Interim Clinical Utility Findings of a Transcriptomic Psoriasis Biologic Test Demonstrate Altered Physician Prescribing Behavior and Improved Patient Outcomes

Objective: This study (MATCH) was designed to assess the clinical utility of a machine-learning based tool (Mind.Px) that predicts patient response to the most common biologic classes used in the management of psoriasis patients. Methods: Psoriasis patients who were biologic naïve or switching biologic were enrolled into the study (N=112). At baseline, a dermal biomarker patch was applied to lesional skin and Mind.Px test results provided to physicians prior to biologic selection. The choice of biologic for each patient was recorded and in the case of physician non-concordance with Mind.Px test results, a questionnaire completed to determine the reason for non-concordance. Patients were evaluated at weeks 4, 8, 12, and 16 and statistical analysis between groups performed. Results: Physician prescribing behavior was measured with and without the inclusion of Mind.Px test results. This data was compared to previously obtained data in which dermal biomarker patches were applied at baseline, but Mind.Px results were not provided to physicians at any point during treatment (N=180). Statistical analysis of concordance between the Mind.Px-informed and Mind.Px-uninformed groups within the MATCH study (84.4% vs 53.8%, respectively) showed that when given access to Mind.Px results, physician behavior was significantly altered (p = 0.0022). Furthermore, improved clinical outcomes in those patients whose physicians were provided Mind.Px test results was observed. Specifically, this cohort reached PASI75 sooner than those who were not provided test results (p = 0.004). Conclusion: These results provide an interim measurement of the clinical utility of Mind.Px by demonstrating that physicians will utilize this test in psoriasis biologic decision making and by doing so, this leads to improved patient outcomes. These improved patient outcomes can potentially translate into tremendous cost savings for healthcare systems.