IDoser: Improving individualized dosing policies with clinical practice and machine learning

Background Finding: the correct drug dose for a specific condition is a key step in many treatments, and failing to do so can lead to deleterious consequences to patient health. Clinical protocols are derived from drug development phase prospective trials. While carefully designed, these often do not include all potential patients, comorbidities or clinical outcomes, ultimately leading to sub-optimal dosing policies. Observational datasets provide real-world information that cannot be substituted with data collected in a controlled environment. Several published methodologies have applied observational datasets for the development of clinical protocols, however these are only applicable whenever these datasets are varied and complete. Often, clinical observational datasets do not comply with these requirements. Computational methods can and should exploit field knowledge to address weaknesses associated with clinical observational data. Methods This paper proposes IDoser, a core dosing model that links drug dose to relevant covariates via a set of coefficients, and includes a loss function to codify needed assumptions and requirements. Coordinate descent is used to obtain a fitted model with minimal loss. The loss function is also used to measure performance when validating the model with unseen data. Our proposal is validated using the case of follicle stimulating hormone (FSH) dosing for controlled ovarian stimulation (COS). Results The proposed Individualized Doser (IDoser) achieved significant improvements when loss values were compared to observed clinical practice and a selected literature benchmark and during the validation phase. Conclusions This methodology constitutes a simple but effective method to bridge the gap between current clinical dosing policies and gold policies based on the true underlying and often unknown dose-response functions.