The stratum corneum is the outermost skin layer with a vital role in skin barrier function. It is comprised of dead keratinocytes (corneocytes) and is known to maintain its thickness by shedding cells, although, the precise mechanisms that safeguard stratum corneum maturation and homeostasis remain unclear. Previous ex vivo studies have suggested a neutral-to-acidic pH gradient in the stratum corneum. Here, we use intravital pH imaging at single-corneocyte resolution to demonstrate that corneocytes actually undergo differentiation to develop three distinct zones in the stratum corneum, each with a distinct pH value. We identified a moderately acidic lower, an acidic middle, and a pH-neutral upper layer in the stratum corneum, with tight junctions playing a key role in their development. The upper pH neutral zone can adjust its pH according to the external environment and has a neutral pH under steady-state conditions owing to the influence of skin microbiota. The middle acidic pH zone provides a defensive barrier against pathogens. With mathematical modeling, we demonstrate the controlled protease activation of kallikrein-related peptidases on the stratum corneum surface that results in proper corneocyte shedding in desquamation. This work adds crucial information to our understanding of how stratum corneum homeostasis is maintained.

This work presents a decision support method for the choice between batch and continuous technologies in solid drug product manufacturing based on the economic evaluation. The method consists of four steps: (I) modeling of operating costs, (II) evaluation, (III) sensitivity analysis, and (IV) interpretation, with iterations. For a given design situation, manufacturing processes are modeled and evaluated with consideration for the characteristics of the two technologies. The sensitivity of the input parameters is analyzed; after interpreting all results, the economically preferable technology is suggested. As a case study, the method was applied to a situation where a new product was in the late development stage, and one of the two technologies needs to be chosen. After executing the four steps, the comparison result of the net present cost was obtained as the decision support information.

Background: Several biologics for atopic dermatitis (AD) have demonstrated good efficacy in clinical trials, but with a substantial proportion of patients being identified as poor responders. This study aims to understand the pathophysiological backgrounds of patient variability in drug response, especially for dupilumab, and to identify promising drug targets in dupilumab poor responders.Methods: We conducted model‐based meta‐analysis of recent clinical trials of AD biologics and developed a mathematical model that reproduces reported clinical efficacies for nine biological drugs (dupilumab, lebrikizumab, tralokinumab, secukinumab, fezakinumab, nemolizumab, tezepelumab, GBR 830, and recombinant interferon‐gamma) by describing system‐level AD pathogenesis. Using this model, we simulated the clinical efficacy of hypothetical therapies on virtual patients.Results: Our model reproduced reported time courses of %improved EASI and EASI‐75 of the nine drugs. The global sensitivity analysis and model simulation indicated the baseline level of IL‐13 could stratify dupilumab good responders. Model simulation on the efficacies of hypothetical therapies revealed that simultaneous inhibition of IL‐13 and IL‐22 was effective, whereas application of the nine biologic drugs was ineffective, for dupilumab poor responders (EASI‐75 at 24 weeks: 21.6% vs. max. 1.9%).Conclusion: Our model identified IL‐13 as a potential predictive biomarker to stratify dupilumab good responders, and simultaneous inhibition of IL‐13 and IL‐22 as a promising drug therapy for dupilumab poor responders. This model will serve as a computational platform for model‐informed drug development for precision medicine, as it allows evaluation of the effects of new potential drug targets and the mechanisms behind patient variability in drug response.