Clozapine Toxicity Predictor: Deep neural network model predicting clozapine toxicity and its therapeutic dose range

Abstract

Clozapine is the gold standard for treatment-resistant schizophrenia; however, its superior efficacy is accompanied by potentially serious adverse events (neutropenia, seizures, constipations, pneumonia), many of which are also concentration-dependent. As such, clozapine dose titration should be guided by therapeutic drug monitoring (TDM). However, access to TDM is often limited. The present study describes a new deep neural network that can predict the concentrations, toxicity and therapeutic dose range for clozapine and norclozapine. The model was trained on basic clinical data (biological sex, age, clozapine daily dose, BMI, CRP and number of CYP 1A2 and 3A4 substrates, inhibitors and inducers) from 69 patients with treatment-refractory patients treated with different clozapine doses. Our findings provide the training efficacy data for the model, as well as an analysis of clozapine and norclozapine blood concentrations in a test group of three additional patients, to demonstrate its practical capabilities. The model is licensed on a free and permissive 2-Clause BSD license and is available to all clinicians; it can be accessed as a web application, available at https://csk.umed.pl/clotop.