Objective: To evaluate the usefulness of intervention in drug interactions of antiretroviral drugs with coadministered agents by a clinical pharmacist in outpatient HIV-treatment.
Methods: The study design included two intervention arms (A and B), which were both preceded by a control observation period. In arm A, a complete list of the currently used drugs, extracted from pharmacy records was provided to the treating physician. In arm B the same list was provided but with a notification when a drug interaction was present and an advice how to handle this. The infectious disease specialist obtained the information before the patient's visit to the outpatient clinic (time point 0). Three months prior (time point -3) and 3 months after (time point +3) the intervention, pharmacy records were also screened for drug interactions. The number of drug interactions (total and per patient) was determined at the three different time points (-3, 0, +3). In addition, drug interactions encountered at time points -3 and 0 were checked for their presence at time points 0 and +3, respectively, for both intervention arms.
Results: Arms A and B included 115 and 105 patients, respectively. Patient characteristics of both intervention arms were similar at time point 0. The number of interactions and the number of patients with interactions were similar in both intervention arms at time point 0. There were 42 and 40 potential drug interactions in 30 and 24 patients in arms A and B, respectively. The reduction in the number of interactions per patient over time and after intervention was small but significant, and was equal in both intervention arms. The advice of the clinical pharmacist had thus no additional value.
Conclusion: Both interventions were effective in reducing the number of drug interactions per patient. The advice of a clinical pharmacist was, however, redundant in the studied setting.
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http://dx.doi.org/10.1111/j.1365-2710.2003.00541.x | DOI Listing |
J Chem Inf Model
January 2025
Geneis (Beijing) Co. Ltd., Beijing 100102, China.
Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively deciphers DTIs, most deep learning-based methods represent drug features from only a single perspective. Moreover, the fusion method of drug and protein features needs further refinement.
View Article and Find Full Text PDFPLoS One
January 2025
Special Infectious Agents Unit-BSL3, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.
The ongoing increase in the prevalence and mutation rate of the influenza virus remains a critical global health issue. A promising strategy for antiviral drug development involves targeting the RNA-dependent RNA polymerase, specifically the PB2-cap binding domain of Influenza A H5N1. This study employs an in-silico approach to inhibit this domain, crucial for viral replication, using potential inhibitors derived from marine bacterial compounds.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
February 2025
Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, People's Republic of China.
Carrier-free nanomedicines exhibited significant potential in elevating drug efficacy and safety for tumor management, yet their self assembly typically relied on chemical modifications of drugs or the incorporation of surfactants, thereby compromising the drug's inherent pharmacological activity. To address this challenge, we proposed a triethylamine (TEA)-mediated protonation-deprotonation strategy that enabled the adjustable-proportion self assembly of dual drugs without chemical modification, achieving nearly 100% drug loading capacity. Molecular dynamic simulations, supported by experiment evidence, elucidated the underlying self-assembly mechanism.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Department of Molecular Pharmacology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China.
Efficient virtual screening methods can expedite drug discovery and facilitate the development of innovative therapeutics. This study presents a novel transfer learning model based on network target theory, integrating deep learning techniques with diverse biological molecular networks to predict drug-disease interactions. By incorporating network techniques that leverage vast existing knowledge, the approach enables the extraction of more precise and informative drug features, resulting in the identification of 88,161 drug-disease interactions involving 7,940 drugs and 2,986 diseases.
View Article and Find Full Text PDFChem Biodivers
January 2025
Ordu University: Ordu Universitesi, Department of Chemistry, Cumhuriyet Mah., Ordu, TURKEY.
The concise synthesis of O-methyled-inositol derivative and conduritol derivatives was obtained starting from p-benzoquinone. Spectroscopic methods have been performed for characterization of new synthesized compounds. Cyclitols are useful molecules with anticancer, antibiotic, antinutrient and antileukemic activity.
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