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

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.

Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level

“…In a scientific study, 28 researchers investigated the possibility of using baseline dermal biomarkers and transcriptomes, collected through genetic testing, to predict the appropriate biologic for individual patients with psoriasis. The results were promising and suggest that predicting the optimal biologic for patients with psoriasis may be achievable to some extent, in accordance with our results.…”

Transcriptional Basis of Psoriasis from Large Scale Gene Expression Studies: The Importance of Moving towards a Precision Medicine Approach

“…Gene expression profiling, through RNA-seq and serum proteome analysis from blood, showed that there was a strong association between clinical response and TNF-regulated genes in skin and blood [ 90 ]. The use of a machine-learning algorithm to predict patients’ response to three of the psoriasis biologics was studied in a recent study [ 91 ]. In this large cohort study, involving 242 psoriasis patients, dermal patch biomarker patches were applied before and after 12 weeks of drug treatment with IL-23, IL-17, and TNF-α.…”

Dermatologic Disease-Directed Targeted Therapy (D3T2): The Application of Biomarker-Based Precision Medicine for the Personalized Treatment of Skin Conditions—Precision Dermatology

“…A study of 242 psoriasis patients was performed; 118 patients were treated with an IL-23 inhibitor, 79 patients were treated with an IL-17 inhibitor, and 35 patients were treated with a TNF-alpha inhibitor. The positive predictive values for IL-23 inhibitors, IL-17 inhibitors, and TNF-alpha inhibitors were 93.1%, 92.3%, and 85.7%, respectively (in contrast to the response rates of about 66% to 86% to IL-23 inhibitors; 60-70% to IL-17 inhibitors, and 25-60% to TNF-alpha inhibitors seen in patients; hence, precision dermatology based on a machine-learning-based test that evaluates baseline mRNA biomarkers can be used to select the biologic drug therapy to which a psoriasis patient is most likely to respond [20,[22][23][24][25][26]. Cost savings may be realized by giving the right drugs to the right patient at the right time: the sine qua non of precision medicine; in psoriasis, these cost savings have been estimated to average $8492 per year when transcriptomics is used to predict responders [21].…”