Class of 2012 Abstract / Objectives: To evaluate the literature with respect to drug-drug interactions of the hydroxymethylglutaryl CoA reductase inhibitors atorvastatin, fluvastatin, lovastatin, pitavastitin, pravastatin, simvastatin, and rosuvastatin with warfarin. Methods: This descriptive retrospective study identified articles reporting on each drug-drug interaction from the online databases PubMed (1970 – February 2012) and the drug compendia Micromedex and Facts & Comparisons. The studies included in this investigation were primary literature reports, written in English with human subjects. All studies included were evaluated using the van Roon 5-point quality of evidence scale developed in the Netherlands to assess drug-drug interactions. This scale rates the study type from lowest to highest quality, from zero to four. Case-reports were evaluated using the Drug Interaction Probability Scale (DIPS). The DIPS tool uses 10 questions to evaluate the probability that an adverse event is caused by a drug-drug interaction. Results: Twenty studies met the inclusion criteria. One study involved atorvastatin, four for fluvastatin, three for lovastatin, 2 for pitavastatin, 1 for pravastatin, 5 for rosuvastatin, and 6 for simvastatin. The mean van Roon quality of evidence score was 2.1+/- 0.74, the mean score for atorvastatin, pitavastatin, and pravastatin was 3, with the mean score of fluvastatin, lovastatin, rosuvastatin, and simvastatin was 2. 70% of the literature reviewed were case-reports or letters. Conclusions: The studies and reports supporting HMG-CoA reductase inhibitors and warfarin drug-drug interactions are most commonly case- reports and are of low quality and quantity.

Class of 2009 Abstract / OBJECTIVES: To assess the performance of drug-drug interaction (DDI) software programs utilized in community and hospital pharmacies located in urban and rural settings. METHODS: A fictitious patient profile with 18 drugs and a penicillin allergy was entered into pharmacy computer systems throughout Arizona. Researchers recorded the software systems’ responses to 20 targeted combinations, 14 of which should have produced an alert and 6 that were not true interactions. The number of true positive, true negative, false positive and false negative responses was determined for each system. These data were subsequently used to calculate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) overall and at each site. RESULTS: There were 35 participating pharmacies that used a total of 18 different software programs. The overall sensitivity was 0.8, and ranged from 0.21 to 1 between sites. Computer software failed to detect important interactions 20% of the time. The specificity ranged from 0.83 to 1; PPV ranged from 0.89 to 1; and NPV ranged from to 0.35 to 1. Nine sites, using five different software programs returned perfect results. However, some of those programs produced different results at other sites. CONCLUSIONS: This study shows that improvements are needed in software programs to help pharmacists accurately identify DDIs which could prevent potential adverse drug events. Many clinically important interactions remain undetected by software programs, and users should be mindful of current limitations in technology.

Class of 2007 Abstract / Objectives: The purpose of this study was to determine if there is a difference between both graduating medical and pharmacy students in their capabilities to appropriately recognize drug-drug interactions that have led or can lead to serious toxicological consequences in humans. The hypothesis of this study was that there would be no difference between the ability of medical and pharmacy students to recognize potential drug-drug interactions. Methods: A two-page questionnaire was giving during the last semester before both the medical and pharmacy students graduate. The first page requested information about demographics, such as: gender, age, current educational program, previous education in healthcare, other degrees held, and average hours worked in healthcare per week for the past year. The second page contained 22 questions on potential drug-drug interactions. Also, there will be some questions that do not contain any drug-drug interactions. The students had four choices, in which they could answer. The choices were (1) The two drugs should not be used together (contraindicated), (2) The two drugs may be used safely together with monitoring, (3) The two drugs may be used safely together without monitoring, and (4) Not sure if the drugs can be used together. Results: Of the 168 questionnaires distributed, 51 were completed and returned. Forty-seven pharmacy students responded, while only 4 medical students responded. Pharmacy students correctly identified 38.4% + 11.7% of the interactions. The minimum correct responses was 13.6% and the maximum was 68.2% Pharmacy students without a bachelor of science (BS) performed slightly better than the students having a BS with a mean score of 40.0% + 3.0% and 37.1% + 9.0%, respectively. There was no significant difference between the groups (p = 0.42). Males had a mean score of 39.1% + 8.2%, while females had a mean score of 38.1% + 13.1%. There was no significant difference between the groups (p = 0.78). Also, there was no significant difference between the student’s age or how many hours they worked per week regarding the percent of correct responses.

Adverse Drug Reactions (ADRs) are a major cause of morbidity and mortality worldwide. Clinical trials, which are extremely costly, human labor intensive and specific to controlled human subjects, are ineffective to uncover all ADRs related to a drug. There is thus a growing need of computing-supported methods facilitating the automated detection of drugs-related ADRs from large reports data sets; especially ADRs that left undiscovered during clinical trials but later arise due to drug-drug interactions or prolonged usage. For this purpose, big data sets available through drug-surveillance programs and social media provide a wealth of longevity information and thus a huge opportunity. In this research, we thus design a system using machine learning techniques to discover severe unknown ADRs triggered by a combination of drugs, also known as drug-drug-interaction. Our proposed Multi-drug Adverse Reaction Analytics System (MARAS) adopts and adapts an association rule mining-based methodology by incorporating contextual information to detect, highlight and visualize interesting drug combinations that are strongly associated with a set of ADRs. MARAS extracts non-spurious associations that are true representations of the combination of drugs taken and reported by patients. We demonstrate the utility of MARAS via case studies from the medical literature, and the usability of the MARAS system via a user study using real world medical data extracted from the FDA Adverse Event Reporting System (FAERS).

The aim of this study was to investigate the induction of CYP1A by DMSO, to determine whether DMSO induced other P450 isozymes (CYP2B) and to compare the effects of DMSO and another differentiating agent, sodium butyrate. Induction of CYP1A-dependent ethoxyresorufin-O-deethylase (EROD) was observed in the presence of increasing concentrations of DMSO. All concentration investigated (1%, 1.5% and 2%) caused induction (2-3 fold), and enhanced BA-induction of EROD. Enhancement of BA-induction was greater with 1% and 1.5% DMSO (2.5-3 fold over BA alone) than with 2% (1.8-fold). DMSO alone did not increase CYP1A1 RNA levels. Hepatocytes treated with BA and DMSO together exhibited a 1.3-fold greater increase in RNA levels than with BA alone. Western blotting indicated that CYP1A1 protein was increased by inducers (BA, DMSO and isosafrole), but that CYP1A2 was not. This indicates that the CYP1A1 isozyme is responsible for EROD activity in these cultures, and that the CYP1A2-induction mechanism is lost in rat hepatocytes cultured under the conditions of these experiments. This observation was confirmed by the lack of CYP1A2-dependent phenacetin-O-deethylase (POD) activity in culture. The substituted benzimidazole omeprazole has been shown to induce CYP1A isozymes in human hepatocyte cultures. In this study omeprazole was not effective in inducing EROD activity in rat hepatocytes or <I>in vivo</I> in the rat. This confirms that rat hepatocytes are not a good model for CYP1A induction in man. DMSO appears to be isozyme specific, since CYP2B-dependent pentoxyresorufin-O-dealkylase (PROD) activity was not increased by DMSO, and phenobarbitone (PB) induction of PROD was enhanced only slightly by DMSO on day 3 of culture (4-fold over control; 1.5-fold over PB alone). Sodium butyrate and DMSO were both shown to induce differentiation of rat hepatocyte, with maintenance of low level of γ-glutamyl transferase activity, and maintenance of a more rounded morphology.

Class of 2008 Abstract / Objectives: The purpose of this study is to compare the critical drug-drug interaction alerting software at the Department of Veteran Affairs with Hansten and Horn's drug analysis and management (DIAM) and Micromedex®. Methods: The Department of Veterans Affairs supplied a list of drug-drug interacting (DDI) pairs. Each pair was labled as significant or critical. The critical interactions were included in the study (n=1018). Two researchers inputed the interactions into Micromedex and looked up the interactions in Hansten and Horn's drug interactions analysis and management (DIAM). A Kappa statistic was used to calculate the agreement between the 2 researchers. Results: The researchers differed in the number of interactions found to be "contraindicated" or "major" in Micromedex and "avoid" or "usually aviod" in DIAM (researcher 1= 683, 330, respectively; researcher 2= 672,176, respectively) with a Kappa of 0.9 for Micromedex and 0.57 for DIAM. Conclusions: Our study suggests that there is a difference between the VA drug interaction alerting system, Micromedex ® and DIAM in regards to the way they list interactions and their method of rating the level of severity of the interactions. Also, there may be a difference in the way each researcher interprets the information.

Purpose: Drug-drug interactions (DDIs) place a burden on our nation and cause potential harm to patients. Awareness of potential DDIs is essential for safe prescribing. Nurse practitioners (NP) have prescriptive authority throughout the nation, however, little is known about NP prescribing habits. The purpose of this study was to identify NPs' demographic and practice characteristics, DDI knowledge and factors that influence this knowledge.Data Sources: A survey was administered to NP prescribers recruited from a national conference. Data was collected on demographics, practice and technological characteristics, and perceptions and knowledge of DDIs.Conclusions: Data from 305 questionnaires were analyzed. NPs correctly classified 31% of drug pairs. Nitroglycerin and Sildenafil (drug combination to avoid) was classified correctly by the most respondents (90.8%, n = 305); Warfarin and Gemfibrozil (drug combination to usually avoid) the fewest 15.7% (n = 302). A positive correlation was found between NPs in acute care hospital settings and DDI knowledge, indicating higher knowledge scores. Neither hierarchical linear regression model was significant at predicting NPs' DDI knowledge.Implications for Practice: Continuing education needs to be targeted to enhance NPs knowledge of potential clinically significant DDIs. The increased recognition of potential DDIs among NPs will enhance patient safety.

Objective: The research sought to evaluate seven drug information resources, specifically designed for analyzing drug interactions for scope, completeness, and ease of use, and determine the consistency of content among the seven resources. Methods: A cross-sectional study was conducted where 100 drug-drug and drug-dietary supplement interactions were analyzed using 7 drug information resources: Lexicomp Interactions module, Micromedex Drug Interactions, Clinical Pharmacology Drug Interaction Report, Facts & Comparisons eAnswers, Stockley's Drug Interactions (10th edition), Drug Interactions Analysis and Management (2014), and Drug Interaction Facts (2015). The interaction sample was developed based on published resources and peer input. Two independent reviewers gathered data for each interaction from each of the 7 resources using a common form. Results: Eighty-two drug-drug and 18 drug-dietary supplement interactions were analyzed. Scope scores were higher for Lexicomp Interactions (97.0%), Clinical Pharmacology Drug Interaction Report (97.0%), and Micromedex Drug Interactions (93.0%) compared to all other resources (p<0.05 for each comparison). Overall completeness scores were higher for Micromedex Drug Interactions (median 5, interquartile range [IQR] 4 to 5) compared to all other resources (p<0.01 for each comparison) and were higher for Lexicomp Interactions (median 4, IQR 4 to 5), Facts & Comparisons eAnswers (median 4, IQR 4 to 5), and Drug Interaction Facts (4, IQR 4 to 5) compared to all other resources, except Micromedex (p<0.05 for each comparison). Ease of use, in terms of time to locate information and time to gather information, was similar among resources. Consistency score was higher for Micromedex (69.9%) compared to all other resources (p<0.05 for each comparison). Conclusions: Clinical Pharmacology Drug Interaction Report, Lexicomp Interactions, and Micromedex Drug Interactions scored highest in scope. Micromedex Drug Interactions and Lexicomp Interactions scored highest in completeness. Consistency scores were overall low, but Micromedex Drug Interactions was the highest.

Class of 2010 Abstract / OBJECTIVES: The purpose of this study is to determine the effects of professional pharmacy educational training and occupational student pharmacist training towards the quantity of medication errors attributed to not investigating drug-drug interactions and/or not acknowledging contraindications for medications and treatment. METHODS: The design was a cross-sectional, analytical study of student pharmacists in their first, second, or third year of a four-year Doctor of Pharmacy program. A questionnaire of patient drug interaction scenarios along with student work experience and demographic survey questions was administered to a class of students to complete and return at the time it was administered. It was a prospective study. RESULTS: Questionnaires were completed by 180 students. None of the classes surveyed scored significantly higher than another class. Students with retail experience did not score significantly higher survey scores than those with hospital experience. Finally, when comparing the scores of students with experience in multiple fields, in comparison to those with experience in only one field of pharmacy, it was noted that there was no statistical significance. CONCLUSIONS: The amount of professional pharmacy education training and occupational student pharmacist experience was not found to have an affect on a student pharmacist’s ability to prevent medication error that was attributed to either not investigating a drug-drug interaction and/or not acknowledging contraindications for medications and treatment.

Class of 2009 Abstract / OBJECTIVES: The purpose of this study is to evaluate and compare the DDI knowledge of pharmacy, medical, and nurse practitioner students who are beginning clinical clerkships. METHODS: This study utilized a prospective evaluation of DDI knowledge among healthcare professional students who were currently enrolled in their final didactic year at the University of Arizona College of Medicine, College of Pharmacy, or College of Nursing’s nurse practitioner course. Students were provided with 15 possible DDI pairs, and asked to select an appropriate management strategy for each pair. Management options included: “Avoid Combination,” “Usually Avoid Combination,” “Take Precaution,” “No Special Precaution,” and “Not Sure.” The primary outcome measure was the ability to correctly categorize each DDI pair into one of the five management responses. The secondary outcome measure was the number of clinically significant DDIs recognized. Analysis of variance was used to evaluate differences between groups. An alpha of 0.05 was set a-priori. RESULTS: Response rates were 61% for medical students (72 of 119), 82% for pharmacy students (64 of 78) and 100% for nurse practitioner students (29 of 29). The mean number correct for management strategies was comparable in the medical students (2.5, SD= 1.9) and nurse practitioner students (3.0, SD= 1.9), while the pharmacy students had a mean score of 6.1 (SD= 2.2) correct answers. There was a significant difference between the groups in correct responses (p< 0.001). In regards to student ability to identify interactions, the mean number correct was 10.1 (SD= 2.6), 5.0 (SD= 3.3), and 4.4 (SD= 3.0) for pharmacy, medicine, and nursing respectively (F= 60.6; p< 0.001). Post hoc analysis demonstrated that pharmacy students performed significantly better than medical and nurse practitioner students in regards to their ability to: 1) select management strategies for DDI pairs; and 2) identify a DDI interaction. No significant differences were found between the medical and nurse practitioner students. CONCLUSIONS: Pharmacy students demonstrated better knowledge than medical and nurse practitioner students with respect to identifying and selecting management strategies for possible DDIs. However, there is much room for improvement for all groups.