Evidence Threshold for a Precision Medicine Test that Predicts Optimal Response to a Biologic Agent in Patients With Psoriasis: A Consensus Panel

June 2022 | Volume 21 | Issue 6 | 630 | Copyright © June 2022


Published online May 20, 2022

doi:10.36849/JDD.6864

Bruce Strober MD PhDa*, John Fox MDb, Eliot Jekowsky MDc, David Pariser MDd, Ken Schaecher MDe

aYale University School of Medicine, New Haven, CT; Central Connecticut Dermatology, Cromwell, CT
bFoxworthy Healthcare Consulting, Grand Rapids, MI
cIndependent Consultant, Boston, MA
dEastern Virginia Medical School and Virginia Clinical Research, Inc., Norfolk, VA
eUniversity of Utah Health Plans, Murray, UT

Abstract
Precision medicine approaches are receiving increased attention in dermatology, including inflammatory skin diseases. In psoriasis, a precision medicine treatment paradigm could temper the rapid increase in pharmacy costs that have resulted from a tremendous expansion in the number of available biologic drug options. However, without a clear and agreed upon proof of clinical utility in a real-world setting, costly new pharmacotherapies are often burdened with barriers to coverage by payers and ultimately, routine patient care. This panel was assembled to discuss the evidence threshold required to demonstrate the clinical utility of a precision medicine diagnostic that predicts the biologic therapeutic class for treating psoriasis patients. The panel reviewed clinical utility study designs and economic impact study designs aimed at delineating net savings and waste reduction. A psoriasis biologic precision medicine test could optimize pharmacotherapy management of psoriasis patients. The consensus opinion of this panel was that positive results from the study described here would prove the clinical utility of this precision medicine test.

J Drugs Dermatol. 2022;21(6):630-636. doi:10.36849/JDD.6864

INTRODUCTION

Precision medicine has been a long-promised goal in patient care, and with the advent of technologies that comprehensively collect large data sets and the corresponding computational tools to parse and analyze this complex data, this aspiration is increasingly becoming reality. This is particularly true in oncology where biomarker-guided paradigms are seeing improved utilization by physicians to optimize patient treatment.1 In dermatology, precision medicine approaches have been adopted with the dual goal of matching a patient with the optimal first-line drug and simultaneously reducing the total cost of care.2 Precision medicine paradigms rely on the use of complex biomarker sets in order to achieve their goals; these biomarkers can be molecular in nature or clinically measurable variables. Sophisticated machine learning methods can then analyze these complex data sets and derive algorithms that have clinically actionable value.

At present, there are limited precision tools currently available for inflammatory conditions such as psoriasis and atopic dermatitis. Psoriasis is an immune-mediated inflammatory skin disease that afflicts upwards of 3% of the population worldwide.3 This autoimmune disease has been well-studied and a complex inflammatory circuit has been implicated in the pathology of the disease that stimulates keratinocyte proliferation via upregulation of a host of cytokines including tumor necrosis factor (TNF), interleukin (IL)-17, and IL-23.4 Current treatment options per American Academy of Dermatology (AAD) guidelines include topical medications, phototherapy, systemic medications, and biologics, depending upon severity of disease.

In recent years, there has been a rapid proliferation of biologic drug options for psoriasis which has revolutionized the management of these patients. This expansion of treatment options has come with a concomitant rapid increase in the annual cost for treatment.5 In the U.S. alone, it is estimated that patients with psoriasis will pay a lifetime cost of $11,498 for relief of physical and emotional symptoms, resulting in an annual national cost of an estimated $112 billion to treat these patients.6 The annual expense paid by payers for psoriasis