• 약학회지
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• 제49권5호
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• pp.399-404
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• 2005

To investigate drug interaction, 23,536 prescriptions published for 1 year were investigated with 'Drug Inter­action Fact 2002'. Dispensing records and a database file written in a local general hospital in South Korea were used as a sample. The number of total cases of drug interaction was 3,238 ($13.76\%$) out of 23,536 prescriptions. The incidence of drug interaction in each prescription the children, the adults, and the elderly were $1.33\%,\;10.97\%,\;25.50\%$, respectively. The incidences of drug interaction per each prescription were $22.03\%,\;20.52\%,\;0.51\%,\;and\;0.36\%$ in neurosurgery, internal med­icine, pediatrics, and orthopedics, respectively. In neurosurgery and internal medicine, risk-high drugs of drug interaction such as antihypertensive drugs, diuretics, and cimetidine were used very often in elderly. In this paper, several suggestions to reduce drug interaction were postulated with regard to the usage of analgesics, non-steroidal antiinflammatory drugs, and antibiotics.

• 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.

• 대한예방한의학회지
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• 제15권1호
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• pp.1-16
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• 2011

Objectives : The mechanisms for korean traditional medicine-drug interaction has not been well reviewed in spite that the chance for co-administration with western drugs or diet supplements has been increased. Especially, it is well known that various cytochrome P450s play a major role in drug-drug interaction. Of course, Korean traditional medicines is not excluded in a view of metabolism or biotransformation by cytochrome P450. This article was focused on reviewing the possible roles of cytochrome P450 in Korean traditional medicine-drug interaction, Also, the directions for further studies were suggested in terms of Korean traditional medicine-drug interaction. Methods : New studies for korean traditional medicine-drug interaction were reviewed and summarized in terms of cytochrome P450 activities by various Korean traditional medicines and western drugs. Results and Conclusions : Even if a few studies related to Korean traditional medicine-drug interactions was carried out, almost no studies for Korean traditional medicine-drug interactions has been found in a view of cytochrome P450. It was suggested that Korean traditional medicines and their decoction should be analyzed that how they effects on cytochrome P450, expecially CYP 1, 2, 3 families and how they interact with western drugs.

• 한국임상약학회지
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• 제25권3호
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• pp.138-144
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• 2015

Objective: It is to evaluate the drug interaction monitoring program as a pilot project to develop a pharmaceutical care model in a medical intensive care unit and to analyze the influencing factors of drug interactions. Method: Electronic medical records were retrospectively investigated for 116 patients who had been hospitalized in a medical intensive care unit from October to December in 2014. The prevalence of adverse reaction with risk rating higher than 'D' was investigated by Lexi-$Comp^{(R)}$ Online database. The factors related with potential drug interaction and with treatment outcomes were analyzed. Results: The number of patients with a potential interaction of drug combination was 92 (79.3%). Average ages, the length of stay in the intensive care unit and the numbers of prescription drugs showed significant differences between drug interaction group and non-drug interaction group. Opioids (14.4%), antibiotics (7.2%), and diuretics (7.2%) were most responsible drug classes for drug interactions and the individual medications included furosemide (6.4%), tramadol (4.9%), and remifentanil (4.5%). There were 950 cases with a risk rating of 'C' (84.6%), 142 cases with a risk rating of 'D' (12.6%), and 31 cases with a risk rating of 'X' (avoid combination) (2.8%). The factors affecting drug interactions were the number of drugs prescribed (p < 0.0001) and the length of stay at intensive care unit (p < 0.01). The patients in intensive care unit showed a high incidence of adverse reactions related to potential drug interaction. Therefore, drug interaction monitoring program as a one of pharmaceutical care services was successfully piloted and it showed to prevent adverse reaction and to improve therapeutic outcomes. Conclusion: Active participation of a pharmacist in the drug management at the intensive care unit should be considered.

• 셀메드
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• 제4권3호
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• pp.17.1-17.8
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• 2014

Herbal remedies are commonly used by patients worldwide. Because these herbal preparations share the same metabolic and transport proteins with prescribed medicines, the potential for a drug-herb interaction is substantial and is an issue of significant concern. This review paper summarizes drug-herb interactions involving inhibition or induction of cytochrome P450 enzymes, drug transporters as well as modulation of drug pharmacodynamics. An increasing number of in vitro and animal studies, case reports and clinical trials evaluating such interactions have been reported, and implications of these studies are discussed in this review. The most commonly implicated drugs in the interaction include anticoagulants, antiplatelets, immunosuppressants, anti-neoplastics, protease inhibitors, and some antidepressants. Pharmacokinetic and/or pharmacodynamic interactions of five commonly used herbal remedies (danshen, garlic, Ginkgo biloba, ginseng, and St John's wort) with these drugs are presented, with focus of discussion being the potentials for interaction, their mechanisms and clinical implications. There is a necessity for adequate pharmacovigilance to be carried out in minimizing unanticipated but often preventable drug-herb interactions.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.276-278
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• 2022

모든 약물은 신체 내에서 특정한 작용을 하며, 많은 경우 합병증 또는 기존 약물치료 중 새롭게 발생하는 증상에 의해 약물이 혼용되는 경우가 발생한다. 이런 경우 신체 내에서 예상치 못한 상호작용이 발생할 수 있다. 따라서 약물 간 상호작용을 예측하는 것은 안전한 약물 사용을 위해 매우 중요한 과제이다. 본 연구에서는 다중 약물 사용 시 발생 가능한 약물 간 상호작용 예측을 위해 약물 정보 문서를 이용해 학습시키는 딥러닝 기반의 예측 모델을 제안한다. 약물 정보 문서는 DrugBank 데이터를 이용해 약물의 작용 기전, 독성, 표적 등 여러 속성을 결합해 생성되었으며, 두 약물 문서가 한 쌍으로 묶여 딥러닝 기반 예측 모델에 입력으로 사용되고 해당 모델은 두 약물 간 상호작용을 출력한다. 해당 연구는 임베딩 방법이나 데이터 전처리 방법 등 다양한 조건의 변화에 따른 실험 결과의 차이를 분석하여 차후 새로운 약물쌍 간 상호작용을 예측하는 데에 활용이 가능하다.

• 생물정신의학
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• 제7권1호
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• pp.34-45
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• 2000

Pharmacotherapy of bipolar disorder is a rapidly evolving field. Mood stabilizers and anticonvulsants have varying biochemical profiles which may predispose them to different adverse effects and drug-drug interactions. Several of the new anticonvulsants appear less likely to have the problems with drug-drug interaction. To provide more effective combination pharmacotherapies, clinicians should be allowed to anticipate and avoid pharmacokinetic and pharmacodynamic drug-drug interactions. We reviewed the role of cytochrome P450 isozymes in the metabolism of the drugs and their interactions. The drug-drug interactions of several classes of drugs which used as mood stabilizers and new anticonvulsants, some of which may have psychotropic profiles, are discussed mainly in this article. Finally, potential pharmacokinetic interactions between the benzodiazepines and other coadministered drugs are discussed briefly.

• 생물정신의학
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• 제7권1호
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• pp.21-33
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• 2000

As the clinical practice of using more than one drug at a time increase, the clinician is faced with ever-increasing number of potential drug interactions. Although many interactions have little clinical significances, some may interfere with treatment or even be life-threatening. This review provides a better understanding of drug-drug interactions often encountered in pharmacotherapy of depression. Drug interactions can be grouped into two principal subdivisions : pharmacokinetic and pharmacodynamic. These subgroups serve to focus attention on possible sites of interaction as a drug moves from the site of administration and absorption to its site of action. Pharmacokinetic processes are those that include transport to and from the receptor site and consist of absorption, distribution on body tissue, plasma protein binding, metabolism, and excretion. Pharmacodynamic interactions occur at biologically active sites. In this review, emphasis is placed on antidepressant medications, how they are metabolized by the P450 system, and how they alter the metabolism of other drugs. When prescribing antidepressant medications, the clinician must consider the drug-drug interactions that are potentially problematic.

• 생물정신의학
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• 제7권1호
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• pp.3-13
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• 2000

There is nothing that is harmless ; the dose alone decides that something is no poison(Paracelsus, 1493-1541). So, in a point of view to maximize the therapeutic efficacy of drug therapy in a way that minimize the drug toxicity, the knowledges of the drug-ineractions as well as the pharmacokinetic and pharmacodynamic principles of every therapeutic drug used in the medical clinic cannot be emphasized too much. Many drug interactions can be predicted if the pharmacokinetic properties, pharmacodynamic mechanisms of action of the interacting drugs are known, and most adverse interactions can be avoided. In this paper, the clinical importance, classification, and general principles of clinical drug-interactions are presentated with a few explanatory examples.

• 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.