Detection Of Drug Interactions Research Articles

Pharmaceutical Care in Patients with Side Effects to Oral Antineoplastic Agents: A Case Report

Background Chronic myeloid leukaemia (CML) is a haematopoietic stem cell disease characterised by the proliferation of granulocytes and their immature myeloid precursors. The treatments recommended are oral treatments dispensed in the outpatient unit (OU) of the hospital’s pharmacy service (PS), where patient-centred pharmaceutical care (PC) is essential to enhance therapeutic adherence, detect drug interactions and create a pharmacist-patient relationship to educate the patient on the main potential toxicities derived from the treatment and warning signs or symptoms requiring immediate attention. Methodology We present the case of a 55-year-old woman, followed up in consultation by the Haematology Service (HS) since October 2021 for CML in the chronic phase. Results She started treatment on nilotinib 300 mg every 12 hours. The patient attended OU and was offered PC. After 12 months, nilotinib was well tolerated, but our patient experienced a loss of effectiveness, so HS decided to switch to dasatinib 100 mg daily. After one year of treatment, our patient was presented with symptoms of swelling and dyspnoea on moderate exertion. In January 2024, following a joint Haematology-Pharmacy session, a switch to bosutinib 400 mg daily was decided. After 15 days of treatment, the patient presented without an appointment due to a pruritic generalised rash on the torso, face, and extremities, being diagnosed with leukocytoclastic vasculitis. Conclusion In conclusion, we describe case reports of a patient who has a lack of effectiveness and ARs in several TKIs and the importance of identifying side effects, through early and close PC, in order for the patient’s evolution to be favourable.

GPMelt: A hierarchical Gaussian process framework to explore the dark meltome of thermal proteome profiling experiments.

Thermal proteome profiling (TPP) is a proteome wide technology that enables unbiased detection of protein drug interactions as well as changes in post-translational state of proteins between different biological conditions. Statistical analysis of temperature range TPP (TPP-TR) datasets relies on comparing protein melting curves, describing the amount of non-denatured proteins as a function of temperature, between different conditions (e.g. presence or absence of a drug). However, state-of-the-art models are restricted to sigmoidal melting behaviours while unconventional melting curves, representing up to 50% of TPP-TR datasets, have recently been shown to carry important biological information. We present a novel statistical framework, based on hierarchical Gaussian process models and named GPMelt, to make TPP-TR datasets analysis unbiased with respect to the melting profiles of proteins. GPMelt scales to multiple conditions, and extension of the model to deeper hierarchies (i.e. with additional sub-levels) allows to deal with complex TPP-TR protocols. Collectively, our statistical framework extends the analysis of TPP-TR datasets for both protein and peptide level melting curves, offering access to thousands of previously excluded melting curves and thus substantially increasing the coverage and the ability of TPP to uncover new biology.

COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL

Objective: The study aims to compare the ability of free artificial intelligence (AI) chatbots to detect drug interactions with freely available drug interaction tools, using clopidogrel as an example. Material and Method: The Lexicomp database was used as a reference to determine drug interactions with clopidogrel. ChatGPT-3.5 AI and Bing AI were selected as the free AI chatbots. Medscape Drug Interaction Checker, DrugBank Drug Interaction Checker and Epocrates Interaction Check were selected as free drug interaction tools. Accuracy score and comprehensiveness score were calculated for each drug interaction tool and AI chatbots. The kappa coefficient was calculated to assess inter-source agreement for interaction severity. Result and Discussion: The results most similar to those of Lexicomp were obtained from the DrugBank and the ChatGPT-3.5 AI chatbot. The ChatGPT-3.5 AI chatbot performed best, with 69 correct results and an accuracy score of 307. ChatGPT-3.5 AI has the highest overall score of 387 points for accuracy and comprehensiveness. In addition, the highest kappa coefficient with Lexicomp was found for ChatGPT-3.5 AI chatbot (0.201, fair agreement). However, some of the results obtained by ChatGPT-3.5 AI need to be improved as they are incorrect/inadequate. Therefore, information obtained using AI tools should not be used as a reference for clinical applications by healthcare professionals and patients should not change their treatment without consulting doctor.

Advocating for the inclusion of therapeutic drug monitoring in the national essential diagnostic list: Perspectives from psychiatrists.

India published its first edition of the National Essential Diagnostics List in 2019. The list depicts the list of diagnostic tests that can ensure affordable and quality healthcare delivery by removing barriers toward accessibility and reducing out-of-pocket expenditure. In 2024, the Indian Council of Medical Research has invited suggestions for revision of the list. Therapeutic drug monitoring (TDM) has been a promising modality and has been useful for a range of indications like monitoring medication adherence, diagnosing suboptimal treatment, detecting drug interactions, and guiding initiation or withdrawal of therapy. In this article, the authors make a case for inclusion of TDM for certain psychotropic drugs like lithium, sodium valproate, carbamazepine, and clozapine at the district hospital level. The authors have tried to justify the inclusion backed by recent evolving evidence.

Literature Review: Development of Electronic Medical Records In Hospital Management Information Systems

Introduction: Health technology today is developing very quickly from the initially conventional using paper to being computerized. This literature review aims to map and critically summarize the scientific evidence on the cost-effectiveness and acceptability of Computerized Physician Order Entry (CPOE) and Electronic Health Reports. This Journal have Question that need to be answered how does the development of CPOE in medical records affect cost-effectiveness improvement so that it can be accepted by many parties? Method used in this journal is literature review is conducted on journal articles related to costs and receipts in CPOE. The systematic search was conducted from 5 databases namely PubMed, Science Direct, ProQuest, DOAJ and Ebscohost. Journal articles are selected and selected following PRISMA guidelines. Twenty-five journal articles qualified based on predetermined criteria. At the end as result, Cost-effectiveness with CPOE is more likely to be found that it is easier to reach compared to conventional methods. In addition, the acceptance of patients and health workers is also high. These factors can have a positive or negative influence on the hospital management system because developing countries still need adequate resources so that they can run these methods. Discussion and conclusion CPOE systems can improve patient safety by detecting drug interactions and actions. It is necessary to develop medical records in order to provide effective financing and acceptance.

Drug interaction detection and glycemic control – telepharmacy’s role in diabetes management: before-after study

Introduction: Diabetes management faces intertwined challenges like polypharmacy and adherence complexities, hindering effective care delivery. Telepharmacy emerges as a promising solution, revolutionizing diabetes care by enhancing pharmacists’ pivotal role. However, a tailored model for Indonesia is still lacking. Purpose: This study aimed to assess the potential effectiveness of a telepharmacy application in improving the detection of drug-drug interactions and glycemic control. Methods: We conducted an observational before-and-after study among type 2 diabetic patients in Indonesia. Results: We noted a significant surge in detected drug interactions. Patients counseled through telepharmacy had 3.653 times higher likelihood of achieving good glycemic control. Conclusion: The study underscores the positive impact of telepharmacy on drug interaction detection and glycemic control among Indonesian diabetics.

An LC-MS/MS Method for Quantification of Lamotrigine and Its Main Metabolite in Dried Blood Spots.

The antiepileptic drug lamotrigine (LTG) shows high pharmacokinetic variability due to genotype influence and concomitant use of glucuronidation inducers and inhibitors, both of which may be frequently taken by elderly patients. Our goal was to develop a reliable quantification method for lamotrigine and its main glucuronide metabolite lamotrigine-N2-glucuronide (LTG-N2-GLU) in dried blood spots (DBS) to enable routine therapeutic drug monitoring and to identify altered metabolic activity for early detection of drug interactions possibly leading to suboptimal drug response. The analytical method was validated in terms of selectivity, accuracy, precision, matrix effects, haematocrit, blood spot volume influence, and stability. It was applied to a clinical study, and the DBS results were compared to the concentrations determined in plasma samples. A good correlation was established for both analytes in DBS and plasma samples, taking into account the haematocrit and blood cell-to-plasma partition coefficients. It was demonstrated that the method is suitable for the determination of the metabolite-to-parent ratio to reveal the metabolic status of individual patients. The clinical validation performed confirmed that the DBS technique is a reliable alternative for plasma lamotrigine and its glucuronide determination.

Critical role of growth medium for detecting drug interactions in Gram-negative bacteria that model in vivo responses.

Drug-resistant bacterial infections are a growing concern and have only continued to increase during the SARS-CoV-2 pandemic. Though not routinely used for Gram-negative bacteria, drug combinations are sometimes used for serious infections and may become more widely used as the prevalence of extremely drug-resistant organisms increases. To date, reliable methods are not available for identifying beneficial drug combinations for a particular infection. Our study shows variability across strains in how drug interactions are impacted by growth conditions. It also demonstrates that testing drug combinations in tissue-relevant growth conditions for some strains better models what happens during infection and may better inform combination therapy selection.

Deep learning for drug‐drug interaction prediction: A comprehensive review

AbstractThe prediction of drug‐drug interactions (DDIs) is a crucial task for drug safety research, and identifying potential DDIs helps us to explore the mechanism behind combinatorial therapy. Traditional wet chemical experiments for DDI are cumbersome and time‐consuming, and are too small in scale, limiting the efficiency of DDI predictions. Therefore, it is particularly crucial to develop improved computational methods for detecting drug interactions. With the development of deep learning, several computational models based on deep learning have been proposed for DDI prediction. In this review, we summarized the high‐quality DDI prediction methods based on deep learning in recent years, and divided them into four categories: neural network‐based methods, graph neural network‐based methods, knowledge graph‐based methods, and multimodal‐based methods. Furthermore, we discuss the challenges of existing methods and future potential perspectives. This review reveals that deep learning can significantly improve DDI prediction performance compared to traditional machine learning. Deep learning models can scale to large‐scale datasets and accept multiple data types as input, thus making DDI predictions more efficient and accurate.

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery.

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

Artificial intelligence-supported web application design and development for reducing polypharmacy side effects and supporting rational drug use in geriatric patients.

The main complications of polypharmacy, which is known as the simultaneous use of more than five drugs, are potentially inappropriate medicines(PIMs), drug-drug, and drug-disease interaction. It is aimed to prepare an auxiliary tool to reduce the complications of polypharmacy and to support rational drug use(RDU), by evaluating the patient with age, drugs, and chronic diseases in this study. In the first phase of this study, as methodological research, an up-to-date and comprehensive auxiliary tool as a reference method was generated with a database containing interaction information of 430 most commonly used drug agents and chronic diseases in geriatrics in the light of current and valid 6 PIM criteria for geriatric patients, and medication prospectuses, relevant current articles, and guidelines. Then, an artificial intelligence(AI) supported web application was designed and developed to facilitate the practical use of the tool. Afterward, the data of a cross-sectional observational single-center study were used for the rate and time of PIM and drug interaction detection with the web application. The proposed web application is publicly available at https://fastrational.com/. While the PIM coverage rate with the proposed tool was 75.3%, the PIM coverage rate of EU(7)-PIM, US-FORTA, TIME-to-STOPP, Beers 2019, STOPP, Priscus criteria in the web application database respectively(63.5%-19.5%) from the highest to the lowest. The proposed tool includes all PIMs, drug-drug, and drug-disease interaction information detected with other criteria. A general practitioner detects interactions for a patient without the web application in 2278 s on average, while the time with the web application is decreased to 33.8 s on average, and this situation is statistically significant. In the literature and this study, the PIM criteria alone are insufficient to include actively used medicines and it shows heterogeneity. In addition, many studies showed that the biggest obstacle to drug regulation in practice is "time constraints." The proposed comprehensive auxiliary tool analyzes age, drugs, and diseases specifically for the patient 60 times faster than the manual method, and it provides quick access to the relevant references, and ultimately supports RDU for the clinician, with the first and only AI-supported web application.

Potential drug-drug interactions in adults receiving oral anticoagulant and antiaggregant therapy

ABSTRACT Background Anticoagulant and antiaggregant drugs are drug groups with high mortality and the most common cause of malpractice. Research design and Methods 18 and 65 years were scheduled for pharmacotherapy in the Family Health Center. 122 patients during their anticoagulant and/or antiaggregant treatment were evaluated in terms of drug-drug interactions. Results Drug-drug interactions were detected in 89.7% of the patients included in the study. A total of 212 drug-drug interactions were found in 122 patients. Of these, 12 (5.6%) were identified as A, 16 (7.5%) B, 146 (68.6%) C, 32 (15.2%) D and 6 (2.8%) X risk category. The number of DDI was found to be significantly higher in patients aged between 56 and 65 years. The most drug interactions are significantly higher in the C and D categories, respectively. The most predicted clinical outcomes of DDI’s were increased in the therapeutic effect and adverse/toxic reactions. Conclusions Contrary to expectations, it is seen that although polypharmacy is relatively less in patients aged 18–65 years compared to patients over 65 years of age, it is very important to detect drug interactions in this age group in terms of safety, efficacy and treatment benefit in terms of drug-drug interaction.

Utilization of Expert Systems as a Source of Information in Detecting Drug Interactions in the Treatment of Diabetes Mellitus Patients: A Systematic Literature Review

Background: The high prevalence of DM, as well as drug interaction problems that increase 2.5 times for each patient prescription and the side effects of individuals with diabetes mellitus including being more susceptible to drug interactions, it is necessary to monitor drug therapy in diabetes patients Mellitus. Methods: in this study, this research uses a literature study or uses a systematic literature review or also known as descriptive analysis based on research data. Results: This study shows that the use of expert systems in pharmacy, especially for disease detection and drug interactions, has been widely developed and proven to be able to detect. Conclusion: Based on the results and discussions that have been obtained, it can be concluded that the expert system is very useful for detecting disease and is also useful for checking drug interactions with a treatment therapy.

A SYSTEMATIC REVIEW OF SOFTWARE AND RESOURCES AVAILABLE IN THE PHARMACEUTICAL SECTOR

The whole pharmaceutical area (pharmacy industry, pharmacy billing, clinical trials, pharmacovigilance, and drug-drug interaction detection) needed various innovative and scientific solutions to solve the problems. A systematic review was performed on various software applications widely used in the pharmaceutical industry, including clinical trials, pharmacy billing, pharmacovigilance, the detection of drug interactions that assist the healthcare professional. Our review concluded that computer software has created a significant role in the pharmaceutical industry, pharmacy billing, and herbal industry. The computer software and programmes aid in reducing manufacturing costs, manufacturing errors, medical errors, and clinical errors.The software performs various safety functions, supplies on-screen access to pertinent drug and patient information, manages data, and assists in the product formulation process. The programmed safeguards can be modified to meet changing requirements.

Small molecule drug and biotech drug interaction prediction based on multi-modal representation learning

BackgroundDrug–drug interactions (DDIs) occur when two or more drugs are taken simultaneously or successively. Early detection of adverse drug interactions can be essential in preventing medical errors and reducing healthcare costs. Many computational methods already predict interactions between small molecule drugs (SMDs). As the number of biotechnology drugs (BioDs) increases, so makes the threat of interactions between SMDs and BioDs. However, few computational methods are available to predict their interactions.ResultsConsidering the structural specificity and relational complexity of SMDs and BioDs, a novel multi-modal representation learning method called Multi-SBI is proposed to predict their interactions. First, multi-modal features are used to adequately represent the heterogeneous structure and complex relationships of SMDs and BioDs. Second, an undersampling method based on Positive-unlabeled learning (PU-sampling) is introduced to obtain negative samples with high confidence from the unlabeled data set. Finally, both learned representations of SMD and BioD are fed into DNN classifiers to predict their interaction events. In addition, we also conduct a retrospective analysis.ConclusionsOur proposed multi-modal representation learning method can extract drug features more comprehensively in heterogeneous drugs. In addition, PU-sampling can effectively reduce the noise in the sampling procedure. Our proposed method significantly outperforms other state-of-the-art drug interaction prediction methods. In a retrospective analysis of DrugBank 5.1.0, 14 out of the 20 predictions with the highest confidence were validated in the latest version of DrugBank 5.1.8, demonstrating that Multi-SBI is a valuable tool for predicting new drug interactions through effectively extracting and learning heterogeneous drug features.

Study of potential interactions of oral antidiabetic drugs in patients with type 2 diabetes mellitus with comorbidities: A retrospective study

Background: Diabetes mellitus (DM) has many complications, such as microvascular and macrovascular complications. Patients are given polypharmacy therapy to combat these issues, which can cause drug interactions. Oral antidiabetic drugs were chosen based on their risk profile. Risk assessment aided treatment intensity targeting. The number of drugs taken can increase the risk of drug interactions causing health issues. Objective: To identify potential drug interactions based on their severity (major, moderate and minor) and the mechanism of the drug interaction (pharmacokinetics and pharmacodynamics), in type two diabetes mellitus patients with comorbidities at the Lamongan Health Center. Methods: This was a cross-sectional study using medical records and patient prescriptions from October 2020 to June 2021 at Puskesmas (Primary Health Centers) Deket, Karanggeneng, and Babat. The data were descriptively analysed. Drug interactions were analysed using drugs.com and Stockley. Purposive sampling was used to select 194 patients for inclusion. Results: From October 2020 to June 2021, 110 out of 194 outpatients at Lamongan Regional Health Center had potential drug interactions (56.7 %). The most common type of drug interaction was of moderate severity, with 120 cases (93.0%), and the most common mechanism was pharmacodynamics (61.2%). Conclusion: Polypharmacy is a difficult problem to avoid, so drug therapy monitoring is required in diabetic patients to minimise unwanted effects. Preventing potential drug interactions requires a system for early detection of potential drug interactions that may occur in patient prescribing and maximising pharmaceutical care. This system would allow for the community to be more proactive in finding out about potential drug interactions.

Safety and Effectiveness of a Pharmacist-Driven High-Dose Methotrexate Monitoring Protocol at an Academic Medical Center

Introduction: High-dose methotrexate (HD-MTX) requires extensive monitoring and implementation of supportive care strategies to prevent associated toxicities. Although monitoring protocols for MTX vary across institutions, standardized supportive care measures can improve treatment outcomes. The purpose of this study was to evaluate the impact of a pharmacist-driven monitoring protocol on management of patients with delayed MTX elimination.Methods: This was a single-center, retrospective analysis that included hospitalized adult cancer patients receiving HD-MTX therapy (≥ 1 g/m 2). The study consisted of a pre-protocol group (August 2014 to July 2017) and a post-protocol group (August 2017 to August 2020). Patients were included in the analysis more than once if they had a repeat but separate hospital encounter. The primary outcome measure was to compare the rate of appropriate leucovorin dosage adjustments, defined by adherence to institution-specific HD-MTX monitoring guidelines, before and after protocol implementation (Table 1). Secondary outcomes included MTX concentrations at 24, 48, and 72 hours; time to MTX elimination; evidence of delayed MTX clearance; urine pH; length of stay; drug-drug interactions; and incidence of toxicities. Group comparisons for continuous data were made using Mann-Whitney U test and Chi-square or Fisher's exact test was used for categorical data. A p-value of < 0.05 was used to indicate significance.Results: Of 272 hospital encounters initially identified, 82 pre-protocol encounters and 59 post-protocol encounters were included in the analysis. Thirty-five percent of patient encounters in the pre-protocol group were excluded as a result of inappropriately timed methotrexate levels compared to only 9% in the post-protocol group, indicating improved MTX monitoring after protocol implementation. Baseline characteristics are summarized in Table 2. The most common malignancy was primary CNS lymphoma (53.8%) followed by non-Hodgkin lymphoma (28.8%). HD-MTX monotherapy was the most common regimen used in the pre-protocol group (57.1%) whereas combination therapy with rituximab +/- procarbazine was the most common regimen used in the post-protocol group (62.5%). Twenty-one encounters in the pre-protocol group and 22 encounters in the post-protocol group required leucovorin dose escalations at 24, 48, or 72 hours (Table 3). Following the implementation of the pharmacist-driven protocol, the rate of appropriate leucovorin dose adjustments increased significantly from 61.9% to 90.9% (p = 0.034). The median methotrexate concentration at 48 and 72 hours was higher in the post-protocol group when compared to the pre-protocol group. [0.22 µM/L vs.0.18 µM/L (p = 0.019) and 0.1 µM/L vs. 0.07 µM/L (p < 0.001), respectively]. Median time to methotrexate elimination was slightly longer in patients managed according to the MTX protocol [73 hours vs. 72.4 hours, p = 0.022] with higher rates of delayed clearance noted in these patients [18 (30.5%) vs. 16 (19.5%), p < 0.001]. There were no statistically significant differences in length of stay, urine pH, or drug-drug interactions between the two groups during methotrexate therapy (Table 4). However, a lower percentage of concomitant drug interactions with methotrexate were identified after protocol implementation, suggesting improved drug interaction screening [29 (49.2%) vs. 53 (64.9%), p = 0.857]. Rates of toxicities are shown in Table 5. The post-protocol group experienced a higher incidence of any-grade hyperbilirubinemia [20 (33.9%) vs. 18 (22%), p = 0.019], encephalopathy [10 (17%) vs. 5 (6.1%), p = 0.043], and grade ≥ 3 neutropenia [4 (6.8%) vs. 4 (4.9%), p = 0.009]. These differences in toxicity rates may in part be due to more frequent use of multi-agent chemotherapy in the post-protocol group and greater number of MTX cycles administered to these patients. The incidence of nephrotoxicity was similar in both groups (6 (7.3%) vs. 5 (8.5%), p= 0.493) and in line with previous reports.Conclusion:The implementation of a pharmacist-driven HD-MTX monitoring protocol improved optimal timing of MTX serum concentrations, resulted in a statistically significant improvement in the rate of appropriate leucovorin dose escalations, and led to enhanced detection of drug interactions. These findings demonstrate the important role pharmacists can play in dosing and monitoring high-risk regimens. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Detect Drug Interactions with Metronidazole

Introduction: Metronidazole has been prescribed to treat infections for over a century and continues to be helpful in the therapy of amoebiasis, trichomoniasis, and giardiasis. Metronidazole is a cost-effective medication because of its low price, few adverse effects, and favorable pharmacokinetic and pharmacodynamic properties; nevertheless, it interacts with a wide variety of other medications. Some interactions with other medicines diminish its effectiveness, while others increase it.&#x0D; Aims: The study aims to detect and evaluate metronidazole interactions with other medicines at King Abdulaziz Specialist Hospital.&#x0D; Methodology: This retrospective study encompasses the review of 360 computerized prescriptions inside the outpatient clinic at King Abdulaziz Specialist Hospital in Saudi Arabia between March and September 2020 to detect and evaluate interactions among metronidazole and different medications.&#x0D; Results: Metronidazole interactions are mostly major or moderate. Metronidazole had the most common interactions with domperidone (15.83 %), famotidine (13.89 %), and ciprofloxacin (11.67 %). Metronidazole contains a nitroimidazole ring, which suppresses the metabolism in the liver of numerous medications, including those that may be metabolized by the CYP3A4 and/or CYP450 2C9 isoenzymes. The combination of metronidazole with phenytoin or phenobarbital can cause metronidazole elimination to be accelerated and phenytoin clearance to be reduced. Metronidazole may improve warfarin's anticoagulant effects, leading to a longer prothrombin time and a higher risk of bleeding. Concurrent use of metronidazole with alfuzosin, escitalopram, and ondansetron may raise the risks of QT-interval prolongation and arrhythmias.&#x0D; Conclusion: Most metronidazole drug interactions can be avoided by following excellent clinical care and clinical pharmacology concepts, such as avoiding complex treatment regimens, educating patients. and identifying patient risk factors. Furthermore, before prescribing and dispensing medicines, physicians and pharmacists should utilize drug-drug interactions checkers such as Micromedex and Lexicomp or a book such as Stockley's Drug Interactions.

Assessment of Drug Interactions with Online Electronic Checkers in Multi-Pathological Patients

Introduction: Multi-pathological patients are at high risk of drug interactions and side effects. We aimed to assess the usefulness of 3 online drug interaction checkers. Methods: In a cross-sectional study, carried out in the Medicine Department of Hospital General of Castellon, Spain, in February 2020, we assessed drug interaction detection with 3 online electronic checkers, Drugs.com, Lexicomp®, and Medscape, and compared results obtained with the 3 tools. From every hospitalized patient, we obtained the list of drugs he or she had been taking until admission. Bivariable tests were used for analysis. p values <0.05 were considered significant. Results: We included data from 134 patients; 68 (51%) were male; median (and interquartile range) of their age was 82 (76–88) years. A total of 1,082 substance drugs were entered in the checkers. The number of highest grade interactions found with every program was Drugs.com 85, Lexicomp® 33, and Medscape 67. Positive correlations were found between age and number of drug substances prescribed (p = 0.009) and between number of drug substances prescribed and interactions found with any of the 3 checkers (p < 0.001 in all 3 cases). Regarding highest grade interactions, agreement among all 3 checkers was poor. Conclusions: The 3 online checkers we assessed found a large number of interactions. The 3 programs gave very discrepant results. Impact on Practice Statements: The analyzed programs, Drugs.com, Lexicomp®, and Medscape Interactions, found a large number of drug interactions in the studied patients. The 3 programs gave very discrepant results among them.

A Multi-disciplinary Approach to Managing Chronic Myelogenous Leukemia Patients on Oral Anticancer Therapy at a Large Academic Medical Center.

Since the discovery of imatinib, an oral breakpoint cluster region-Abelson murine leukemia tyrosine kinase inhibitor, chronic myelogenous leukemia transformed from a hematologic malignancy primarily treated with intravenous chemotherapy to a disease almost solely managed with oral agents. While certainly there are benefits to taking a medication at home, this change in treatment modality also came with unique challenges, including patient adherence, medication acquisition and cost, and toxicity management. Pharmacists are uniquely equipped to assist with educating patients, safe prescribing, and access to medications. Several studies have described the benefits of an integrated oral anticancer medication management program in the ambulatory setting, including improvements in patient adherence, side effect management, patient comprehension, and drug-interaction detection. Pharmacists are also specially trained to assist with medication dose adjustments, relative lab monitoring, and co-pay assistance. Here, we describe the multidisciplinary workflows established to manage oral therapies in chronic myelogenous leukemia patients in a malignant hematology clinic at a large academic medical center. By using the unique talents of the clinic pharmacist, clinic nurse, and specialty retail pharmacy group, patients can be triaged to help ensure the correct skill set is used to optimally care for patients. An acuity-based monitoring structure can improve the ability to reach and safely monitor a large volume of patients.