Supervised Machine-Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations

Area of Science:

• Pharmacology
• Clinical Medicine
• Obesity Research

Background:

• Rising global obesity rates present challenges for drug efficacy.
• Dapsone is a widely used medication for various conditions, including dermatological disorders and infections.
• The impact of obesity on dapsone pharmacokinetics remains incompletely understood.

Purpose of the Study:

• To investigate the effect of obesity on dapsone pharmacokinetic parameters.
• To identify predictors of dapsone absorption, clearance, and distribution in relation to body weight and age.
• To determine if standard dapsone dosing is adequate for obese and older populations.

Main Methods:

• A clinical experiment was conducted using optimal design in volunteers with a weight range of 44-150 kg.
• Maximum-likelihood estimation via the expectation-maximization algorithm was employed for pharmacokinetic analysis.
• Artificial intelligence-based multivariate adaptive regression splines were utilized for covariate selection.

Main Results:

• Weight and/or age were identified as significant predictors of dapsone absorption, systemic clearance, and volume of distribution.
• Complex, non-linear relationships were observed between patient weight/age ranges and drug pharmacokinetics.
• Older and obese individuals exhibited lower dapsone concentrations following standard dosing regimens.

Conclusions:

• Obesity significantly alters dapsone pharmacokinetics, leading to reduced drug concentrations in standard doses.
• Current dosing strategies may be suboptimal for obese and elderly patients, potentially impacting treatment efficacy.
• Personalized dapsone dosing is essential for obese patients to achieve therapeutic concentrations and ensure treatment effectiveness.