• Korean Journal of Clinical Pharmacy
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• v.25 no.2
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• pp.120-129
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• 2015

Objectives: Drug utilization review program in Korea has provided 'drug combinations to avoid (DCA)' alerts to physicians and pharmacists to prevent potential adverse drug events or inappropriate drug use. Seven hundred and six DCA pairs have been announced officially by the Ministry of Food and Drug Safety (MFDS) by March, 2015. Some DCA pairs could be grouped based on the drug interaction mechanism and its consequences. This study aimed to investigate the drug-drug interaction (DDI) pairs, which may be potential DCAs, generated by the drug class-drug class interaction method. Methods: Eleven additive/synergistic and one antagonistic drug class-drug class interaction groups were identified. By combining drugs of two interacting drug class groups, numerous DDI pairs were made. The status and severity of DDI pairs were examined using Lexicomp and Micromedex. Also, the DCA listing rate was calculated. Results: Among 258 DDI pairs generated by the drug class-drug class interaction method, only 142 pairs were identified as official DCA pairs by the MFDS. One hundred and four pairs were identified as potential DCA pairs to be listed. QT prolonging agents-QT prolonging agents, triptans-ergot alkaloids, tricyclic antidepressants-monoamine oxidase inhibitors, and dopamine agonists-dopamine antagonists were identified as drug class-drug class interaction groups which have less than 50 % DCA listing rate. Conclusion: To improve the clinicians' adaptability to DCA alerts, the list of DCA pairs needs to be continuously updated.

• Journal of Pharmaceutical Investigation
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• v.36 no.4
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• pp.239-243
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• 2006

Due to the development of new dosage forms and the improvement or pharmaceutics, the pharmaceutical excipients have become more specified and diverse, and the reclassification on them became necessary. Also with the increasing interests on the kinds and usage amount, related provisions, and evaluation of the pharmaceutical excipients, the systemic and effective control of them was in its demand. Therefore, in this research, we provided the following information on excipients: the type, amount and specification. In order to provide the information, we investigated, analysed and summarized the excipients that are approved by KFDA and published $\ulcorner$Handbook of Pharmaceutical Excipients$\lrcorner$). This handbook is expected to be used as a reference in the development of the pharmaceutics and evaluation in them. As the importance of excipients in pharmaceutics are increasing, IPEC which consist of IPEC-America, IPEC-Europe and JPEC, PDG and ICH have tried to make an international harmonization on excipient. This current status was not an exception to Korea, therefore, the result of this research is expected to make a progress in the evaluation on the excipients to an advanced level.

• Natural Product Sciences
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• v.25 no.3
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• pp.275-283
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• 2019

In this study, we described the new developed method to simultaneously discriminate two herbal drugs of Artemisiae Argyi Folium and Artemisiae Iwayomogii Herba using eight marker compounds (1 - 8) on an HPLC-PDA system. The developed method was applied to quantify the major components of two herbal drugs. The pattern analysis successfully discriminated and evaluated different components between Artemisiae Argyi Folium and Artemisiae Iwayomogii Herba. Results were used for classification of different species from collected samples.

• Korean Journal of Clinical Pharmacy
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• v.23 no.2
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• pp.114-122
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• 2013

Background: Drugs should be evaluated in appropriate subjects representing potential population to take the drugs. This study focuses on gender factor and aims to make known the appropriateness of considering gender difference on clinical evaluation of drug with domestic data related to drug use. Methods: To understand gender difference shown in drug use, three types of domestic statistical data (prevalence of chronic disease, number of outpatient with major concerning disease, and consumption of medicine) were analyzed and compared according to gender. Results: Three of fifteen chronic diseases which were analyzed, showed significantly higher prevalence in women than in men, and three were vice versa. Meanwhile, the sex ratio of outpatients was significantly different in 22 major concerning diseases. Among the drug groups coded by Anatomical Therapeutic Chemical (ATC) Classification System, the consumption of most drug groups was generally higher in women than in men except for one group coded G (genito-urinary system and sex hormones). Conclusion: Gender difference should be considered in domestic clinical evaluation of drug and domestic guidance for reflecting gender difference should be established.

• Korean Journal of Clinical Pharmacy
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• v.17 no.1
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• pp.46-51
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• 2007

This study was aimed to provide the controlled terminology for adverse drug reactions by selecting an appropriate internationally standardized classifications (WHO-ART or MedDRA). We collected the relevant information on ADR terminology systems including WHO-ART and MedDRA by online searching and visiting pharmaceutical companies and WHO UMC (Uppsala Monitoring Centre, Uppsala, Sweden). For MedDRA, project leader directly communicated with the officer of MSSO (Maintenance and Support Services Organization). Collecting all the pertinent information, two possible terminology classifications or systems (WHO-ART and MedDRA) were compared in the views of acceptability, cost-effectiveness and international feasibility and reviewed by the consultation committee and finally WHO-ART was selected.

• Journal of Information Science Theory and Practice
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• v.3 no.1
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• pp.6-23
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• 2015

This study develops an effective method for sentiment analysis of user-generated content on drug review websites, which has not been investigated extensively compared to other general domains, such as product reviews. A clause-level sentiment analysis algorithm is developed since each sentence can contain multiple clauses discussing multiple aspects of a drug. The method adopts a pure linguistic approach of computing the sentiment orientation (positive, negative, or neutral) of a clause from the prior sentiment scores assigned to words, taking into consideration the grammatical relations and semantic annotation (such as disorder terms) of words in the clause. Experiment results with 2,700 clauses show the effectiveness of the proposed approach, and it performed significantly better than the baseline approaches using a machine learning approach. Various challenging issues were identified and discussed through error analysis. The application of the proposed sentiment analysis approach will be useful not only for patients, but also for drug makers and clinicians to obtain valuable summaries of public opinion. Since sentiment analysis is domain specific, domain knowledge in drug reviews is incorporated into the sentiment analysis algorithm to provide more accurate analysis. In particular, MetaMap is used to map various health and medical terms (such as disease and drug names) to semantic types in the Unified Medical Language System (UMLS) Semantic Network.

• Journal of Korean Neurosurgical Society
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• v.66 no.3
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• pp.308-315
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• 2023

Recent updates in genomic-integrated glioma classification have caused confusion in current clinical practice, as management protocols and health insurance systems are based on evidence from previous diagnostic classifications. The Korean Brain Tumor Society conducted an electronic questionnaire for society members, asking for their ideas on risk group categorization and preferred treatment for each individual diagnosis listed in the new World Health Organization (WHO) classification of gliomas. Additionally, the current off-label drug use (OLDU) protocols for glioma management approved by the Health Insurance Review and Assessment Service (HIRA) in Korea were investigated. A total of 24 responses were collected from 20 major institutes in Korea. A consensus was reached on the dichotomic definition of risk groups for glioma prognosis, using age, performance status, and extent of resection. In selecting management protocols, there was general consistency in decisions according to the WHO grade and the risk group, regardless of the individual diagnosis. As of December 2022, there were 22 OLDU protocols available for the management of gliomas in Korea. The consensus and available options described in this report will be temporarily helpful until there is an accumulation of evidence for effective management under the new classification system for gliomas.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.258-270
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• 2022

Cancer has become a common disease for the past two decades throughout the globe and there is significant increase of cancer among women. Breast cancer and ovarian cancers are more prevalent among women. Majority of the patients approach the physicians only during their final stage of the disease. Early diagnosis of cancer remains a great challenge for the researchers. Although several drugs are being synthesized very often, their multi-benefits are less investigated. With millions of drugs synthesized and their data are accessible through open repositories. Drug repurposing can be done using machine learning techniques. We propose a feature selection technique in this paper, which is novel that generates multiple populations for the grey wolf algorithm and classifies breast cancer drugs efficiently. Leukemia drug dataset is also investigated and Multilayer perceptron achieved 96% prediction accuracy. Three supervised machine learning algorithms namely Random Forest classifier, Multilayer Perceptron and Support Vector Machine models were applied and Multilayer perceptron had higher accuracy rate of 97.7% for breast cancer drug classification.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.65-71
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• 2016

To reduce the expenses for development a novel drug, systems biology has been studied actively. Target prediction, a part of systems biology, contributes to finding a new purpose for FDA(Food and Drug Administration) approved drugs and development novel drugs. In this paper, we propose a classification model for predicting novel target genes based on relation between target genes and disease related genes. After collecting known target genes from TTD(Therapeutic Target Database) and disease related genes from OMIM(Online Mendelian Inheritance in Man), we analyzed the effect of target genes on disease related genes based on PPI(Protein-Protein Interactions) network. We focused on the distinguishing characteristics between known target genes and random target genes, and used the characteristics as features for building a classifier. Because our model is constructed using information about only a disease and its known targets, the model can be applied to unusual diseases without similar drugs and diseases, while existing models for finding new drug-disease associations are based on drug-drug similarity and disease-disease similarity. We validated accuracy of the model using LOOCV of ten times and the AUCs were 0.74 on Alzheimer's disease and 0.71 on Breast cancer.

• Health Policy and Management
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• v.16 no.4
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• pp.147-168
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• 2006

Technological innovation has been regarded as the core competence for the economic growth of individual, as well as organization and country. Pharmaceutical innovation, what we call new medicines, influence people's longevity and productivity by increasing output per hour worked. Therefore, using claims data on virtually all the drugs and diseases of over 550,000 people enrolled in National Health Insurance Program in Korea, we examined the impact of the vintage (original FDA and KFDA approval year) of drugs used to treat a patients from July 1st to December 31st in 2002 on the patient's mortality at the end of 2004, controlling for demographic characteristics(age and sex), utilization of medical services, and the nature and complexity of illness. We found that people using newer drugs are less likely to die at the end of 2004, conditional on covariates. The estimated mortality rates were declining with respect to drug vintage for 1970s, 1980s and 1990s and highly significant. In addition to estimating the model for the entire sample, we estimated the model separately for several disease categories classified by Korean Classification of Disease. Estimates of three drug vintage variables for subgroups of people with (1)neoplasms, (2)endocrine, nutritional and metabolic diseases, and (3)the diseases of circulatory system displayed similar patterns.