Psoriasis and lasting implications

Raju, Sudesh S.

doi:10.1586/1744666x.2014.872033

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Psoriasis is an immune‐mediated inflammatory skin dermatosis characterized by discrete, erythematous plaques and papules with a micaceous scale 14 . This common disease affects more than 125 million people worldwide 15 .…”

Section: Psoriasismentioning

confidence: 99%

Getting personal about skin: Realizing precision medicine in dermatology

Brownstone¹,

Strober

et al. 2021

Dermatological Reviews

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With the massive influx of data from “omics” methods combined with sophisticated informatics platforms, personalized medicine, defined as the tailoring of medical decision‐making and/or treatments to an individual patient, has led to the promise of better and more efficient patient care. As a concept, personalized medicine could fundamentally alter how clinicians think about the diagnosis and treatment of disease. The introduction of a precision medicine tool has benefits for multiple stakeholders; patients benefit by having improved outcomes, physicians can make confident decisions in managing their patients, and the healthcare system saves significant dollars through the reduction of wasted spend. This paradigm has been particularly true in oncology where biomarker‐driven treatment paradigms have become increasingly common. In this review, personalized medicine and biomarkers in the field of dermatology are reviewed. Specifically, three disease states are discussed including psoriasis, atopic dermatitis, and melanoma. For each disease state, biomarkers and personalized medicine tests are reviewed focusing on diagnosis and treatment for these conditions. The ethics, implementation, advantages, and disadvantages are discussed for the adaptation of personalized medicine in clinical dermatology. The advent of personalized medicine in dermatology could lead to a paradigm shift in how patients are treated, with the resulting improved clinical outcomes leading to concomitant reductions in wasted healthcare expenditures.

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Section: Psoriasismentioning

confidence: 99%

Getting personal about skin: Realizing precision medicine in dermatology

Brownstone¹,

Strober

et al. 2021

Dermatological Reviews

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“…1 Many studies have suggested that psoriasis is an imbalance in the regulation of the immune system under the interaction of a variety of factors, which ultimately causes abnormal epidermal keratinocyte function including abnormal proliferation and excessive secretion of inflammatory factors by mediating different pathways. [2][3][4] Previous studies have indicated that the secretion of pro-inflammatory cytokines including IL-6 and IL-1β 5 in the skin can promote chemokine production and induce immune cell infiltration, contributing toward the development of various inflammatory skin diseases, including psoriasis. 2 Nevertheless, the pathogenesis of psoriasis is not yet fully understood.…”

Section: Introductionmentioning

confidence: 99%

MicroRNA-17-3p is upregulated in psoriasis and regulates keratinocyte hyperproliferation and pro-inflammatory cytokine secretion by targeting <em>CTR9</em>

Zhang

Liu

et al. 2022

Eur J Histochem

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Psoriasis is a chronic inflammatory skin disease. Although miRNAs are reported to be associated with the pathogenesis of psoriasis, the contribution of individual microRNAs toward psoriasis remains unclear. The miR-17-92 cluster regulates cell growth and immune functions that are associated with psoriasis. miR-17-3p is a member of miR-17-92 cluster; however, its role in dermatological diseases remains unclear. Our study aims at investigating the effects of miR-17-3p and its potential target gene on keratinocytes proliferation and secretion of pro-inflammatory cytokine and their involvement in psoriasis. Initially, we found that miR-17-3p was upregulated in psoriatic skin lesions, and bioinformatic analyses suggested that CTR9 is likely to be a target gene of miR-17-3p. Quantitative reverse-transcriptase PCR and immunohistochemical analysis revealed that CTR9 expression was downregulated in psoriatic lesions. Using dual-luciferase reporter assays, we identified CTR9 as a direct target of miR-17-3p. Further functional experiments demonstrated that miR-17-3p promoted the proliferation and pro-inflammatory cytokine secretion of keratinocytes, whereas CTR9 exerted the opposite effects. Gain-of-function studies confirmed that CTR9 suppression partially accounted for the effects of miR-17-3p in keratinocytes. Furthermore, Western blot revealed that miR-17-3p activates the downstream STAT3 signaling pathway while CTR9 inactivates the STAT3 signaling pathway. Together, these findings indicate that miR-17-3p regulates keratinocyte proliferation and pro-inflammatory cytokine secretion partially by targeting the CTR9, which inactivates the downstream STAT3 protein, implying that miR-17-3p might be a novel therapeutic target for psoriasis.

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“…4 Psoriasis is a T-cell mediated inflammatory skin disease characterized by discrete erythematous plaques and papules with micaceous scale. 5 Worldwide, this is a common disease, with approximately 2.8% of the United States population, or 7.5 million people, diagnosed with psoriasis. The pathology of the disease has been heavily studied and is known to be triggered by a complex inflammatory circuit that stimulates keratinocyte proliferation via upregulation of a host of cytokines including tumor necrosis factor-alpha (TNF), interleukin (IL)-17, and IL-23.…”

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confidence: 99%

A Machine Learning-Based Test for Predicting Response to Psoriasis Biologics

Bagel

Wang²,

Montgomery³

et al. 2021

J of Skin

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Objective: This study was designed to develop and prospectively validate a machine learning based algorithm that could predict patient response to the most common biologic drug classes used in the management of psoriasis patients. This type of tool would allow clinicians to have greater confidence that a given patient will respond to a specific drug class, which could lead to improved health outcomes and reduced wasted healthcare spend. Methods: Patients were enrolled into one of two observational studies (STAMP studies) where dermal biomarker patches (DBPs) were applied at baseline prior to drug exposure, followed by clinical evaluations at 12 weeks after exposure. PASI measurements were made at baseline and 12 weeks to evaluate clinical response to a clinical phenotype. Responders were defined as those who reached PASI75 at 12 weeks. The transcriptomes obtained from the DBPs were sequenced and analyzed to derive and/or validate classifiers for each biologic class, which were then combined to yield predictive responses for all three biologic drug classes (IL-23i, IL-17i, and TNFai). Results: A total of 242 psoriasis patients were enrolled in these studies, including 118 patients (49.6%) treated with IL-23i, 79 patients (33.2%) treated with IL-17i, 35 patients (14.7%) treated with TNFai, and 6 patients (2.5%) treated with IL-12/23i. The IL-23i predictive classifier was developed from the earlier enrolled patients and independently validated with the latter enrolled patients. IL-17i and TNFai predictive classifiers were developed using publicly available datasets and independently validated with patients from the STAMP studies. In the independent validation, positive predictive values for three classifiers (IL-23i, IL-17i, and TNFai) were 93.1%, 92.3% and 85.7% respectively. Over the entire cohort, 99.5% of patients were predicted to respond to at least one drug class. Conclusion: This study demonstrates the power of using baseline dermal biomarkers and machine learning methods as applied to the prediction of psoriasis biologic prior to drug exposure. Using this test, patients, physicians, and the health care system all can benefit in distinct ways. Precision medicine can be realized for individual patients as most will likely respond to their prescribed biologic the first time. Physicians can prescribe these drugs with increased confidence, and the healthcare system will realize lower net costs as well as greatly reduced wasted spend by significantly improving initial response rates to expensive biologic therapeutics.

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Psoriasis and lasting implications

Cited by 4 publications

References 21 publications

Getting personal about skin: Realizing precision medicine in dermatology

Getting personal about skin: Realizing precision medicine in dermatology

MicroRNA-17-3p is upregulated in psoriasis and regulates keratinocyte hyperproliferation and pro-inflammatory cytokine secretion by targeting <em>CTR9</em>

A Machine Learning-Based Test for Predicting Response to Psoriasis Biologics

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