Increasing use of the botanical kratom to self-manage opioid withdrawal and pain has led to increased kratom-linked overdose deaths. Despite these serious safety concerns, rigorous fundamental pharmacokinetic knowledge of kratom in humans remains lacking. We assessed the pharmacokinetics of a single low dose (2 g) of a well-characterized kratom product administered orally to six healthy participants. Median concentration-time profiles for the kratom alkaloids examined were best described by a two-compartment model with central elimination. Pronounced pharmacokinetic differences between alkaloids with the 3S configuration (mitragynine, speciogynine, paynantheine) and alkaloids with the 3R configuration (mitraciliatine, speciociliatine, isopaynantheine) were attributed to differences in apparent intercompartmental distribution clearance, volumes of distribution, and clearance. Based on noncompartmental analysis of individual concentration-time profiles, the 3S alkaloids exhibited a shorter median time to maximum concentration (1–2 vs. 2.5–4.5 h), lower area under the plasma concentration-time curve (430–490 vs. 794–5120 nM × h), longer terminal half-life (24–45 vs. ~12–18 h), and higher apparent volume of distribution during the terminal phase (960–12,700 vs. ~46–130 L) compared to the 3R alkaloids. Follow-up mechanistic in vitro studies suggested differential hepatic/intestinal metabolism, plasma protein binding, blood-to-plasma partitioning, and/or distribution coefficients may explain the pharmacokinetic differences between the two alkaloid types. This first comprehensive pharmacokinetic characterization of kratom alkaloids in humans provides the foundation for further research to establish safety and effectiveness of this emerging botanical product.

Background: Pharmacokinetic drug–drug interactions (DDIs) can be caused by the effect of a pharmaceutical compound on the activity of one or more subtypes of the Cytochrome P450 (CYP) family, UDP-glucuronosyltransferases (UGTs), and/or transporters. As the number of therapeutic areas with polypharmacy has increased, interest has grown in assessing the risk of DDIs during the early phases of drug development. Various lines of research have led to improved mathematical models to predict DDIs, culminating in the Food and Drug Administration’s (FDA) guidelines on evaluating pharmacokinetic DDI risks. However, the recommended static models are highly conservative and often result in false positive predictions. The current research aims to improve the workflow for assessing CYP-mediated DDI risk using Boehringer Ingelheim (BI) proprietary compounds. Methods: The Drug–drug Interaction Risk Calculator (PharmaPendium) was used to evaluate the mechanistic static model, and predictions were correlated with human pharmacokinetic studies from Phase I clinical trials. Results: The results demonstrated that the FDA formula performed well in predicting DDIs for BI proprietary compounds. Furthermore, the integration of either human renal excretion or preclinical species total excretion data into the mechanistic static model enhanced the predictive performance for candidate drugs as victims in DDIs. Conclusions: The basic static models (BSMs) for drug interactions should be used in early drug discovery to “rule out” DDI risks because of the minimal inputs required and the low rate of false negative predictions. Mechanistic static models (MSMs) can then be implemented for compounds that require additional evaluation.