Fexofenadine Plasma Concentrations to Estimate Systemic Exposure in Healthy Adults Using a Limited Sampling Strategy with a Population Pharmacokinetic Approach.

BACKGROUND: Fexofenadine is a recommended in vivo probe drug for phenotyping P-glycoprotein (P-gp) and organic anion transporting polypeptide (OATP) 1B1/3 transporter activities. This study evaluated a limited sampling strategy using a population pharmacokinetic approach to estimate plasma fexofenadine exposure as an index of P-gp and OATP activities. METHODS: In a previous study, a single oral dose of fexofenadine (120 mg) was administered alone or in combination with grapefruit juice, Panax ginseng , or Echinacea purpurea to healthy adult participants. Serial plasma samples were collected up to 72 hours after administration and fexofenadine concentrations were measured. A population pharmacokinetic model was developed using nonlinear mixed-effects modeling. Limited sampling models (LSMs) using single and 2-timepoint fexofenadine concentrations were compared with full profiles from intense sampling using empirical Bayesian post hoc estimations of systemic exposure derived from the population pharmacokinetic model. Predefined criteria for LSM selection and validation included a coefficient of determination (R 2 ) ≥ 0.90, relative percent mean prediction error ≥ -5 to ≤5%, relative percent mean absolute error ≤ 10%, and relative percent root mean square error ≤ 15%. RESULTS: Fexofenadine concentrations (n = 1520) were well described using a 2-compartment model. Grapefruit juice decreased the relative oral bioavailability of fexofenadine by 25%, whereas P. ginseng and E. purpurea had no effect. All the evaluated single timepoint fexofenadine LSMs showed unacceptable percent mean prediction error, percent mean absolute error, and/or percent root mean square error. Although adding a second time point improved precision, the predefined criteria were not met. CONCLUSIONS: Identifying novel fexofenadine LSMs to estimate P-gp and OATP1B1/3 activities in healthy adults for future transporter-mediated drug-drug interaction studies remains elusive.

Limitations of fexofenadine limited sampling strategy using plasma concentrations and partial area under the concentration-time curve to estimate transporter activity in healthy adults.

OBJECTIVE: Fexofenadine is a probe drug used to phenotype P-glycoprotein (P-gp) and organic anion transporting polypeptide (OATP) 1B1/3 activities. This study evaluated a limited sampling strategy using plasma concentrations and/or partial area under the concentration versus time curves (AUCs) to estimate systemic exposure and, potentially, P-gp and OATP1B1/3 activities. MATERIALS AND METHODS: Plasma concentration versus time data were obtained from 53 healthy adult participants (22 females) from four published studies. Participants were administered a single oral dose (120 mg) of fexofenadine during constitutive P-gp and OATP1B1/3 conditions. Concentration-time data were divided into a training (n = 18) and validation (n = 35) set. Backwards stepwise linear regression generated single-, 2-timepoint, and partial AUC limited sampling models (LSMs). Noncompartmental analysis methods were used to determine total AUC (AUC0-lNF) from intensive sampling. Coefficient of determination (r2) and bias and precision were assessed via relative percent mean prediction error (%MPE), relative percent mean absolute error (%MAE), and relative percent root mean square error (%RMSE). RESULTS: The geometric mean observed AUC0-INF was 1,680 ng×h/mL. The 2-, 5-, and 2- plus 5-hour LSMs met backwards stepwise linear regression significance (p < 0.15) to remain in the model but had unacceptable %RMSE (17 - 29%). The majority of partial AUC LSMs had unacceptable r2 (0.21 - 0.83), with all models having unacceptable %MAE (12 - 35%). CONCLUSION: Fexofenadine limited sampling strategy using single-timepoint, 2-timepoint, and partial AUCs were unable to accurately estimate AUC0-lNF and thus constitutive P-gp and OATB1B1/3 activities in healthy adults. Timepoints that were not measured or selected may have improved LSM performance.

S-warfarin limited sampling strategy with a population pharmacokinetic approach to estimate exposure and cytochrome P450 (CYP) 2C9 activity in healthy adults.

PURPOSE: S-warfarin is used to phenotype cytochrome P450 (CYP) 2C9 activity. This study evaluated S-warfarin limited sampling strategy with a population pharmacokinetic (PK) approach to estimate CYP2C9 activity in healthy adults. METHODS: In 6 previously published studies, a single oral dose of warfarin 10 mg was administered alone or with a CYP2C9 inducer to 100 healthy adults. S-warfarin concentrations were obtained from adults during conditions when subjects were not on any prescribed medications. A population PK model was developed using non-linear mixed effects modeling. Limited sampling models (LSMs) using single- or 2-timepoint concentrations were compared with full PK profiles from intense sampling using empiric Bayesian post hoc estimations of S-warfarin AUC derived from the population PK model. Preset criterion for LSM selection and validation were a correlation coefficient (R2) >0.9, relative percent mean prediction error (%MPE) >-5 to <5%, relative percent mean absolute error (%MAE) ≤ 10%, and relative percent root mean squared error (%RMSE) ≤ 15%. RESULTS: S-warfarin concentrations (n=2540) were well described with a two-compartment model. Mean apparent oral clearance was 0.56 L/hr and volume of distribution was 35.5 L. Clearance decreased 33% with the CYP2C9 *3 allele and increased 42% with lopinavir/ritonavir co-administration. During CYP2C9 constitutive conditions, LSMs at 48 hr and at 72 hr as well as 2-timepoint LSMs were within acceptable limits for R2, %MPE, %MAE, and %RMSE. During CYP2C9 induction, S-warfarin LSMs had unacceptable %MPE, %MAE, and %RMSE. CONCLUSIONS: Phenotyping studies with S-warfarin in healthy subjects can utilize a single- and/or a 2-timepoint LSM with a population PK approach to estimate constitutive CYP2C9 activity.

More opioids, more constipation? Evaluation of longitudinal total oral opioid consumption and self-reported constipation in patients with cancer.

PURPOSE: Opioid-induced constipation (OIC) is a distressing physical symptom for patients with cancer taking opioids. Total opioid consumption may contribute to developing worsening OIC-related symptoms. We completed a retrospective analysis examining the association of total daily opioid consumption on self-reported constipation in patients with cancer. METHODS: In over 5 clinic visits, we collected self-reported constipation scores and 24-h oral morphine equivalents (OME). We examined the association between OME and the presence of constipation (i.e., score > 3) and the relationship of OME between patients with or without constipation. RESULTS: Of 297 patients with cancer, we observed 57.8% with constipation and 42.4% without constipation at the first clinic visit. Age was similar in both groups (54.2 ± 14.5 vs. 56.4 ± 14.8 years [mean ± SD]) and the majority of patients were women (63.7% vs. 61.1%). The most common cancer type in patients with constipation was non-colorectal gastrointestinal (n = 25, 14.6%), while in patients without constipation was colorectal gastrointestinal (n = 25; 19.8%). Across visits, we observed weak or no association between OME and self-reported constipation (r = 0.01-0.27). At the first visit, higher mean OME was seen in patients who self-reported constipation (133.4 vs 76; p < 0.05). Age, sex, metastatic disease, and stimulant laxative use were not associated with constipation. CONCLUSIONS: We observed weak to no association between OME and constipation in patients with cancer. These results suggest a lack of a clear association between total opioid consumption and self-reported constipation.

Evaluation of Omeprazole Limited Sampling Strategies to Estimate Constitutive Cytochrome P450 2C19 Activity in Healthy Adults.

BACKGROUND: Limited sampling strategy (LSS) is a validated method to estimate pharmacokinetic (PK) parameters from a reduced number of samples. Omeprazole is used to phenotype in vivo cytochrome P450 (CYP) 2C19 activity. This study examined an LSS using 2 estimation methods to determine apparent oral clearance (CL/F) and thus CYP2C19 activity. METHODS: Data from 7 previously published studies included healthy subjects receiving a single, oral dose of omeprazole with intensive PK sampling. CL/F was estimated using noncompartmental analysis (NCA) and population PK modeling. LSS was simulated by selecting the 1, 2, 4, and/or 6-hour postdose time points. Linear regression was performed to assess whether CL/F estimated from limited sampling could accurately predict CL/F from the full PK profile. RESULTS: Median CL/F was 23.7 L/h by NCA and 19.3 L/h by population PK modeling. In comparing the LSS NCA estimated versus observed CL/F, all evaluated linear regression models had unacceptable coefficients of determination (r, range: 0.14-0.81). With the population PK approach, 737 plasma concentrations (n = 71) and CYP2C19 genotype data were described with a 1-compartment structural model with mixed zero and first-order absorption and lag time. In comparing the population PK LSS estimated versus observed CL/F, all evaluated linear regression models had unacceptable r (range: 0.02-0.74). Post hoc comparison of CYP2C19 poor metabolizers versus CYP2C19 extensive metabolizers resulted in significantly lower CL/F in poor metabolizers versus extensive metabolizers. CONCLUSIONS: Omeprazole LSS performed poorly in estimating CL/F using 2 separate estimation approaches and does not seem to be a suitable method for determining CYP2C19 activity.

Transfusion practices at end of life for hematopoietic stem cell transplant patients.

PURPOSE: Limited data exist regarding transfusion practices at end of life (EOL) for hematopoietic stem cell transplant (HSCT) patients. The purpose of this study was to examine red blood cell (RBC) and platelet transfusion practices in HSCT patients who enrolled or did not enroll in hospice. METHODS: This was a single-center, retrospective chart review in deceased HSCT patients. The primary objective was to determine the mean difference between the last transfusion and death in HSCT patients (n = 116) who enrolled or did not enroll in hospice. RESULTS: Sixteen (14%) and 100 (86%) patients were enrolled in hospice and not enrolled in hospice, respectively. Hospice patients observed a larger mean difference between death and last transfusion (45.9 ± 66.7 vs. 14.6 ± 48.1 days, p < 0.0001). A higher amount of platelet, but not RBC, transfusions occurred in patients not enrolled in hospice (p = 0.04). The last transfusion that occurred more than 96 h before death was observed in 12 (75%) and 22 (22%) in hospice and non-hospice patients, respectively. For HSCT patients not enrolled in hospice, 17 patients received a transfusion on the same day of death and 31 patients received the last transfusion 24 h before death. CONCLUSIONS: Blood transfusion practices differed in HSCT patients enrolled and not enrolled in hospice. For most patients not enrolled in hospice, the last transfusion occurred 24 h before death. Future efforts should explore if limited access to blood products is a barrier to hospice enrollment for HSCT patients.

Weight loss versus muscle loss: re-evaluating inclusion criteria for future cancer cachexia interventional trials.

PURPOSE: Participation in cancer cachexia clinical trials requires a defined weight loss (WL) over time. A loss in skeletal muscle mass, measured by cross-sectional computed tomography (CT) image analysis, represents a possible alternative. Our aim was to compare WL versus muscle loss in patients who were screened to participate in a cancer cachexia clinical trial. METHODS: This was a single-center, retrospective analysis in metastatic colorectal cancer patients screened for an interventional cancer cachexia trial requiring a ≥5 % WL over the preceding 6 months. Concurrent CT images obtained as part of standard oncology care were analyzed for changes in total muscle and fat (visceral, subcutaneous, and total). RESULTS: Of patients screened (n = 36), 3 (8 %) enrolled in the trial, 17 (47 %) were excluded due to insufficient WL (<5 %), 3 (8 %) were excluded due to excessive WL (>20 %), and 16 (44 %) met inclusion criteria for WL. Patients who met screening criteria for WL (5-20 %) had a mean ± SD of 7.7 ± 8.7 % muscle loss, 24.4 ± 37.5 % visceral adipose loss, 21.6 ± 22.3 % subcutaneous adipose loss, and 22.1 ± 24.7 % total adipose loss. Patients excluded due to insufficient WL had 2 ± 6.4 % muscle loss, but a gain of 8.5 ± 39.8 % visceral adipose, and 4.2 ± 28.2 % subcutaneous adipose loss and 0.8 ± 28.4 % total adipose loss. Of the patients excluded due to WL <5 % (n = 17), 7 (41 %) had a skeletal muscle loss >5 %. CONCLUSIONS: Defining cancer cachexia by WL over time may be limited as it does not capture skeletal muscle loss. Cross-sectional CT body composition analysis may improve early detection of muscle loss and patient participation in future cancer cachexia clinical trials.

S-Warfarin Limited Sampling Models to Estimate Area Under the Concentration Versus Time Curve for Cytochrome P450 2C9 Baseline Activity and After Induction.

BACKGROUND: Phenotyping cytochrome P450 (CYP) 2C9 activity using S-warfarin has routinely required extensive blood sampling over at least 96 hours after dose to estimate the area under the concentration time curve from zero to infinity (AUC). Alternatively, S-warfarin limited sampling models (LSMs) using one or 2 concentration timepoints have been proposed to estimate AUC. This study evaluated whether S-warfarin LSMs accurately estimate CYP2C9 baseline and induction conditions in healthy adults and in advanced-stage cancer patients. METHODS: Plasma S-warfarin concentrations from healthy adults (n = 92) and in advanced-stage cancer patients (n = 22) were obtained from 6 published studies where a single 10 mg dose of oral warfarin was administered at CYP2C9 baseline and induction conditions. S-warfarin observed AUC was determined by noncompartmental analysis, whereas estimated AUC was calculated from the LSMs. Bias and precision were assessed by percent mean prediction error, percent mean absolute error, and percent root mean square error. RESULTS: Different results were observed for S-warfarin LSMs in estimating CYP2C9 baseline activity, with most studies resulting in unacceptable bias and precision. The percent mean prediction error, percent mean absolute error, and/or percent root mean square error exceeded acceptable limits for LSMs in patients with advanced-stage cancer and during CYP2C9 induction with lopinavir/ritonavir. CONCLUSIONS: The differing results during CYP2C9 baseline conditions, as well as unacceptable bias and precision in patients with advanced cancer and during CYP2C9 induction, considerably limit the widespread use of previously published S-warfarin LSMs.

Retrospective analysis of pharmacist interventions in an ambulatory palliative care practice.

BACKGROUND: We have previously reported the development of an outpatient palliative care practice under pharmacist-physician collaboration. The Doris A. Howell Service at the University of California, San Diego Moores Cancer Center includes two pharmacists who participate in a transdisciplinary clinic and provide follow-up care to patients. OBJECTIVE: This study evaluated pharmacist interventions and patient outcomes of a pharmacist-led outpatient palliative care practice. METHODS: This was a retrospective data analysis conducted at a single, academic, comprehensive cancer center. New (first visit) patient consultations were referred by an oncologist or hematologist to an outpatient palliative care practice. A pharmacist evaluated the patient at the first visit and at follow-up (second, third, and fourth visits). Medication problems identified, medication changes made, and changes in pain scores were assessed. RESULTS: Eighty-four new and 135 follow-up patient visits with the pharmacist occurred from March 2011 to March 2012. All new patients (n = 80) were mostly women (n = 44), had localized disease (n = 42), a gastrointestinal cancer type (n = 21), and were on a long-acting (n = 61) and short-acting (n = 70) opioid. A lack of medication efficacy was the most common problem for symptoms of pain, constipation, and nausea/vomiting that was identified by the pharmacist at all visits. A change in pain medication dose and initiation of a new medication for constipation and nausea/vomiting were the most common interventions by the pharmacist. A statistically significant change in pain score was observed for the third visit, but not for the second and fourth visits. CONCLUSIONS: A pharmacist-led outpatient palliative care practice identified medication problems for management of pain, constipation, and nausea/vomiting. Medication changes involved a change in dose and/or initiating a new medication. Trends were observed in improvement and stabilization of pain over subsequent clinic visits.

Limited sampling strategy of partial area under the concentration-time curves to estimate midazolam systemic clearance for cytochrome P450 3A phenotyping.

OBJECTIVE: Intravenous (IV) midazolam is the preferred cytochrome P450 (CYP) 3A probe for phenotyping, with systemic clearance (CL) estimating hepatic CYP3A activity. A limited sampling strategy was conducted to determine whether partial area under the concentration-time curves (AUCs) could reliably estimate midazolam systemic CL during conditions of CYP3A baseline activity, inhibition, and induction/activation. METHODS: Midazolam plasma concentrations during CYP3A baseline (n = 93), inhibition (n = 40), and induction/activation (n = 33) were obtained from 7 studies in healthy adults. Noncompartmental analysis determined observed CL (CL(obs)) and partial AUCs. Linear regression equations were derived from partial AUCs to estimate CL (CL(pred)) during CYP3A baseline, inhibition, and induction/activation. Preestablished criterion for linear regression analysis was r(2) ≥ 0.9. CL(pred) was compared with CL(obs), and relative bias and precision were assessed using percent mean prediction error and percent mean absolute error. RESULTS: During CYP3A baseline and inhibition, all evaluated partial AUCs failed to meet criterion of r(2) ≥ 0.9 and/or percent mean absolute error <15%. During CYP3A induction/activation, equations derived from partial AUCs from 0 to 1 hour (AUC0-1), 0 to 2 hours (AUC0-2), and 0 to 4 hours (AUC0-4) were acceptable, with good precision and minimal bias. These equations provided the same conclusions regarding equivalency testing compared with intense sampling. CONCLUSIONS: During CYP3A induction/activation, but not baseline or inhibition, midazolam partial AUC0-1, AUC0-2, and AUC0-4 reliably estimated systemic CL and consequently hepatic CYP3A activity in healthy adults.

Tricyclic antidepressants: is your patient taking them? Observations on adherence and unreported use using prescriber-reported medication lists and urine drug testing.

OBJECTIVE: Tricyclic antidepressants (TCAs) are first-line treatment for neuropathic pain. Despite widespread use, many health care providers do not know which patients are currently taking TCAs. The objective of this retrospective data analysis was to determine adherence rates to amitriptyline, nortriptyline, or imipramine. The rate at which patients used TCAs (confirmed by presence of TCA in the urine) but did not inform their health care provider is also reported (non-informed prescriber rate). Finally, the effects of age, sex, and number of prescriptions on adherence and non-informed prescriber rates were assessed. METHODS: Urinary excretion data were obtained from 55,296 patients with pain and were analyzed using liquid chromatography tandem mass spectrometry in a multiplex assay which included amitriptyline, nortriptyline, and imipramine. RESULTS: The adherence rate was 66% (1,407/2,137); the rate of non-informed prescribers was 3% (1,547/55,296) among the general population, and 52% (1,547/2,954) when only TCA users were considered. While adherence was higher among older and female subjects, the number of other medications did not affect adherence rate. CONCLUSIONS: This analysis reveals that many prescribers are not informed when patients start and stop using TCAs.

Evaluating the Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose in Patients with Cancer.

The Commercially Insured health Plan Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (CIP-RIOSORD) is an evidence-based tool to determine serious opioid-induced respiratory depression (OIRD) or overdose risk. The CIP-RIOSORD total score determines a risk class and estimates the probability for an OIRD event within the next 6 months. We performed a single-center, retrospective analysis to determine CIP-RIOSORD baseline scores and the most common predictive factors in patients with cancer. Patients (n = 160) were split into new consultations (n = 83, Group 1) versus the first documented follow-up consultation (n = 77, Group 2). Most patients were Caucasian women with metastatic gastrointestinal cancer. CIP-RIOSORD scores for Group 1 patients were 14.8 ± 15.2 (mean ± SD, risk class 4). Group 2 patients had higher CIP-RIOSORD scores (16.6 ± 14.9, risk class 4). For Group 1, the most common CIP-RIOSORD predictive factors were use of a long-acting opioid formulation (n = 24, 29%) and daily oral morphine equivalent (OME) ≥100 (n = 20, 24%); for Group 2, predictive factors were use of an antidepressant (n = 34, 44%) and a long-acting opioid formulation (n = 27, 35%). Based on the CIP-RIOSORD, there is a 15% probability of experiencing a serious OIRD event or overdose within the next 6 months.

Omeprazole limited sampling strategies to predict area under the concentration-time curve ratios: implications for cytochrome P450 2C19 and 3A phenotyping.

PURPOSE: To develop a limited sampling strategy (LSS) to predict area under the concentration-time curve (AUC) ratios of omeprazole (AUC(OPZ)) to its metabolites 5-hydroxyomeprazole (AUC(5OH)) and omeprazole sulfone (AUC(SUL)) as phenotyping parameters for cytochrome P450 (CYP) 2C19 and 3A. METHODS: Data were obtained from 37 (4 women) Caucasian, Chinese, and Korean healthy adults from three published studies. The AUC(OPZ), AUC(5OH), and AUC(SUL) were calculated via noncompartmental analysis. Observed AUC(OPZ, OBS)/AUC(5OH, OBS) and AUC(OPZ, OBS)/AUC(SUL, OBS) were determined. Plasma concentrations of omeprazole, 5-hydroxyomeprazole, and omeprazole sulfone at 1, 1.5, 2, 3, 4, 6, and 8 h post-dose were used to generate limited sampling strategy (LSS) models to predict AUC(OPZ,PRE)/AUC(5OH,PRE) and AUC(OPZ,PRE/)AUC(SUL,PRE). Bias and precision were assessed via percentage mean prediction error (%MPE) and percentage mean absolute error (%MAE), with acceptable limits being <15%. RESULTS: For CYP2C19, the AUC(OPZ,OBS)/AUC(5OH,OBS) was [mean ± standard deviation (SD)] 2.10 ± 1.63. Five LSS models of AUC(OPZ,PRE)/AUC(5OH,PRE) were generated, but none met the bias or precision criteria. Upon stratification by CYP2C19 genotype and ethnicity, a three-timepoint (at 1, 2, and 4 h) LSS model accurately predicted AUC(OPZ)/AUC(5OH) in Caucasian CYP2C19*1/*1 subjects. For CYP3A, AUC(OPZ,OBS)/AUC(SUL,OBS) (mean ± SD) was 1.79 ± 0.67. All LSS models had unacceptable %MAE, even when stratified by CYP2C19 genotype and ethnicity. CONCLUSIONS: A LSS model to predict AUC(OPZ)/AUC(5OH), and thus CYP2C19 activity, was generated for Caucasian CYP2C19*1/*1 subjects. However, additional model validation is needed prior to general use. LSS models to predict AUC(OPZ)/AUC(SUL), and thus CYP3A activity, were not possible, even upon stratification by CYP2C19 genotype and ethnicity.

Evaluation of intravenous midazolam limited sampling models to determine area under the concentration time curve during cytochrome P450 3A baseline, inhibition and induction or activation.

OBJECTIVE: This study evaluated if previously published limited sampling models (LSMs) accurately predict midazolam area under the concentration time curve (AUC) during cytochrome P450 (CYP) 3A baseline, inhibition and induction/activation. MATERIALS AND METHODS: Plasma midazolam concentrations (n = 108) were obtained where intravenous midazolam was co-administered alone or concomitantly with ketoconazole, itraconazole, aprepitant, rifampin, or pleconaril. Observed AUC was calculated using noncompartmental analysis. Predicted AUC was calculated from the LSMs. Bias and precision were determined by percent mean prediction error (%MPE), percent mean absolute error (%MAE), and percent root mean squared error (%RMSE). RESULTS: Contrasting results were observed for LSMs in predicting CYP3A baseline activity, with the majority of studies resulting in unacceptable bias and precision. During CYP3A inhibition, unacceptable bias and precision were observed from single- and 2-time point LSMs. %MAE and %RMSE values exceeded acceptable limits during CYP3A induction with rifampin. Contrasting results were observed with pleconaril. CONCLUSION: The contrasting results during CYP3A baseline and induction/activation, as well as the unacceptable bias and precision during CYP3A inhibition, limits the widespread use of the previously published LSMs.

Spiritual AIM: assessment and documentation of spiritual needs in patients with cancer.

The chaplain is an essential member of the palliative care (PC) team, yet, standard methods to document chaplain assessments are lacking. The study team performed a retrospective analysis of chaplaincy documentation in an outpatient PC clinic at an academic medical center over 6 months (April 2017 to October 2017). The study team identified unique adult patients with cancer, then manually extracted variables from the electronic medical record. The primary objective was to assess the number of spiritual assessments documented by the chaplain. Secondary objectives included descriptive analysis of identified spiritual needs. Out of the 376 total patient encounters, 292 (77.8%) included documentation of a chaplain's spiritual assessment. The most frequent spiritual need was self-worth/community (n = 163, 55.8%).This study demonstrates that chaplains can effectively document Spiritual AIM-based screening and assessment. Moreover, this may be an effective documentation method across institutions to facilitate chaplain-based data.

Contribution of the Gut Microbiome to Drug Disposition, Pharmacokinetic and Pharmacodynamic Variability.

The trillions of microbes that make up the gut microbiome are an important contributor to health and disease. With respect to xenobiotics, particularly orally administered compounds, the gut microbiome interacts directly with drugs to break them down into metabolic products. In addition, microbial products such as bile acids interact with nuclear receptors on host drug-metabolizing enzyme machinery, thus indirectly influencing drug disposition and pharmacokinetics. Gut microbes also influence drugs that undergo enterohepatic recycling by reversing host enzyme metabolic processes and increasing exposure to toxic metabolites as exemplified by the chemotherapy agent irinotecan and non-steroidal anti-inflammatory drugs. Recent data with immune checkpoint inhibitors demonstrate the impact of the gut microbiome on drug pharmacodynamics. We summarize the clinical importance of gut microbe interaction with digoxin, irinotecan, immune checkpoint inhibitors, levodopa, and non-steroidal anti-inflammatory drugs. Understanding the complex interactions of the gut microbiome with xenobiotics is challenging; and highly sensitive methods such as untargeted metabolomics with molecular networking along with other in silico methods and animal and human in vivo studies will uncover mechanisms and pathways. Incorporating the contribution of the gut microbiome to drug disposition, pharmacokinetics, and pharmacodynamics is vital in this era of precision medicine.

Exploring the Expanded Role of Pharmacists in Advance Care Planning.

PURPOSE: Advance care planning (ACP) is a clinical skill that can be taught. An opportunity exists to teach how to conduct ACP to clinicians not typically engaged in these conversations to increase the likelihood that patients and caregivers engage in ACP. We conducted a prospective study exploring the feasibility of a pharmacist-led ACP intervention. METHODS: We completed a prospective, single-center study from July 2015 to July 2017. We included patients of age ≥ 18 years with incurable cancer referred to the palliative care clinic. A trained pharmacist led an ACP discussion with the patient and selected proxy. We defined feasibility as completion of ≥ 30 pharmacist-led ACP discussions over the study period. Additionally, we defined an informed healthcare proxy as someone who understood three key end-of-life (EOL) treatment preferences: the patient's personal definition of quality of life, desired resuscitation status, and preferred location of death (in or out of the hospital). Patients were followed until the end of the study or death. For those patients who died, the pharmacist contacted the proxy for follow-up and explored satisfaction with the ACP intervention. RESULTS: Thirty-four patients completed the study. All selected proxies completed the intervention and were able to understand the three EOL preferences. At the time of the patient's death (n = 20), proxies reported that 66.6% received their preferred resuscitation status and 72.2% died in their preferred location. Proxy satisfaction with the ACP process was 7.6 ± 2.5 (mean ± SD) on a 11-point Likert scale. CONCLUSION: These findings indicate the potential for pharmacists to lead and engage in ACP in the outpatient setting.

Evaluation of current and upcoming therapies in oral mucositis prevention.

Cancer chemotherapy has evolved from a few therapeutic agents in three drug classes to more than 50 drugs in over ten drug classes. With generally cytotoxic mechanisms of action, there is continued research interest in preventing and managing adverse events of chemotherapy. Although treatment-induced symptom management has made significant progress, most therapies lead to intolerable reactions that result in a dose reduction or discontinuation of therapy. Mucositis is a common adverse event that can occur after administration of systemic chemotherapy and/or radiation therapy leading to inflammatory lesions anywhere from the oral cavity to the GI tract. Although pathophysiologically similar, gastrointestinal mucositis and oral mucositis (OM) differ in terms of symptom presentation and offending therapies. The focus of the article will be on OM; gastrointestinal mucositis will be mentioned when therapy efficacy is relevant to OM. OM prophylaxis has been a subject of interest for at least the past 30 years, yet progress has been limited due to a lack of understanding of the condition. With the recent introduction of palifermin (Kepivance™), novel therapies continue to be developed that may significantly reduce the incidence, duration and/or severity of OM. In addition, outcomes including an improvement in patient quality of life, increasing treatment dose intensity or reducing healthcare costs may result from successful management of OM prophylaxis. This article will review currently available OM prophylactic therapies. Agents in preclinical or clinical development and natural supplements will also be discussed.

Pharmacogenomics: bridging the gap between science and practice.

OBJECTIVE: To educate pharmacists about principles and concepts in pharmacogenomics, clinical applications of pharmacogenomic information, and the social, ethical, and legal aspects of pharmacogenomics and to describe a Centers for Disease Control and Prevention (CDC)-supported pharmacogenomics education program for pharmacists and other health professionals. DATA SOURCES: Primary literature from PubMed, recommendations from the Food and Drug Administration and Evaluation of Genomic Applications in Practice and Prevention Working Group, prescribing information, websites of government agencies and professional organizations, and relevant textbooks. STUDY SELECTION: Not applicable. DATA EXTRACTION: Not applicable. DATA SYNTHESIS: Principles and concepts of pharmacogenomic nomenclature, polymorphism types, and systematic approach to understanding polymorphisms were reviewed. Drug therapy for select therapeutic areas that highlight the applicability of pharmacogenomics are presented, including abacavir, selective serotonin reuptake inhibitors, tamoxifen, and warfarin. Challenges of translating pharmacogenomics into clinical practice included ethical, social, legal, and economic issues. We have developed a pharmacogenomics education program to disseminate evidence-based pharmacogenomics information and provide a resource for health professionals, including pharmacists. CONCLUSION: Pharmacists play a critical role in the education of patients and health professionals in the area of pharmacogenomics.