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Background & Objective: High medicine diversity has always been a significant challenge for prescription, causing confusion or doubt in physicians’ decision-making process. This paper aims to develop a medicine recommender system called RecoMed to aid the physician in the prescription process of hypertension by providing information about what medications have been prescribed by other doctors and figuring out what other medicines can be recommended in addition to the one in question. Methods: There are two steps to the developed method: First, association rule mining algorithms are employed to find medicine association rules. The second step entails graph mining and clustering to present an enriched recommendation via ATC code, which itself comprises several steps. First, the initial graph is constructed from historical prescription data. Then, data pruning is performed in the second step, after which the medicines with a high repetition rate are removed at the discretion of a general medical practitioner. Next, the medicines are matched to a well-known medicine classification system called the ATC code to provide an enriched recommendation. And finally, the DBSCAN and Louvain algorithms cluster medicines in the final step. Results: A list of recommended medicines is provided as the system's output, and physicians can choose one or more of the medicines based on the patient's clinical symptoms. Only the medicines of class #2, related to high blood pressure medications, are used to assess the system's performance. Unlike other research studies, in this work, the framework proposed is entirely based on unsupervised learning methods and so it is more likely to be used in real-world applications as medical data are generally unlabeled, and supervised learning models cannot be directly built using these types of data. The results obtained from this system have been reviewed and confirmed by an expert in this field.

Background: The ability to account for comorbidity when estimating survival in a population diagnosed with cancer could be improved by using a drug comorbidity index based on filled drug prescriptions.Methods: We created a drug comorbidity index from age-stratified univariable associations between filled drug prescriptions and time to death in 326,450 control males randomly selected from the general population to men with prostate cancer. We also evaluated the index in 272,214 control females randomly selected from the general population to women with breast cancer.Results: The new drug comorbidity index predicted survival better than the Charlson Comorbidity Index (CCI) and a previously published prescription index during 11 years of follow-up. The concordance (C)-index for the new index was 0.73 in male and 0.76 in the female population, as compared with a C-index of 0.67 in men and 0.69 in women for the CCI. In men of age 75-84 years with CCI = 0, the median survival time was 7.1 years (95% confidence interval [CI] = 7.0, 7.3) in the highest index quartile. Comparing the highest to the lowest drug comorbidity index quartile resulted in a hazard ratio (HR) of 2.2 among men (95% CI = 2.1, 2.3) and 2.4 among women (95% CI = 2.3, 2.6).Conclusions: A new drug comorbidity index based on filled drug prescriptions improved prediction of survival beyond age and the CCI alone. The index will allow a more accurate baseline estimation of expected survival for comparing treatment outcomes and evaluating treatment guidelines in populations of people with cancer. [ABSTRACT FROM AUTHOR]

Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug–gene interaction data. We obtained drug–gene interaction data from an earlier version of DrugBank, built a drug–gene interaction network, and projected it as a drug–drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug–gene interaction data to generate a comprehensive drug repurposing hint list.

Objectives: This technical report intends to share a process of compiling a comprehensive drug list for pharmacoepidemiology studies in Taiwan that utilize the National Health Insurance (NHI) data. In addition, it also explains the application of World Health Organization Anatomical Therapeutic Chemical (WHO ATC) codes in research settings. Methods: Publicly available (web-based) resources provided by Taiwan Food and Drug Administration (TFDA) and National Health Insurance Administration (NHIA) were utilized in a study that evaluated drugs for obstructive airway diseases (R03 ATC category). We searched ATC codes and generic names in order to compile a comprehensive drug list for the R03 drugs. ATC codes for drugs from TFDA and NHIA information was compared against the official ATC categorization by World Health Organization. Results: Within the R03 ATC category, 958 TFDA-approved drug entities and 1071 unique NHI drug codes from NHIA were identified. Drugs in the information provided by NHIA were all coded with ATC codes whereas 143 TFDA-approved drug entities did not have ATC code from the TFDA file. In addition, information from the TFDA file for 117 TFDA-approved drug entities had ATC codes that were different from the ATC codes provided by NHIA. Six TFDA-approved drug entities (corresponding to 18 NHI drug items) had ATC code that were different from the WHO ATC coding system. Finally, 5 R03 ATC codes provided by NHIA did not have corresponding code in the WHO ATC system. Conclusions: In pharmacoepidemiology studies utilizing Taiwan NHI data, compilation of drug list of interest could start with ATC code since ATC code represents not only a molecule but also specifies its formulation. However, there may be minor discrepancy between the ATC information provided by TFDA and NHIA, and they may also differ from the WHO ATC coding. When applying ATC code to drug list construction, cross-referencing generic names, especially for combination products, and ATC codes from TFDA and NHIA is highly recommended. [ABSTRACT FROM AUTHOR]

Predicting Anatomical Therapeutic Chemical (ATC) code of drugs is of vital importance for drug classification and repositioning. Discovering new association information related to drugs and ATC codes is still difficult for this topic. We propose a novel method named drug-domain hybrid (dD-Hybrid) incorporating drug-domain interaction network information into prediction models to predict drug's ATC codes. It is based on the assumption that drugs interacting with the same domain tend to share therapeutic effects. The results demonstrated dD-Hybrid has comparable performance to other methods on the gold standard dataset. Further, several new predicted drug-ATC pairs have been verified by experiments, which offer a novel way to utilize drugs for new purposes effectively. [ABSTRACT FROM AUTHOR]

Fifty samples of finished drinking waters (FDWs) from Spain covering 12 million inhabitants were tested for 53 pharmaceuticals pertaining to 12 different Anatomical Therapeutic Chemical (ATC) classification system codes. The studied compounds are a combination of most commonly consumed pharmaceuticals with other barely reported in the literature. Five compounds, azithromycin, clarithromycin, erythromycin, sulfamethoxazole, and ibuprofen were tentatively identified by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) in some samples (2 to 15 %), but only ibuprofen and azithromycin could be confirmed when analyzed by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) with a quadrupole-Orbitrap instrument. Concentration levels of ibuprofen in the positive samples ranged from 12 to 17 ng/L ( n = 6) while for azithromycin values from 5 to 9.5 ng/L ( n = 3) were found. Ibuprofen fragmentation behaviour in different mass spectrometry instrument configurations (triple quadrupole, quadrupole-ion trap, and quadrupole-Orbitrap) was evaluated. [ABSTRACT FROM AUTHOR]

Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses. [ABSTRACT FROM AUTHOR]

Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug–gene interaction data. We obtained drug–gene interaction data from an earlier version of DrugBank, built a drug–gene interaction network, and projected it as a drug–drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug–gene interaction data to generate a comprehensive drug repurposing hint list. [ABSTRACT FROM AUTHOR]

Background: Ships should carry mandatory given amounts of medicinal products and basic first aid items, collectively known as the ship's medicine chest. Type and quantities of these products/items are suggested by the World Health Organisation (WHO) and regulated by individual flag states. In countries that lack national legislation, it is assumed that ships should follow WHO indications. An objective difficulty mainly involving vessels of international long-haul routes could be to recognise medicinal compounds obtained in other countries for replacing products used or expired. Language barrier may complicate, if not make it impossible to interpret the name of the medicinal product and/or of the active principle as indicated in a box printed in a completely different language. Handling of the ship's pharmacy may be difficult in case of purchasing of drugs abroad due to language barriers. Medicinal products are identified by the international non-proprietary name of the active principle and/or by their chemical or invented (branded) names. This may make the identification of a medicinal product difficult, primarily if it is purchased abroad and the box and instructions are written in the language of the country where it is marketed. Therefore, there is a simpler classification system of the medicinal compounds the ATC (ATC: Anatomy, Therapeutic properties, Chemical, pharmacological properties). This paper has reviewed the list of medicinal products recommended by WHO and assigned to each one the ATC code as a solution to the problem of medicinal compounds organisation., Materials and Methods: Two researchers independently examined the list of medicinal compounds indicated in the third edition of the International Medical Guide for Ships and attributed to each compound the ATC code according to the 2013 Guidelines for ATC classification and Defined Daily Dose (DDD) assignment., Results: The ATC code was attributed to the medicinal compounds indicated in the third edition of the International Medical Guide for Ships., Conclusions: The availability of an objective system to identify medicinal products is required for ships, which will contribute in making the identification of items purchased simpler, making it easier to understand which drug seafarers need to be administer, and consequently reduce possible therapeutic mistakes.