Three stepwise pH progressions in stratum corneum for homeostatic maintenance of the skin.

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.

In Silico Elucidation of Key Drivers of Staphyloccocus aureus-Staphyloccocus epidermidis-Induced Skin Damage in Atopic Dermatitis Lesions.

Staphylococcus aureus (SA) colonizes and can damage skin in atopic dermatitis lesions, despite being commonly found with Staphylococcus epidermidis (SE), a commensal that can inhibit SA's virulence and kill SA. In this study, we developed an in silico model, termed a virtual skin site, describing the dynamic interplay between SA, SE, and the skin barrier in atopic dermatitis lesions to investigate the mechanisms driving skin damage by SA and SE. We generated 106 virtual skin sites by varying model parameters to represent different skin physiologies and bacterial properties. In silico analysis revealed that virtual skin sites with no skin damage in the model were characterized by parameters representing stronger SA and SE growth attenuation than those with skin damage. This inspired an in silico treatment strategy combining SA-killing with an enhanced SA-SE growth attenuation, which was found through simulations to recover many more damaged virtual skin sites to a non-damaged state, compared with SA-killing alone. This study demonstrates that in silico modelling can help elucidate the key factors driving skin damage caused by SA-SE colonization in atopic dermatitis lesions and help propose strategies to control it, which we envision will contribute to the design of promising treatments for clinical studies.

Circulating microRNA Profiles Identify a Patient Subgroup with High Inflammation and Severe Symptoms in Schizophrenia Experiencing Acute Psychosis.

Schizophrenia is a complex and heterogenous psychiatric disorder. This study aimed to demonstrate the potential of circulating microRNAs (miRNAs) as a clinical biomarker to stratify schizophrenia patients and to enhance understandings of their heterogenous pathophysiology. We measured levels of 179 miRNA and 378 proteins in plasma samples of schizophrenia patients experiencing acute psychosis and obtained their Positive and Negative Syndrome Scale (PANSS) scores. The plasma miRNA profile revealed three subgroups of schizophrenia patients, where one subgroup tended to have higher scores of all the PANSS subscales compared to the other subgroups. The subgroup with high PANSS scores had four distinctively downregulated miRNAs, which enriched 'Immune Response' according to miRNA set enrichment analysis and were reported to negatively regulate IL-1ß, IL-6, and TNFα. The same subgroup had 22 distinctively upregulated proteins, which enriched 'Cytokine-cytokine receptor interaction' according to protein set enrichment analysis, and all the mapped proteins were pro-inflammatory cytokines. Hence, the subgroup is inferred to have comparatively high inflammation within schizophrenia. In conclusion, miRNAs are a potential biomarker that reflects both disease symptoms and molecular pathophysiology, and identify a patient subgroup with high inflammation. These findings provide insights for the precision medicinal strategies for anti-inflammatory treatments in the high-inflammation subgroup of schizophrenia.

Model-Based Meta-Analysis to Optimize Staphylococcus aureus‒Targeted Therapies for Atopic Dermatitis.

Several clinical trials of Staphylococcus aureus (S. aureus)‒targeted therapies for atopic dermatitis (AD) have shown conflicting results about whether they improve AD severity scores. This study performs a model-based meta-analysis to investigate the possible causes of these conflicting results and suggests how to improve the efficacies of S. aureus‒targeted therapies. We developed a mathematical model that describes systems-level AD pathogenesis involving dynamic interactions between S. aureus and coagulase-negative Staphylococcus (CoNS). Our model simulation reproduced the clinically observed detrimental effects of the application of S. hominis A9 and flucloxacillin on AD severity and showed that these effects disappeared if the bactericidal activity against CoNS was removed. A hypothetical (modeled) eradication of S. aureus by 3.0 log10 colony-forming unit per cm2 without killing CoNS achieved Eczema Area and Severity Index 75 comparable with that of dupilumab. This efficacy was potentiated if dupilumab was administered in conjunction with S. aureus eradication (Eczema Area and Severity Index 75 at week 16) (S. aureus eradication: 66.7%, dupilumab 61.6% and combination 87.8%). The improved efficacy was also seen for virtual dupilumab poor responders. Our model simulation suggests that killing CoNS worsens AD severity and that S. aureus‒specific eradication without killing CoNS could be effective for patients with AD, including dupilumab poor responders. This study will contribute to designing promising S. aureus‒targeted therapy.

A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis.

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.

Scale-Free Soft Sensor for Monitoring of Water Content in Fluid Bed Granulation Process.

In-line monitoring of granule water content during fluid bed granulation is important to control drug product qualities. In this study, a practical scale-free soft sensor to predict water content was proposed to cope with the manufacturing scale changes in drug product development. The proposed method exploits two key ideas to construct a scale-free soft sensor. First, to accommodate the changes in the manufacturing scale, the process parameters (PPs) that are critical to water content at different manufacturing scales were selected as input variables. Second, to construct an accurate statistical model, locally weighted partial least squares regression (LW-PLSR), which can cope with collinearity and nonlinearity, was utilized. The soft sensor was developed using both laboratory (approx. 4 kg) data and pilot (approx. 25 kg) scale data, and the prediction accuracy in the commercial (approx. 100 kg) scale was evaluated based on the assumption that the process was scaled-up from the pilot scale to the commercial scale. The developed soft sensor exhibited a high prediction accuracy, which was equivalent to the commonly used near-infrared (NIR) spectra-based method. The proposed method requires only standard instruments; therefore, it is expected to be a cost-effective alternative to the NIR spectra-based method.

Establishment of a clinically relevant specification for dissolution testing using physiologically based pharmacokinetic (PBPK) modeling approaches.

This paper presented how to establish a clinically relevant specification (CRS) using in silico physiologically based pharmacokinetic (PBPK) modeling. Three different formulations of model drug products were used in the clinical studies in order to distinguish between bioequivalent (BE) batches from non-BE batches. A human PBPK model was constructed by integrating the clinical and non-clinical observations by using GastroPlusTM software. The developed model was verified by the comparison between human PK behavior observed in clinical studies and human PK behavior predicted from the software. The simulation results were obtained by using the dissolution profiles showing clinically relevant discriminatory power as input variables for the developed PBPK model. For three investigated formulations, the simulated PK behavior was comparable to the PK behavior observed in clinical studies. In addition, in silico BE simulation studies confirmed that the verified PBPK model can successfully reproduce the clinical study results. In conclusion, a CRS was established with the BE simulation by using the verified PBPK model, in order to detect and reject non-BE batches of drug products. The establishment of the CRS is useful for a quality control and finding optimal formulation to accomplish target PK behavior, safety, and efficacy.

Spectral fluctuation dividing for efficient wavenumber selection: application to estimation of water and drug content in granules using near infrared spectroscopy.

In process analytical technology (PAT) based on near infrared (NIR) spectroscopy, wavenumber selection is crucial to develop an accurate and robust calibration model. The present research proposes new efficient spectral dividing and wavenumber selection methods to significantly reduce the computational load required by conventional wavenumber selection methods such as interval partial least squares (iPLS). The proposed method, named spectral fluctuation dividing (SFD), divides a whole spectrum into multiple spectral intervals at local minimum points of the spectral fluctuation profile, which consists of the standard deviation of absorbance at each wavenumber in a calibration set. SFD is combined with PLS (SFD-PLS) to select the spectral intervals at which input variables have significant influence on a target response. The usefulness of SFD-PLS was demonstrated through its application to the problems of estimating water and drug content in granules. PLS models based on SFD-PLS achieved higher estimation accuracy than those based on conventional methods including iPLS, PLS-beta, and variable influence on projection (VIP). In addition, SFD-PLS was more than 10 times faster than the conventional variable selection methods including PLS-beta and VIP; in particular, SFD-PLS was more than 25 times faster than iPLS. Consequently, the proposed SFD-PLS is a promising wavenumber selection method.

Verification of model development technique for NIR-based real-time monitoring of ingredient concentration during blending.

There has been a considerable research on the process analytical technology (PAT) and real-time monitoring based on NIR, but the model development is still an important issue and persons in charge have difficulty in building good models. In this study, to realize efficient NIR-based real-time monitoring of ingredient concentration and establish a model development method, we investigated the effect of a calibration set, spectral preprocessing, wavelengths, and other factors on the prediction error through pilot and commercial scale blending experiments. The results confirmed that the small prediction error was realized by a calibration set, including dynamic measurement spectra acquired with the target blender. In addition, the results demonstrated that locally weighted partial least squares (LW-PLS) achieved the smaller prediction error than conventional PLS. The present study has also clarified that spectral preprocessing methods and wavelengths selected to build a model affect the prediction error of ingredient concentration interactively. A wide wavelength range should be selected when the spectral preprocessing does not lessen the effect of baseline variation, while a narrow wavelength range should be selected when it strongly decreases the effect.

Anionic amino acid dendrimer-trastuzumab conjugates for specific internalization in HER2-positive cancer cells.

Trastuzumab, a humanized monoclonal antibody against human epidermal growth factor receptor 2 (HER2), offers a promising strategy of anticancer drug targeting to HER2-expressing cancer cells. Conjugation of trastuzumab to dendrimers, repeatedly branched polymers with a highly functionalized surface, can enhance the drug loading capacity. However, typical dendrimers such as cationic polyamidoamine dendrimers have exhibited a nonspecific cytotoxicity. In the present study, we developed a novel biocompatible amino acid dendrimer with potentially less toxicity by surface modification of the sixth generation lysine dendrimer with glutamate (KG6E). The synthesized KG6E showed a well-controlled particle size around 5-6 nm with low polydispersibility and negative surface potentials for negligible cytotoxicity. Next, the targeting efficiency of the fluorescent-labeled KG6E-trastuzumab conjugate was evaluated in HER2-positive (SKBR3) and -negative (MCF7) human breast cancer cell lines compared to free trastuzumab and KG6E dendrimers. The KG6E-trastuzumab conjugate was specifically bound to SKBR3 cells in a dose-dependent manner with low binding affinity to MCF7 cells. Furthermore, the conjugate was significantly internalized in SKBR3 cells and then trafficked to lysosomes. These results indicate the potential of KG6E-trastuzumab conjugates as HER2-targeting carriers for therapeutic and diagnostic approaches to cancer therapy.