Finding the optimal dose for psoriasis ultraviolet radiation therapy through multi-objective evolutionary optimization on a skin model

Abstract

Ultraviolet radiation (UVR) is proven to be an effective treatment for psoriasis yet it needs to be carefully controlled since it is a widely known carcinogen. Due to the time-consuming, expensive and hazardous nature of clinical tests on actual patients, the range of possible values for the optimal regimen set has not yet been thoroughly explored. An agent-based model was constructed in order to study the complex role of UVR in psoriasis phototherapy, including possible contribution to the formation of skin cancers. The treatment progression of the simulated psoriasis undergoing UVR phototherapy was monitored weekly and compared with the corresponding clinical data. For model calibration, a single-objective optimization via genetic algorithm was employed that aimed to minimize the discrepancy between the model output and the expected clinical result. After fitting and validation, the model was then subjected to multi-objective evolutionary optimization using Non-dominated Sorting Genetic Algorithm-II (NSGA-II) in order to suggest sets of optimal UVR phototherapy regimen for psoriasis taking into account the safety, clearance time as well as aggressiveness of the therapy. Results show a good model fit against clinical data and gave some plausible sets of dosimetry for UVR phototherapy that maximize its therapeutic efficacy while minimizing the associated skin cancer risk.