Drug Information Research Articles

E006 Providing information on arthritis medication: a service evaluation

Abstract Background/Aims Traditionally patients commencing disease modifying anti-rheumatic medications (DMARDs) attended a face to face consultation with a rheumatology nurse or pharmacist, to discuss benefits, concerns and safety monitoring. The pandemic has led to an increase in the use of remote methods of information sharing including telephone consultations. The aim of this service evaluation was to elicit patients preferences on receiving information on arthritis medications to help inform service development. Methods A service evaluation with contributions from patients and stakeholders was designed and registered with the trust. Patients who were commencing or taking DMARDs attending, one of two hospital sites, in North Staffordshire were asked to complete a short questionnaire from November 2022-April 2023. Some patients completed the questionnaire at the end of their telephone drug counselling appointment with the assistance of the nurse whilst others completed the questionnaire whilst attending in person for their appointment. The questionnaire focused on the preferred format for receiving information on arthritis medications including a face to face appointment with a health professional (even if this meant a wait), telephone consultation, attending a group counselling session, having a drug information leaflet with the option to contact a health professional to answer any questions or watching a video on a device with internet access (computer, smart phone or tablet). The importance of receiving information on the same day as commencing treatment was also explored. Results 64 patients took part in the survey. No demographic data was collected. The most popular method for receiving information on arthritis medications was a face to face appointment, even if it meant a delay in starting medication (n = 32,50%). The second most popular method was an information leaflet with the option to discuss any concerns via the telephone (n = 12,19 %). The third preference to receive information were via a video, again with the option to discuss concerns remotely (n = 10, 16%) or a telephone appointment, even if it meant a delay in starting medication (n 10,16%) Surprisingly although no patients expressed a preference to attend a group counselling session, 36 (56%) would attend if offered. When presented with the option of having information on the same day as the decision was made to commence medication, or, having to wait to discuss the mediation with a health professional, there was a strong preference to see a health professional (n = 46, 72%) Conclusion Patients clearly value discussing commencing DMARDs with a health professional even if this means a delay in treatment. Having just one approach to information giving does not equate with individual needs and a range of options regarding information delivery that the patient can select from is required. Disclosure S. Ryan: None. S. Hider: None. Z. Paskins: None.

Accuracy of a chatbot in answering questions that patients should ask before taking a new medication

BackgroundThe potential uses of artificial intelligence have extended into the fields of health care delivery and education. However, challenges are associated with introducing innovative technologies into health care, particularly with respect to information quality. ObjectiveThis study aimed to evaluate the accuracy of answers provided by a chatbot in response to questions that patients should ask before taking a new medication. MethodsTwelve questions obtained from the Agency for Healthcare Research and Quality were asked to a chatbot for the top 20 drugs. Two reviewers independently evaluated and rated each response on a 6-point scale for accuracy and a 3-point scale for completeness with a score of 2 considered adequate. Accuracy was determined using clinical expertise and a drug information database. After the independent reviews, answers were compared, and discrepancies were assigned a consensus score. ResultsOf 240 responses, 222 (92.5%) were assessed as completely accurate. Of the inaccurate responses, 10 (4.2%) were mostly accurate, 5 (2.1%) were more accurate than inaccurate, 2 (0.8%) were equal parts accurate and inaccurate, and 1 (0.4%) was more inaccurate than accurate. Of the 240 responses, 194 (80.8%) were comprehensively complete. There were 235 (97.9%) responses that scored 2 or higher. Five responses (2.1%) were considered incomplete. ConclusionUsing a chatbot to answer questions commonly asked by patients is mostly accurate but may include inaccurate information or lack valuable information for patients.

Drug trials are more likely to disclose full placebo control information than non-drug trials: A cross-sectional study of participant information leaflets of placebo-controlled trials

BackgroundThis study aimed to investigate whether placebo control is differently disclosed in drug and non-drug randomised clinical trial (RCT) participant information leaflets (PILs) and how this might affect participant blinding and direction of study outcomes. MethodsPILs were obtained from trials registered in the International Standard Randomised Controlled Trial Number database via email. Placebo descriptions in PILs were categorised as Full Disclosure (FD), Partial Disclosure (PD), or Missing Information (MI). Associations between intervention type (drug or non-drug)/placebo disclosure (FD or PD/MI) and participant blinding success/trial outcome direction (positive or non-positive) were examined using a two-sided Fisher's exact test. ResultsOf 116 collected PILs, 56 % were for drug trials and 44 % were for non-drug trials. Among them, 88 PILs had the corresponding publications available and 68 reports specified primary outcomes. Drug trials were more likely to fully disclose placebo information than non-drug trials (92.3 % vs. 74.5 %, p < 0.05). However, the success rate of blinding was only reported in 3 out of 88 trial publications (3.4 %), precluding further analysis. Furthermore, there was no significant association between the direction of trial results and the type of intervention or placebo disclosure. ConclusionOur study findings suggest that drug and non-drug RCTs might differ in the way they reveal placebo control information. Further research is warranted to understand what leads to more common PD of placebo information in non-drug trials than drug trials and to determine the optimal placebo control disclosure in specific trial context.

Development and implementation of ambulatory care pharmacy services at an internal medicine clinic.

This report describes the step-by-step process that led to expansion of ambulatory care pharmacy services at a newly established internal medicine clinic within a patient-centered medical home in North Carolina. Implementation of clinical pharmacist services at the clinic was led by a postgraduate year 2 (PGY2) pharmacy resident and guided by the 9 steps described in the book Building a Successful Ambulatory Care Practice: A Complete Guide for Pharmacists. After a needs assessment and review of the demographics and insurance status of the clinic's target population, it was determined that pharmacist services would focus on quality measures including diabetes nephropathy screening, diabetes eye examination, blood glucose control in diabetes, discharge medication reconciliation, annual wellness visits, and medication adherence in diabetes, hypercholesterolemia, and hypertension. Clinic appointments were conducted under 3 models: a pharmacist-physician covisit model, a "floor model" of pharmacist consultation on drug information or medication management issues during medical resident sign-out sessions with supervising physicians (medical residents could also see patients along with the pharmacist at a covisit appointment), and a covisit model of stacked physician and pharmacist appointments. The pharmacist's services were expanded from 2 half-day clinic sessions per week initially to 5 or 6 half-day clinic sessions by the end of the residency year. By the fourth quarter of the first PGY2 residency year in which ambulatory care pharmacy services were provided in the clinic, the clinical and financial impact of those services justified the addition of a second full-time pharmacist to the clinic team.

Public Health Outreach in Impoverished Areas of Cambodia: Addressing the Issues Related to Prescription Practices

During a two-week public health internship organized by Projects Abroad in impoverished areas in Cambodia, the authors participated in health check-ups and outreach activities. We identified issues such as polypharmacy and medication misuse. These problems stem from symptom-based prescriptions without considering individual patient conditions due to limited diagnostic equipments and medications. Our solution involved suggesting a documentation akin to Japan's prescription record books, and Drug Information Leaflets (DILs). Prescription record books would enable patients to record the medications they purchased at the pharmacy, so that physicians would know what medications patients are taking during their health checkups, and thus prevent polypharmacy. In addition, the DILs included medication details and illustrations, considering the low literacy rates in the areas. We emphasized the need for sustained non-communicable diseases (NCDs) treatment and the potential of external perspectives to introduce innovative healthcare approaches and improvements within local communities.

Design of a multi-category drug information integration platform for intelligent pharmacy management: A needs analysis study.

A drug store was never just an area to fill personal solution. Patients considered drug specialists to be counsels, somebody who could help them pick an over-the-counter treatment or understanding the portion and directions for a solution. Drug stores, similar to the remainder of the medical services business, are going through changes. Nowadays, one of the main highlights of any structure is the board. The executives give the refinement needed to wrap up any responsibility in a particular way. The executive framework of a drug store can be utilized to deal with most drug store related errands. This report has provided data on the best way to fabricate and execute a Pharmacy Management System. The primary objective of this system is to expand exactness, just as security and proficiency, in the drug shop. This undertaking is focused on the drug store area, determined to offer engaging and reasonable programming answers to assist them with modernizing to rival shops (helping out other equal modules in a similar examination program). This study will clarify the system's thoughts concerning the board issues and arrangements of a drug store. Likewise, this study covers the main parts of the Pharmacy application's investigation, execution, and look.

Fair Balance of Prescription Drug Information on Legitimate and Illegitimate Online Pharmacy Websites.

Patients require important information when prescribed medications. For example, the U.S. Food and Drug Administration (FDA) requires that promotional information includes a fair balance of risks and benefits. This study evaluates how well legitimate online pharmacies (LOPs) and illegitimate online pharmacies (IOPs) comply with the spirit of the FDA's fair balance guidelines by examining the extent and equivalence of risk and benefit information on their websites. This study analyzed the content of 307 online pharmacy websites. Most (90.3%) communicated drug benefits, while 84.7% provided risks. Both risk and benefit information was moderately extensive. Presentation of risks and benefits differed between online pharmacy types. Compared to LOPs, IOPs were more likely to present risk information but also exaggerate benefits. Four in ten online pharmacies presented a fair balance of risks and benefits. However, LOPs (47.4%) were more likely to present a fair balance than IOPs (36.5%). Interestingly, IOPs were more likely to disclose instructions for use and overdose information than LOPs. These findings underscore the need for regulatory guidelines to encourage online pharmacies to present a fair balance of benefit and risk information. Also, pharmacists should develop online approaches to better fulfill their professional responsibility as drug information providers while maintaining their integrity and objectivity.

DTKGIN: Predicting drug-target interactions based on knowledge graph and intent graph

Knowledge graph intent graph attention mechanism Predicting drug-target interactions (DTIs) plays a crucial role in drug discovery and drug development. Considering the high cost and risk of biological experiments, developing computational approaches to explore the interactions between drugs and targets can effectively reduce the time and cost of drug development. Recently, many methods have made significant progress in predicting DTIs. However, existing approaches still suffer from the high sparsity of DTI datasets and the cold start problem. In this paper, we develop a new model to predict drug-target interactions via a knowledge graph and intent graph named DTKGIN. Our method can effectively capture biological environment information for targets and drugs by mining their associated relations in the knowledge graph and considering drug-target interactions at a fine-grained level in the intent graph. DTKGIN learns the representation of drugs and targets from the knowledge graph and the intent graph. Then the probabilities of interactions between drugs and targets are obtained through the inner product of the representation of drugs and targets. Experimental results show that our proposed method outperforms other state-of-the-art methods in 10-fold cross-validation, especially in cold-start experimental settings. Furthermore, the case studies demonstrate the effectiveness of DTKGIN in predicting potential drug-target interactions. The code is available on GitHub: https://github.com/Royluoyi123/DTKGIN.

Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST.

The completeness of adverse event (AE) reports, crucial for assessing putative causal relationships, is measured using the vigiGrade completeness score in VigiBase, the World Health Organization global database of reported potential AEs. Malaysian reports have surpassed the global average score (approximately 0.44), achieving a 5-year average of 0.79 (SD 0.23) as of 2019 and approaching the benchmark for well-documented reports (0.80). However, the contributing factors to this relatively high report completeness score remain unexplored. This study aims to explore the main drivers influencing the completeness of Malaysian AE reports in VigiBase over a 15-year period using vigiGrade. A secondary objective was to understand the strategic measures taken by the Malaysian authorities leading to enhanced report completeness across different time frames. We analyzed 132,738 Malaysian reports (2005-2019) recorded in VigiBase up to February 2021 split into historical International Drug Information System (INTDIS; n=63,943, 48.17% in 2005-2016) and newer E2B (n=68,795, 51.83% in 2015-2019) format subsets. For machine learning analyses, we performed a 2-stage feature selection followed by a random forest classifier to identify the top features predicting well-documented reports. We subsequently applied tree Shapley additive explanations to examine the magnitude, prevalence, and direction of feature effects. In addition, we conducted time-series analyses to evaluate chronological trends and potential influences of key interventions on reporting quality. Among the analyzed reports, 42.84% (56,877/132,738) were well documented, with an increase of 65.37% (53,929/82,497) since 2015. Over two-thirds (46,186/68,795, 67.14%) of the Malaysian E2B reports were well documented compared to INTDIS reports at 16.72% (10,691/63,943). For INTDIS reports, higher pharmacovigilance center staffing was the primary feature positively associated with being well documented. In recent E2B reports, the top positive features included reaction abated upon drug dechallenge, reaction onset or drug use duration of <1 week, dosing interval of <1 day, reports from public specialist hospitals, reports by pharmacists, and reaction duration between 1 and 6 days. In contrast, reports from product registration holders and other health care professionals and reactions involving product substitution issues negatively affected the quality of E2B reports. Multifaceted strategies and interventions comprising policy changes, continuity of education, and human resource development laid the groundwork for AE reporting in Malaysia, whereas advancements in technological infrastructure, pharmacovigilance databases, and reporting tools concurred with increases in both the quantity and quality of AE reports. Through interpretable machine learning and time-series analyses, this study identified key features that positively or negatively influence the completeness of Malaysian AE reports and unveiled how Malaysia has developed its pharmacovigilance capacity via multifaceted strategies and interventions. These findings will guide future work in enhancing pharmacovigilance and public health.

GCARDTI: Drug–target interaction prediction based on a hybrid mechanism in drug SELFIES

AbstractThe prediction of the interaction between a drug and a target is the most critical issue in the fields of drug development and repurposing. However, there are still two challenges in current deep learning research: (i) the structural information of drug molecules is not fully explored in most drug target studies, and the previous drug SMILES does not correspond well to effective drug molecules and (ii) exploration of the potential relationship between drugs and targets is in need of improvement. In this work, we use a new and better representation of the effective molecular graph structure, SELFIES. We propose a hybrid mechanism framework based on convolutional neural network and graph attention network to capture multi‐view feature information of drug and target molecular structures, and we aim to enhance the ability to capture interaction sites between a drug and a target. In this study, our experiments using two different datasets show that the GCARDTI model outperforms a variety of different model algorithms on different metrics. We also demonstrate the accuracy of our model through two case studies.

Improving Drug- and Disease-related Knowledge of Patients with Chronic Kidney Disease: Randomized Controlled Trial from a Sri Lankan Outpatient Clinic

ABSTRACT Context: During past decades, the prevalence of chronic kidney disease (CKD) has increased significantly. Individuals with CKD are on complex drug regimens. Enhancing patients’ disease and medication knowledge can be achieved by pharmacist-conducted patient education. Aims: This study aimed to assess disease-specific knowledge changes in patients with stage 4 or 5 CKD after providing patient education. Settings and Design: A randomized controlled clinical trial was conducted at an outpatient renal clinic, Teaching Hospital, Anuradhapura, Sri Lanka. Subjects and Methods: The clinic admission register was used as the sampling frame. Patient consent was obtained, and demographic data were collected at commencement. Four counseling sessions were provided to the intervention group (IG) by “clinic-based pharmacist,” at commencement, and 2, 6, and 10 months, post-recruitment. The control group (CG) received usual care. A validated “drug and disease knowledge questionnaire” was interviewer administered to both groups at 6 months and a “drug and disease information booklet” was provided to IG. The same questionnaire was readministered at 12 months to reassess disease-specific knowledge. CG received an education booklet at 12 months. Statistical Analysis Used: The Mann–Whitney U test was used to analyze initial and 12 months in-between group data. The Wilcoxon signed-rank test was used to analyze initial and 12-month data within CG and IG. Results: One hundred patients were enrolled in CG and 101 in IG. The demographic profile of the two groups was broadly similar. Median drug and disease knowledge questionnaire scores were similar at 6 months at 23.00 (19.25–26.00) in CG and 24.00 (20.00–27.00) in IG (P &gt; 0.05). At 12-month scores were 22.00 (18.00–25.50) in CG compared to 35.00 (31.00–37.00) in IG (P &lt; 0.05). Conclusions: The provision of medication counseling and a “drug and disease information booklet” is beneficial in improving disease-specific knowledge in patients with CKD.

Dispensing Errors and Self-medication Practices-Pharmacists' Experience in Tabuk: A Cross-sectional Study.

Community pharmacists are engaged in various professional activities starting from drug dispensing to promoting the well-being of patients. They dispense medications as stated in the prescription and are also licensed to prescribe over-the-counter (OTC) medications. Self-medication is widely practiced in various countries, which may lead to irrational drug use. The objectives of this study were to identify the factors associated with dispensing errors, to find ways to minimize dispensing errors, to identify patients' reasons for self-medication, and to find the drugs commonly utilized by patients as OTC medications and the sources of their drug information. A cross-sectional survey of a convenience sample of 286 registered community pharmacists all over Tabuk was conducted using a self-administered questionnaire. Physician's unclear handwriting in the prescription was the major factor for dispensing error (2.6 out of 3) and writing the prescription clearly by the physician or using a printed form of prescription was an important factor in minimizing dispensing errors (2.91 out of 3). Previous similar complaints in the past were the main reason for self-medication (2.45 out of 3) with analgesics and antipyretics being the commonly dispensed drug groups dispensed as OTC medications (2.95 out of 3). Self-medication practices and dispensing errors are widespread in Tabuk. Antibiotics were dispensed as OTC medication, which may lead to more chance of irrational drug use. Writing the prescription clearly and legibly would reduce dispensing errors. It is the community pharmacists' responsibility to increase awareness regarding the appropriate use of drugs to the public.

Conformity of Package Inserts Information to Regulatory Requirements

ABSTRACT Background: Accurate and reliable information regarding drugs is essential for safe and effective use of drugs. It is not possible for the clinician to refer research articles every time for complete updates. In such situations, package inserts (PIs) form an authentic information for the doctor and patient. The study was intended to assess the information completeness and precision of PIs using the criteria laid down by, sections 6.2 and 6.3 of Schedule D (II), Schedule Y of Drugs and Cosmetic Act (1940) and Rules (1945). Materials and Methods: PIs of different formulations were collected from various departments, and also local and regional chemists. The information adequacy was done by 25 checklist proposed by the Indian Drug and Cosmetic Act (1940) and Rules, 1945 under sections 6.2 and 6.3 of Schedule D and Schedule Y. A score of “1” was given, if information was present, and “0” if there was no information. Information adequacy scores also calculated. Results: A total of 207 PIs were analyzed. Majority of PIs belong Grade B category of drug information (174). Generic name, composition, posology and method of administration, undesirable effects, contraindications, drug interactions, special precautions, legibility, storage and contraindications during pregnancy and breast feeding were mentioned by nearly all. Gaps were observed in handling instructions, influence on person’s self-driving and machinery operation capabilities, pharmaceutical incompatibilities, shelf life, excipients and Pharmacokinetic and dynamic information not updated regularly. Conclusion: The current study shows that there is still inadequacy of particulars regarding drug information. Hence, it is essential to establish proper uniform standards for the currently available PIs. An absolute and flawless PI should provide complete information. This requires continuous and constant effort from manufacturer and regulatory authorities to promote safe and rational use of medicines.

Baseline antipsychotic prescription and short-term outcome indicators in individuals at clinical high-risk for psychosis: Findings from an Italian longitudinal study

IntroductionThe prognostic prediction of outcomes in individuals at clinical high-risk for psychosis (CHR-P) is still a significant clinical challenge. Among multiple baseline variables of risk calculator models, the role of ongoing pharmacological medications has been partially neglected, despite meta-analytical evidence of higher risk of psychosis transition associated with baseline prescription exposure to antipsychotics (AP) in CHR-P individuals. In particular, baseline AP exposure in CHR-P individuals may be considered as a functional equivalent of the psychometric transition to psychosis, as already postulated in the original ‘Ultra High-Risk’ model.ObjectivesThe main aim of the current study was to test the hypothesis that ongoing AP need at baseline indexes a subgroup of CHR-P individuals with more severe psychopathology and worse prognostic trajectories along a 1-year follow-up period.MethodsThis research was settled within the ‘Parma At-Risk Mental States’ program. Baseline and 1-year follow-up assessment included the Positive And Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF). CHR-P individuals who were taking AP medications at entry were included in the CHR-P-AP+ subgroup. The remaining participants were grouped as CHR-P-AP-. The acquisition of drug and outcome information was collected both at baseline and across the follow-up period. Finally, logistic regression analyses with dichotomized 1-year outcome parameters (previously showing statistically significant differences in inter-group comparisons) as dependent measures and sociodemographic and clinical characteristics as independent variables were also performed.ResultsHundred and seventy-eight CHR-P individuals (aged 12–25 years) were enrolled (91 CHR-P-AP+, 87 CHR-P-AP-). Compared to CHR-P AP-, CHR-P AP+ individuals had older age, greater baseline PANSS ‘Positive Symptoms’ and ‘Negative Symptoms’ factor subscores and a lower GAF score. At the end of our follow-up, CHR-P-AP+ subjects showed higher rates of psychosis transition, new hospitalizations and urgent/non-planned visits compared to CHRP- AP- individuals.ConclusionsThe current study suggests that AP need is a significant prognostic variable in cohorts of CHR-P individuals and should be included in the current risk calculators. In particular, the results of this study conducted in a realworld clinical setting indicate that the rate of CHR-P individuals who were already exposed to AP at the time of CHR-P status ascription was higher than those reported in recent meta-analyses on this topic. Moreover, our findings confirm that baseline AP prescription appears to increase psychotic transition risk.Disclosure of InterestNone Declared

Analyzing the Iatrogenic Triad: Discovering Strategies for Preventing Harm in the Elderly.

The iatrogenic triad is a significant global health problem in the elderly population. This study aimed to evaluate the iatrogenic triad in the elderly and identify potential preventive measures to mitigate its occurrence. A preliminary observational study was conducted on 150 ambulatory elderly patients to assess potentially inappropriate medications (PIMs), polypharmacy, and drug interactions. The AGS Beers Criteria 2019, Polypharmacy, Medication Complexity Regimen Index (MRCI), and Micromedex (a drug information software) were used to assess the harmful triad. Before and after data collection, we observed, identified, and unfolded potential strategies to avoid the harmful triad in the elderly population. MRCI is 30.49 ± 13.77, suggesting a moderate level of complexity in the drug regimens of elderly patients. Among the PIMs identified by the AGS Beer criteria for 2019, glimepiride (45) and diclofenac (23) were found to be the most frequently prescribed. Moderate-level drug-drug interactions were identified between aspirin and metoprolol (20), metoprolol and metformin (13), and aspirin and enalapril (11). All drug-ethanol and drug-food interactions were rapid and often unknown to patients. Furthermore, the study found that MRCI and polypharmacy were significantly associated with the number of PIMs and drug interactions (p < 0.01). Based on data collection, this study identified three possible ways to prevent the iatrogenic triad in elderly patients: interaction, collaboration, and continuing education. In conclusion, this study sheds light on medication regimen complexity, PIMs, and drug interactions in elderly patients. The study also highlights three possible ways to prevent the iatrogenic triad: interaction, collaboration, and continuing education. By implementing these strategies, healthcare providers can help prevent harm and improve the quality of care for elderly patients.

Analysis of drug-drug interactions in spontaneous adverse drug reaction reports from EudraVigilance focusing on psychiatric drugs and somatic medication

IntroductionPatients with severe mental illnesses (SMI) are often exposed to polymedication. Additionally, the risk of somatic diseases is twice as high in patients with SMI as in individuals without a psychiatric disorder. Furthermore, drug–drug interactions (DDI) between psychiatric drugs and somatic medications are a well-known cause of adverse drug reactions (ADR).ObjectivesThe aim of this study was to analyse whether already known DDI related to psychiatric drugs and somatic medication still occur in everyday clinical practice.MethodsTherefore we identified all spontaneous ADR reports contained in the European ADR database EudraVigilance from Germany received between 01/2017 and 12/2021 reported for patients older than 17 years in which antidepressants, antipsychotics and mood stabilizers were reported as suspected/interacting (n= 9,665). ADR reports referring to intentional overdoses and suicide attempts were excluded (n= 9,276 left). We used the ABDATA drug information system in order to identify all potential DDI (pDDI). The identified reports with pDDI were then assessed individually to determine whether the respective DDI occurred.Results 1,271 reports with 728 potentially interacting drugs pairs related to psychiatric drugs and somatic medications with 2,655 pDDI were found. Restricted to potentially interacting drug pairs with more than 10 reports, (i) hyponatremias related to antidepressants and diuretics (n= 362, 32.6%), (ii) bleeding events related to selective serotonin reuptake inhibitors (SSRI) and platelet aggregation inhibitors, anticoagulants or non-steroidal antiinflammatory drugs (NSAID) (n= 295, 17.5%), and (iii) increased beta-blocker effects related to SSRIs and beta-blockers (n= 126, 11.3%) were the most frequently identified pDDI. After individual case assessment, in 33.3% (14/42), 23.7% (45/190) and 17.4% (8/46) of the reports bleeding events related to SSRIs and anticoagulants, SSRIs and platelet aggregation inhibitors and SSRIs and NSAIDs were reported. Hyponatremia was reported in 7.6% (22/289) of the reports related to antidepressants and diuretics and increased beta-blocker effects in 6.9% (8/116) of the reports related to SSRIs and beta-blockers.ConclusionsAccording to our analysis, well-known DDI still occur in the treatment of psychiatric patients with psychiatric drugs and somatic medication. Whenever possible, alternative drug combinations with a lower potential of DDIs may be considered or appropriate monitoring measures should be conducted.Disclosure of InterestNone Declared

Health Technology Assessment: Evaluation of 7 Glucagon-Like Peptide-1 Receptor Agonists for the Treatment of Type 2 Diabetes Mellitus.

This study provides a reference for healthcare organizations in the selection and rational use of glucagon-like peptide-1 receptor agonists (GLP-1RAs), based on the Rapid Guide for Drug Evaluation and Selection in Chinese Medical Institutions (Second Edition). According to the Rapid Guide for Drug Evaluation and Selection in Chinese Medical Institutions (Second Edition) released in 2023, relevant databases such as PubMed, Cochrane, and Embase, drug labels, and clinical guidelines were searched for drug information. We systematically evaluated 7 GLP-1RAs marketed in China for safety, efficacy, economy, pharmacological properties, and other attributes using a percentage scoring method. The final assessment result scores from highest to lowest were semaglutide (71.5 points), dulaglutide (68.9 points), liraglutide (68.7 points), exenatide (62.5 points), lixisenatide (59.9 points), polyethylene glycol loxenatide (55.9 points), and benaglutide (45.1 points). When a healthcare organization introduces GLP-1RAs to their hospital, they can refer to the assessment results and use the top three recommended medications: semaglutide, dulaglutide, and liraglutide.

The Effect Implementation of Pharmaceutical Service Standards on Increasing Patient Satisfaction at Puskesmas Sedinginan Kab. Rokan Hilir

The achievement of pharmaceutical services in Indonesia in 2022 reached 68.87%, exceeding the target of 60%, showing an increase from the previous year which was only 57.10% of the target of 55%. This research aims to see the effect of implementing pharmaceutical service standards on increasing patient satisfaction at the Sejiwan Rokan Hilir Community Health Center in 2023. The research method used was quantitative with a cross-sectional design. The results of the research show that reviewing prescriptions, drug information services, counseling, monitoring drug side effects, monitoring drug therapy, and evaluating drug use influence increasing patient satisfaction at the Sejiwan Rokan Hilir Community Health Center in 2023.

Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction.

Personalized cancer treatments require accurate drug response predictions. Existing deep learning methods show promise but higher accuracy is needed to serve the purpose of precision medicine. The prediction accuracy can be improved with not only topology but geometrical information of drugs. A novel deep learning methodology for drug response prediction is presented, named Hi-GeoMVP. It synthesizes hierarchical drug representation with multi-omics data, leveraging graph neural networks and variational autoencoders for detailed drug and cell line representations. Multi-task learning is employed to make better prediction, while both 2D and 3D molecular representations capture comprehensive drug information. Testing on the GDSC dataset confirms Hi-GeoMVP's enhanced performance, surpassing prior state-of-the-art methods by improving the Pearson correlation coefficient from 0.934 to 0.941 and decreasing the root mean square error from 0.969 to 0.931. In the case of blind test, Hi-GeoMVP demonstrated robustness, outperforming the best previous models with a superior Pearson correlation coefficient in the drug-blind test. These results underscore Hi-GeoMVP's capabilities in drug response prediction, implying its potential for precision medicine. The source code is available at https://github.com/matcyr/Hi-GeoMVP.

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.