Identification Of Drug-drug Interactions Research Articles

Drug-Drug Interactions in Nosocomial Infections: An Updated Review for Clinicians.

Prevention, assessment, and identification of drug-drug interactions (DDIs) represent a challenge for healthcare professionals, especially in nosocomial settings. This narrative review aims to provide a thorough assessment of the most clinically significant DDIs for antibiotics used in healthcare-associated infections. Complex poly-pharmaceutical regimens, targeting multiple pathogens or targeting one pathogen in the presence of another comorbidity, have an increased predisposition to result in life-threatening DDIs. Recognising, assessing, and limiting DDIs in nosocomial infections offers promising opportunities for improving health outcomes. The objective of this review is to provide clinicians with practical advice to prevent or mitigate DDIs, with the aim of increasing the safety and effectiveness of therapy. DDI management is of significant importance for individualising therapy according to the patient, disease status, and associated comorbidities.

PREDICTING DRUG-DRUG INTERACTION USING MACHINE LEARNING

The ever-expanding landscape of pharmaceutical compounds and medical treatments has underscored the critical need for an advanced system to predict drug-drug interactions (DDIs) using machine learning. This paper delves into the development of a novel predictive model that leverages diverse data sources, including chemical structures, pharmacological properties, and scientific literature, to comprehensively analyse and foresee potential DDIs. The current state of DDIs is rife with limitations in scope and accuracy, making the development of an advanced system not only a necessity for enhancing patient safety but also a boon to pharmaceutical research and regulatory oversight. The relationship between two medications in which one drug’s pharmacological actions are altered by another is known as a drug-drug interaction (DDI). Favourable drug-drug interactions (DDIs) generally contribute to improved therapeutic results for patients. On the contrary, adverse DDIs pose a significant risk, often resulting in undesirable drug reactions that can potentially lead to the withdrawal of a drug from the market and even patient fatalities. Consequently, the identification of DDIs is essential for advancing new drug development and effectively managing diseases. Keywords: Drug, Drug-drug interaction, Machine learning, Bioinformatics, Drug development.

MATT-DDI: Predicting multi-type drug-drug interactions via heterogeneous attention mechanisms

The joint use of multiple drugs can result in adverse drug-drug interactions (DDIs) and side effects that harm the body. Accurate identification of DDIs is crucial for avoiding accidental drug side effects and understanding potential mechanisms underlying DDIs. Several computational methods have been proposed for multi-type DDI prediction, but most rely on the similarity profiles of drugs as the drug feature vectors, which may result in information leakage and overoptimistic performance when predicting interactions between new drugs. To address this issue, we propose a novel method, MATT-DDI, for predicting multi-type DDIs based on the original feature vectors of drugs and multiple attention mechanisms. MATT-DDI consists of three main modules: the top k most similar drug pair selection module, heterogeneous attention mechanism module and multi‑type DDI prediction module. Firstly, based on the feature vector of the input drug pair (IDP), k drug pairs that are most similar to the input drug pair from the training dataset are selected according to cosine similarity between drug pairs. Then, the vectors of k selected drug pairs are averaged to obtain a new drug pair (NDP). Next, IDP and NDP are fed into heterogeneous attention modules, including scaled dot product attention and bilinear attention, to extract latent feature vectors. Finally, these latent feature vectors are taken as input of the classification module to predict DDI types. We evaluated MATT-DDI on three different tasks. The experimental results show that MATT-DDI provides better or comparable performance compared to several state-of-the-art methods, and its feasibility is supported by case studies. MATT-DDI is a robust model for predicting multi-type DDIs with excellent performance and no information leakage.

An update on drug-drug interactions for care of the acutely ill in the era of COVID-19.

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. To provide key pharmacological concepts underlying drug-drug interactions (DDIs), a decision-making framework, and a list of DDIs that should be considered in the context of contemporary acutely ill patients with COVID-19. DDIs are frequently encountered in the acutely ill. The implications of DDIs include either increased risk of drug toxicity or decreased effectiveness, which may have severe consequences in the acutely ill due to lower physiological and neurocognitive reserves in these patients. In addition, an array of additional therapies and drug classes have been used for COVID-19 that were not typically used in the acute care setting. In this update on DDIs in the acutely ill, we provide key pharmacological concepts underlying DDIs, including a discussion of the gastric environment, the cytochrome P-450 (CYP) isozyme system, transporters, and pharmacodynamics in relation to DDIs. We also provide a decision-making framework that elucidates the identification of DDIs, risk assessment, selection of alternative therapies, and monitoring. Finally, important DDIs pertaining to contemporary acute care clinical practice related to COVID-19 are discussed. Interpreting and managing DDIs should follow a pharmacologically based approach and a systematic decision-making process to optimize patient outcomes.

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug-Drug Interactions.

The identification of drug-drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN is used to learn the embedding representation containing high-order structural information and semantic information in the knowledge graph (KG). The embedding and the Morgan molecular fingerprint of drugs are then used as input of NFMs to predict DDIs. The performance and effectiveness of the current method have been evaluated and confirmed based on the two real-world datasets with different sizes, and the results demonstrate that KGCN_NFM outperforms the state-of-the-art algorithms. Moreover, the identified interactions between topotecan and dantron by KGCN_NFM were validated through MTT assays, apoptosis experiments, cell cycle analysis, and molecular docking. Our study shows that the combination therapy of the two drugs exerts a synergistic anticancer effect, which provides an effective treatment strategy against lung carcinoma. These results reveal that KGCN_NFM is a valuable tool for integrating heterogeneous information to identify potential DDIs.

Chemoproteomic Identification of Serine Hydrolase RBBP9 as a Valacyclovir-Activating Enzyme.

Prodrug discovery and development in the pharmaceutical industry have been hampered by a lack of knowledge of prodrug activation pathways. Such knowledge would minimize the risks of prodrug failure by enabling proper selection of preclinical animal models, prediction of pharmacogenomic variability, and identification of drug-drug interactions. Technologies for annotation of activating enzymes have not kept pace with the growing need. Activity-based protein profiling (ABPP) has matured considerably in recent decades, leading to widespread use in the pharmaceutical industry. Here, we report the extension of competitive ABPP (cABPP) to prodrug-activating enzyme identification in stable isotope-labeled cell lysates using a modified fluorophosphonate probe. Focusing on the antiviral ester prodrug valacyclovir (VACV), we identified serine hydrolase RBBP9 as an activating enzyme in Caco-2 cells via shotgun proteomics, validating the activity via the selective inhibitor emetine (EME). Kinetic characterization of RBBP9 revealed a catalytic efficiency (kcat·KM-1 = 104 mM-1·s-1) comparable to that of BPHL, the only known VACV-activating enzyme prior to this work. EME incubation in wild-type and Bphl-knockout jejunum and liver lysates demonstrated the near-exclusivity of VACV activation by RBBP9 in the intestine. Additionally, these studies showed that RBBP9 and BPHL are the two major and coequal VACV-activating enzymes in the liver. Single-pass intestinal perfusions of VACV ± EME in mice showed EME coperfusion significantly inhibited the intestinal activation of VACV, implying the in vivo relevance of RBBP9-mediated VACV activation. We envision that others might use the cABPP approach in the future for global, rapid, and efficient discovery of prodrug-activating enzymes.

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis.

Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2000000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.

Drug-drug interactions in an intensive care unit of a tertiary hospital in southern Chile: Evaluating databases agreement

Context: Patients in intensive care units have a high risk of experiencing a pharmacological interaction due to complex pharmacotherapy, severe disease, and comorbidities; increasing the risk of adverse effects of medications. Electronic databases are useful sources to identify drug-drug interactions (DDI), especially when new therapeutic alternatives are added to conventional treatments. Aim: To identify the frequency and severity of potential drug-drug interactions (pDDIs) in ICU patients using three electronic databases. Methods: Clinical pharmacists collected data on medication dosage and route of administration, sex, age, length of stay, comorbidities, and APACHE II score using patient records. Micromedex, Medscape, and Lexicomp databases were used to identify and categorize pDDIs. Intensivists confirmed if a pDDI was clinically present. kappa concordance test was utilized as a measure of agreement among databases. Results: Of the 93 ICU patients studied, pDDIs were identified in 89. A positive incremental relationship was found between number of medications, length of stay, and number of pDDIs. Patients with respiratory pathologies were most predisposed to presenting DDIs. Agreement among databases was mixed. Intensivists confirmed 5% of pDDIs. Conclusions: Discrepancies among databases and in intensivist judgment highlight a significant information gap in the identification of DDIs.

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies.

Drug-drug interactions (DDI) are frequent causes of adverse clinical drug reactions. Efforts have been directed at the early stage to achieve accurate identification of DDI for drug safety assessments, including the development of in silico predictive methods. In particular, similarity-based in silico methods have been developed to assess DDI with good accuracies, and machine learning methods have been employed to further extend the predictive range of similarity-based approaches. However, the performance of a developed machine learning method is lower than expectations partly because of the use of less diverse DDI training data sets and a less optimal set of similarity measures. In this work, we developed a machine learning model using support vector machines (SVMs) based on the literature-reported established set of similarity measures and comprehensive training data sets. The established similarity measures include the 2D molecular structure similarity, 3D pharmacophoric similarity, interaction profile fingerprint (IPF) similarity, target similarity and adverse drug effect (ADE) similarity, which were extracted from well-known databases, such as DrugBank and Side Effect Resource (SIDER). A pairwise kernel was constructed for the known and possible drug pairs based on the five established similarity measures and then used as the input vector of the SVM. The 10-fold cross-validation studies showed a predictive performance of AUROC >0.97, which is significantly improved compared with the AUROC of 0.67 of an analogously developed machine learning model. Our study suggested that a similarity-based SVM prediction is highly useful for identifying DDI. in silico methods based on multifarious drug similarities have been suggested to be feasible for DDI prediction in various studies. In this way, our pairwise kernel SVM model had better accuracies than some previous works, which can be used as a pharmacovigilance tool to detect potential DDI.

TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs

BackgroundA significant number of adverse drug reactions is caused by unexpected Drug-drug interactions (DDIs). The identification of DDIs becomes crucial before the co-prescription of multiple drugs is made. Such a task in clinics or in drug discovery usually requires high costs and numerous limitations, while computational approaches are able to predict potential DDIs effectively by utilizing diverse drug attributes (e.g. side effects). Nevertheless, they’re incapable when required to predict enhancive and degressive DDIs, which change increasingly and decreasingly the pharmacological behavior of interacting drugs respectively. The pharmacological change of DDIs is one of the most important factors when making a multi-drug prescription.ResultsIn this work, we design a Triple Matrix Factorization-based Unified Framework (TMFUF) to address the above issue. By leveraging a group of side effect entries of drugs, TMFUF achieves the inspiring result (AUC = 0.842 and AUPR = 0.526) in the case of conventional DDI prediction under the traditional screening task. In the comparison with two state-of-the-art approaches, TMFUF demonstrates it superiority by ~ 7% and ~ 20% improvement in terms of AUC and AUPR respectively. More importantly, TMFUF shows its ability in the comprehensive DDI prediction under different screening tasks. Finally, a utilization TMFUF reveals the significant pairs of side effects, which contribute to form enhancive and degressive DDIs, for further clinical validation.ConclusionsThe proposed TMFUF is first capable to predict both conventional binary DDIs and comprehensive DDIs such that it captures the pharmacological changes caused by DDIs. Furthermore, it provides a unified solution of DDI prediction for two screening scenarios, which involves newly given drugs having no prior interaction. Another advantage is its ability to indicate how significantly the pairs of drug features contribute to form DDIs.

Characterization of drug-drug interactions in patients whose substance intake was objectively identified by detection in urine

ABSTRACTBackground: Identification of drug-drug interactions (DDIs) typically relies on patient medication lists which are prone to inaccuracies. This study describes use of a mass spectrometry test to detect recently ingested substances in urine with subsequent identification of DDIs.Research design and methods: This was a retrospective analysis of the prevalence of DDIs identified in patients with chronic pain, addiction and/or behavioral health conditions in the U.S. Relationships between patient demographics, polypharmacy and the occurrence of DDIs were also described.Results: Of 15,004 patients, 2964 (20%) had a DDI identified. There was a positive association between the number of substances detected in urine and the number of interactions identified (r = 0.5033, p-value = 0.0001). Of patients with polypharmacy, 15.6% had contraindicated or severe interactions identified compared to only 3.2% of those without polypharmacy. For polypharmacy patients, the youngest population studied had a much higher likelihood of having one or more DDIs identified compared to the other age groups (p-value = 0.0002).Conclusions: By utilizing a mass spectrometry test to objectively detect recently ingested substances followed by identification of DDIs, healthcare providers may be able to better characterize the true incidence of DDIs. Study findings may not be generalizable to healthcare populations outside of pain management, addiction treatment, and behavioral health.

Identification of Drug-Drug Interactions Using Chemical Interactions

Background: One drug can affect the activity of another when they are administered together, which can cause adverse drug reactions or sometimes improve therapeutic effects. Therefore, correct identification of drug-drug interactions (DDIs) can help medical workers use various drugs effectively, avoiding adverse effects and improving therapeutic effects. Methods: This study proposed a novel prediction model to identify DDIs. A new metric was constructed to evaluate the similarity of two pairs of drugs using chemical interaction information retrieved from STITCH. Validated DDIs retrieved from DrugBank were employed, from which we constructed all possible pairs of drugs that were deemed as negative samples. The whole dataset was divided into one training dataset and one test dataset. To address the imbalanced data, a complicated dataset compilation strategy was adopted to construct nine training datasets from the original training dataset, reducing the ratio of positive samples and negative samples. Nine predictors based on the nearest neighbor algorithm were built based on these training datasets. The proposed model integrated the above nine predictors by majority voting and its performance was evaluated on the test dataset. Results: The predicted results indicate that the method is quite effective for identification of DDIs. Finally, we also discussed the ability of the method for identifying novel DDIs by investigating the likelihood of some negative samples in the test dataset that were predicted as DDIs being novel DDIs. Conclusion: The proposed method has a good ability for identification of potential DDIs. Keywords: Drug-drug interaction, chemical interaction, chemical structure similarity, nearest neighbor algorithm, majority voting, imbalanced dataset.

Computational prediction of drug-drug interactions based on drugs functional similarities

Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs.

Drug-drug interactions: experience at a tertiary care hospital

Background: Drug-drug interaction (DDI) is one important factor that influences relationship between prescribed dose and drug-effects by interfering with either pharmacokinetics or pharmacodynamics of the co-administered drug. DDIs can cause toxicity or inhibit the drug effect, both of which have deleterious effect on patient care. This study was done to report the impact of prevention of DDIs. Methods: In this retrospective study, demographic details, relevant clinical information of the cases with suspected DDIs and the opinion given regarding suspected DDIs, with anticipated outcomes and further management were recorded and analyzed. Results: Of the 124 cases, 21 (16.9%) cases had suspected DDIs, among them 5 (23.8%) were pharmacodynamic and 13 (61.9%) were pharmacokinetic. Of the 21 DDIs, in 38.1% interactions, concomitant administration of interacting drugs was to be avoided and in 33.3% interactions, monitoring of effect was necessary to guide dosage adjustment. In ten (47.6%) cases, the DDIs were identified on day one and necessary action was taken to prevent the deleterious outcome and in rest of the 11 (52.4%) cases, adverse events have occurred due to DDIs, for which most of the patients were hospitalized. Conclusion: The present demonstrated that early identification of DDIs on day one, could prevent undesired consequences in 10 cases (47.6%). As DDIs is an important factor that can be prevented, if identified early, clinicians should be vigilant regarding DDIs when more than two drugs are prescribed.

Identification of drug-drug interaction of multi-component in sochengryoung-tang

Identification of drug-drug interaction of multi-component in sochengryoung-tang

Identification of Drug-Drug Interactions In Vitro: A Case Study Evaluating the Effects of Sofosbuvir and Amiodarone on hiPSC-Derived Cardiomyocytes.

Drug-drug interactions pose a difficult drug safety problem, given the increasing number of individuals taking multiple medications and the relative complexity of assessing the potential for interactions. For example, sofosbuvir-based drug treatments have significantly advanced care for hepatitis C virus-infected patients, yet recent reports suggest interactions with amiodarone may cause severe symptomatic bradycardia and thus limit an otherwise extremely effective treatment. Here, we evaluated the ability of human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) to recapitulate the interaction between sofosbuvir and amiodarone in vitro, and more generally assessed the feasibility of hiPSC-CMs as a model system for drug-drug interactions. Sofosbuvir alone had negligible effects on cardiomyocyte electrophysiology, whereas the sofosbuvir-amiodarone combination produced dose-dependent effects beyond that of amiodarone alone. By comparison, GS-331007, the primary circulating metabolite of sofosbuvir, had no effect alone or in combination with amiodarone. Further mechanistic studies revealed that the sofosbuvir-amiodarone combination disrupted intracellular calcium (Ca2+) handling and cellular electrophysiology at pharmacologically relevant concentrations, and mechanical activity at supra-pharmacological (30x Cmax) concentrations. These effects were independent of the common mechanisms of direct ion channel block and P-glycoprotein activity. These results support hiPSC-CMs as a comprehensive, yet scalable model system for the identification and evaluation of cardioactive pharmacodynamic drug-drug interactions.

Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling.

Identification of Drug-Drug Interactions (DDIs) is a significant challenge during drug development and clinical practice. DDIs are responsible for many adverse drug effects (ADEs), decreasing patient quality of life and causing higher care expenses. DDIs are not systematically evaluated in pre-clinical or clinical trials and so the FDA U. S. Food and Drug Administration relies on post-marketing surveillance to monitor patient safety. However, existing pharmacovigilance algorithms show poor performance for detecting DDIs exhibiting prohibitively high false positive rates. Alternatively, methods based on chemical structure and pharmacological similarity have shown promise in adverse drug event detection. We hypothesize that the use of chemical biology data in a post hoc analysis of pharmacovigilance results will significantly improve the detection of dangerous interactions. Our model integrates a reference standard of DDIs known to cause arrhythmias with drug similarity data. To compare similarity between drugs we used chemical structure (both 2D and 3D molecular structure), adverse drug side effects, chemogenomic targets, drug indication classes, and known drug-drug interactions. We evaluated the method on external reference standards. Our results showed an enhancement of sensitivity, specificity and precision in different top positions with the use of similarity measures to rank the candidates extracted from pharmacovigilance data. For the top 100 DDI candidates, similarity-based modeling yielded close to twofold precision enhancement compared to the proportional reporting ratio (PRR). Moreover, the method helps in the DDI decision making through the identification of the DDI in the reference standard that generated the candidate.

Electronic database to detect drug–drug interactions between antidepressants and oral anticancer drugs from a cancer center in Singapore: implications to clinicians

Electronic drug interaction databases are often utilized in clinical practice to detect for possible drug-drug interactions between drug pairs. It is uncertain, however, whether most of these detections interactions are clinically important in practice. To demonstrate these issues, this study utilized a comprehensive drug-drug interaction (DDI) electronic database to elucidate the prevalence of DDIs at a cancer centre between antidepressants and oral anticancer drugs (ACDs). Drug utilization reports were retrieved to determine the patients who were prescribed with antidepressants oral ACDs between 2006 and 2009 at a cancer center. Medication records of these patients were retrospectively examined using OncoRx, an internet-based oncology-specific database that allows the identification of DDIs. Out of 910 users of antidepressants, about one-third (281 patients, 30.9%) used an oral ACD and an antidepressant concomitantly. From these patients, about one-fifth (21.0%) had potential DDIs. These patients were users of 17 potentially interacting drug pairs. Ten out of the 17 drug pairs could potentially cause pharmacokinetic interactions, and the rest were pharmacodynamic interactions, with only three out of the 17 drug pairs were clinically documented to cause interacting events. The lack of screening conditions may have led to an over detection of DDI combinations by electronic DDI databases. Many of the detected interactions may not deem high significance in clinical practice. This study exposed a major weakness of current electronic DDI databases for detecting oral ACDs and antidepressants DDIs.

Assessment tool for pharmacy drug–drug interaction software

ObjectivesTo assess the performance of pharmacy clinical decision support (CDS) systems for drug–drug interaction (DDI) detection and to identify approaches for improving the ability to recognize important DDIs. Practice descriptionPharmacists rely on CDS systems to assist in the identification of DDIs, and research suggests that these systems perform suboptimally. The software evaluation tool described here may be used in all pharmacy settings that use electronic decision support to detect potential DDIs, including large and small community chain pharmacies, community independent pharmacies, hospital pharmacies, and governmental facility pharmacies. Practice innovationA tool is provided to determine the ability of pharmacy CDS systems to identify established DDIs. It can be adapted to evaluate potential DDIs that reflect local practice patterns and patient safety priorities. Beyond assessing software performance, going through the evaluation processes creates the opportunity to evaluate inadequacies in policies, procedures, workflow, and training of all pharmacy staff relating to pharmacy information systems and DDIs. ConclusionThe DDI evaluation tool can be used to assess pharmacy information systems’ ability to recognize relevant DDIs. Suggestions for improvement include determining whether the software allows for customization, creating standard policies for handling specific interactions, and ensuring that drug knowledge database updates occur frequently.

Evaluation of Croatian Community Pharmacists' Patient Care Competencies Using the General Level Framework

To assess Croatian community pharmacists' patient care competencies using the General Level Framework (GLF). The competencies of 100 community pharmacists working in 38 community pharmacies were evaluated using an adapted version of the GLF. Pharmacists demonstrated the best performance in the competency areas drug specific issues and provision of drug products; the poorest performance was in the competency areas evaluation of outcomes and monitoring drug therapy. Pharmacists' behavior varied the most in the following areas: ensuring that the prescription is legal, prioritization of medication management problems, and identification of drug-drug interactions. Competencies were identified that need to be developed to improve pharmacist interventions in community settings. This study provides the first data on pharmacists' performance in Croatia and serves as a starting point for future studies and actions.