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

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.63-65
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• 2022

DDI(Drug-Drug Interaction)는 병원에서 발생하는 약물이상반응의 30%를 유발하는 부작용이지만, 현실적으로 모든 약물쌍의 DDI 를 기존 in vivo, in vitro 방식으로 예측하는 것은 불가능하다. 그렇기에, 다양한 in silico 방식의 DDI 예측 모델이 연구되고 있다. 본 연구에서는, 단백질 네트워크 상에서 RWR(Random Walk with Restart) 알고리즘을 통해 약물과 직접적으로 상호작용하는 단백질과 간접적으로 상호작용하는 단백질의 정보를 사용하여 DDI 를 예측하는 모델을 개발하였다. 이 모델을 통하여 기존에 발견하지 못한 DDI 를 새롭게 발견하고, 신약 개발 시에도, 신약과 함께 복용 시 문제를 일으킬 수 있는 약물을 예측하여 약물 이상반응을 방지하고자 한다.

• 한국임상약학회지
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• 제19권1호
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• pp.1-17
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• 2009

Adverse drug reactions (ADR) caused by inappropriate prescription are responsible for major socioeconomic loss. Drug-drug interactions (DDI) has been recognized as a major part of ADRs and, therefore, healthcare professionals should prevent possible DDIs to minimize preventable ADRs. This study aimed to examine DDI information in drug information references and Korea Food & Drug Administration (KFDA) drug labeling information. Drug ingredients from the formulary of Health Insurance Review and Assessment Service in Korea (HIRA) were included for the study. DDI information source used for the study were Micromedex Drugdex and Drug Information Facts (DIF) with the DDI severity level of "moderate" or more. The DDI information in KFDA drug labeling were collected and compared. Drug ingredients were classified with KFDA Drug Classification and ATC Classification of WHO for the analysis. Among the total 1,355 drug ingredients satisfying inclusion criteria, 738 ingredients involved at least one DDI, which was described in Micromedex and/or DIF. Drug Ingredients of 176 involved DDI only described in KFDA drug labeling, but not Micromedex nor DIF. Drug ingredients of 35 which DDIs were described in Micromedex or DIF did not have DDI based on KFDA drug labeling. Micromedex and DIF retrieved 7,582 and 3,071 DDIs, respectively 57.6% and 58.5% of DDIs were also described in KFDA drug labeling. Central nervous system (CNS) drugs, cardiovascular system (CVS) drugs and the antiinfectives appeared to have higher frequency of DDIs among all drug classes. The highest number of DDIs with high severity level ("contraindicated" or "major") were the DDIs of CNS drugs. The antiinfectives are the second drug group having serious DDIs. The DDI pairs of the CNS drug and the antiinfective had the highest contraindication risk (13.6%). DDI information from Micromedex and DIF were not consistent with the result that only 465 ingredients' DDIs are common in both literature (total DDI numbers were 715 vs 488, respectively). And 1,652 DDI information are common in both references among 7,582 vs 3,071 DDIs, respectively. Only 55.2% of DDI information in the database contained in the KFDA drug labeling. Prescribers and pharmacists should pay attention to the drugs for CV system, CNS and infections because of higher risk of possible DDIs compared to other drug classes. KFDA drug labeling is not likely to be recommended as a good information source for DDI due to significant inconsistency of information. Drug information providers should be aware that DDI information from different sources are not consistent and therefore multiple references should be used.

• 한국임상약학회지
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• 제25권2호
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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.

• 한국임상약학회지
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• 제21권3호
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• pp.249-255
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• 2011

Objective: To examine the drug use (prescribing) pattern of serious drug-drug interactions (DDIs, contraindicated drug interactions) using real world data. Prescription patterns were examined in terms of dispensing types. Method: Retrospective drug utilization review (DUR) study was performed. One hundred and six datasets of serious DDIs (DDI pairs) were determined among DDI datasets that Ministry of Health & Welfare announced for the DUR system from 2004 to 2005. Electronically transacted ambulatory patients' prescription database to Health Insurance Assessment and Review Services (HIRA) from July, 2005 to June, 2006 was collected with personal information deidentified and analyzed in terms of types of dispensing as a contributing factor. Results: After prescription data analysis per each patient, total number of DDI cases using 95 DDI pairs was 5,511, which accounted for 2.6 cases per patients. DDI cases between two drugs from each of community pharmacy dispensing- type prescription were considerable (63% vs. 24% in those from each of in-institutional dispensing-type prescription and vs. 13% in those from a community pharmacy dispensing-type prescription and an in-institutional dispensingtype prescription). Conclusions: DDI cases from different prescribers were found to be significant. Thus, the concurrent DUR process between prescriptions from different physicians and institutions should be implemented for the safe drug use.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2019년도 제59차 동계학술대회논문집 27권1호
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• pp.33-36
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• 2019

DDI 추출은 생물 의학 문헌으로부터 약물-약물 상호작용(Drug-Drug Interaction) 관계를 추출하는 작업으로, 기존에 알려지지 않은 인체 내 약물 간의 효과 또는 부작용 정보를 제공하는데 중요한 역할을 한다. 본 연구에서는 PCNN 모델을 활용하여 특징 추출 과정을 자동화하고 약물 개체 간의 구조 정보를 포착해 개체 간 관계를 효율적으로 추출하였으며, 생물 의학 문헌에서 쓰이는 생소한 용어를 보다 풍부하게 표현하기 위해 5가지 버전의 단어 임베딩을 PCNN의 채널로 사용하였다. 본 연구에서 제안하는 MC-PCNN 모델의 성능 평가를 위해 DDI'13 Corpus 데이터를 사용하여 비교 실험을 진행하였으며, 그 결과 기존 연구보다 $F_1$ 점수 기준 최대 2.05%p 향상된 성능을 보이며 DDI 관계 추출에서 효과적인 방법론임을 확인하였다.

• The Korean Journal of Physiology and Pharmacology
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• 제25권6호
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• pp.545-553
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• 2021

Fixed-dose combinations development requires pharmacokinetic drugdrug interaction (DDI) studies between active ingredients. For some drugs, pharmacokinetic properties such as long half-life or delayed distribution, make it difficult to conduct such clinical trials and to estimate the exact magnitude of DDI. In this study, the conventional (non-compartmental analysis and bioequivalence [BE]) and model-based analyses were compared for their performance to evaluate DDI using amlodipine as an example. Raw data without DDI or simulated data using pharmacokinetic models were compared to the data obtained after concomitant administration. Regardless of the methodology, all the results fell within the classical BE limit. It was shown that the model-based approach may be valid as the conventional approach and reduce the possibility of DDI overestimation. Several advantages (i.e., quantitative changes in parameters and precision of confidence interval) of the model-based approach were demonstrated, and possible application methods were proposed. Therefore, it is expected that the model-based analysis is appropriately utilized according to the situation and purpose.

• The Korean Journal of Physiology and Pharmacology
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• 제21권1호
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• pp.107-115
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• 2017

Over the last decade, physiologically based pharmacokinetics (PBPK) application has been extended significantly not only to predicting preclinical/human PK but also to evaluating the drug-drug interaction (DDI) liability at the drug discovery or development stage. Herein, we describe a case study to illustrate the use of PBPK approach in predicting human PK as well as DDI using in silico, in vivo and in vitro derived parameters. This case was composed of five steps such as: simulation, verification, understanding of parameter sensitivity, optimization of the parameter and final evaluation. Caffeine and ciprofloxacin were used as tool compounds to demonstrate the "fit for purpose" application of PBPK modeling and simulation for this study. Compared to caffeine, the PBPK modeling for ciprofloxacin was challenging due to several factors including solubility, permeability, clearance and tissue distribution etc. Therefore, intensive parameter sensitivity analysis (PSA) was conducted to optimize the PBPK model for ciprofloxacin. Overall, the increase in $C_{max}$ of caffeine by ciprofloxacin was not significant. However, the increase in AUC was observed and was proportional to the administered dose of ciprofloxacin. The predicted DDI and PK results were comparable to observed clinical data published in the literatures. This approach would be helpful in identifying potential key factors that could lead to significant impact on PBPK modeling and simulation for challenging compounds.

• 한국임상약학회지
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• 제32권3호
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• pp.204-214
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• 2022

Objective: This study aimed to investigate the occurrence and types of the adverse events (AEs) associated with oral fluconazole and itraconazole and factors associated with specific types of AEs. Methods: We analyzed AEs reported by community pharmacies nationwide over 10 years using the Korea Adverse Event Reporting System database. Various AE terms were categorized into 18 types, and concomitant medications were classified by drug-drug interaction (DDI) severity. The relationship between the specific type of AE and age, sex, and number of concomitant medications was investigated using multiple logistic regression analysis. Results: A total of 879 AE reports of fluconazole and 401 reports of itraconazole were analyzed; of these reports, 321 and 83 reports of fluconazole and itraconazole, respectively, described concomitant drug administration categorized as DDI severity of contraindicated or major. Women had a higher risk of psychiatric AEs associated with fluconazole use (OR, 1.587; p=0.042). Polypharmacy increased the risk for psychiatric AEs (OR, 3.598; p<0.001 for fluconazole and OR, 2.308; p=0.046 for itraconazole). In dermatologic AEs, the mean age of patients who received itraconazole was lower than that of patients who received fluconazole (46.3±16.8 vs. 54.9±15.4; p<0.001). Co-administration of fluconazole with 1-3 drugs increased the risk of neurological AEs (OR, 1.764; p=0.028). Conclusion: When using fluconazole and itraconazole, psychiatric AEs should be noted, particularly in women and in case of polypharmacy; moreover, when fluconazole is co-administered with other drugs, attention should be paid to the occurrence of neurological AEs.

• 한국임상약학회지
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• 제23권2호
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• pp.129-136
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• 2013

Objectives: Among many new drugs that are under investigation with intent to treat cancer, oral kinase inhibitors are proven to be effective in numerous clinical trials and easy to administer. Due to these advantages the use of oral kinase inhibitors is increasing. Oral kinase inhibitors are metabolized by CYP450 which can result either increase of adverse effect or decrease of drug effect by drug interaction when used concurrently with other agents. In this research, the medication records of patients on oral kinase inhibitors from Oct. 2010 to Nov. 2011 were reviewed to investigate potential drug interactions. Methods: From Oct. 2010 to Nov. 2011, cancer patients in Inha University Hospital who took oral kinase inhibitors more than once were included. The patients' medication records were reviewed to list out concurrent medications that have interaction potential with oral kinase inhibitors, the frequency of concurrent use, and the severity of interaction result using Micromedex$^{(R)}$ and Lexicomp-online$^{(R)}$ as references. Results: As a result, 90 cases of drug with interaction potential were prescribed by Micromedex$^{(R)}$ and 179 cases by Lexicomp-online$^{(R)}$ data. In case of severity, 33.3% by Micromedex$^{(R)}$ and 26.3% by Lexicomp-online$^{(R)}$ were categorized as Major and 65.6% by Micromedex$^{(R)}$ and 72.6% by Lexicomp-online$^{(R)}$ as Moderate. The number of adverse events was 92 cases which 58.7% were on skin and 19.6% on Gastro-intestinal tract. Conclusions: Considerable number of drug with interaction potential was used though each oral kinase inhibitors showed differences in extent. Hence there exists the risk of drug interaction in patients taking oral kinase inhibitors with other drugs.