Secukinumab Efficacy in Psoriatic Arthritis: Machine Learning and Meta-analysis of Four Phase 3 Trials

Image-Based Artificial Intelligence in Psoriasis Assessment: The Beginning of a New Diagnostic Era?

Psoriasis, a chronic inflammatory skin disease, affects millions of people worldwide. It imposes a significant burden on patients' quality of life and healthcare systems, creating an urgent need for optimized diagnosis, treatment, and management. In recent years, image-based artificial intelligence (AI) applications have emerged as promising tools to assist physicians by offering improved accuracy and efficiency. In this review, we provide an overview of the current landscape of image-based AI applications in psoriasis. Emphasis is placed on machine learning (ML) algorithms, a key subset of AI, which enable automated pattern recognition for various tasks. Key AI applications in psoriasis include lesion detection and segmentation, differentiation from other skin conditions, subtype identification, automated area involvement, and severity scoring, as well as personalized treatment selection and response prediction. Furthermore, we discuss two commercially available systems that utilize standardized photo documentation, automated segmentation, and semi-automated Psoriasis Area and Severity Index (PASI) calculation for patient assessment and follow-up. Despite the promise of AI in this field, many challenges remain. These include the validation of current models, integration into clinical workflows, the current lack of diversity in training-set data, and the need for standardized imaging protocols. Addressing these issues is crucial for the successful implementation of AI technologies in clinical practice. Overall, we underscore the potential of AI to revolutionize psoriasis management, highlighting both the advancements and the hurdles that need to be overcome. As technology continues to evolve, AI is expected to significantly improve the accuracy, efficiency, and personalization of psoriasis treatment.

Targeting cytokines in psoriatic arthritis

Psoriatic arthritis (PsA) is part of the psoriatic disease spectrum and is characterized by a chronic inflammatory process that affects entheses, tendons and joints. Cytokines produced by immune and non-immune cells play a central role in the pathogenesis of PsA by orchestrating key aspects of the inflammatory response. Pro-inflammatory cytokines such as TNF, IL-23 and IL-17 have been shown to regulate the initiation and progression of PsA, ultimately leading to the destruction of the architecture of the local tissues such as soft tissue, cartilage and bone. The important role of cytokines in PsA has been underscored by the clinical success of antibodies that neutralize their function. In addition to biologic agents targeting individual pro-inflammatory cytokines, signaling inhibitors that block multiple cytokines simultaneously such as JAK inhibitors have been approved for PsA therapy. In this review, we will focus on our current understanding of the role of cytokines in the disease process of PsA and discuss potential new treatment options based on modulation of cytokine function.

Sustainable biofabrication: from bioprinting to AI-driven predictive methods

Biofabrication is potentially an inherently sustainable manufacturing process of bio-hybrid systems based on biomaterials embedded with cell communities. These bio-hybrids promise to augment the sustainability of various human activities, ranging from tissue engineering and robotics to civil engineering and ecology. However, as routine biofabrication practices are laborious and energetically disadvantageous, our society must refine production and validation processes in biomanufacturing. This opinion highlights the research trends in sustainable material selection and biofabrication techniques. By modeling complex biosystems, the computational prediction will allow biofabrication to shift from an error-trial method to an efficient, target-optimized approach with minimized resource and energy consumption. We envision that implementing bionomic rationality in biofabrication will render bio-hybrid products fruitful for greening human activities.

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

Identification of ADAM23 as a Potential Signature for Psoriasis Using Integrative Machine-Learning and Experimental Verification

Background

Psoriasis is a common chronic, recurrent, and inflammatory skin disease. Identifying novel and potential biomarkers is valuable in the treatment and diagnosis of psoriasis. The goal of this study was to identify novel key biomarkers of psoriasis and analyze the potential underlying mechanisms.

Methods

Psoriasis-related datasets were downloaded from the Gene Expression Omnibus database to screen differential genes in the datasets. Functional and pathway enrichment analyses were performed on the differentially expressed genes (DEGs). Candidate biomarkers for psoriasis were identified from the GSE30999 and GSE6710 datasets using four machine learning algorithms, namely, random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression, weighted gene co-expression network analysis (WGCNA), and support vector machine recursive feature elimination (SVM-RFE), and were validated using the GSE41662 dataset. Next, we used CIBERSORT and single-cell RNA analysis to explore the relationship between ADAM23 and immune cells. Finally, we validated the expression of the identified biomarkers expressions in human and mouse experiments.

Results

A total of 709 overlapping DEGs were identified, including 426 upregulated and 283 downregulated genes. Enhanced by enrichment analysis, the differentially expressed genes (DEGs) were spatially arranged in relation to immune cell involvement, immune-activating processes, and inflammatory signals. Based on the enrichment analysis, the DEGs were mapped to immune cell involvement, immune-activating processes, and inflammatory signals. Four machine learning strategies and single-cell RNA sequencing analysis showed that ADAM23, a disintegrin and metalloprotease, may be a unique, critical biomarker with high diagnostic accuracy for psoriasis. Based on CIBERSORT analysis, ADAM23 was found to be associated with a variety of immune cells, such as macrophages and mast cells, and it was upregulated in the macrophages of psoriatic lesions in patients and mice.

Conclusion

ADAM23 may be a potential biomarker in the diagnosis of psoriasis and may contribute to the pathogenesis by regulating immunological activity in psoriatic lesions.

Artificial intelligence in psoriasis: Where we are and where we are going

Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual-based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e-health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

There has been rapid growth in the use of artificial intelligence (AI) analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers, given the "black box" nature of most algorithms, the unfamiliarity of the terms, and the lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this subject area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas, with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.

Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature

Background

Rheumatological and dermatological disorders contribute to a significant portion of the global burden of disease. Big Data are increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including dermatology and rheumatology. Rheumatology and dermatology can potentially benefit from Big Data.

Methods

A systematic review of the literature was conducted according to the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines, mining “Uno per tutti”, a highly integrated and automated tool/meta-database developed at the University of Genoa, Genoa, Italy, and consisting of 20 major scholarly electronic databases, including PubMed/MEDLINE. Big Data- or artificial intelligence-based studies were judged based on the modified Qiao’s critical appraisal tool for critical methodological quality assessment of Big Data/machine learning-based studies. Other studies designed as cross-sectional, longitudinal, or randomized investigations, reviews/overviews or expert opinions/commentaries were evaluated by means of the relevant “Joanna Briggs Institute” (JBI)’s critical appraisal tool for the critical methodological quality assessment.

Results

Fourteen papers were included in the present systematic review of the literature. Most of the studies included concerned molecular applications of Big Data, especially in the fields of genomics and post-genomics. Other studies concerned epidemiological applications, with a practical dearth of studies assessing smart and digital applications for psoriatic arthritis patients.

Conclusions

Big Data can be a real paradigm shift that revolutionizes rheumatological and dermatological practice and clinical research, helping to early intercept psoriatic arthritis patients. However, there are some methodological issues that should be properly addressed (like recording and association biases) and some ethical issues that should be considered (such as privacy). Therefore, further research in the field is warranted.

Systematic Review Registration

Registration code 10.17605/OSF.IO/4KCU2.

Evidence for the Use of Secukinumab in Patients with Radiographic and Non-radiographic Axial Spondyloarthritis in the Last 5 Years

Axial spondyloarthritis (axSpA) is an inflammatory rheumatic disorder that causes chronic pain, primarily in the spine and sacroiliac joints. It is characterized by the presence of type 1 major histocompatibility complex HLA-B27 genetic marker, arthritis in peripheral joints, enthesitis and/or dactylitis and extra-articular manifestations. Current guidelines recommend biological therapy when first-line therapy is not sufficiently effective. The finding that the interleukin (IL)-17 axis is vital for the pathogenesis of axSpA propelled the development of secukinumab, a fully human monoclonal antibody directed against IL-17A. The present review provides evidence on the efficacy and safety of secukinumab in the treatment of radiographic and non-radiographic axSpA from nine randomized controlled phase III trials, as well as evidence from real-world observational analyses. The primary endpoint in six clinical trials was the proportion of patients meeting the Assessment of SpondyloArthritis international Society criteria for either 20% or 40% improvement (ASAS20, ASAS40) at week 16. Significantly more patients achieved the primary endpoint with secukinumab compared with placebo in all the studies except MEASURE 4. Both clinical trials and real-world studies showed significant improvements in the secondary endpoints of disease activity, quality of life, and pain and fatigue relative to placebo. The benefits of secukinumab were generally sustained during longer-term (up to 5 years) treatment. Overall, secukinumab was well tolerated with a low frequency of adverse events and treatment persistence was high in the real-world setting. Although indirect comparisons suggest that secukinumab and adalimumab have comparable efficacy and safety, they are being directly compared in the ongoing SURPASS study. During the current coronavirus disease 2019 (COVID-19) pandemic, it is advisable to continue biological therapy in patients who do not have severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection, but interrupt treatment during an infection, reinitiating once the patient has recovered from the infection. In conclusion, secukinumab is a largely safe and effective treatment for radiographic and non-radiographic axSpA.

An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Sequential knockoffs for continuous and categorical predictors: With application to a large psoriatic arthritis clinical trial pool

Knockoffs provide a general framework for controlling the false discovery rate when performing variable selection. Much of the Knockoffs literature focuses on theoretical challenges and we recognize a need for bringing some of the current ideas into practice. In this paper we propose a sequential algorithm for generating knockoffs when underlying data consists of both continuous and categorical (factor) variables. Further, we present a heuristic multiple knockoffs approach that offers a practical assessment of how robust the knockoff selection process is for a given dataset. We conduct extensive simulations to validate performance of the proposed methodology. Finally, we demonstrate the utility of the methods on a large clinical data pool of more than 2000 patients with psoriatic arthritis evaluated in four clinical trials with an IL-17A inhibitor, secukinumab (Cosentyx), where we determine prognostic factors of a well established clinical outcome. The analyses presented in this paper could provide a wide range of applications to commonly encountered datasets in medical practice and other fields where variable selection is of particular interest.

Efficacy and safety of secukinumab in patients with psoriatic arthritis: A meta-analysis of different dosing regimens

The appropriate dosing regimens of secukinumab for psoriatic arthritis (PsA) are not well defined. We performed a meta-analysis to evaluate the efficacy and safety of different dosing regimens of secukinumab in the treatment of PsA. A systematic search was conducted using major electronic databases to identify relevant randomized controlled trials (RCTs) comparing secukinumab 300 mg versus secukinumab 150 mg in patients with PsA. Meta-analysis was performed using Review Manager software (version 5.3). Six studies with a total of 1141 patients were included. At week 24, secukinumab 300 mg was associated with a higher American College of Rheumatology 20% response (ACR 20), ACR 50, PASI 75 response rate, and dactylitis resolution rate than secukinumab 150 mg, especially in the anti-TNF-IR subgroup. At week 52, secukinumab 300 mg was associated with a higher psoriasis area and severity index (PASI) 75 and PASI 90 response rate than secukinumab 150 mg. There was no significant difference between secukinumab 300 mg and secukinumab 150 mg in the risk of any adverse events (AEs) and serious AEs at either week 24 or week 52. Secukinumab 300 mg was significantly more effective than 150 mg, especially for patients with PsA who have failed TNF therapy, and it was well tolerated.

Short-Term Efficacy and Safety of Secukinumab for Ankylosing Spondylitis: A Systematic Review and Meta-Analysis of RCTs

Secukinumab is a novel IL-17A inhibitor that has been confirmed to be effective for treating PsA and RA. Several studies have demonstrated that secukinumab also provides benefits for AS patients. Thus, we performed a meta-analysis of RCTs to evaluate the short-term efficacy and safety of secukinumab for the management of AS. The PubMed, Medline, Embase, Web of Science, and Cochrane Library databases were searched for RCTs published prior to March 2020 on the treatment of AS with secukinumab. The primary outcome was the ASAS20 response, and the secondary outcomes included the ASAS40 response, ASAS5/6 response, SF-36 PCS score, ASQoL score, and AEs. Dichotomous data were expressed as pooled RRs with 95% CIs, while continuous data were expressed as pooled MDs with 95% CIs. Subgroup analysis was conducted based on whether the AS patients previously underwent treatment with TNFi. A total of 4 RCTs with 1166 patients were included in our meta-analysis. At week 16, secukinumab 150 mg yielded significant improvements in the clinical response and patient-reported outcomes for AS patients. There was no increased risk of AEs. Consistent results were detected in the meta-analysis of secukinumab 75 mg versus a placebo. Furthermore, no significant difference was detected between the secukinumab 75 mg group and secukinumab 150 mg group. We concluded that secukinumab is effective for treating AS and generally well tolerated by AS patients in the short term, regardless of whether they previously underwent TNFi treatment. The superiority of secukinumab 150 mg over secukinumab 75 mg seems to be limited, since no significant difference in any endpoint was detected between the two groups.

[Role of artificial intelligence in assessing the extent and progression of dermatoses]

BACKGROUND

In recent years, many medical specialties with a visual focus have been revolutionized by image analysis algorithms using artificial intelligence (AI). As dermatology belongs to this field, it has the potential to play a pioneering role in the use of AI.

OBJECTIVE

The current use of AI for the diagnosis and follow-up of dermatoses is reviewed and the future potential of these technologies is discussed.

MATERIALS AND METHODS

This article is based on a selective review of the literature using Embase and MEDLINE and the keywords "psoriasis", "eczema", "dermatoses" and "acne" combined with "artificial intelligence", "machine learning", "deep learning", "neural network", "computer-guided", "supervised machine learning" or "unsupervised machine learning" were searched.

RESULTS

In comparison to examiner-dependent intra- and interindividually fluctuating scores for the assessment of inflammatory dermatoses (e.g. the Psoriasis Areas Severity Index [PASI] and body surface area [BSA]), AI-based algorithms can potentially offer reproducible, standardized evaluations of these scores. Whereas promising algorithms have already been developed for the diagnosis of psoriasis, there is currently only scarce work on the use of AI in the context of eczema.

CONCLUSIONS

The latest developments in this field show the enormous potential of AI-based diagnostics and follow-up of dermatological clinical pictures by means of an autonomous computer-based image analysis. These noninvasive, optical examination methods provide valuable additional information, but dermatological interaction remains indispensable in daily clinical practice.