• 한국식품위생안전성학회지
• /
• 제26권4호
• /
• pp.302-307
• /
• 2011

One of the most important objectives of post-marketing monitoring of dietary supplements is the early detection of unknown and unexpected adverse events (AEs). Several causality algorithms, such as the Naranjo scale, the RUCAM scale, and the M & V scale are available for the estimation of the likelihood of causation between a product and an AE. Based on the existing algorithms, the Korea Food & Drug Administration has developed a new algorithm tool to reflect the characteristics of dietary supplements in the causality analysis. However, additional work will be required to confirm if the newly developed algorithm tool has reasonable sensitivity and not to generate an unacceptable number of false positives signals.

• International Journal of Computer Science & Network Security
• /
• 제21권6호
• /
• pp.319-328
• /
• 2021

Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.

• International journal of advanced smart convergence
• /
• 제12권4호
• /
• pp.434-442
• /
• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• 약학회지
• /
• 제50권3호
• /
• pp.145-153
• /
• 2006

The adverse events which do not appear in the approval process tend to occur more frequently at the early stage of the use. Therefore new drugs, drugs with different active substances or routes of administration, or drugs with explicitly different efficacy added are particularly chosen for re-examination, and go through a study, which is conducted on 600 to 3,000 subjects over 4 to 6 years. Since the re-examination system was implemented in January 1995, 880 drug products have been designated as the subject of re-examination and among them 194 drugs have been completed their re-examination as of until March 2005. Post Marketing Surveillance to insure drug safety should be correlated with re-examination of new drug, re-evaluation of drug, and adverse event monitoring system. And the first labeling change should reflect all information collected for a defined period of time after the marketing authorization is granted. Furthermore centralized management through spontaneous reporting system of adverse event for whole period of time would be the most desirable type of system.

• 대한의용생체공학회:의공학회지
• /
• 제43권4호
• /
• pp.230-240
• /
• 2022

Drug regeneration technology is an efficient strategy than the existing new drug development process, which requires large costs and time by using drugs that have already been proven safe. In this study, we recognize the importance of the new drug regeneration aspect of new drug development and research in predicting functional similarities through the basic molecular structure that forms drugs. We test four string-based algorithms by using SMILES data and searching for their similarities. And by using the ATC codes, pair them with functional similarities, which we compare and validate to select the optimal model. We confirmed that the higher the molecular structure similarity, the higher the ATC code matching rate. We suggest the possibility of additional potency of random drugs, which can be predicted through data that give information on drugs with high molecular similarities. This model has the advantage of being a great combination with additional data, so we look forward to using this model in future research.

• 통합자연과학논문집
• /
• 제3권3호
• /
• pp.162-167
• /
• 2010

A pseudoreceptor combines structure-based and ligand-based techniques to represent a unifying concept for both receptor mapping and ligand matching. In this molecular modeling approach, there are opportunities to construct the pseudoreceptor models using a set of small molecules. To build a reliable pseudoreceptor model, we need a set of ligand molecules with known affinity (biological activity) to generate 3D bioactive conformation for each of these ligand molecules. Several software packages are available to generate a pseudoreceptor model and this can provide an entry point for structure based drug discovery in cases where receptor structure information is not available. In this review, we presented the concept of pseudoreceptor, as well as discussed about various software packages available to generate a pseudoreceptor model.

• 한국농촌간호학회지
• /
• 제12권1호
• /
• pp.3-11
• /
• 2017

Purpose: This study was done to investigate the status of drug misuse and abuse in community-dwelling elders. Methods: The participants in this study were elders who were 65 years or over, and lived in ChungNam province. Descriptive statistics were used to analyze the data. Results: All of the participants reported taking both prescription and non-prescription drugs, and 78.4% used two or more kinds of drugs. Of the elders, 74.5% reported that they did not receive any education about drug misuse and abuse. The mean score for behaviors related to drug misuse was 8.76. Conclusions: Results indicate that many elders take medicine frequently, but they do not have any knowledge about drugs and possible side effects. This lack of knowledge might mean that they continue to use and misuse prescription and non-prescription drugs. It is important that elders be provided with precise information about medicines.

• Genomics & Informatics
• /
• 제17권2호
• /
• pp.18.1-18.10
• /
• 2019

Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.

• 보건행정학회지
• /
• 제24권2호
• /
• pp.164-171
• /
• 2014

Background: Safety of drugs has become a major issue in public healthcare. Spontaneous reporting of adverse drug reaction (ADR) is the cornerstone in management of drug safety. We aimed to investigate the awareness and knowledge of spontaneous ADR reporting in general public of Korea. Methods: A total of 1,500 study subjects aged 19-69 years were interviewed with a questionnaire for their awareness and knowledge related to spontaneous ADR reporting. Computer assisted telephone interview was performed from 27th February 2013 to 4th March 2013. Target population was selected with quota sampling, using age, sex, and residence area. Healthcare professionals such as physicians, pharmacists, and nurses were excluded. The survey questions included awareness of spontaneous ADR reporting, opinions on ways to activate ADR reporting, and sociodemographic characteristics. Results: Overall awareness of spontaneous ADR reporting system was 8.3% (${\pm}2.53%$) among general population of Korea. Major source from which people got the information regarding ADR reporting was television/radio (69.9%), followed by internet (19.3%), and poster/brochure (6.1%). Awareness level differed between age groups (p<0.0001) and education levels (p<0.0001). Upon learning about the ADR reporting system, 88.5% of study subjects agreed on the necessity of ADR reporting system, while 46.6% thought promotion through internet and mass media as an effective way to activate ADR reporting. Conclusion: The overall awareness of spontaneous ADR reporting should be enhanced in order to establish a firm national system for drug safety. Adequate promotions should be performed targeting lower awareness groups, as well as various publicity activities via effective channels for the general population.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제11권1호
• /
• pp.11-18
• /
• 2022

신약 디자인은 단백질 수용체와 같은 생물학적 표적과 상호작용할 수 있는 약물 후보물질을 식별하는 과정이다. 전통적인 신약 디자인 연구는 약물 후보 물질 탐색과 약물 개발 단계로 구성되어 있으나, 하나의 신약을 개발하기 위해서는 10년 이상의 장시간이 요구된다. 이러한 기간을 단축하고 효율적으로 신약 후보 물질을 발굴하기 위하여 심층 학습 기반의 방법들이 연구되고 있다. 많은 심층학습 기반의 모델들은 SMILES 문자열로 표현된 화합물을 재귀신경망을 통해 학습 및 생성하고 있으나, 재귀신경망은 훈련시간이 길고 복잡한 분자식의 규칙을 학습시키기 어려운 단점이 있어서 개선의 여지가 남아있다. 본 연구에서는 self-attention과 variational autoencoder를 활용하여 SMILES 문자열을 생성하는 딥러닝 모델을 제안한다. 제안된 모델은 최신 신약 디자인 모델 대비 훈련 시간을 1/26로 단축하는 것뿐만 아니라 유효한 SMILES를 더 많이 생성하는 것을 확인하였다.