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 a semantic map for three main profiles: responders, non-responders and an intermediate profile. The algorithm yielded 5 of the 30 attributes to describe the 3 profiles. This allowed us to set up the predictive model. It displayed after training testing protocol an overall accuracy of 91.88% (90% for responders and 93,75% for non-responders). CONCLUSIONS: The present study is possibly the first approach employing ANNs to predict drug efficacy in dermatology; a wider use of ANNs may be conducive to useful both theoretical and clinical insight. J Drugs Dermatol. 2020;19(12) doi:10.36849/JDD.2020.5006.
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 psoriasispatients, 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 psoriasispatients: 19 patients were responders and 4 were non-responders. 30 attributes were examined by Auto-CM, creating a semantic map for three main profiles: responders, non-responders and an intermediate profile. The algorithm yielded 5 of the 30 attributes to describe the 3 profiles. This allowed us to set up the predictive model. It displayed after training testing protocol an overall accuracy of 91.88% (90% for responders and 93,75% for non-responders). CONCLUSIONS: The present study is possibly the first approach employing ANNs to predict drug efficacy in dermatology; a wider use of ANNs may be conducive to useful both theoretical and clinical insight. J Drugs Dermatol. 2020;19(12) doi:10.36849/JDD.2020.5006.
Authors: Alex J Allphin; Yvonne M Mowery; Kyle J Lafata; Darin P Clark; Alex M Bassil; Rico Castillo; Diana Odhiambo; Matthew D Holbrook; Ketan B Ghaghada; Cristian T Badea Journal: Tomography Date: 2022-03-10
Authors: Andrea Conti; Giovanni Damiani; Roberta Ruggeri; Giulia Odorici; Francesca Farnetani; Paolo Daniele Maria Pigatto; Giovanni Pellacani Journal: Dermatol Ther Date: 2021-08-16 Impact factor: 3.858