The influence of polymorbidity, revascularization, and wound therapy on the healing of arterial ulceration

Joerg Tautenhahn1, Ralf Lobmann2, Brigitte Koenig3, Zuhir Halloul1, Hans Lippert1, Thomas Buerger11Department of General, Visceral and Vascular Surgery; 2Department of Endocrinology and Metabolism; 3Institute for Medical Microbiology, Medical School, Otto-von-Guericke University, Magdeburg, GermanyObjective: An ulcer categorized as Fontaine’s stage IV represents a chronic wound, risk factor of arteriosclerosis, and co-morbidities which disturb wound healing. Our objective was to analyze wound healing and to assess potential factors affecting the healing process.Methods: 199 patients were included in this 5-year study. The significance levels were determined by chi-squared and log-rank tests. The calculation of patency rate followed the Kaplan-Meier method.Results: Mean age and co-morbidities did not differ from those in current epidemiological studies. Of the patients with ulcer latency of more than 13 weeks (up to one year), 40% required vascular surgery. Vascular surgery was not possible for 53 patients and they were treated conservatively. The amputation rate in the conservatively treated group was 37%, whereas in the revascularizated group it was only 16%. Ulcers in patients with revascularization healed in 92% of cases after 24 weeks. In contrast, we found a healing rate of only 40% in the conservatively treated group (p < 0.001). Revascularization appeared more often in diabetic patients (n = 110; p < 0.01) and the wound size and number of infections were elevated (p = 0.03). Among those treated conservatively, wound healing was decelerated (p = 0.01/0.02; χ² test).Conclusions: The success of revascularization, presence of diabetes mellitus, and wound treatment proved to be prognostic factors for wound healing in arterial ulcers.Keywords: arterial leg ulcer, wound management, risk factors, revascularizatio

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

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

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

International audienceIntroduction 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

Open Systems Pharmacology Community-An Open Access, Open Source, Open Science Approach to Modeling and Simulation in Pharmaceutical Sciences

Systems pharmacology integrates structural biological and pharmacological knowledge and experimental data, enabling dissection of organism and drug properties and providing excellent predictivity. The development of systems pharmacology models is a significant task requiring massive amounts of background information beyond individual trial data. The qualification of models needs repetitive demonstration of successful predictions. Open Systems Pharmacology is a community that develops, qualifies, and shares professional open source software tools and models in a collaborative open-science way