October 2023), will be included in the ATC/DDD Index the year after the following year (i.e. January 2024).ĪTC codes, DDDs and ATC/DDD alterations approved at the October meeting of the Working Group (e.g. March 2023) will be included in the ATC/DDD index the following year (i.e. ATC codes are also included in the WHO Essential Drug List. the Martindale) and in several national drug catalogues. ATC codes are included in some international drug textbooks (e.g. Summary of Product Characteristics) approved by EMA, the regulatory medicines agency in EU. We get it Open DevOps makes it easier to do both regardless of the tools you use. ATC codes are included in the pharmaceutical product information (e.g.
#Who atc code update#
The involved parties (applicants) will be informed directly but the deadline will also be published in the WHO Drug Information and on this website.ĪTC codes, DDDs and ATC/DDD alterations approved at the March meeting of the Working Group (e.g. Developers want to focus on code, not update issues. The deadlines for the October meeting 2023 are:Ĭomments on or objections to the temporary assigned new ATC/DDDs and alterations which are agreed at meetings of the WHO International Working Group for Drug Statistics Methodology should be forwarded to the WHO Collaborating Centre for Drug Statistics Methodology, before the above mentioned deadlines. For combinations products, see list of DDDs for combinations, ATC code. The DDD of selexipag is based on treatment of pulmonary arterial hypertension. The DDD of vorapaxar is based on the content of one tablet (2.08 mg). The deadlines for the March meeting 2023 are:ġ5 January (new requests and alterations) The DDD of iloprost is based on treatment of peripheral vascular disease. In order to include requests for new ATC codes, new DDDs, ATC/DDD alterations on the agenda for the Working Group meetings, they should normally be forwarded to the Centre before 15 January (March meeting) and before 15 August (October meeting).
#Who atc code code#
ĭrug ATC code drug discovery graph transformer network interaction information multi-label classification.Ĭopyright © 2022 Yan, Suo, Wang, Zhang and Luo.In order to include requests for new ATC codes, new DDDs, ATC/DDD alterations on the agenda for the Working Group meetings, they should normally be forwarded to the Centre before 15 January (March meeting) and before 15 August (October meeting). The strengths of the various components are not taken into consideration. These DDDs are based on an average dose regimen of three times daily, and dosages in the upper area of the recommended dose ranges are chosen. The source codes of our method are available at. Fixed DDDs are assigned for combinations. Experiments on the benchmark datasets demonstrate that the proposed DACPGTN model can achieve better prediction performance than the existing methods. Based on the constructed composite features and learned heterogeneous networks, we employ graph convolution network to generate the embedding of drug nodes, which are further used for the multi-label learning tasks in drug discovery. Inspired by the application of Graph Transformer Network, we learn potential novel interactions among drugs diseases and targets from the known interactions to construct drug-target-disease heterogeneous networks containing comprehensive interaction information. DACPGTN constructs composite features of drugs, diseases and targets by applying diverse biomedical information. In this article, we propose an end-to-end model DACPGTN to predict the ATC code for the given drug. Predicting the ATC code of a given drug helps to understand the indication and potential toxicity of the drug, thus promoting its use in the therapeutic phase and accelerating its development. The ATC system assigns different ATC codes to drugs based on their anatomy, pharmacological, therapeutics and chemical properties. The Anatomical Therapeutic Chemical (ATC) classification system is a drug classification scheme proposed by the World Health Organization, which is widely used for drug screening, repositioning, and similarity research.
0 kommentar(er)
