Abstract
COVID-19 together with variants have caused an unprecedented amount of mental and economic turmoil with ever increasing fatality and no proven therapies in sight. The healthcare industry is racing to find a cure with multitude of clinical trials underway to access the efficacy of repurposed antivirals, however the much needed insights into the dynamics of pathogenesis of SARS-CoV-2 and corresponding pharmacology of antivirals are lacking. This paper introduces systematic pathological model learning of COVID-19 dynamics followed by derivative free optimization based multi objective drug rescheduling. The pathological model learnt from clinical data of severe COVID-19 patients treated with remdesivir could additionally predict immune T cells response and resulted in a dramatic reduction in remdesivir dose and schedule leading to lower toxicities, however maintaining a high virological efficacy.
【초록키워드】 COVID-19, Efficacy, clinical trial, therapy, Pathogenesis, Antiviral, antivirals, T cells, variant, Remdesivir, clinical trials, variants, immune, T cell, healthcare, predict, fatality, cure, dose, followed by, in sight, Virological, Healthcare industry, Clinical data, toxicities, severe COVID-19 patients, multi, schedule, multitude, sight, pathogenesis of SARS-CoV-2, Mental, caused, treated, reduction in, severe COVID-19 patient, 【제목키워드】 COVID-19, antiviral therapy, pathogenic,