Predicting Secukinumab Fast-Responder Profile in Psoriatic Patients: Advanced Application of Artificial-Neural-Networks (ANNs)

Abstract

Background Drug resistance to biologics in psoriasis therapy can occur – it may be acquired during a treatment or else present itself from the beginning. To date, no biomarkers are known that may reliably guide clinicians in predicting responsiveness to biologics. Biologics may pose a substantial economic burden. Secukinumab efficiently targets IL-17 in the treatment of psoriasis. Objective To assess the “fast responder” patient profile, predicting it from the preliminary complete blood count (CBC) and clinical examination. Materials and methods From November 2016 to May 2017 we performed a multicenter prospective open label pilot study in three Italian reference centers enrolling bio-naive plaque psoriasis patients, undergoing the initiation phase secukinumab treatment (300mg subcutaneous at week 0,1,2,3,4). We define fast responders as patients having achieved at least PASI 75 at the end of secukinumab induction phase. Clinical and CBC data at week 0 and at week 4 were analyzed with linear statistics, principal component analysis, and artificial neural networks (ANNs), also known as deep learning. Two different ANNs were employed: Auto Contractive Map (Auto-CM), an unsupervised ANNs, to study how this variables cluster and a supervised ANNs, Training with Input Selection and Testing (TWIST), to build the predictive model. Results We enrolled 23 plaque psoriasis patients: 19 patients were responders and 4 were non-responders. 30 attributes were examined by Auto-CM, creating…