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

Abstract

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.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was supported by Doctorat Industrial funded by Generalitat de Catalunya [DI-2019-24], by project CI-SUSTAIN funded by the Spanish Ministry of Science and Innovation [PID2019-104156GB-I00], by EUROVA Innovative Training Network (MSCA- ITN-2019-860960), and by intramural funding by Clinica Eugin-Eugin Group. Nuria Correa is a PhD Student of the doctoral program in Computer Science at the Universitat Autonoma de Barcelona.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics Committee for Research of Clinica Eugin gave ethical approval of this work (approval code: ALGO2) on the 20th of October of 2020.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

All data produced in the present study are available upon reasonable request to the authors

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