Skin Pharmacokinetics of Transdermal Scopolamine:Measurements and Modeling

Prediction of skin absorption and local bioavailability from topical formulations remains a difficult task. An important challenge in forecasting topical bioavailability is the limited information available about local and systemic drug concentrations post application of topical drug products. Commercially available transdermal patches, such as Scopoderm (Novartis Consumer Health UK), offer an opportunity to test these experimental approaches as systemic pharmacokinetic data are available with which to validate a predictive model. The long-term research aim, therefore, is to develop a physiologically based pharmacokinetic model (PBPK) to predict the dermal absorption and disposition of actives included in complex dermatological products. This work explored whetherin vitrorelease and skin permeation tests (IVRT and IVPT, respectively), andin vitroandin vivostratum corneum (SC) and viable tissue (VT) sampling data, can provide a satisfactory description of drug “input rate” into the skin and subsequently into the systemic circulation.In vitrorelease and skin permeation results for scopolamine were consistent with the previously reported performance of the commercial patch investigated. New skin sampling data on the dermatopharmacokinetics (DPK) of scopolamine also accurately reflected the rapid delivery of a “priming” dose from the patch adhesive, superimposed on a slower, rate-controlled input from the drug reservoir. The scopolamine concentration versus time profiles in SC and VT skin compartments,in vitroandin vivo, taken together with IVRT release and IVPT penetration kinetics, reflect the input rate and drug delivery specifications of the Scopoderm transdermal patch and reveal the importance of skin binding with respect to local drug disposition. Further data analysis and skin PK modeling are indicated to further refine and develop the approach outlined.</p

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis