Characterization of the Nuclear Pore Complex in Red Alga, Cyanidioschyzon merolae

Cyanidioschyzon merolae (C. merolae) is a primitive, unicellular species of red alga that is considered to be one of the simplest self-sustaining eukaryotes. The highly elementary nature of C. merolae makes it an excellent model organism for studying evolution as well as cell function and organelle communication. In our study, we hypothesize that C. merolae contains the minimal assembly of proteins to make up their Nuclear Pore Complexes (NPCs), and hence are the first ancestral NPCs. NPCs are essential for basic nuclear transport in the cell. They are embedded in the double membrane of the nucleus, the nuclear envelope (NE), which separates nuclear DNA from cytoplasmic organelles. The NE acts as a selective protective barrier, and active transport of molecules between the nucleus and the cytoplasm is facilitated mainly by nuclear NPCs in higher and lower eukaryotic cells. When not functioning properly or fully, NPCs are known to be involved in several types of human disease, including cancer, accelerated aging and Huntington’s Disease (HD)

Opioid Adjunct Drug Therapy: Evaluating Effectiveness Using Text Analytics of Real World Data

Opioid analgesics continue to be the mainstay of pharmacologic treatment of moderate to severe pain. An adjunct is a drug that in its pharmacological characteristic is not identified primarily as an analgesic, but that has been found in clinical practice to have either an independent analgesic effect or additive analgesic properties when used with opioids. By using an adjunct to maximize the level of analgesia, the required opioid dosage may be reduced, together with concomitant adverse effects. BACKGROUND Real World Data (RWD) refers to data that describe observations in normal clinical practice obtained by any non- interventional methodology, such as Randomized Controlled Trials (RCTs). The U.S. Food and Drug Administration (FDA) maintains one of the largest government databases in the country, the FDA Adverse Event Reporting System (FAERS). It is comprised of adverse event reports submitted to the FDA through the “MedWatch” reporting program and contains a plethora of Real World Data: thousands of case reports on opioids and adjunct drugs, comprised of unstructured textual data. The objective of this study is to identify the therapeutic effectiveness of adjunct drugs with opioids by examination of narrative text in MedWatch cases. METHODS This project follows the traditional approach of knowledge discovery in databases, comprised of five steps: 1) Data selection, 2) Pre-processing, 3) Transformation, 4) Data mining and 5) Interpretation. The strategy employed will transform the narrative text data into an organized and concise summary of key endpoints. An appropriate sample (500 to 1,000 relevant patient cases) that describe opioids and adjunct drugs will be included in the case report data set. Key task 1: Data selection and pre-processing (Steps 1,2). MedWatch narratives of patient cases that describe the types of opioid and adjunct drug combinations used in real-life clinical settings will be obtained from the FAERS database. Key task 2: Data transformation and mining (Steps 3,4). Cases will be organized in a Structured Query Language (SQL) database. A lexicon of words and terms clinically or theoretically related to opioid and adjunct drug therapy will be developed, which will serve as a reference for analysis of the text. Using Natural Language Processing (NLP) techniques, textual data will be transformed into n-grams using a MySQL n-gram parser. N-gram extraction will identify notes containing n-grams matching terms from the theory-and expert-derived lexicon. Categories will be formed from the most frequently identified n-grams and their total frequency. RESULTS (PROJECTED) Key task 3: Evaluate and interpret results (Step 5) and compile the information into a useful format for healthcare providers. The most commonly extracted n-grams will be identified by category, then frequency, and displayed in tabular format. N-gram analysis of the corpus of case reports reveals the frequency with which and adjunct drug was used with an opioid, and indicate impact on analgesic effect. Completion of key tasks provides evidence on the associated outcomes of treatment; whether the adjunct drug therapy indicates treatment success or failure. CONCLUSION Findings of this project will add to the existing body of knowledge on opioid adjunct therapy for analgesia and may corroborate or refute other existing evidence for adjunct drug therapeutic effectiveness derived from case reports or clinical trials

Enhancing Personal Health Record Adoption Through the Community Pharmacy Network: A Service Project

Personal Health Records, or PHRs, are designed to be created, maintained and securely managed by patients themselves. PHRs can reduce medical errors and increase quality of care in the health care system through efficiency and improving accessibility of health information. Adoption of PHRs has been disappointingly low. In this paper a project is described—essentially a call for action—whereby the skills, expertise, and accessibility of the community pharmacist is utilized to address the problem of poor PHR adoption. The objective of this proposed project is to promote the expansion of PHR adoption directly at the consumer level by utilizing the existing infrastructure of community pharmacies. The ADDIE model can provide the framework for PHR adoption in community pharmacies. ADDIE is an acronym that stands for the 5 phases contained in the model: 1) Analysis, 2) Design, 3) Development, 4) Implementation, and 5) Evaluation. ADDIE is a versatile educational model used for creating instructional materials, and has found utility as a guidance model for managing projects of all types. By bringing together these concepts: the highly accessible infrastructure of community pharmacies with the educational resources to inform consumers on the proper use of PHRs, the quality of care for patients will be greatly enhanced.   Type: Idea Pape

A Framework for a New Paradigm of Opioid Drug Tapering Using Adjunct Drugs

Michael A Veronin,1 Justin P Reinert2 1Department of Pharmaceutical Sciences and Health Outcomes The University of Texas at Tyler Ben and Maytee Fisch College of Pharmacy, Tyler, TX, USA; 2Department of Pharmacy Practice The University of Toledo College of Pharmacy and Pharmaceutical Sciences, Toledo, OH, USACorrespondence: Michael A Veronin, Email [email protected]: The misuse of and dependency on prescription opioids represents a significant crisis at the national level, impacting not only the health of the public but also the societal and economic well-being. There is a critical need for strategies to reduce the dosage of prescribed opioids to limit opioid-associated adverse effects and lower the risk of addiction development in patients experiencing chronic pain. Opioid-sparing medications, when co-administered with opioids, enable a reduced opioid dose without loss of efficacy. This suggests the potential for using opioid adjunct drugs in opioid tapering, whereby opioid doses are lowered incrementally in a systematic manner to improve a patient’s safety profile or quality of life. The objective of this report is two-fold: 1) to illustrate the potential for adjunct drugs in opioid tapering, and 2) to describe the steps needed to be taken to develop a framework for the use of adjunct drugs in opioid tapering. This can provide the impetus for further investigation into opioid tapering and the development of improved clinical care. The proposed project implements knowledge synthesis methods to develop the framework for a new paradigm of opioid drug tapering that incorporates opioid dosage reductions with adjunct drugs. Framework development is organized into three major phases: 1) Adjunct drug characterization, 2) Assessment of the opioid-sparing effect, and 3) Usability of data for clinicians. The knowledge gained from this project can provide a foundation for improved analgesia protocols for opioids and adjunctive drug therapy.Keywords: opioid adjunct, opioid-sparing, opioid tapering, addiction risk, knowledge synthesi

Comparison of Tertiary Drug Information Resources With the CDC Guideline for Oxycodone Dosing: Are Patients at Risk?

Background: Inappropriate prescribing of opioids is thought to play a central role in the ongoing opioid health crisis. Tertiary information resources are commonly used by clinicians for obtaining opioid dosing information. To assist health care providers in pain management, the Centers for Disease Control and Prevention (CDC) developed a guideline for prescribing opioids. Objective: To identify discrepancies for dosing information on oxycodone between commonly used tertiary drug information resources and the CDC Guideline. Methods: Searches of the tertiary drug information resources were conducted in the following order: Facts and Comparisons, Lexicomp, Medscape, and Micromedex. The term “oxycodone” was entered in the search box in the tertiary resources’ applications. Drug information items retrieved were organized in tabular format. In the Google Chrome version 106.0.5249.119 search box, the term “CDC guideline for opioid dosing” was entered to retrieve current information on the CDC Guideline. Results: Searches produced drug information on oxycodone for available formulations, dosing regimens, recommended dosing, and maximum daily dose (MDD). Searches revealed discrepancies in dosing recommendations for oxycodone among tertiary drug resources and between tertiary drug resources and the CDC Guideline. Conclusions: When considering maximum daily dosing information for oxycodone from the selected tertiary drug information resources, the potential exists for patients to be at risk of addiction, overdose, and perhaps death. Improving the way opioids are prescribed through the CDC Clinical Practice Guideline can ensure patients have access to safer, more effective chronic pain treatment while reducing the number of people who misuse or overdose from inappropriate dosing information

An Analysis of Covid-19 Vaccine Allergic Reactions

From our study, all three covid-19 vaccines have a similar proportion of adverse reaction reports in which the patient had a history of allergies. However, the proportion of life-threatening outcomes were lower for those with the Janssen vaccine (0.62% hospitalization rate for Janssen versus 2.59% for Pfizer and 0.60% death for Janssen versus 5.15% for Moderna). In terms of specific allergies, patients with *cillin or sulfa allergies had the most adverse reactions to covid-19 vaccines, however, Janssen again had the lowest percentage of reported deaths (1.39% for *cillin-related allergy deaths for Janssen versus 6.10% for Pfizer). In terms of patient age and gender, females has 2.9x the number of adverse reactions than males and a lower average age for reactions for the Pfizer and Moderna vaccines. We feel this data could be used by individuals and medical professionals to assist in choosing a vaccine to maximize patient safety based on their allergy history, age and gender

A Data Driven Approach to Profile Potential SARS-CoV-2 Drug Interactions Using TylerADE

We use a data driven approach on a cleaned adverse drug reaction database to determine the reaction severity of several covid-19 drug combinations currently under investigation. We further examine their safety for vulnerable populations such as individuals 65 years and older. Our key findings include 1. hydroxychloroquine/chloroquine are associated with increased adverse drug event severity versus other drug combinations already not recommended by NIH treatment guidelines, 2. hydroxychloroquine/azithromycin are associated with lower adverse drug event severity among older populations, 3. lopinavir/ritonavir had lower adverse reaction severity among toddlers and 4. the combination of azithromycin, hydroxychloroquine and tocilizumab is safer than its component drugs. While our approach does not consider drug efficacy, it can help prioritize clinical trials for drug combinations by focusing on those with the lowest reaction severity and thus increase potential treatment options for covid-19 patients

A Framework for a New Paradigm of Opioid Drug Tapering Using Adjunct Drugs

The misuse of and dependency on prescription opioids represents a significant crisis at the national level, impacting not only the health of the public but also the societal and economic well-being. There is a critical need for strategies to reduce the dosage of prescribed opioids to limit opioid-associated adverse effects and lower the risk of addiction development in patients experiencing chronic pain. Opioid-sparing medications, when co-administered with opioids, enable a reduced opioid dose without loss of efficacy.This suggests the potential for using opioid adjunct drugs in opioid tapering, whereby opioid doses are lowered incrementally in a systematic manner to improve a patient’s safety profile or quality of life. The objective of this report is two-fold: 1) to illustrate the potential for adjunct drugs in opioid tapering, and 2) to describe the steps needed to be taken to develop a framework for the use of adjunct drugs in opioid tapering. This can provide the impetus for further investigation into opioid tapering and the development of improved clinical care. The proposed project implements knowledge synthesis methods to develop the framework for a new paradigm of opioid drug tapering that incorporates opioid dosage reductions with adjunct drugs. Framework development is organized into three major phases: 1) Adjunct drug characterization, 2) Assessment of the opioid-sparing effect, and 3) Usability of data for clinicians. The knowledge gained from this project can provide a foundation for improved analgesia protocols for opioids and adjunctive drug therapy

A Decision Tree Analysis of Opioid and Prescription Drug Interactions Leading to Death Using the FAERS Database

Can unknown and possibly dangerous interactions between opioids and prescription drugs be identified? Is it possible? Our research seeks to answer these questions by applying a supervised machine learning algorithm to the FDA’s Adverse Event Reporting System (FAERS). We trained a decision tree classifier to investigate heroin and prescription drug interactions with an accuracy of 84.9%. We found that heroin and buprenorphine, a commonly prescribed opioid detox drug, led to a 28.0% survival rate among patients. Heroin, buprenorphine, and quinine were even deadlier with a 24.0% survival rate. Our technique can be applied to previously unknown drug combinations to predict mortality and perhaps improve patient safety