Application of deep neural networks as a prescreening tool to assign individualized absorption models in pharmacokinetic analysis

Mu'taz Jaber, Burhaneddin Yaman, Kyriakie Sarafoglou, Richard C. Brundage

Research output: Contribution to journalArticlepeer-review

Abstract

A specific model for drug absorption is necessarily assumed in pharmacokinetic (PK) analyses following extravascular dosing. Unfortunately, an inappropriate absorption model may force other model parameters to be poorly estimated. An added complexity arises in population PK analyses when different individuals appear to have different absorption patterns. The aim of this study is to demonstrate that a deep neural network (DNN) can be used to prescreen data and assign an individualized absorption model consistent with either a first-order, Erlang, or split-peak process. Ten thousand profiles were simulated for each of the three aforementioned shapes and used for training the DNN algorithm with a 30% hold-out validation set. During the training phase, a 99.7% accuracy was attained, with 99.4% accuracy during in the validation process. In testing the algorithm classification performance with external patient data, a 93.7% accuracy was reached. This algorithm was developed to prescreen individual data and assign a particular absorption model prior to a population PK analysis. We envision it being used as an efficient prescreening tool in other situations that involve a model component that appears to be variable across subjects. It has the potential to reduce the time needed to perform a manual visual assignment and eliminate inter-assessor variability and bias in assigning a sub-model.

Original languageEnglish (US)
Article number797
JournalPharmaceutics
Volume13
Issue number6
DOIs
StatePublished - May 26 2021

Bibliographical note

Funding Information:
Research reported in this publication was partially supported by the Office of Orphan Products Development of the Food and Drug Administration under award number R01FDR0006100. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the FDA nor the FDA?s Office of Orphan Products Development.

Funding Information:
Funding: Research reported in this publication was partially supported by the Office of Orphan Products Development of the Food and Drug Administration under award number R01FDR0006100. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the FDA nor the FDA’s Office of Orphan Products Development Institutional Review Board Statement: The clinical data collected was approved for reporting by the Institutional Review Board of University of Minnesota (protocol code 1209M21101 approved on 5 July 2017) under the study “Outcomes in Congenital Adrenal Hyperplasia”.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Absorption models
  • Deep learning
  • Individualized models
  • Machine learning
  • Pharmacokinetics
  • Visual inspection

PubMed: MeSH publication types

  • Journal Article

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