Drug Information Research Articles

Better Medicines for Children: Lessons Learnt and Share Learnings at the EFGCP Annual Paediatric Conferences.

For many years, the European Forum for Good Clinical Practice (EFGCP) Children Medicines Working Party has organised a Paediatric conference annually. In the past, this event was organised jointly with the European Medicines Agency who was used to host it, along with the Drug Information Association (DIA). This conference is the opportunity for all involved in paediatric drug development, i.e., regulators, HTA bodies, patients' representatives, academia and industry, to share learnings and raise awareness about new regulatory requirements of interest to optimise paediatric drug development. The theme of the 2021 conference was "Challenges and Solutions - the path forward" while in 2022 it focused on "Progress made and Continuing Challenges". Because of the COVID-19 pandemic these two conferences were organised virtually. However, this has not impacted the attendance and value of the conference, since because of a broad and attractive agenda there was a wide stakeholder participation, which provided a compendious overview of the leading issues to improve children's access to innovative medicines.

Abstract 4126893: Feasibility and Acceptability of a Nurse-Pharmacist Post-Discharge Telehealth Model of Care for Heart Failure Patients

Background: Suboptimal medication management is common in patients with heart failure (HF), particularly during transitions-of-care. To date, there are few studies assessing the feasibility of a nurse-pharmacist post-discharge telehealth service for medication optimisation in patients with HF. We performed a feasibility study to determine service uptake and acceptability, and ability to identify medication-related issues for HF patients as they transition from hospital to home. Methods: HF patients were referred to an existing post-discharge telehealth service and offered medication reconciliation and education in addition to their usual care; a service we termed ‘MedRec’ (MR). Primary outcomes were feasibility, measured through recruitment and successful MR completion, and acceptability, measured by an investigator-developed survey. Secondary outcomes were medication-related issues detected during MR. Results: A total of 100 HF patients were offered a post-discharge MR. Mean age of patients was 68.5 ±14.2 years, and mostly male sex (62%). Pharmacist MRs were requested by 80% of patients. In total 62 MRs (77.5%) were performed; 9 patients declined MR during follow-up and an additional 9 patients were uncontactable. Mean time to MR following nurse referral was 10.98 ±9.74 days. Drug-related toxicity or adverse effect presentation was identified in 25 (40.3%) MR recipients at the time of consultation and subsequently required general practitioner follow-up. Medication compliance issues were detected by the pharmacist in 13 (20.9%) patients; forgotten doses being the most common concern. Undertreated medical conditions, such as symptomatic HF and chronic pain, were identified in 12 (19.3%) MR recipients. Medications prescribed without any apparent indication were found in 8 (12.9%) patients. Drug or disease management information was requested by 35 (56.4%) MR recipients. A total of 35 (56.5%) post-MR surveys were successfully completed. All participants who completed a post-MR survey agreed that a post-discharge telehealth MR was an acceptable form of education provision. Engagement with a pharmacist MR was perceived to ease anxiety associated with understanding medication-related changes and empowered greater medication self-management. Conclusions: A post-discharge nurse-pharmacist telehealth service is a feasible and acceptable model of care. Inclusion of a routine MR post-discharge may be an effective means of maintaining continuity of care for HF patients.

Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.

Accurate prediction of anticancer drug responses is essential for developing personalized treatment plans in order to improve cancer patient survival rates and reduce healthcare costs. To this end, we propose a drug sensitivity prediction model based on multi-stage multi-modal drug representations (ModDRDSP) to reflect the properties of drugs more comprehensively, and to better model the complex interactions between cells and drugs. Specifically, we adopt the SMILES representation learning method based on the deep hierarchical bi-directional GRU network (DSBiGRU) and the molecular graph representation learning method based on the deep message-crossing network (DMCN) for the multi-modal information of drugs. Additionally, we integrate the multi-omics information of cell lines based on a convolutional neural network (CNN). Finally, we use an ensemble deep forest algorithm for the prediction of drug sensitivity. After validation, the ModDRDSP shows impressive performance which outperforms the four current industry-leading models. More importantly, ablation experiments demonstrate the validity of each module of the proposed model, and case studies show the good results of ModDRDSP for predicting drug sensitivity, further establishing the superiority of ModDRDSP in terms of performance.

Assessment of potential drug-drug interactions in hospitalized patients with infectious diseases: an experience from a secondary care hospital

Background Polypharmacy is common among hospitalized patients with infectious infections owing to comorbidities or concomitant illnesses. This raises the likelihood of drug-drug interactions and creates uncertainty for healthcare providers. This study aimed to assess the potential drug-drug interactions (pDDIs) among hospitalized patients with infectious diseases in a secondary care hospital. Methods A prospective observational study was conducted in the internal medicine ward for six months after the ethics committee’s approval. Data were collected from patient case records, and prescriptions were screened for pDDIs from a portable electronic physician information database (PEPID) resource analyzed using SPSS, version 27.0. Results In total, 148 patient case records were analyzed, and 549 pDDIs were identified, with 66.8% having at least one or more DDIs. The mean number of drug interactions was 3.70 ± 4.58 per prescription. The most frequently encountered drug interactions were drug combinations such as bisoprolol with atorvastatin and aspirin with tazobactam/piperacillin. Bivariate analysis showed that age, comorbidities, length of hospital stay, and the number of drugs prescribed were risk factors associated with DDIs (p<0.05). In the multiple binary logistic regression analysis, DDIs were significantly associated with comorbidities and the number of prescribed medications (p<0.0001). Conclusions This study observed the prevalence of DDIs in hospitalized patients with infectious diseases of ‘moderate’ severity. Prescription screening using a drug information database assists in early identification and prevention of DDIs, enhancing drug safety and quality of patient-centered care.

Assessment of Errors in Handwritten Prescriptions in Zliten City- Libya

Errors in handwritten prescriptions are a major factor in medical errors and are the main cause of harm to patients due to the lack of clarity of the prescription or the lack of correct data, which leads to the prescription of the wrong medication, causing adverse events. The present study aimed to identify the common errors in handwriting prescriptions in Zliten City. To collect information about errors in handwritten prescriptions, we used two models: first, we distributed fifty-one questionnaires to fifty-one pharmacies distributed in all areas of Zliten city, and second, we collected one hundred and fifty prescriptions from different health facilities distributed in different areas within Zliten city. The questionnaires and prescriptions then analysed to identify medical errors. In the first model of the questionnaire, the results are as follows: the patient's name and age are often present, while the gender, doctor's name, and stamp are often absent. The main issue lies in the clarity of the font for the drug's name, dose, and dosage form. In the second model of the prescriptions, the results are as follows: the patient's name is mostly present, while gender and diagnosis are absent in about 40% of the prescriptions, the doctor's name is mostly absent, and only 2% of the prescriptions have the doctor's stamp and number in the syndicate, and the main issue lies in the clarity of the font for the drug's name, dose, and dosage form., as it is unclear in between 20-30% of the prescriptions. We found that the patient's information is present in most prescriptions the opposite of the questionnaire in which it is less. Also in the prescriber's information, the analysis in our result for prescription contradicted the questionnaires, as most prescribers in the questionnaire are present, we also found that during the analysis of drug information in prescription not clear more than in the questionnaire. From this research, we concluded that there are many errors in prescriptions, possibly because of the large number of cases that need to be seen by the doctor and the speed of writing, and perhaps because doctors forget the vocabulary of the names of medicines. Because of the many errors in handwritten prescriptions, we must immediately switch to computerized prescription writing.

Characteristics, Perspectives, and Experiences of Cyproheptadine Users at Community Pharmacies in the Context to Prescription Medication Misuse

Some people abuse the antihistamine medication cyproheptadine in an attempt to reach their desired shape, weight, and appearance. This study aims to investigate the characteristics of cyproheptadine misusers as well as their behaviors, perceptions, experiences, and sources of information regarding using this medication. A descriptive cross-sectional study was carried out in Sebha City among customers of community pharmacies. Each customer who has taken cyproheptadine was invited to participate in this research. The data was collected from April 2022 until September 2022. Almost all of the participants (n=99, 96.1%) reported they purchased cyproheptadine from community pharmacies and obtained the drug without a prescription n=79 (76.7%). Over half of the respondents (n=53, 51.5%) use this drug for increasing appetite, compared to (n=25,24.3%) and (n=19, 18.4%) respondents who said they used it to improve their shape and lose weight, respectively. Most participants indicated they were unaware of other uses of a drug (n=72, 69.9%) or its side effects (n=61, 59.2%). Furthermore, twenty participants (19.4%) denied receiving information concerning the precautions and contraindications of using cyproheptadine. The highest rate of source of drug information reported by participants was “friends or family member” (n=34, 33%) followed by pharmacists (n=29, 28.2%). Community pharmacies must employ qualified pharmacists on staff to give customers trustworthy information regarding prescription pharmaceuticals such as cyproheptadine.

Prediction of Drug Pathway-based Disease Classes using Multiple Properties of Drugs

Background: Drug repositioning now is an important research area in drug discovery as it can accelerate the procedures of discovering novel effects of existing drugs. However, it is challenging to screen out possible effects for given drugs. Designing computational methods are a quick and cheap way to complete this task. Most existing computational methods infer the relationships between drugs and diseases. The pathway-based disease classification reported in KEGG provides us a new way to investigate drug repositioning as such classification can be applied to drugs. A predicted class of a given drug suggests latent diseases it can treat. Objective: The purpose of this study is to set up efficient multi-label classifiers to predict the classes of drugs. Methods: We adopt three types of drug information to generate drug features, including drug pathway information, label information and drug network. For the first two types, drugs are first encoded into binary vectors, which are further processed by singular value decomposition. For the third type, the network embedding algorithm, Mashup, is employed to yield drug features. Above features are combined and fed into RAndom k-labELsets (RAKEL) to construct multi-label classifiers, where support vector machine is selected as the base classification algorithm. Results: The ten-fold cross-validation results show that the classifiers provide high performance with accuracy higher than 0.95 and absolute true higher than 0.92. The case study indicates the novel effects of three drugs, i.e., they may treat new diseases. Conclusion: The proposed classifiers have high performance and are superiority to the classifiers with other classic algorithms and drug information. Furthermore, they have the ability to discover new effects of drugs.

Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications

Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDIs), potentially compromising treatment outcomes or causing toxicity. Although machine learning (ML) models have made strides in DDI prediction, existing approaches lack specificity for oral diabetes medications and face challenges in interpretability. To address these limitations, we propose a novel ML-based framework utilizing the Simplified Molecular Input Line Entry System (SMILES) to encode structural information of oral diabetes drugs. Using this representation, we developed an XGBoost model, selecting molecular features through LASSO. Our dataset, sourced from DrugBank, included 42 oral diabetes drugs and 1,884 interacting drugs, divided into training, validation, and testing sets. The model identified 606 optimal features, achieving an F1-score of 0.8182. SHAP analysis was employed for feature interpretation, enhancing model transparency and clinical relevance. By predicting adverse DDIs, our model offers a valuable tool for clinical decision-making, aiding safer prescription practices. The 606 critical features provide insights into atomic-level interactions, linking computational predictions with biological experiments. We present a classification model specifically designed for predicting DDIs associated with oral diabetes medications, with an openly accessible web application to support diabetes management in multi-drug regimens and comorbidity settings.

Evaluation of the Knowledge, Attitudes and Practice of Veterinary Professionals and Senior Animal Health Students in Central Gondar Zone, Gondar, Ethiopia: Antimicrobial Use and Resistance Perspectives.

Antimicrobial resistance (AMR) poses a worldwide challenge to the well-being of animals and humans. The emergence of AMR is largely attributed to the improper utilization and overuse of antimicrobials. In order to combat this issue and promote responsible antimicrobial use (AMU), it is imperative for veterinary professionals and graduating students to possess extensive knowledge, maintain positive attitudes, and adhere to good practices regarding AMU and AMR. To assess the knowledge, attitude and practices (KAPs) of veterinary professionals and senior animal health students' towards veterinary AMU and AMR. A cross-sectional study design was conducted on purposefully selected 200 participants (60 of them were veterinary professionals from the districts and towns, and the remaining 140 were senior animal health university students). A self-administered, structured questionnaire was used to collect the necessary data, and descriptive statistics were used to summarize the data. The study revealed that, overall, study participants' KAP were not satisfactory. From veterinary professionals, about 56.7% of respondents scored average knowledge, whereas 66.7% and 61.66% of them had negative attitudes and poor practices, respectively, on AMU and AMR. The study found that 56% of the participants' think the previous knowledge they acquired regarding AMU and AMR was not sufficient, and the internet was used as a source of drug information by the majority of participants. The majority (72%) of them were not taking special training on AMU and AMR. Regarding senior animal health students, this study indicated that 52% and 69.2% of the respondents had poor knowledge and a negative attitude, respectively, towards AMU and AMR. Overall, veterinary professionals in the central Gondar zone have average knowledge, a negative attitude and poor practices. Poor knowledge and a negative attitude were also observed among senior animal health students towards AMU and AMR. Therefore, to fill the observed gaps, activities that enhance the skills of veterinary professionals and students should be implemented. Ingreted measures with animicrobial stewardship program and Nationwide studies are highly suggested to evaluate the overall status of veterinary professionals towards AMU and AMR.

A comparison of drug information question responses by a drug information center and by ChatGPT.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. A study was conducted to assess the accuracy and ability of Chat Generative Pre-trained Transformer (ChatGPT) to systematically respond to drug information inquiries relative to responses of a drug information center (DIC). Ten drug information questions answered by the DIC in 2022 or 2023 were selected for analysis. Three pharmacists created new ChatGPT accounts and submitted each question to ChatGPT at the same time. Each question was submitted twice to identify consistency in responses. Two days later, the same process was conducted by a fourth pharmacist. Phase 1 of data analysis consisted of a drug information pharmacist assessing all 84 ChatGPT responses for accuracy relative to the DIC responses. In phase 2, 10 ChatGPT responses were selected to be assessed by 3 blinded reviewers. Reviewers utilized an 8-question predetermined rubric to evaluate the ChatGPT and DIC responses. When comparing the ChatGPT responses (n = 84) to the DIC responses, ChatGPT had an overall accuracy rate of 50%. Accuracy across the different question types varied. In regards to the overall blinded score, ChatGPT responses scored higher than the responses by the DIC according to the rubric (overall scores of 67.5% and 55.0%, respectively). The DIC responses scored higher in the categories of references mentioned and references identified. Responses generated by ChatGPT have been found to be better than those created by a DIC in clarity and readability; however, the accuracy of ChatGPT responses was lacking. ChatGPT responses to drug information questions would need to be carefully reviewed for accuracy and completeness.

Investigating the capabilities of advanced large language models in generating patient instructions and patient educational material

ObjectivesLarge language models (LLMs) with advanced language generation capabilities have the potential to enhance patient interactions. This study evaluates the effectiveness of ChatGPT 4.0 and Gemini 1.0 Pro in providing...

The Züri Can study: Can regulated cannabis sales promote lower-risk cannabis use? Mini-review and study protocol

Background and AimsRecreational use of cannabis is illegal in most countries. Despite this, it is the third most commonly used psychoactive substance worldwide. As a result of this discrepancy, a growing number of countries have begun to reassess their legal approach to cannabis in recent years. While the health risks of cannabis and potential harm reduction measures are increasingly well understood, there are still significant gaps in knowledge about which regulatory and supply models are effective in promoting lower-risk cannabis use.In this paper, we outline the Züri Can study, which implements and evaluates a regulatory framework for cannabis sales in the city of Zurich, Switzerland, between 2023 and 2026. In addition, we illustrate how the study addresses current knowledge gaps to provide further insight into the potential future regulation of cannabis in Switzerland.To embed the study in the present scientific and political context, we first provide a brief overview of the state of knowledge on cannabis-related health risks and means of reducing them, along with lessons learned from other countries that have implemented varying regulatory systems. Design and Measurements2,100 participants will be able to legally purchase cannabis either at a pharmacy, a cannabis social club, or the municipal drug information center over a three-year period. As part of this observational study, participants will be evaluated regarding their cannabis use habits and motives, their knowledge of lower-risk use, and their mental and physical health, among other parameters.Established harm reduction strategies are implemented as an integral part of the study design. CommentsThe study will contribute to a better understanding of the impact of different cannabis distribution models on cannabis use patterns and related health outcomes. The results are expected to assist Swiss and international policy makers in developing evidence-based and public health-oriented regulatory frameworks for cannabis.

A Drug Repositioning Approach Reveals Ergotamine May Be a Potential Drug for the Treatment of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that is the most common form of dementia in the elderly. The drugs currently used to treat AD only have limited effects and are not able to cure the disease. Drug repositioning has increasingly become a promising approach to find potential drugs for diseases like AD. To screen potential drug candidates for AD based on the relationship between risk genes of AD and drugs. We collected the risk genes of AD and retrieved the information of known drugs from DrugBank. Then, the AD-related genes and the targets of each drug were mapped to the human protein-protein interaction network (PPIN) to represent AD and the drugs on the network. The network distances between each drug and AD were calculated to screen the drugs proximal to AD-related genes on PPIN, and the screened drug candidates were further analyzed by molecular docking and molecular dynamics simulations. We compiled a list of 714 genes associated with AD. From 5,833 drugs used for human diseases, we identified 1,044 drugs that could be potentially used to treat AD. Then, amyloid-β (Aβ) protein, the key molecule involved in the pathogenesis of AD was selected as the target to further screen drugs that may inhibit Aβ aggregation by molecular docking. We found that ergotamine and RAF-265 could bind stably with Aβ. In further analysis by molecular dynamics simulations, both drugs exhibited reasonable stability. Our work indicated that ergotamine and RAF-265 may be potential candidates for treating AD.

Assessing Immunogenicity in Drug Reviews and Prescribing Information in Japan.

Anti-drug antibodies (ADAs) generated in response to biopharmaceuticals can significantly impact pharmacokinetics (PK) and overall drug efficacy. Thus, the ICH M4 guidelines mandate summarizing immunogenicity data from clinical trials in drug approval applications. However, following the approval of the first antibody drug in Japan in 2001, no cross-sectional investigation has focused on immunogenicity during the regulatory review process. Therefore, this study aims to examine the review reports and prescribing information of antibody drugs approved in Japan to identify key points related to immunogenicity evaluation. We conducted a cross-sectional analysis of review reports for antibody drugs approved between June 2001 and July 2022 by the Japanese regulatory authority. Specifically, we evaluated the ADA positivity rate, presence of neutralizing antibodies, antibody titers, and effects of ADA on PK, efficacy, and safety. We also compared this information with that provided in the prescribing information. Our analysis revealed that the ADA positivity rate and its effects on PK, efficacy, and safety were critical aspects of the review process. The emphasis on these factors varied depending on the number of applications and disease area. The information presented in the prescribing information was largely consistent with that discussed in the review reports. Overall, this study provides the first cross-sectional evaluation of immunogenicity considerations in the regulatory review of antibody drugs in Japan. Our findings can contribute to the efficiency of clinical trial planning and preparation of approval applications, to potentially improve the overall drug development process and address the drug loss problem in Japan.

FDA Approval Summary: Capecitabine Labeling Update under Project Renewal.

On December 14, 2022, the U.S. Food and Drug Administration (FDA) approved revisions to the United States Prescribing Information (USPI) for capecitabine that revised existing indications and dosage regimens, added new indications and their recommended dosage regimens, revised safety information, updated the description of the risk of capecitabine in patients with dihydropyrimidine dehydrogenase (DPD) deficiency, and edited other sections of the USPI to conform with FDA's current labeling guidance. These supplements were reviewed and approved under Project Renewal, a public health initiative established by the FDA's Oncology Center of Excellence that aims to update the prescribing information of certain older oncology drugs to ensure information is clinically meaningful and scientifically up to date. This article summarizes the FDA approach that supported revisions to the capecitabine USPI within the context of Project Renewal.

ChemAP: predicting drug approval with chemical structures before clinical trial phase by leveraging multi-modal embedding space and knowledge distillation

Recent studies showed that the likelihood of drug approval can be predicted with clinical data and structure information of drug using computational approaches. Predicting the likelihood of drug approval can be innovative and of high impact. However, models that leverage clinical data are applicable only in clinical stages, which is not very practical. Prioritizing drug candidates and early-stage decision-making in the de novo drug development process is crucial in pharmaceutical research to optimize resource allocation. For early-stage decision-making, we need a computational model that uses only chemical structures. This seemingly impossible task may utilize the predictive power with multi-modal features including clinical data. In this work, we introduce ChemAP (Chemical structure-based drug Approval Predictor), a novel deep learning scheme for drug approval prediction in the early-stage drug discovery phase. ChemAP aims to enhance the possibility of early-stage decision-making by enriching semantic knowledge to fill in the gap between multi-modal and single-modal chemical spaces through knowledge distillation techniques. This approach facilitates the effective construction of chemical space solely from chemical structure data, guided by multi-modal knowledge related to efficacy, such as clinical trials and patents of drugs. In this study, ChemAP achieved state-of-the-art performance, outperforming both traditional machine learning and deep learning models in drug approval prediction, with AUROC and AUPRC scores of 0.782 and 0.842 respectively on the drug approval benchmark dataset. Additionally, we demonstrated its generalizability by outperforming baseline models on a recent external dataset, which included drugs from the 2023 FDA-approved list and the 2024 clinical trial failure drug list, achieving AUROC and AUPRC scores of 0.694 and 0.851. These results demonstrate that ChemAP is an effective method in predicting drug approval only with chemical structure information of drug so that decision-making can be done at the early stages of drug development process. To the best of our knowledge, our work is the first of its kind to show that prediction of drug approval is possible only with structure information of drug by defining the chemical space of approved and unapproved drugs using deep learning technology.

A phenotypic drug discovery approach by latent interaction in deep learning

Contemporary drug discovery paradigms rely heavily on binding assays about the bio-physicochemical processes. However, this dominant approach suffers from overlooked higher-order interactions arising from the intricacies of molecular mechanisms, such as those involving cis -regulatory elements. It introduces potential impairments and restrains the potential development of computational methods. To address this limitation, I developed a deep learning model that leverages an end-to-end approach, relying exclusively on therapeutic information about drugs. By transforming textual representations of drug and virus genetic information into high-dimensional latent representations, this method evades the challenges arising from insufficient information about binding specificities. Its strengths lie in its ability to implicitly consider complexities such as epistasis and chemical–genetic interactions, and to handle the pervasive challenge of data scarcity. Through various modeling skills and data augmentation techniques, the proposed model demonstrates outstanding performance in out-of-sample validations, even in scenarios with unknown complex interactions. Furthermore, the study highlights the importance of chemical diversity for model training. While the method showcases the feasibility of deep learning in data-scarce scenarios, it reveals a promising alternative for drug discovery in situations where knowledge of underlying mechanisms is limited.

Learning personalized drug features and differentiated drug-pair interaction information for drug–drug interaction prediction

Multi-drug combination therapies are increasingly used for complex diseases but carry risks of harmful drug interactions. Effective drug–drug interaction prediction (DDIP) is essential for assessing risks among numerous drug pairs. Most DDIP methods involve two main steps: drug representation and drug pair interaction extraction, respectively challenged by the loss of personalized drug information and the need for differentiated interaction data, and are rarely studied. Specifically, personalized drug information refers to the distinct features of each drug. These properties can be easily confused by neighboring information during graph propagation. This issue is especially prominent in drug interaction graphs with long-tail distributions, which poses challenges for personalized drug information learning. Furthermore, it is crucial to learn interactions with differentiation in order to identify diverse drug relationships. Some methods simply concatenate drug features, often ignoring the differences of different drug relationships, while other methods based on substructures rely on professional pharmacological knowledge and are computationally complex. To address these issues, we propose a novel method, learning personalized Drug Features and differentiated Drug-Pair interaction information for drug–drug interaction prediction (DFPDDI). This approach employs a contrastive learning network with edge-aware augmentations and mutual information estimators to capture personalized drug features across various graph distributions. Furthermore, it applies a mutual information constraint to drug-pair representations, enhancing the accuracy of interaction predictions by better distinguishing between different types of drug relationships. The results evaluated on three public datasets demonstrate competitive performance compared to baselines. It also shows potential for accurate predictions, particularly in imbalanced-distribution graphs.

Appropriateness of ChatGPT as a resource for medication-related questions.

With its increasing popularity, healthcare professionals and patients may use ChatGPT to obtain medication-related information. This study was conducted to assess ChatGPT's ability to provide satisfactory responses (i.e., directly answers the question, accurate, complete and relevant) to medication-related questions posed to an academic drug information service. ChatGPT responses were compared to responses generated by the investigators through the use of traditional resources, and references were evaluated. Thirty-nine questions were entered into ChatGPT; the three most common categories were therapeutics (8; 21%), compounding/formulation (6; 15%) and dosage (5; 13%). Ten (26%) questions were answered satisfactorily by ChatGPT. Of the 29 (74%) questions that were not answered satisfactorily, deficiencies included lack of a direct response (11; 38%), lack of accuracy (11; 38%) and/or lack of completeness (12; 41%). References were included with eight (29%) responses; each included fabricated references. Presently, healthcare professionals and consumers should be cautioned against using ChatGPT for medication-related information.

Clinical drug interactions between voriconazole and 38 other drugs: a retrospective analysis of adverse events.

Voriconazole (VRZ) is involved in a variety of drug‒drug interactions (DDIs), but few studies have reported adverse events (AEs) associated with the DDIs of VRZ. The primary goal of this study was to analyse the potential risk factors for AEs caused by DDIs between VRZ and other drugs via the OpenVigil FDA platform and to provide a reference for preventing VRZ DDIs and monitoring clinically related adverse drug events. A retrospective pharmacovigilance study was conducted to investigate the AEs related to DDIs between VRZ and four categories of drugs: proton pump inhibitors (PPIs), non-steroidal anti-inflammatory drugs (NSAIDs), immunosuppressants, and other antibacterial drugs. AE information for the target drugs from the first quarter of 2004 to the third quarter of 2022 was downloaded from the OpenVigil FDA data platform. Four frequency statistical models-the reporting ratio method, Ω shrinkage measure model, combination risk ratio model, and the chi-square statistics model-were used to analyse the AEs related to DDIs and evaluate the correlation and influence of sex and age between the drug(s) and the target AEs detected. A total of 38 drugs were included, with 262 AEs detected by at least one of the four models and 48 AEs detected by all four models. Some 77 detected AEs were significantly positively correlated with DDIs and were related to higher reporting rates of AEs than when used alone. Graft-versus-host disease was the AE that had the strongest correlation with the drug interaction between VRZ and immunosuppressants (tacrolimus, mycophenolate mofetil, cyclophosphamide, and cyclosporine), and multiple organ dysfunction syndrome was correlated with VRZ in combination with other antibacterial drugs (linezolid, meropenem, cefepime, and vancomycin). Significant sex and age differences in the target AEs were detected for five and nine target drugs, respectively. For VRZ in combination with linezolid, aggravated conditions and respiratory failure should be given more attention in male patients, and mycophenolate mofetil and respiratory failure in female patients. When conditions are aggravated, febrile neutropenia and septic shock should be of particular concern in patients over 18years of age who use VRZ in combination with ceftazidime, ciprofloxacin, or cytarabine. In patients aged under 18, septic shock should be considered when VRZ is used in combination with meropenem and dexamethasone. AEs related to DDIs should receive more attention when VRZ is used in combination with PPIs (renal impairment), NSAIDs (constipation and renal failure), immunosuppressants (graft versus host disease, septic shock) and other antibacterial drugs (multiple organ dysfunction syndrome, febrile neutropenia, and respiratory failure). Considering the influence of sex and age differences in VRZ DDIs, these factors need to be considered when assessing the risk of AEs in patients receiving VRZ and other drugs.