A non-parametric optimal design algorithm for population pharmacokinetics

TL;DR

This paper presents a non-parametric optimal design (NPOD) algorithm for population pharmacokinetics that improves efficiency over previous methods by using a gradient approach to select support points, reducing computation time.

Contribution

The NPOD algorithm introduces a gradient-based support point selection method, enhancing efficiency in non-parametric population pharmacokinetics modeling compared to the NPAG algorithm.

Findings

NPOD achieves similar accuracy to NPAG on two datasets.

NPOD requires fewer cycles and less runtime than NPAG.

The algorithm is suitable for rapid drug dose determination.

Abstract

This paper introduces a non-parametric estimation algorithm designed to effectively estimate the joint distribution of model parameters with application to population pharmacokinetics. Our research group has previously developed the non-parametric adaptive grid (NPAG) algorithm, which while accurate, explores parameter space using an ad-hoc method to suggest new support points. In contrast, the non-parametric optimal design (NPOD) algorithm uses a gradient approach to suggest new support points, which reduces the amount of time spent evaluating non-relevant points and by this the overall number of cycles required to reach convergence. In this paper, we demonstrate that the NPOD algorithm achieves similar solutions to NPAG across two datasets, while being significantly more efficient in both the number of cycles required and overall runtime. Given the importance of developing robust and efficient algorithms for determining drug doses quickly in pharmacokinetics, the NPOD algorithm represents a valuable advancement in non-parametric modeling. Further analysis is needed to determine which algorithm performs better under specific conditions.