Comparison of a New Intranasal Naloxone Formulation to Intramuscular Naloxone: Results from Hypothesis-generating Small Clinical Studies.

Easy-to-use naloxone formulations are needed to help address the opioid overdose epidemic. The pharmacokinetics of i.v., i.m., and a new i.n. naloxone formulation (2 mg) were compared in six healthy volunteers. Relative to i.m. naloxone, geometric mean (90% confidence interval [CI]) absolute bioavailability of i.n. naloxone was modestly lower (55%; 90% CI, 43-70% vs. 41%; 90% CI, 27-62%), whereas average (±SE) mean absorption time was substantially shorter (74 ± 8.8 vs. 6.7 ± 4.9 min). The opioid-attenuating effects of i.n. naloxone were compared with i.m. naloxone (2 mg) after administration of oral alfentanil (4 mg) to a separate group of six healthy volunteers pretreated with 240 mL of water or grapefruit juice. The i.m. and i.n. naloxone attenuated miosis by similar extents after water (40 ± 15 vs. 41 ± 21 h*%) and grapefruit juice (49 ± 18 vs. 50 ± 22 h*%) pretreatment. Results merit further testing of this new naloxone formulation.

Implementation of a pharmacogenomics consult service to support the INGENIOUS trial.

Hospital systems increasingly utilize pharmacogenomic testing to inform clinical prescribing. Successful implementation efforts have been modeled at many academic centers. In contrast, this report provides insights into the formation of a pharmacogenomics consultation service at a safety-net hospital, which predominantly serves low-income, uninsured, and vulnerable populations. The report describes the INdiana GENomics Implementation: an Opportunity for the UnderServed (INGENIOUS) trial and addresses concerns of adjudication, credentialing, and funding.

Quantitative prediction and clinical evaluation of an unexplored herb-drug interaction mechanism in healthy volunteers.

Quantitative prediction of herb-drug interaction risk remains challenging. A quantitative framework to assess a potential interaction was used to evaluate a mechanism not previously tested in humans. The semipurified milk thistle product, silibinin, was selected as an exemplar herbal product inhibitor of raloxifene intestinal glucuronidation. Physiologically based pharmacokinetic (PBPK) model simulations of the silibinin-raloxifene interaction predicted up to 30% increases in raloxifene area under the curve (AUC0-inf) and maximal concentration (Cmax). Model-informed clinical evaluation of the silibinin-raloxifene interaction indicated minimal clinical interaction liability, with observed geometric mean raloxifene AUC0-inf and Cmax ratios lying within the predefined no effect range (0.75-1.33). Further refinement of PBPK modeling and simulation approaches will enhance confidence in predictions and facilitate generalizability to additional herb-drug combinations. This quantitative framework can be used to develop guidances to evaluate potential herb-drug interactions prospectively, providing evidenced-based information about the risk or safety of these interactions.

Physiologically based pharmacokinetic modeling framework for quantitative prediction of an herb-drug interaction.

Herb-drug interaction predictions remain challenging. Physiologically based pharmacokinetic (PBPK) modeling was used to improve prediction accuracy of potential herb-drug interactions using the semipurified milk thistle preparation, silibinin, as an exemplar herbal product. Interactions between silibinin constituents and the probe substrates warfarin (CYP2C9) and midazolam (CYP3A) were simulated. A low silibinin dose (160 mg/day × 14 days) was predicted to increase midazolam area under the curve (AUC) by 1%, which was corroborated with external data; a higher dose (1,650 mg/day × 7 days) was predicted to increase midazolam and (S)-warfarin AUC by 5% and 4%, respectively. A proof-of-concept clinical study confirmed minimal interaction between high-dose silibinin and both midazolam and (S)-warfarin (9 and 13% increase in AUC, respectively). Unexpectedly, (R)-warfarin AUC decreased (by 15%), but this is unlikely to be clinically important. Application of this PBPK modeling framework to other herb-drug interactions could facilitate development of guidelines for quantitative prediction of clinically relevant interactions.CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e107; doi:10.1038/psp.2013.69; advance online publication 26 March 2014.