The administered dose of a drug modulates whether patients will experience optimal effectiveness, toxicity including death, or no effect at all. Dosing is particularly important for diseases and/or drugs where the drug can decrease severe morbidity or prolong life. Likewise, dosing is important where the drug can cause death or severe morbidity. Since we believe there are many examples where more precise dosing could benefit patients, it is worthwhile to consider how to prioritize drug-disease targets. One key consideration is the quality of information available from which more precise dosing recommendations can be constructed. When a new more precise dosing scheme is created and differs significantly from the approved label, it is important to consider the level of proof necessary to either change the label and/or change clinical practice. The cost and effort needed to provide this proof should also be considered in prioritizing drug-disease precision dosing targets. Although precision dosing is being promoted and has great promise, it is underutilized in many drugs and disease states. Therefore, we believe it is important to consider how more precise dosing is going to be delivered to high priority patients in a timely manner. If better dosing schemes do not change clinical practice resulting in better patient outcomes, then what is the use? This review paper discusses variables to consider when prioritizing precision dosing candidates while highlighting key examples of precision dosing that have been successfully used to improve patient care.

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The objectives of this manuscript are to describe a case report of a patient whose phenelzine maintenance therapy was discontinued due to concern for a phenelzine-morphine drug interaction, to review the available literature regarding the potential for this drug-drug interaction, and provide recommendations for this clinical scenario. A PubMed/MEDLINE literature search was conducted and all publications determined to be relevant to this case report were included. Literature describing in vitro data, case reports/human studies, and review articles concerning the interaction between morphine and monoamine oxidase inhibitors (MAOIs) were included. A total of 14 publications pertinent to the potential phenelzine-morphine interaction were included in this review including 5 in vitro studies, 4 human studies, and 6 review articles detailing the drug interaction profile between opioids and antidepressants. Of these publications, only a single case report of a potential drug interaction between morphine and phenelzine was identified. The literature suggesting a drug interaction between morphine and phenelzine is limited. The combination of phenelzine and morphine, with close monitoring for signs and symptoms of serotonin syndrome, is reasonable for patients with appropriate indications for both agents.

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Population pharmacokinetic (PK)/pharmacodynamic models are commonly used to inform drug dosing; however, often real‐world patients are not well represented in the clinical trial population. We sought to determine how well dosing recommended in the rivaroxaban drug label results in exposure for real‐world patients within a reference area under the concentration–time curve (AUC) range. To accomplish this, we assessed the utility of a prior published rivaroxaban population PK model to predict exposure in real‐world patients. We used the model to predict rivaroxaban exposure for 230 real‐world patients using 3 methods: (1) using patient phenotype information only, (2) using individual post hoc estimates of clearance from the prior model based on single PK samples of rivaroxaban collected at steady state without refitting of the prior model, and (3) using individual post hoc estimates of clearance from the prior model based on PK samples of rivaroxaban collected at steady state after refitting of the prior model. We compared the results across 3 software packages (NONMEM, Phoenix NLME, and Monolix). We found that while the average patient‐assigned dosing per the drug label will likely result in the AUC falling within the reference range, AUC for most individual patients will be outside the reference range. When comparing post hoc estimates, the average pairwise percentage differences were all <10% when comparing the software packages, but individual pairwise estimates varied as much as 50%. This study demonstrates the use of a prior published rivaroxaban population PK model to predict exposure in real‐world patients.