also may be used to track primary tumor recurrence after resection. This orthotopic model is suited to both syngeneic and xenograft models and may be used in pre-clinical trials to investigate the impact of novel anti-cancer therapeutics on the growth of the primary pancreatic tumor and metastasis.

VEGF-D promotes tumor metastasis by regulating prostaglandins produced by the collecting lymphatic endothelium.

Lymphatic metastasis is facilitated by lymphangiogenic growth factors VEGF-C and VEGF-D that are secreted by some primary tumors. We identified regulation of PGDH, the key enzyme in prostaglandin catabolism, in endothelial cells of collecting lymphatics, as a key molecular change during VEGF-D-driven tumor spread. The VEGF-D-dependent regulation of the prostaglandin pathway was supported by the finding that collecting lymphatic vessel dilation and subsequent metastasis were affected by nonsteroidal anti-inflammatory drugs (NSAIDs), known inhibitors of prostaglandin synthesis. Our data suggest a control point for cancer metastasis within the collecting lymphatic endothelium, which links VEGF-D/VEGFR-2/VEGFR-3 and the prostaglandin pathways. Collecting lymphatics therefore play an active and important role in metastasis and may provide a therapeutic target to restrict tumor spread.