scholarly journals Non-interacting, Non-opioid, and Non-barbiturate Containing Acute Medication Combinations in Headache: A Pilot Combinatorics Approach Based on DrugBank Database

2021 ◽  
Vol 12 ◽  
Author(s):  
Victor Kaytser ◽  
Pengfei Zhang
Keyword(s):  
Drug Interactions ◽  
Drug Combinations ◽  
Exhaustive Search ◽  
Common Elements ◽  
Acute Medication ◽  
Drugbank Database ◽  
Exhaustive List

Background: Polypharmacy in abortive medications is often inevitable for patients with refractory headaches.Objective: We seek to enumerate an exhaustive list of headaches abortive medications that are without drug-drug interactions.Methods: We updated a list of acute medications based on the widely used Jefferson Headache Manual with novel abortive medications including ubrogepant, lasmiditan, and rimegepant. Opioids and barbiturate-containing products are excluded. From this resultant list of medications, we then conducted an exhaustive search of all pair-wise interactions via DrugBank's API. Using this interaction list, we filtered all possible two, three, and four drug combinations of abortive medications. The list of medications was then reapplied to DrugBank to verify the lack of known drug-drug interactions.Results: There are 192 medication combinations that do not contain any drug-drug interactions. Most common elements in these combinations are ubrogepant, prochlorperazine, followed by tizanidine. There are 67 three-drug combinations that do not contain interactions. Only two of the four-drug combinations do not yield some form of drug-drug interactions.Conclusion: This list of headaches abortive medications without drug-drug interactions is a useful tool for clinicians seeking to more effectively manage refractory headaches by implementing a rational polypharmacy.

scholarly journals Safe Non-opioid and Non-barbiturate containing Acute Medication Combinations in Headache: An Exhaustive Approach Based on DrugBank Database

2020 ◽  
Author(s):  
Victor Kaytser ◽  
Pengfei Zhang
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Drug Combinations ◽  
Exhaustive Search ◽  
Common Elements ◽  
Acute Medication ◽  
Drug Drug Interaction ◽  
Drugbank Database ◽  
Exhaustive List

Structured AbstractBackgroundRational polypharmacy in abortive medications use is often inevitable for patients with refractory headaches.ObjectiveWe seek to enumerate an exhaustive list of headaches abortive medications that are without drug-drug interactions.MethodsWe updated a list of acute medications based on the widely used Jefferson Headache Manual with novel abortive medications including ubrogepant, lasmiditan, and rimegepant. Opioids and barbiturate containing products are excluded. We then conducted an exhaustive search of all pair-wise interactions for this list of medication via DrugBank API. Using this interaction list, we filtered all possible two, three, and four drug combinations of abortive medications. The resultant list of medication was then reapplied to DrugBank to verify the lack of known drug-drug interaction.ResultsThere are 192 medication combinations that do not contain any drug-drug interactions. Most common elements in these combinations are ubrogepant, prochlorperazine, followed by tizanidine. There are 67 three-drug combinations that do not contain interaction. Only 2 of the four-drug combinations do not yield some form of drug-drug interactions.ConclusionThis list of headaches abortive medications without drug-drug interactions is a useful tool for clinicians seeking to more effectively manage refractory headaches.

scholarly journals Potential drug-drug interactions in the psychiatric hospital: Frequency analysis

2019 ◽  
Vol 5 (4) ◽  
pp. 1-6
Author(s):  
Oleg O. Kirilochev ◽  
Inna P. Dorfman ◽  
Adelya R. Umerova ◽  
Svetlana E. Bataeva
Keyword(s):  
Drug Interactions ◽  
Clinical Significance ◽  
Psychiatric Hospital ◽  
Psychiatric Inpatient ◽  
Clinical Problem ◽  
Drug Combinations ◽  
Drug Prescriptions ◽  
Inpatient Setting ◽  
Potential Drug ◽  
Important Clinical Problem

Introduction: Drug-drug interactions are an important clinical problem in pharmacotherapy. This study is focused on different types of drugs used in a psychiatric hospital. Materials and methods: The pharmacoepidemiological study included the analysis of medical records of 500 psychiatric inpatients. The patients were divided into 2 groups: under 65 and over 65 years of age. All the drug prescriptions were analyzed to identify the combinations of drugs that can induce drug-drug interactions and determine their clinical significance. Results and discussion: Over 77% of hospitalized patients were administered drug combinations that could induce drug-drug interactions, most of which were of moderate clinical significance. A reliable association was found between the patient’s age, the clinical significance of drug-drug interactions, and the pharmacotherapy structure. The most common irrational drug combinations were identified. Conclusion: Timely analysis of drug prescriptions for potential drug-drug interactions can enhance the safety of pharmacotherapy and decrease the risk of adverse drug reactions in the psychiatric inpatient setting.

scholarly journals Drug combinations as effective anti-leishmanials against drug resistant Leishmania mexicana

2020 ◽  
Vol 11 (8) ◽  
pp. 905-912
Author(s):  
Humera Ahmed ◽  
Charlotte R. Curtis ◽  
Sara Tur-Gracia ◽  
Toluwanimi O. Olatunji ◽  
Katharine C. Carter ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Cell Lines ◽  
Resistant Cell ◽  
Drug Combinations ◽  
Drug Resistant ◽  
Leishmania Mexicana

Synergistic and antagonist drug interactions of drug combinations against Leishmania drug sensitive and resistant cell lines.

scholarly journals Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

2020 ◽  
Vol 10 (7) ◽  
pp. 2376 ◽  
Author(s):  
Rob C. van Wijk ◽  
Rami Ayoun Alsoud ◽  
Hans Lennernäs ◽  
Ulrika S. H. Simonsson
Keyword(s):  
Drug Discovery ◽  
Drug Interactions ◽  
Development Strategy ◽  
Drug Exposure ◽  
Rapid Development ◽  
Drug Combinations ◽  
Pharmacodynamic Modeling ◽  
Drug Discovery And Development

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Drug information

2016 ◽  
Author(s):  
Andrew Dickman ◽  
Jennifer Schneider
Keyword(s):  
Palliative Care ◽  
Clinical Pharmacology ◽  
Adverse Effects ◽  
Drug Interactions ◽  
Drug Information ◽  
Route Of Administration ◽  
Stability Data ◽  
Pertinent Information ◽  
Exhaustive List

This chapter provides a concise summary of pertinent information for 37 drugs that are administered by CSCI. Each monograph includes information relating to clinical pharmacology, indications, adverse effects, doses, drug interactions, and an exhaustive list of compatibility and stability data. Opioid equianalgesia is discussed because several opioids are used in palliative care and it is often necessary to either change the drug or route of administration as a patient’s condition changes.

scholarly journals An interpretable flux-based machine learning model of drug interactions across metabolic space and time

2021 ◽  
Author(s):  
Carolina H Chung ◽  
Sriram Chandrasekaran
Keyword(s):  
Machine Learning ◽  
Combination Therapy ◽  
Drug Interactions ◽  
Growth Conditions ◽  
Drug Combinations ◽  
Time Interval ◽  
Therapy Outcomes ◽  
Data Types ◽  
Growth Environment ◽  
Therapy Design

Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of the growth environment, drug order, and time interval. To address these limitations, we present an approach that uses genome-scale metabolic modeling and machine learning to explain and guide combination therapy design. Our approach (a) accommodates diverse data types, (b) accurately predicts drug interactions in various growth conditions, (c) accounts for time- and order-specific interactions, and (d) identifies mechanistic factors driving drug interactions. The entropy in bacterial stress response, time between treatments, and gluconeogenesis activation were the most predictive features of combination therapy outcomes across time scales and growth conditions. Analysis of the vast landscape of condition-specific drug interactions revealed promising new drug combinations and a tradeoff in the efficacy between simultaneous and sequential combination therapies.

Drug-Drug Combinations Can Enhance Toxicity as Shown by Monocyte-Derived Hepatocyte-like Cells From Patients With Idiosyncratic Drug-Induced Liver Injury

2019 ◽  
Vol 171 (2) ◽  
pp. 296-302 ◽  
Author(s):  
Andreas Benesic ◽  
Kowcee Jalal ◽  
Alexander L Gerbes
Keyword(s):  
Liver Injury ◽  
Liver Failure ◽  
Drug Interactions ◽  
Acute Liver Failure ◽  
Causality Assessment ◽  
Drug Combinations ◽  
Positive Interactions ◽  
In Vitro Test ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Abstract Drug-induced liver injury (DILI) is a major cause for acute liver failure and regulatory actions on novel drugs. Individual patient characteristics are the main determinant of idiosyncratic DILI, making idiosyncratic DILI (iDILI) one of the most challenging diagnoses in hepatology. Individual drug-drug interactions might play a role in iDILI. However, the current approaches to iDILI diagnosis are focused on single drugs as causative agents. For the present analysis, 48 patients with acute liver injury who took 2 drugs and who were diagnosed as iDILI were investigated. A novel in vitro test was employed using monocyte-derived hepatocyte-like cells (MH cells) generated from these patients. iDILI diagnosis and causality were evaluated using clinical causality assessment supported by Roussel-Uclaf Causality Assessment Method. In 13 of these 48 patients (27%), combinations of drugs increased toxicity in the MH test when compared with the single drugs. Interestingly, whereas in 24 cases (50%) drug-drug combinations did not enhance toxicity, in 11 cases (23%) only the combinations caused toxicity. The incidence of severe cases fulfilling Hy’s law was higher in patients with positive interactions (57% vs 43%; p = .04), with acute liver failure occurring in 40% versus 8% (p = .01). The most common drug combinations causing increased toxicity were amoxicillin/clavulanate (8 of 9 cases) and diclofenac in combination with steroid hormones (4 of 9 cases). Drug-drug interactions may influence the incidence and/or the severity of idiosyncratic DILI. MH cell testing can identify relevant drug-drug interactions. The data generated by this approach may improve patient safety. Study identifier ClinicalTrials.gov NCT 02353455.

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