Abstract
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.
Highlights
The promise of personalized medicine has been touted for many years but has been elusive in some specialties.[1]
A total of 242 psoriasis patients were enrolled in the STAMP studies (Figure 1) at time of data lock, including 38 patients who were still in follow up
With regard to drug class, 49.6% patients were treated with IL-23 inhibitor (IL-23i), 33.2% were treated with IL-17 inhibitor (IL-17i), 14.7% were treated with tumor necrosis factor-alpha (TNF) i, and 2.5% were treated with IL-12/23i
Summary
The promise of personalized medicine has been touted for many years but has been elusive in some specialties.[1] Recently, with the influx of large data sets from “omics”based methods including genomics, transcriptomics, and metabolomics, personalized approaches to medical practice have come to the forefront and many specialties use some form of personalized medicine in research and clinical care This is true in oncology where biomarker-guided treatment paradigms are increasingly commonplace.[2]. Advances in the molecular understanding of the skin as well as advances in cutaneous pathophysiology have initiated new lines of thinking for the application of personalized medicine to the treatment of the skin These successes were first realized in melanoma and current treatment guidelines for metastatic melanoma recommend testing tissue for relevant mutations (NRS, BRAF, KIT, GNAQ/11, and/or BAP1) with the goal of treatment that is personalized for a specific patient.[3] other inflammatory skin diseases continue to have a need for personalized approaches. The American Academy of Dermatology (AAD) and National Psoriasis Foundation (NPF) joint guidelines on the treatment of psoriasis with biologic agents stated the urgent need for the identification of biomarkers that can guide efficient biologic selection for individual patients was highlighted.[4]
Published Version (Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have