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Predicting Maternal and Infant Tetrahydrocannabinol Exposure in Lactating Cannabis Users: A Physiologically Based Pharmacokinetic Modeling Approach.

Babajide ShenkoyaVenkata Kashyap YellepeddiKatrina MarkMathangi Gopalakrishnan
Published in: Pharmaceutics (2023)
A knowledge gap exists in infant tetrahydrocannabinol (THC) data to guide breastfeeding recommendations for mothers who use cannabis. In the present study, a paired lactation and infant physiologically based pharmacokinetic (PBPK) model was developed and verified. The verified model was used to simulate one hundred virtual lactating mothers (mean age: 28 years, body weight: 78 kg) who smoked 0.32 g of cannabis containing 14.14% THC, either once or multiple times. The simulated breastfeeding conditions included one-hour post smoking and subsequently every three hours. The mean peak concentration (C max ) and area under the concentration-time curve (AUC (0-24 h) ) for breastmilk were higher than in plasma (C max : 155 vs. 69.9 ng/mL; AUC (0-24 h) : 924.9 vs. 273.4 ng·hr/mL) with a milk-to-plasma AUC ratio of 3.3. The predicted relative infant dose ranged from 0.34% to 0.88% for infants consuming THC-containing breastmilk between birth and 12 months. However, the mother-to-infant plasma AUC (0-24 h) ratio increased up to three-fold (3.4-3.6) with increased maternal cannabis smoking up to six times. Our study demonstrated the successful development and application of a lactation and infant PBPK model for exploring THC exposure in infants, and the results can potentially inform breastfeeding recommendations.
Keyphrases
  • dairy cows
  • body weight
  • preterm infants
  • smoking cessation
  • clinical practice
  • human milk
  • machine learning
  • pregnant women
  • physical activity
  • weight loss
  • big data