Pharmacokinetics and pharmacodynamics of finerenone in patients with chronic kidney disease and type 2 diabetes: Insights based on FIGARO-DKD and FIDELIO-DKD.

AIMS: To perform dose-exposure-response analyses to determine the effects of finerenone doses. MATERIALS AND METHODS: Two randomized, double-blind, placebo-controlled phase 3 trials enrolling 13 026 randomized participants with type 2 diabetes (T2D) from global sites, each with an estimated glomerular filtration rate (eGFR) of 25 to 90 mL/min/1.73 m2 , a urine albumin-creatinine ratio (UACR) of 30 to 5000 mg/g, and serum potassium ≤ 4.8 mmol/L were included. Interventions were titrated doses of finerenone 10 or 20 mg versus placebo on top of standard of care. The outcomes were trajectories of plasma finerenone and serum potassium concentrations, UACR, eGFR and kidney composite outcomes, assessed using nonlinear mixed-effects population pharmacokinetic (PK)/pharmacodynamic (PD) and parametric time-to-event models. RESULTS: For potassium, lower serum levels and lower rates of hyperkalaemia were associated with higher doses of finerenone 20 mg compared to 10 mg (p < 0.001). The PK/PD model analysis linked this observed inverse association to potassium-guided dose titration. Simulations of a hypothetical trial with constant finerenone doses revealed a shallow but increasing exposure-potassium response relationship. Similarly, increasing finerenone exposures led to less than dose-proportional increasing reductions in modelled UACR. Modelled UACR explained 95% of finerenone's treatment effect in slowing chronic eGFR decline. No UACR-independent finerenone effects were identified. Neither sodium-glucose cotransporter-2 (SGLT2) inhibitor nor glucagon-like peptide-1 receptor agonist (GLP-1RA) treatment significantly modified the effects of finerenone in reducing UACR and eGFR decline. Modelled eGFR explained 87% of finerenone's treatment effect on kidney outcomes. No eGFR-independent effects were identified. CONCLUSIONS: The analyses provide strong evidence for the effectiveness of finerenone dose titration in controlling serum potassium elevations. UACR and eGFR are predictive of kidney outcomes during finerenone treatment. Finerenone's kidney efficacy is independent of concomitant use of SGLT2 inhibitors and GLP-1RAs.

Baseline clusters and the response to positive airway pressure treatment in obstructive sleep apnoea patients: longitudinal data from the European Sleep Apnea Database cohort.

Introduction: The European Sleep Apnea Database was used to identify distinguishable obstructive sleep apnoea (OSA) phenotypes and to investigate the clinical outcome during positive airway pressure (PAP) treatment. Method: Prospective OSA patient data were recruited from 35 sleep clinics in 21 European countries. Unsupervised cluster analysis (anthropometrics, clinical variables) was performed in a random sample (n=5000). Subsequently, all patients were assigned to the clusters using a conditional inference tree classifier. Responses to PAP treatment change in apnoea severity and Epworth sleepiness scale (ESS) were assessed in relation to baseline patient clusters and at short- and long-term follow-up. Results: At baseline, 20 164 patients were assigned (mean age 54.1±12.2 years, 73% male, median apnoea-hypopnoea index (AHI) 27.3 (interquartile range (IQR) 14.1-49.3) events·h-1, and ESS 9.8±5.3) to seven distinct clusters based on anthropometrics, comorbidities and symptoms. At PAP follow-up (median 210 [IQR 134-465] days), the observed AHI reduction (n=1075) was similar, whereas the ESS response (n=3938) varied: largest reduction in cluster 3 (young healthy symptomatic males) and 6 (symptomatic males with psychiatric disorders, -5.0 and -5.1 units, respectively (all p<0.01), limited reduction in clusters 2 (obese males with systemic hypertension) and 5 (elderly multimorbid obese males, -4.2 (p<0.05) and -3.7 (p<0.001), respectively). Residual sleepiness in cluster 5 was particularly evident at long-term follow-up (p<0.05). Conclusion: OSA patients can be classified into clusters based on clinically identifiable features. Importantly, these clusters may be useful for prediction of both short- and long-term responses to PAP intervention.

Dose-Exposure-Response Analysis of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone on UACR and eGFR: An Analysis from FIDELIO-DKD.

BACKGROUND AND OBJECTIVE: Finerenone reduces the risk of kidney failure in patients with chronic kidney disease and type 2 diabetes. Changes in the urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) are surrogates for kidney failure. We performed dose-exposure-response analyses to determine the effects of finerenone on these surrogates in the presence and absence of sodium glucose co-transporter-2 inhibitors (SGLT2is) using individual patient data from the FIDELIO-DKD study. METHODS: Non-linear mixed-effects population pharmacokinetic/pharmacodynamic models were used to quantify disease progression in terms of UACR and eGFR during standard of care and pharmacodynamic effects of finerenone in the presence and absence of SGLT2i use. RESULTS: The population pharmacokinetic/pharmacodynamic models adequately described effects of finerenone exposure in reducing UACR and slowing eGFR decline over time. The reduction in UACR achieved with finerenone during the first year predicted its subsequent effect in slowing progressive eGFR decline. SGLT2i use did not modify the effects of finerenone. The population pharmacokinetic/pharmacodynamic model demonstrated with 97.5% confidence that finerenone was at least 94.1% as efficacious in reducing UACR in patients using an SGLT2i compared with patients not using an SGLT2i based on the 95% confidence interval of the SGLT2i-finerenone interaction from 94.1 to 122%. The 95% confidence interval of the SGLT2i-finerenone interaction for the UACR-mediated effect on chronic eGFR decline was 9.5-144%. CONCLUSIONS: We developed a model that accurately describes the finerenone dose-exposure-response relationship for UACR and eGFR. The model demonstrated that the early UACR effect of finerenone predicted its long-term effect on eGFR decline. These effects were independent of concomitant SGLT2i use.

Using real-world data in pediatric clinical trials: Lessons learned and future applications in studies of persistent pulmonary hypertension of the newborn.

Persistent pulmonary hypertension of the newborn (PPHN) is a complication of term birth, characterized by persistent hypoxemia secondary to failure of normal postnatal reduction in pulmonary vascular resistance, with potential for short- and long-term morbidity and mortality. The primary pharmacologic goal for this condition is reduction of the neonate's elevated pulmonary vascular resistance with inhaled nitric oxide, the only approved treatment option. Various adjunctive, unapproved therapeutics have been trialed with mixed results, likely related to challenges with recruiting the full, intended patient population into clinical studies. Recently, real-world data and subsequent derived evidence have been utilized to improve the efficiency of various pediatric clinical trials. We aim to provide recent perspectives regarding the use of real-world data in the planning and execution of pediatric clinical trials and how this may facilitate more streamlined assessment of future therapeutics for the treatment of PPHN and other neonatal conditions.

Early prediction of decompensation (EPOD) score: Non-invasive determination of cirrhosis decompensation risk.

BACKGROUND & AIMS: Decompensation is a hallmark of disease progression in cirrhotic patients. Early detection of a phase transition from compensated cirrhosis to decompensation would enable targeted therapeutic interventions potentially extending life expectancy. This study aims to (a) identify the predictors of decompensation in a large, multicentric cohort of patients with compensated cirrhosis, (b) to build a reliable prognostic score for decompensation and (c) to evaluate the score in independent cohorts. METHODS: Decompensation was identified in electronic health records data from 6049 cirrhosis patients in the IBM Explorys database training cohort by diagnostic codes for variceal bleeding, encephalopathy, ascites, hepato-renal syndrome and/or jaundice. We identified predictors of clinical decompensation and developed a prognostic score using Cox regression analysis. The score was evaluated using the IBM Explorys database validation cohort (N = 17662), the Penn Medicine BioBank (N = 1326) and the UK Biobank (N = 317). RESULTS: The new Early Prediction of Decompensation (EPOD) score uses platelet count, albumin, and bilirubin concentration. It predicts decompensation during a 3-year follow-up in three validation cohorts with AUROCs of 0.69, 0.69 and 0.77, respectively, and outperforms the well-known MELD and Child-Pugh score in predicting decompensation. Furthermore, the EPOD score predicted the 3-year probability of decompensation. CONCLUSIONS: The EPOD score provides a prediction tool for the risk of decompensation in patients with cirrhosis that outperforms well-known cirrhosis scores. Since EPOD is based on three blood parameters, only, it provides maximal clinical feasibility at minimal costs.

Finerenone Dose-Exposure-Serum Potassium Response Analysis of FIDELIO-DKD Phase III: The Role of Dosing, Titration, and Inclusion Criteria.

BACKGROUND: Finerenone is a nonsteroidal selective mineralocorticoid receptor antagonist (MRA) that demonstrated efficacy in delaying the progression of chronic kidney disease (CKD) and reducing cardiovascular events in patients with CKD and type 2 diabetes mellitus in FIDELIO-DKD, where 5734 patients were randomized 1:1 to receive either finerenone or placebo, with a median follow-up of 2.6 years. Doses of finerenone 10 or 20 mg once daily were titrated based on (serum) potassium and estimated glomerular filtration rate. The MRA mode of action increases potassium. METHODS: Nonlinear mixed-effects population pharmacokinetic/pharmacodynamic models were used to analyze the finerenone dose-exposure-response relationship for potassium in FIDELIO-DKD. Individual time-varying exposures from pharmacokinetic analyses were related to the potassium response via a maximal effect, indirect-response model informed by 148,384 serum potassium measurements. RESULTS: Although observed potassium levels decreased with increasing dose (i.e., inverse relation), model-based simulations for a fixed-dose setting (i.e., no dose titration) revealed the intrinsic finerenone dose-exposure-potassium response, with potassium levels increasing in a dose- and exposure-dependent manner, thus explaining the apparent conflict. The potassium limit for inclusion and uptitration from finerenone 10 to 20 mg in FIDELIO-DKD was ≤ 4.8 mmol/L. Modified limits of ≤ 5.0 mmol/L were simulated, resulting in higher hyperkalemia frequencies for both the finerenone and the placebo arms, whereas the relative hyperkalemia risk of a finerenone treatment compared with placebo did not increase. CONCLUSIONS: The analyses demonstrated the effectiveness of finerenone dose titration in managing serum potassium and provide a quantitative basis to guide safe clinical use.

Finerenone Dose-Exposure-Response for the Primary Kidney Outcome in FIDELIO-DKD Phase III: Population Pharmacokinetic and Time-to-Event Analysis.

BACKGROUND: Finerenone is a nonsteroidal selective mineralocorticoid receptor antagonist that recently demonstrated efficacy in delaying chronic kidney disease progression and reducing cardiovascular events in patients with chronic kidney disease and type 2 diabetes in FIDELIO-DKD, where 5734 patients were randomized 1:1 to receive either titrated finerenone doses of 10 or 20 mg once daily or placebo, with a median follow-up of 2.6 years. METHODS: Nonlinear mixed-effects population pharmacokinetic models were used to analyze the pharmacokinetics in FIDELIO-DKD, sparsely sampled in all subjects receiving finerenone. Post-hoc model parameter estimates together with dosing histories allowed the computation of individual exposures used in subsequent parametric time-to-event analyses of the primary kidney outcome. RESULTS: The population pharmacokinetic model adequately captured the typical pharmacokinetics of finerenone and its variability. Either covariate effects or multivariate forward-simulations in subgroups of interest were contained within the equivalence range of 80-125% around typical exposure. The exposure-response relationship was characterized by a maximum effect model estimating a low half-maximal effect concentration at 0.166 µg/L and a maximal hazard decrease at 36.1%. Prognostic factors for the treatment-independent chronic kidney disease progression risk included a low estimated glomerular filtration rate and a high urine-to-creatinine ratio increasing the risk, while concomitant sodium-glucose transport protein 2 inhibitor use decreased the risk. Importantly, no sodium-glucose transport protein 2 inhibitor co-medication-related modification of the finerenone treatment effect per se could be identified. CONCLUSIONS: None of the tested pharmacokinetic covariates had clinical relevance in FIDELIO-DKD. Finerenone effects on kidney outcomes approached saturation towards 20 mg once daily and sodium-glucose transport protein 2 inhibitor use provided additive benefits.

Algorithmic surveillance of ICU patients with acute respiratory distress syndrome (ASIC): protocol for a multicentre stepped-wedge cluster randomised quality improvement strategy.

INTRODUCTION: The acute respiratory distress syndrome (ARDS) is a highly relevant entity in critical care with mortality rates of 40%. Despite extensive scientific efforts, outcome-relevant therapeutic measures are still insufficiently practised at the bedside. Thus, there is a clear need to adhere to early diagnosis and sufficient therapy in ARDS, assuring lower mortality and multiple organ failure. METHODS AND ANALYSIS: In this quality improvement strategy (QIS), a decision support system as a mobile application (ASIC app), which uses available clinical real-time data, is implemented to support physicians in timely diagnosis and improvement of adherence to established guidelines in the treatment of ARDS. ASIC is conducted on 31 intensive care units (ICUs) at 8 German university hospitals. It is designed as a multicentre stepped-wedge cluster randomised QIS. ICUs are combined into 12 clusters which are randomised in 12 steps. After preparation (18 months) and a control phase of 8 months for all clusters, the first cluster enters a roll-in phase (3 months) that is followed by the actual QIS phase. The remaining clusters follow in month wise steps. The coprimary key performance indicators (KPIs) consist of the ARDS diagnostic rate and guideline adherence regarding lung-protective ventilation. Secondary KPIs include the prevalence of organ dysfunction within 28 days after diagnosis or ICU discharge, the treatment duration on ICU and the hospital mortality. Furthermore, the user acceptance and usability of new technologies in medicine are examined. To show improvements in healthcare of patients with ARDS, differences in primary and secondary KPIs between control phase and QIS will be tested. ETHICS AND DISSEMINATION: Ethical approval was obtained from the independent Ethics Committee (EC) at the RWTH Aachen Faculty of Medicine (local EC reference number: EK 102/19) and the respective data protection officer in March 2019. The results of the ASIC QIS will be presented at conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: DRKS00014330.

Immunogenicity of COVID-19 Tozinameran Vaccination in Patients on Chronic Dialysis.

Patients with kidney failure have notoriously weak responses to common vaccines. Thus, immunogenicity of novel SARS-CoV-2 vaccines might be impaired in this group. To determine immunogenicity of SARS-CoV-2 vaccination in patients with chronic dialysis, we analyzed the humoral and T-cell response after two doses of mRNA vaccine Tozinameran (BNT162b2 BioNTech/Pfizer). This observational study included 43 patients on dialysis before vaccination with two doses of Tozinameran 21 days apart. Overall, 36 patients completed the observation period until three weeks after the second dose and 32 patients were further analyzed at week 10. Serum samples were analyzed by SARS-CoV-2 specific IgG and IgA antibodies ~1, ~3-4 and ~10 weeks after the second vaccination. In addition, SARS-CoV-2-specific T-cell responses were assessed at ~3-4 weeks by an interferon-gamma release assay (IGRA). Antibody and T cell outcomes at this timepoint were compared to a group of 44 elderly patients not on dialysis, after immunization with Tozinameran. Median age of patients on chronic dialysis was 74.0 years (IQR 66.0, 82.0). The proportion of males was higher (69.4%) than females. Only 20/36 patients (55.6%, 95%CI: 38.29-71.67) developed SARS-CoV-2-IgG antibodies at the first sampling, whereas 32/36 patients (88.9%, 95%CI: 73.00-96.38) demonstrated IgG detection at the second sampling. In a longitudinal follow-up at ~10 weeks after the second dose, the proportion of dialysis patients reactive for anti-SARS-CoV-2-IgG decreased to 27/32 (84.37%, 95%CI: 66.46-94.10) The proportion of anti-SARS-CoV-2 S1 IgA decreased from 33/36 (91.67%; 95%CI: 76.41-97.82) at weeks 3-4 down to 19/32 (59.38; 95%CI: 40.79-75.78). Compared to a cohort of vaccinees with similar age but not on chronic dialysis seroconversion rates and antibody titers were significantly lower. SARS-CoV-2-specific T-cell responses 3 weeks after second vaccination were detected in 21/31 vaccinated dialysis patients (67.7%, 95%CI: 48.53-82.68) compared to 42/44 (93.3%, 95%CI: 76.49-98.84) in controls of similar age. Patients on dialysis demonstrate a delayed, but robust immune response three to four weeks after the second dose, which indicates effective vaccination of this vulnerable group. However, the lower immunogenicity of Tozinameran in these patients needs further attention to develop potential countermeasures such as an additional booster vaccination.

Translational learning from clinical studies predicts drug pharmacokinetics across patient populations.

Early indication of late-stage failure of novel candidate drugs could be facilitated by continuous integration, assessment, and transfer of knowledge acquired along pharmaceutical development programs. We here present a translational systems pharmacology workflow that combines drug cocktail probing in a specifically designed clinical study, physiologically based pharmacokinetic modeling, and Bayesian statistics to identify and transfer (patho-)physiological and drug-specific knowledge across distinct patient populations. Our work builds on two clinical investigations, one with 103 healthy volunteers and one with 79 diseased patients from which we systematically derived physiological information from pharmacokinetic data for a reference probe drug (midazolam) at the single-patient level. Taking into account the acquired knowledge describing (patho-)physiological alterations in the patient cohort allowed the successful prediction of the population pharmacokinetics of a second, candidate probe drug (torsemide) in the patient population. In addition, we identified significant relations of the acquired physiological processes to patient metadata from liver biopsies. The presented prototypical systems pharmacology approach is a proof of concept for model-based translation across different stages of pharmaceutical development programs. Applied consistently, it has the potential to systematically improve predictivity of pharmacokinetic simulations by incorporating the results of clinical trials and translating them to subsequent studies.

Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling.

Active processes involved in drug metabolization and distribution mediated by enzymes, transporters, or binding partners mostly occur simultaneously in various organs. However, a quantitative description of active processes is difficult because of limited experimental accessibility of tissue-specific protein activity in vivo. In this work, we present a novel approach to estimate in vivo activity of such enzymes or transporters that have an influence on drug pharmacokinetics. Tissue-specific mRNA expression is used as a surrogate for protein abundance and activity and is integrated into physiologically based pharmacokinetic (PBPK) models that already represent detailed anatomical and physiological information. The new approach was evaluated using three publicly available databases: whole-genome expression microarrays from ArrayExpress, reverse transcription-polymerase chain reaction-derived gene expression estimates collected from the literature, and expressed sequence tags from UniGene. Expression data were preprocessed and stored in a customized database that was then used to build PBPK models for pravastatin in humans. These models represented drug uptake by organic anion-transporting polypeptide 1B1 and organic anion transporter 3, active efflux by multidrug resistance protein 2, and metabolization by sulfotransferases in liver, kidney, and/or intestine. Benchmarking of PBPK models based on gene expression data against alternative models with either a less complex model structure or randomly assigned gene expression values clearly demonstrated the superior model performance of the former. Besides accurate prediction of drug pharmacokinetics, integration of relative gene expression data in PBPK models offers the unique possibility to simultaneously investigate drug-drug interactions in all relevant organs because of the physiological representation of protein-mediated processes.

Development of a physiologically based computational kidney model to describe the renal excretion of hydrophilic agents in rats.

A physiologically based kidney model was developed to analyze the renal excretion and kidney exposure of hydrophilic agents, in particular contrast media, in rats. In order to study the influence of osmolality and viscosity changes, the model mechanistically represents urine concentration by water reabsorption in different segments of kidney tubules and viscosity dependent tubular fluid flow. The model was established using experimental data on the physiological steady state without administration of any contrast media or drugs. These data included the sodium and urea concentration gradient along the cortico-medullary axis, water reabsorption, urine flow, and sodium as well as urea urine concentrations for a normal hydration state. The model was evaluated by predicting the effects of mannitol and contrast media administration and comparing to experimental data on cortico-medullary concentration gradients, urine flow, urine viscosity, hydrostatic tubular pressures and single nephron glomerular filtration rate. Finally the model was used to analyze and compare typical examples of ionic and non-ionic monomeric as well as non-ionic dimeric contrast media with respect to their osmolality and viscosity. With the computational kidney model, urine flow depended mainly on osmolality, while osmolality and viscosity were important determinants for tubular hydrostatic pressure and kidney exposure. The low diuretic effect of dimeric contrast media in combination with their high intrinsic viscosity resulted in a high viscosity within the tubular fluid. In comparison to monomeric contrast media, this led to a higher increase in tubular pressure, to a reduction in glomerular filtration rate and tubular flow and to an increase in kidney exposure. The presented kidney model can be implemented into whole body physiologically based pharmacokinetic models and extended in order to simulate the renal excretion of lipophilic drugs which may also undergo active secretion and reabsorption.

Evaluation of the efficacy and safety of rivaroxaban using a computer model for blood coagulation.

Rivaroxaban is an oral, direct Factor Xa inhibitor approved in the European Union and several other countries for the prevention of venous thromboembolism in adult patients undergoing elective hip or knee replacement surgery and is in advanced clinical development for the treatment of thromboembolic disorders. Its mechanism of action is antithrombin independent and differs from that of other anticoagulants, such as warfarin (a vitamin K antagonist), enoxaparin (an indirect thrombin/Factor Xa inhibitor) and dabigatran (a direct thrombin inhibitor). A blood coagulation computer model has been developed, based on several published models and preclinical and clinical data. Unlike previous models, the current model takes into account both the intrinsic and extrinsic pathways of the coagulation cascade, and possesses some unique features, including a blood flow component and a portfolio of drug action mechanisms. This study aimed to use the model to compare the mechanism of action of rivaroxaban with that of warfarin, and to evaluate the efficacy and safety of different rivaroxaban doses with other anticoagulants included in the model. Rather than reproducing known standard clinical measurements, such as the prothrombin time and activated partial thromboplastin time clotting tests, the anticoagulant benchmarking was based on a simulation of physiologically plausible clotting scenarios. Compared with warfarin, rivaroxaban showed a favourable sensitivity for tissue factor concentration inducing clotting, and a steep concentration-effect relationship, rapidly flattening towards higher inhibitor concentrations, both suggesting a broad therapeutic window. The predicted dosing window is highly accordant with the final dose recommendation based upon extensive clinical studies.

A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks.

Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim(®) and MoBi(®) capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug-drug, or drug-metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.

On transferring the grid technology to the biomedical community.

Natural scientists such as physicists pioneered the sharing of computing resources, which resulted in the Grid. The inter domain transfer process of this technology has been an intuitive process. Some difficulties facing the life science community can be understood using the Bozeman's "Effectiveness Model of Technology Transfer". Bozeman's and classical technology transfer approaches deal with technologies that have achieved certain stability. Grid and Cloud solutions are technologies that are still in flux. We illustrate how Grid computing creates new difficulties for the technology transfer process that are not considered in Bozeman's model. We show why the success of health Grids should be measured by the qualified scientific human capital and opportunities created, and not primarily by the market impact. With two examples we show how the Grid technology transfer theory corresponds to the reality. We conclude with recommendations that can help improve the adoption of Grid solutions into the biomedical community. These results give a more concise explanation of the difficulties most life science IT projects are facing in the late funding periods, and show some leveraging steps which can help to overcome the "vale of tears".