Simultaneous LC-MS/MS method for the quantitation of Azithromycin, Hydroxychloroquine and its metabolites in SARS-CoV-2(-/ +) populations using dried blood spots.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to a global pandemic of coronavirus disease 2019 (COVID-19). Early in the pandemic, efforts were made to test the SARS-CoV-2 antiviral efficacy of repurposed medications that were already approved and available for other indications, including hydroxychloroquine (HCQ) and azithromycin (AZI). To reduce the risk of SARS-CoV-2 exposure for clinical-trial study participants and to conform with lockdowns and social distancing guidelines, biospecimen collection for HCQ and AZI included at-home dried blood spot (DBS) collection rather than standard venipuncture by trained clinicians. In this study, we developed and validated the first sensitive and selective simultaneous LC-MS/MS method to accurately quantitate the concentration of HCQ, HCQ metabolites (Desethylchloroquine [DCQ], Bisdesethylchloroquine [BDCQ], Monodesethylhydroxychloroquine [DHCQ]) and AZI extracted from DBS. The validated method was successfully applied for the quantification of over 2000 DBS specimens to evaluate the pharmacokinetic profile of AZI, HQC, and its metabolites. This new method has a small sample volume requirement (~ 10 µL), results in high sensitivity (1 ng/mL), and would facilitate remotely conducted therapeutic drug monitoring.

A linked physiologically based pharmacokinetic model for hydroxychloroquine and metabolite desethylhydroxychloroquine in SARS-CoV-2(-)/(+) populations.

Hydroxychloroquine (HCQ) is Food and Drug Administration (FDA)-approved for malaria, systemic and chronic discoid lupus erythematosus, and rheumatoid arthritis. Because HCQ has a proposed multimodal mechanism of action and a well-established safety profile, it is often investigated as a repurposed therapeutic for a range of indications. There is a large degree of uncertainty in HCQ pharmacokinetic (PK) parameters which complicates dose selection when investigating its use in new disease states. Complications with HCQ dose selection emerged as multiple clinical trials investigated HCQ as a potential therapeutic in the early stages of the COVID-19 pandemic. In addition to uncertainty in baseline HCQ PK parameters, it was not clear if disease-related consequences of SARS-CoV-2 infection/COVID-19 would be expected to impact the PK of HCQ and its primary metabolite desethylhydroxychloroquine (DHCQ). To address the question whether SARS-CoV-2 infection/COVID-19 impacted HCQ and DHCQ PK, dried blood spot samples were collected from SARS-CoV-2(-)/(+) participants administered HCQ. When a previously published physiologically based pharmacokinetic (PBPK) model was used to fit the data, the variability in exposure of HCQ and DHCQ was not adequately captured and DHCQ concentrations were overestimated. Improvements to the previous PBPK model were made by incorporating the known range of blood to plasma concentration ratios (B/P) for each compound, adjusting HCQ and DHCQ distribution settings, and optimizing DHCQ clearance. The final PBPK model adequately captured the HCQ and DHCQ concentrations observed in SARS-CoV-2(-)/(+)participants, and incorporating COVID-19-associated changes in cytochrome P450 activity did not further improve model performance for the SARS-CoV-2(+) population.

Analysis of Drug-Drug Interaction Labeling Language and Clinical Recommendations for Newly Approved Drugs Evaluated With Digoxin, Midazolam, and S-Warfarin.

PURPOSE: To best promote drug tolerability and efficacy in the clinic, data from drug-drug interaction (DDI) evaluations and subsequent translation of the results to DDI prevention and/or management strategies must be incorporated into the US Food and Drug Administration (FDA) product labeling in a consistent manner because differences in language might result in varied interpretations. This analysis aimed to assess the consistency in DDI labeling language in New Drug Applications (NDAs). METHODS: NDAs of recently approved drugs (2012-2020) that increase the exposure of digoxin, midazolam, and S-warfarin, index substrates of P-glycoprotein, cytochrome P450 (CYP) 3A, and CYP2C9 activity, respectively, were fully reviewed. Noninhibitors were also evaluated to appreciate the extent of mechanistic extrapolation in case of negative index studies. FINDINGS: After a systematic review of the DDI studies available in NDAs, FDA-approved labeling, and commonly used clinical tertiary resources, differences in DDI results presentation and resulting clinical recommendations were found, even for inhibitors that affect similarly the exposure of the same index substrate. Studies with negative results were often reported in the labels without providing mechanistic interpretation, thus limiting the possible extrapolation of this information to other known substrates. IMPLICATIONS: The variability in language affects how the information was presented to clinicians in tertiary resources. Strategies that aim to improve the translation of mechanistic DDI index studies into consistent labeling recommendations are briefly discussed in this review.

Do Inhibitory Metabolites Impact DDI Risk Assessment? Analysis of in vitro and in vivo Data from NDA Reviews Between 2013 and 2018.

Evaluating the potential of new drugs and their metabolites to cause drug-drug interactions (DDIs) is critical for understanding drug safety and efficacy. Although multiple analyses of proprietary metabolite testing data have been published, no systematic analyses of metabolite data collected according to current testing criteria have been conducted. To address this knowledge gap, 120 new molecular entities approved between 2013 and 2018 were reviewed. Comprehensive data on metabolite-to-parent area under the curve ratios (AUCM /AUCP ), inhibitory potency of parent and metabolites, and clinical DDIs were collected. Sixty-four percent of the metabolites quantified in vivo had AUCM /AUCP  ≥ 0.25 and 75% of these metabolites were tested for cytochrome P450 (CYP) inhibition in vitro, resulting in 15 metabolites with potential DDI risk identification. Although 50% of the metabolites with AUCM /AUCP  < 0.25 were also tested in vitro, none of them showed meaningful CYP inhibition potential. The metabolite percentage of plasma total radioactivity cutoff of ≥ 10% did not appear to add value to metabolite testing strategies. No relationship between metabolite versus parent drug polarity and inhibition potency was observed. Comparison of metabolite and parent maximum concentration (Cmax ) divided by inhibition constant (Ki ) values suggested that metabolites can contribute to in vivo DDIs and, hence, quantitative prediction of clinical DDI magnitude may require both parent and metabolite data. This systematic analysis of metabolite data for newly approved drugs supports an AUCM /AUCP cutoff of ≥ 0.25 to warrant metabolite in vitro CYP screening to adequately characterize metabolite inhibitory DDI potential and support quantitative DDI predictions.

Changing student attitudes and perceptions toward opioid use disorder.

INTRODUCTION: With the opioid epidemic creating a group of patients with unique health care needs, pharmacists have an opportunity to be a good resource for patients recovering from opioid use disorder (OUD). To accomplish this, it is essential that pharmacists are knowledgeable and unbiased toward this patient population. METHODS: Because the curriculum in place to obtain a PharmD at Drake University does not include in-depth information on substance use disorders, study investigators offered students an opportunity to receive more intensive education. Faculty members at Drake University provided didactic and panel discussion presentations on topics such as opioid pharmacology, OUD, and treatment options. The students were assessed for their perception of knowledge and stigma before and after the summit by using a 5-point Likert scale to measure their attitudes toward 10 statements. RESULTS: Total knowledge scores showed a significant change of 3.1, indicating an increase in perceived understanding of materials presented (P < .0001). Total stigma scores also changed by 1.4, illustrating a statistically significant decrease in negative perceptions (P = .0198). DISCUSSION: By providing more in-depth education, the summit showed that increasing pharmacy student knowledge about OUD and its treatment may decrease associated stigma.