OBJECTIVE: Several drug-drug interaction (DDI) checkers such as DDI-Predictor have been developed to detect and grade DDIs. DDI-Predictor gives an estimate of the magnitude of an interaction based on the ratio of areas under the curve. The objective of the present study was to analyse the frequencies of DDIs involving well-known strong interactors such as rifampicin and selective serotonin reuptake inhibitors (SSRIs), as reported by a clinical pharmacy team using DDI-Predictor, and the pharmacist intervention acceptance rate.
METHODS: The pharmacist intervention rate and the physician acceptance rate were calculated for DDIs involving rifampicin or the SSRIs fluoxetine, paroxetine, duloxetine and sertraline. The rates were compared with a bilateral χ2 test or Fisher\'s exact test.
RESULTS: Of the 284 DDIs recorded, 38 (13.4%) involved rifampicin and 78 (27.5%) involved SSRIs. The pharmacist intervention rate differed significantly (68.4% for rifampicin vs 48.8% for SSRIs; p=0.045) but the physician acceptance rate did not (84.6% for rifampicin vs 81.6% for SSRIs; p=1). Pharmaceutical interventions for SSRIs were more frequent when the ratio of the area under the drug concentration versus time curve in DDI-Predictor was >2. Pharmacists were more likely to issue a pharmacist intervention for DDIs involving rifampicin because of a high perceived risk of treatment failure and were less likely to issue a pharmacist intervention for DDIs involving an SSRI, except when the suspected interaction was strong.
CONCLUSIONS: DDI checkers can help pharmacists to manage DDIs involving strong interactors. DDIs involving strong inhibitors versus a strong inducer differ with regard to their intervention and acceptance rates, notably due to the estimation of the magnitude of the DDI.

OBJECTIVE: To determine the pharmaceutical interventions in patients eligible for phase I cancer clinical trials, focusing specifically on exclusion criteria related to medication or relevant interactions.
METHODS: Descriptive, observational study conducted at a comprehensive cancer centre. Patients undergoing screening for phase I clinical trials (March 2019-December 2022) were included. The pharmacist reviewed concomitant medication and provided a recommendation.
RESULTS: The concomitant medication of 512 patients eligible to participate in 84 phase I clinical trials was analysed. In 230 (44.9%) patients, the clinical trial treatment included oral medication. The median number of concomitant medications was 5 (IQR 3-8) per patient.A total of 280 pharmaceutical interventions were performed in 140 (27.3%) patients: 240 (85.7%) were due to interactions in 124 (24.2%) patients, and 40 (14.3%) were due to exclusion criteria in 34 (6.6%) patients. Interactions and exclusion criteria were detected in 18 (3.5%) patients. The main groups of drugs involved were 68 (24.3%) antacids and antiulcer drugs, 28 (10.0%) antidepressants and 26 (9.3%) opioids. Acceptance analysis of the recommendation was applicable in 215 cases; in 208 (96.7%), the pharmaceutical intervention was accepted.Differences were identified for exclusion criteria (7 vs 27) and interactions (37 vs 87) between parenteral and oral clinical trial medication (p<0.001).
CONCLUSIONS: The pharmacist\'s review of concomitant medication during the screening period in phase I clinical trials enables the detection of prohibited medication or relevant interactions, potentially avoiding screening failures and increasing the efficacy and safety of treatments.

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OBJECTIVE: Pharmaceutical interventions are proposals made by hospital clinical pharmacists to address sub-optimal uses of medications during prescription review. Pharmaceutical interventions include the identification of drug-related problems, their prevention and resolution. The objective of this study was to exploit a newly developed deep neural network classifier to identify drug-related problems from pharmaceutical interventions and perform a large retrospective descriptive analysis of them in a French university hospital over a 3-year period.
METHODS: Data were collected from prescription support software from 2018 to 2020. A classifier running in Python 3.8 and using Keras library was then used to automatically categorise drug-related problems from pharmaceutical interventions according to the coding of the French Society of Clinical Pharmacy.
RESULTS: 2 930 656 prescription lines were analysed for a total of 119 689 patients. Among these prescription lines, 153 335 (5.2%) resulted in pharmaceutical interventions (n=48 202 patients; 40.2%). Pharmaceutical interventions were predominantly observed in patients aged 65 years or older (n=26 141 patients out of 53 186; 49.1%) and in patients taking five or more medications (44 702 patients out of 93 419; 47.8%). The most frequently identified types of drug-related problems associated with pharmaceutical interventions were \'Non-conformity to guidelines or contra-indication\' (n=88 523; 57.7%), \'Overdosage\' (16 975; 11.1%) and \'Improper administration\' (13 898; 9.1%). The most frequently encountered drugs were: paracetamol (n=10 585; 6.9%), esomeprazole (6031; 3.9%), hydrochlorothiazide (2951; 1.9%), enoxaparin (2191; 1.4%), tramadol (1879; 1.2%), calcium (2073; 1.3%), perindopril (1950; 1.2%), amlodipine (1716; 1.1%), simvastatin (1560; 1.0%) and insulin (1019; 0.7%).
CONCLUSIONS: The deep neural network classifier used met the challenge of automatically classifying drug-related problems from pharmaceutical interventions from a large database without mobilising significant human resources. The use of such a classifier can lead to alerting caregivers about certain risky practices in prescription and administration, and triggering actions to improve patients\' therapeutic outcomes.

Hospital Pharmacy is today a profession marked by therapeutic advances, with a proactive attitude, focussed on people and their health. The evolution of processes is constant, with the full presence of digitalisation, robotisation, and even artificial intelligence, in an environment that also requires the efficient and sustainable use of these tools. In this context, it is necessary to have a roadmap that guides the advancement of the profession and Hospital Pharmacy Services. Continuing with the philosophy of the 2020 initiative which, with the slogan \"Towards the future, safely\", defined the strategic lines to advance in the improvement of Hospital Pharmacy practice, the Spanish Society of Hospital Pharmacy wanted to raise the challenges the profession is currently facing and with a view to 2030. With this strategic planning objective, 20 challenges have been identified and developed, which cover the different areas of action and involvement of Hospital Pharmacy and which cover clinical activities, transversal aspects, training, and research, as well as areas related to people and to the organisations or health systems. For each of them, the objectives, standards, tools, and resources have been defined. It is also planned to provide tools that facilitate monitoring of implementation and the impact on the profession, patients, and the environment.

The training of hospital pharmacists in the coming years must adapt and respond to constant current and future social and technological challenges, without neglecting the basic areas of the profession. It is necessary to acquire knowledge in what is known as digital comprehensive health: artificial intelligence, technology and automation, digital skills, and new forms of communication with patients, such as telemedicine and telepharmacy that are already a reality in many hospitals. We must provide knowledge in automated systems for the distribution and dispensing of medicines, robots for preparing sterile preparations, traceability systems, the use of drones in clinical care, etc. as well as training in the application of technology in pharmaceutical care, through devices and applications that help identify patients who require specific care early and effectively. In this digital scenario, new risks and challenges must be faced, such as cybersecurity and cyber resilience, which makes the training and education of healthcare professionals in general, and hospital pharmacists in particular, inexcusable. On the other hand, the appearance of increasingly complex and innovative therapies has a great impact not only on health population but also on economic and environmental issues, which makes new competencies and skills essential to develop and implement disruptive and competent financing, equity, and sustainability strategies. In this demanding and hyper-connected environment, it is understandable that the well-known \"burned out worker syndrome\" appears, which prevents the correct personal and professional development of the team and highlights the importance of quality training for its prevention and management. In short, in the next decade, the training of hospital pharmacists must be aimed at providing knowledge in innovation and in basic skills needed to adapt and succeed to current demands and changes.

Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.

OBJECTIVE: To develop by consensus a dashboard model to standardise and promote the evaluation of research activity in Spanish Hospital Pharmacy Services.
METHODS: The study was carried out in 5 phases following the modified Delphi methodology: constitution of the coordinating group, elaboration of a list of scenarios, selection of participating centres, evaluation of the list of scenarios, and analysis of the results. The coordinating group designed a questionnaire with 114 questions. General research questions and different scenarios (indicators) were included to form the dashboard. The Hospital Pharmacy Services with the highest number of publications were identified to participate in the Delphi consultation. Two rounds of consultations were conducted in which the \"Need\" and/or \"Feasibility\" of their measurement was evaluated for each of the scenarios, using a numerical scale from 1 (lowest score) to 9 (highest score).
RESULTS: Sixteen Hospital Pharmacy Services, belonging to 8 different Autonomous Communities, participated in the Delphi consultation. A total of 100% of them responded to all the questions in the 2 rounds of consultations. It was considered that the Hospital Pharmacy Services should have a research dashboard (Need=100%) with a basic structure and a common minimum set of data for all them (Need=87.5%). The consensus was reached on distinguishing research projects led by the Hospital Pharmacy Services from those led by other groups in which the Hospital Pharmacy Services collaborate (Need=87.5%), and a definition was approved on the leadership of these projects according to whether they are single-centre or multicentre. A consensus was reached on 40 indicators to form the dashboard, which evaluates publications (13 indicators), human resources (12 indicators), research projects (9 indicators), doctoral theses (4 indicators), and patents and intellectual property registrations (2 indicators).
CONCLUSIONS: This is the first consensus dashboard developed to evaluate the research activity of the Hospital Pharmacy Services, which will help to analyse the productivity and impact of research systematically and continuously. In addition, it will allow comparison between them and will help to establish synergies and identify trends, patterns, and challenges.

The huge development that advanced therapy medicinal products (AMTPs) have experienced in recent years, both commercial and research, represent a challenge for hospital pharmacy at all levels. The aim of this article is to describe the implementation of an advanced therapies unit (AUT) and the process of preparation of the AMTPs according to the \"good manufacturing practices\" (GMP), as well as the results obtained in a tertiary hospital, as an example of the challenges posed by MTA\'s academic production. The AUT meets the requirements established in the GMP by guaranteeing that the medicines produced therein are of the quality required for the use for which they are intended, and also provides support to various research groups involved in the development of AMTPs. The AUT is composed of a highly qualified multidisciplinary team, qualified and trained in GMP, and is authorized for the preparation of 5 types of AMTPs consisting of allogeneic virus-specific T cells (VST) with various viral specificities. A circuit has been established in collaboration between the UTA and the pharmacy service with the hematology service for the assessment of the clinical indication, the request, and preparation of VST, which allows the treatment of patients receiving hematopoietic stem cell transplants who present viral reactivations resistant or refractory to standard treatment, or who cannot tolerate it due to toxicity. Preliminary results from these AMTPs suggest that VSTs are an effective and safe alternative. Academic AMTPs have special interest in orphan indications or in the absence of alternative treatments, and their production through the \"hospital exemption\" can favor early access in the initial phases of development and at a lower cost. It is essential to promote the training of hospital pharmacists in GMP and their participation in collaboration with other clinicians and researchers to develop AMTPs that meet all logistical and regulatory requirements.

Drugs do not disappear once they have been excreted. In fact, 992 active principles have already been measured in the different environmental matrices. A recent study led by scientists from the University of York has studied the presence of drugs in the rivers of more than 100 different countries, showing that environmental contamination by pharmaceuticals is a global issue and that, concentrations found are frequently harmful to the environment. In this work, we have tried to briefly expose the problem of environmental contamination with medicines, but above all, we have tried to address the possible solutions, with a perspective from the field of hospital pharmacy. This is a very complex matter (a wicked problem), since it involves multiple stakeholders with different visions and interests regarding medicines. In order to find solutions, we will probably need to act at all steps of the drug\'s life cycle. Until now, health professionals have been part of the problem. It is time for us to be part of the solution.