Identification of Prescribing Patterns in Hemodialysis Outpatients Taking Multiple Medications

We investigated the relationship between multidrug administration and the characteristics, pathophysiology, and drug class in outpatients with hemodialysis. A retrospective cross-sectional study was conducted at Saitama Medical University Hospital in October 2018. Multidrug administration was defined as receiving either more than six drugs or more than the median number of drugs. The drugs used were represented by their anatomical classification codes in the Anatomical Therapeutic Chemistry Classification System (ATC classification). A latent class analysis (LCA) was used to identify clusters at risk of receiving multiple medications. A stepwise logistic regression analysis was performed to select ATC classifications prone to being involved in multidrug administration. As of October 2018, 98 outpatients with hemodialysis were enrolled in the study. In the LCA, when diabetes was the main primary disease, oral hypoglycemic agents available to dialysis patients were limited, but the number of drugs administered was large. Old age, poor nourishment, a long history of dialysis, and chronic nephritis were associated with multidrug administration among nondiabetic patients. In the second level of the ATC classification, the drugs frequently used were coded A02 (drugs for acid-related disorders), A07 (antidiarrheal agents, intestinal anti-inflammatory/anti-infective agents), B01 (antithrombotic agents), and N05 (psycholeptics). The prescribing patterns for either diabetic patients or nondiabetic elderly patients were identified in outpatients with hemodialysis taking multiple medications, and drugs for acid-related disorders, antidiarrheal agents, intestinal anti-inflammatory/anti-infective agents, antithrombotic agents, and psycholeptics are frequently used in those patients.

Visual Comparison of Guidelines: Method and Application to Potentially Inappropriate Medication Lists

Therapeutic guidelines developed by experts are essential tools for improving therapy and drug prescription. Several guidelines often exist that target the same patient, from different organizations and countries. The case of lists for the detection of potentially inappropriate medications (PIMs) is an example which illustrates how these guidelines can be varied and multiple. In order to have an overview to the divergences and similarities between different lists of PIMs, we propose a visual method to compare PIMs lists, based on set visualization, and we apply it to 5 guidelines.

National Antibiotic Consumption for Human Use in Sierra Leone (2017-2019): A Cross-Sectional Study

Monitoring antibiotic consumption is crucial to tackling antimicrobial resistance. However, currently there is no system in Sierra Leone for recording and reporting on antibiotic consumption. We therefore conducted a cross-sectional study to assess national antibiotic consumption expressed as defined daily dose (DDD) per 1000 inhabitants per day using all registered and imported antibiotics (categorized under the subgroup J01 under the anatomical and therapeutic classification (ATC) system) as a proxy. Between 2017–2019, total cumulative consumption of antibiotics was 19 DDD per 1000 inhabitants per day. The vast majority consisted of oral antibiotics (98.4%), while parenteral antibiotics made up 1.6%. According to therapeutic/pharmacological subgroups (ATC level 3), beta-lactam/penicillins, quinolones, and other antibacterials (mainly oral metronidazole) comprised 65% of total consumption. According to WHO Access, Watch, and Reserve (AWaRe), 65% of antibiotics consumed were Access, 31% were Watch, and no Reserve antibiotics were reported. The top ten oral antibiotics represented 97% of total oral antibiotics consumed, with metronidazole (35%) and ciprofloxacin (15%) together constituting half of the total. Of parenteral antibiotics consumed, procaine penicillin (32%) and ceftriaxone (19%) together comprised half of the total. Policy recommendations at global and national levels have been made to improve monitoring of antibiotic consumption and antibiotic stewardship.

A two-tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration

Background

Drug repositioning is the process of identifying new uses for existing drugs. Computational drug repositioning methods can reduce the time, costs and risks of drug development by automating the analysis of the relationships in pharmacology networks. Pharmacology networks are large and heterogeneous. Clustering drugs into small groups can simplify large pharmacology networks, these subgroups can also be used as a starting point for repositioning drugs. In this paper, we propose a two-tiered drug-centric unsupervised clustering approach for drug repositioning, integrating heterogeneous drug data profiles: drug-chemical, drug-disease, drug-gene, drug-protein and drug-side effect relationships.

Results

The proposed drug repositioning approach is threefold; (i) clustering drugs based on their homogeneous profiles using the Growing Self Organizing Map (GSOM); (ii) clustering drugs based on drug-drug relation matrices based on the previous step, considering three state-of-the-art graph clustering methods; and (iii) inferring drug repositioning candidates and assigning a confidence value for each identified candidate. In this paper, we compare our two-tiered clustering approach against two existing heterogeneous data integration approaches with reference to the Anatomical Therapeutic Chemical (ATC) classification, using GSOM. Our approach yields Normalized Mutual Information (NMI) and Standardized Mutual Information (SMI) of 0.66 and 36.11, respectively, while the two existing methods yield NMI of 0.60 and 0.64 and SMI of 22.26 and 33.59. Moreover, the two existing approaches failed to produce useful cluster separations when using graph clustering algorithms while our approach is able to identify useful clusters for drug repositioning. Furthermore, we provide clinical evidence for four predicted results (Chlorthalidone, Indomethacin, Metformin and Thioridazine) to support that our proposed approach can be reliably used to infer ATC code and drug repositioning.

Conclusion

The proposed two-tiered unsupervised clustering approach is suitable for drug clustering and enables heterogeneous data integration. It also enables identifying reliable repositioning drug candidates with reference to ATC therapeutic classification. The repositioning drug candidates identified consistently by multiple clustering algorithms and with high confidence have a higher possibility of being effective repositioning candidates.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2123-4) contains supplementary material, which is available to authorized users.

iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals

Recommended by the World Health Organization (WHO), drug compounds have been classified into 14 main ATC (Anatomical Therapeutic Chemical) classes according to their therapeutic and chemical characteristics. Given an uncharacterized compound, can we develop a computational method to fast identify which ATC class or classes it belongs to? The information thus obtained will timely help adjusting our focus and selection, significantly speeding up the drug development process. But this problem is by no means an easy one since some drug compounds may belong to two or more than two ATC classes. To address this problem, using the DO (Drug Ontology) approach based on the ChEBI (Chemical Entities of Biological Interest) database, we developed a predictor called iATC-mDO. Subsequently, hybridizing it with an existing drug ATC classifier, we constructed a predictor called iATC-mHyb. It has been demonstrated by the rigorous cross-validation and from five different measuring angles that iATC-mHyb is remarkably superior to the best existing predictor in identifying the ATC classes for drug compounds. To convenience most experimental scientists, a user-friendly web-server for iATC-mHyd has been established at http://www.jci-bioinfo.cn/iATC-mHyb, by which users can easily get their desired results without the need to go through the complicated mathematical equations involved.

Medication use for chronic health conditions among adults in Saudi Arabia: findings from a national household survey

PURPOSE

Chronic diseases and their risk factors are believed to be common in the Kingdom of Saudi Arabia (KSA). Most of them require long-term management through medications. We examined patterns of medication use for chronic health conditions (CHC) in KSA based on a national survey.

METHODS

The Saudi Health Interview Survey was a cross-sectional nationally representative household survey of 10,735 individuals aged 15 years or older in 2013. The survey consisted of a detailed health questionnaire. Current medications for CHC were assessed and classified based on the Anatomical Therapeutic Chemical classification.

RESULTS

Among the respondents, 11.8% (standard error = 0.4) reported taking at least one medication for CHC with a mean number of 2.05 (standard error = 0.05) medication items. In addition to older age (odds ratio = 1.94 per each decade, 95%CI: 1.83-2.05) and male gender (odds ratio = 1.22, 95%CI: 1.06-1.41), those with higher income were more likely to take medication. The most common medicines were drugs used for diabetes (A10 Anatomical Therapeutic Chemical code). The top 20 drugs accounted for about 80% of all medications. Only 32.7% of medications were reported to be used exactly as prescribed.

CONCLUSIONS

Compared with the prevalence of CHC in KSA, our study indicates a potential underuse of medications as well as non-adherence to the directions for use. Interventions such as improved clinical guidelines for healthcare providers to increase utilization of necessary medication and educational programs to improve patients' adherence are needed.

Traditional knowledge of medicinal plants in the Serra de Mariola Natural Park, South-Eastern Spain

The present study aims to inventory and analyse the ethnobotanical knowledge about medicinal plants in the Serra de Mariola Natural Park. In respect to traditional uses, 93 species reported by local informants were therapeutic, 27 food, 4 natural dyes and 13 handcrafts. We developed a methodology that allowed the location of individuals or vegetation communities with a specific popular use. We prepared a geographic information system (GIS) that included gender, family, scientific nomenclature and common names in Spanish and Catalan for each species. We also made a classification of 39 medicinal uses from ATC (Anatomical, Therapeutic, Chemical classification system). Labiatae (n=19), Compositae (n=9) and Leguminosae (n=6) were the families most represented among the plants used to different purposes in humans. Species with the most elevated cultural importance index (CI) values were Thymus vulgaris (CI=1.431), Rosmarinus officinalis (CI=1.415), Eryngium campestre (CI=1.325), Verbascum sinuatum (CI=1.106) and Sideritis angustifolia (CI=1.041). Thus, the collected plants with more therapeutic uses were: Lippia triphylla (12), Thymus vulgaris and Allium roseum (9) and Erygium campestre (8). The most repeated ATC uses were: G04 (urological use), D03 (treatment of wounds and ulcers) and R02 (throat diseases). These results were in a geographic map where each point represented an individual of any species. A database was created with the corresponding therapeutic uses. This application is useful for the identification of individuals and the selection of species for specific medicinal properties. In the end, knowledge of these useful plants may be interesting to revive the local economy and in some cases promote their cultivation.