A randomized trial of 7-day doripenem versus 10-day imipenem-cilastatin for ventilatorassociated pneumonia

INTRODUCTION: The aim of this study was to compare a 7-day course of doripenem to a 10-day course of imipenem-cilastatin for ventilator-associated pneumonia (VAP) due to Gram-negative bacteria. METHODS: This was a prospective, double-blinded, randomized trial comparing a fixed 7-day course of doripenem one gram as a four-hour infusion every eight hours with a fixed 10-day course of imipenem-cilastatin one gram as a one-hour infusion every eight hours (April 2008 through June 2011). RESULTS: The study was stopped prematurely at the recommendation of the Independent Data Monitoring Committee that was blinded to treatment arm assignment and performed a scheduled review of data which showed signals that were close to the pre-specified stopping limits. The final analyses included 274 randomized patients. The clinical cure rate at the end of therapy (EOT) in the microbiological intent-to-treat (MITT) population was numerically lower for patients in the doripenem arm compared to the imipenem-cilastatin arm (45.6% versus 56.8%; 95% CI, -26.3% to 3.8%). Similarly, the clinical cure rate at EOT was numerically lower for patients with Pseudomonas aeruginosa VAP, the most common Gram-negative pathogen, in the doripenem arm compared to the imipenem-cilastatin arm (41.2% versus 60.0%; 95% CI, -57.2 to 19.5). All cause 28-day mortality in the MITT group was numerically greater for patients in the doripenem arm compared to the imipenem-cilastatin arm (21.5% versus 14.8%; 95% CI, -5.0 to 18.5) and for patients with P. aeruginosa VAP (35.3% versus 0.0%; 95% CI, 12.6 to 58.0). CONCLUSIONS: Among patients with microbiologically confirmed late-onset VAP, a fixed 7-day course of doripenem was found to have non-significant higher rates of clinical failure and mortality compared to a fixed 10-day course of imipenem-cilastatin. Consideration should be given to treating patients with VAP for more than seven days to optimize clinical outcome. TRIAL REGISTRATION: ClinicalTrials.gov: NCT0058969

Pressurized metered-dose inhalers using next-generation propellant HFO-1234ze(E) deposit negligible amounts of trifluoracetic acid in the environment

Pressurized metered-dose inhalers (pMDIs) deliver life-saving medications to patients with respiratory conditions and are the most used inhaler delivery device globally. pMDIs utilize a hydrofluoroalkane (HFA), also known as an F-gas, as a propellant to facilitate the delivery of medication into the lungs. Although HFAs have minimal impact on ozone depletion, their global warming potential (GWP) is more than 1,000 times higher than CO2, bringing them in scope of the F-Gas Regulation in the European Union (EU). The pharmaceutical industry is developing solutions, including a near-zero GWP “next-generation propellant,” HFO-1234ze(E). At the same time, the EU is also evaluating a restriction on per-and polyfluoroalkyl substances (PFAS) under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) regulation. Trifluoroacetic acid (TFA) is a persistent PFAS and a potential degradation product of HFO-1234ze(E). We quantified yield of TFA from HFO-1234ze(E) using a computational model under Europe-relevant atmospheric conditions. The modeling suggests that most HFO-1234ze(E) degrades into formyl fluoride within 20 days (≥85%) even at the highest examined altitude. These results suggest that TFA yield from HFO-1234ze(E) varies between 0%–4% under different atmospheric conditions. In 2022, France represented the highest numbers of pMDI units sold within the EU, assuming these pMDIs had HFO-1234ze(E) as propellant, we estimate an annual rainwater TFA deposition of ∼0.025 μg/L. These results demonstrate negligible formation of TFA as a degradation product of HFO-1234ze(E), further supporting its suitability as a non-persistent, non-bioaccumulative, and non-toxic future propellant for pMDI devices to safeguard access for patients to these essential medicines

Quantitative systems modeling approaches towards model-informed drug development: Perspective through case studies

Quantitative systems pharmacology (QSP) modeling has become an increasingly popular approach impacting our understanding of disease mechanisms and helping predict patients’ treatment responses to facilitate study design or development go/no-go decisions. In this paper, we highlight the notable contributions and opportunities that QSP approaches are to offer during the drug development process by sharing three examples that have facilitated internal decisions. The barriers to successful applications and the factors that facilitate the success of the modeling approach is discussed

A Viral Dynamic Model for Treatment Regimens with Direct-acting Antivirals for Chronic Hepatitis C Infection

We propose an integrative, mechanistic model that integrates in vitro virology data, pharmacokinetics, and viral response to a combination regimen of a direct-acting antiviral (telaprevir, an HCV NS3-4A protease inhibitor) and peginterferon alfa-2a/ribavirin (PR) in patients with genotype 1 chronic hepatitis C (CHC). This model, which was parameterized with on-treatment data from early phase clinical studies in treatment-naïve patients, prospectively predicted sustained virologic response (SVR) rates that were comparable to observed rates in subsequent clinical trials of regimens with different treatment durations in treatment-naïve and treatment-experienced populations. The model explains the clinically-observed responses, taking into account the IC50, fitness, and prevalence prior to treatment of viral resistant variants and patient diversity in treatment responses, which result in different eradication times of each variant. The proposed model provides a framework to optimize treatment strategies and to integrate multifaceted mechanistic information and give insight into novel CHC treatments that include direct-acting antiviral agents

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition

Abstract Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models

A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor–Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy

Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that these data reflecting tumor spatial heterogeneity be utilized to inform the QSP models to enhance their predictive power. We developed a hybrid computational model platform, spQSP-IO, to extend QSP models of immuno-oncology with spatially resolved agent-based models (ABM), combining their powers to track whole patient-scale dynamics and recapitulate the emergent spatial heterogeneity in the tumor. Using a model of non-small-cell lung cancer developed based on this platform, we studied the role of the tumor microenvironment and cancer–immune cell interactions in tumor development and applied anti-PD-1 treatment to virtual patients and studied how the spatial distribution of cells changes during tumor growth in response to the immune checkpoint inhibition treatment. Using parameter sensitivity analysis and biomarker analysis, we are able to identify mechanisms and pretreatment measurements correlated with treatment efficacy. By incorporating spatial data that highlight both heterogeneity in tumors and variability among individual patients, spQSP-IO models can extend the QSP framework and further advance virtual clinical trials

Impact of low percentage of data below the quantification limit on parameter estimates of pharmacokinetic models

The objectives of the simulation study were to evaluate the impact of BQL data on pharmacokinetic (PK) parameter estimates when the incidence of BQL data is low (e.g. a parts per thousand currency sign10%), and to compare the performance of commonly used modeling methods for handling BQL data such as data exclusion (M1) and likelihood-based method (M3). Simulations were performed by adapting the method of a recent publication by Ahn et al. (J Phamacokinet Pharmacodyn 35(4):401-421, 2008). The BQL data in the terminal elimination phase were created at frequencies of 1, 2.5, 5, 7.5, and 10% based on a one- and a two-compartment model. The impact of BQL data on model parameter estimates was evaluated based on bias and imprecision. The simulations demonstrated that for the one-compartment model, the impact of ignoring the low percentages of BQL data (a parts per thousand currency sign10%) in the elimination phase was minimal. For the two-compartment model, when the BQL incidence was less than 5%, omission of the BQL data generally did not inflate the bias in the fixed-effect parameters, whereas more pronounced bias in the estimates of inter-individual variability (IIV) was observed. The BQL data in the elimination phase had the greatest impact on the volume of distribution estimate of the peripheral compartment of the two-compartment model. The M3 method generally provided better parameter estimates for both PK models than the M1 method. However, the advantages of the M3 over the M1 method varied depending on different BQL censoring levels, PK models and parameters. As the BQL percentages decreased, the relative gain of the M3 method based on more complex likelihood approaches diminished when compared to the M1 method. Therefore, it is important to balance the trade-off between model complexity and relative gain in model improvement when the incidence of BQL data is low. Understanding the model structure and the distribution of BQL data (percentage and location of BQL data) allows selection of an appropriate and effective modeling approach for handling low percentages of BQL data

Population pharmacokinetics of Rabeprazole and dosing recommendations for the treatment of gastroesophageal reflux disease in children aged 1–11\ua0years

Background and Objective: Rabeprazole sodium is a proton pump inhibitor used for the treatment of gastroesophageal reflux disease (GERD). The objective of this study was to develop a population pharmacokinetic model for rabeprazole that describes concentration–time data arising from phase\ua0I and phase\ua0III studies in adult and pediatric subjects, including neonates and preterm infants, and propose dosing recommendations for pediatric subjects aged 1–11\ua0years.Methods: A total of 4,417 pharmacokinetic observations from 597 subjects aged 6\ua0days to 55.7\ua0years with body weights of 1.15–100\ua0kg were used to develop the population pharmacokinetic model using non-linear mixed-effects modeling techniques. Weight and age were included in the structural model to describe clearance (CL) and central volume of distribution (V). Other covariates considered during model development included sex, race, creatinine clearance, hepatic function, formulation, feeding status, and route of administration. The final model was used to determine doses for pediatric subjects aged 1–11\ua0years to achieve a steady-state area under the plasma concentration–time curve across the dose interval of 24\ua0h (AUC) within the target adult AUC range obtained following a rabeprazole 10\ua0mg dose.Results: The best model was a two-compartment disposition model with a sequential zero-order duration of input (Dur), first-order absorption (k) following a lag time (T), with weight and age effects on CL and V. Formulation type and feeding status described some of the variability in bioavailability and the absorption parameters T, Dur, and k. A dosage regimen of 5\ua0mg once daily for childre

Population Pharmacokinetic Analysis of Ceftobiprole for Treatment of Complicated Skin and Skin Structure Infections▿

Population pharmacokinetic analysis demonstrated that renal function, as assessed by creatinine clearance (CLCR), was the patient characteristic that had a clinically relevant impact on ceftobiprole pharmacodynamics. Dosing adjustments based on CLCR for subjects with renal impairment should provide ceftobiprole exposure similar to that in patients with normal renal function

Pressurized metered-dose inhalers using next-generation propellant HFO-1234ze(E) deposit negligible amounts of trifluoracetic acid in the environment

Pressurized metered-dose inhalers (pMDIs) deliver life-saving medications to patients with respiratory conditions and are the most used inhaler delivery device globally. pMDIs utilize a hydrofluoroalkane (HFA), also known as an F-gas, as a propellant to facilitate the delivery of medication into the lungs. Although HFAs have minimal impact on ozone depletion, their global warming potential (GWP) is more than 1,000 times higher than CO2, bringing them in scope of the F-Gas Regulation in the European Union (EU). The pharmaceutical industry is developing solutions, including a near-zero GWP ‘next-generation propellant,’ HFO-1234ze(E). At the same time, the EU is also evaluating a restriction on per-and polyfluoroalkyl substances (PFAS) under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) regulation. Trifluoroacetic acid (TFA) is a persistent PFAS and a potential degradation product of HFO-1234ze(E). We quantified yield of TFA from HFO-1234ze(E) using a computational model under Europe-relevant atmospheric conditions. The modeling suggests that most HFO-1234ze(E) degrades into formyl fluoride within 20 days (≥ 85%) even at the highest examined altitude. These results suggest that TFA yield from HFO-1234ze(E) varies between 0-4% under different atmospheric conditions. In 2022, France represented the highest numbers of pMDI units sold within the EU, assuming these pMDIs had HFO-1234ze(E) as propellant, we estimate an annual rainwater TFA deposition of ~0.025 µg/L. These results demonstrate negligible formation of TFA as a degradation product of HFO-1234ze(E), further supporting its suitability as a non-persistent, non-bioaccumulative, and non-toxic future propellant for pMDI devices to safeguard access for patients to these essential medicines