Supervised machine-learning reveals that old and obese people achieve low dapsone concentrations

R. G.I.I. Hall, J. G. Pasipanodya, M. A. Swancutt, C. Meek, R. Leff, T. Gumbo

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

The human species is becoming increasingly obese. Dapsone, which is extensively used across the globe for dermatological disorders, arachnid bites, and for treatment of several bacterial, fungal, and parasitic diseases, could be affected by obesity. We performed a clinical experiment, using optimal design, in volunteers weighing 44-150 kg, to identify the effect of obesity on dapsone pharmacokinetic parameters based on maximum-likelihood solution via the expectation-maximization algorithm. Artificial intelligence-based multivariate adaptive regression splines were used for covariate selection, and identified weight and/or age as predictors of absorption, systemic clearance, and volume of distribution. These relationships occurred only between certain patient weight and age ranges, delimited by multiple hinges and regions of discontinuity, not identified by standard pharmacometric approaches. Older and obese people have lower drug concentrations after standard dosing, but with complex patterns. Given that efficacy is concentration-dependent, optimal dapsone doses need to be personalized for obese patients.

Original languageEnglish (US)
Pages (from-to)552-559
Number of pages8
JournalCPT: Pharmacometrics and Systems Pharmacology
Volume6
Issue number8
DOIs
StatePublished - Aug 2017

ASJC Scopus subject areas

  • Modeling and Simulation
  • Pharmacology (medical)

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