Dissertations / Theses on the topic 'Drug interaction database'

Purpose: Drug interactions are a concern in oncology with the shift toward oral antineoplastics (OAs). Using electronic databases to screen for drug interactions with OAs is a common practice. There is little literature to guide clinicians on the reliability of these systems with OAs. The primary objective of this study was to explore the sensitivity of commonly available drug interaction databases in detecting drug interactions with OAs. Methods: A list of 20 drug interactions with OAs was developed by two Board-certified oncology pharmacists. The list included multiple types of drug interactions. The sensitivity in detecting these interactions by MicroMedex, Facts & Comparisons, Lexi-Interact, and Epocrates were evaluated. These databases were chosen based on their local availability and widespread use in practice. Drugs.com was evaluated as a surrogate for a patient-accessible drug interaction database. The Cochran Q test was used to assess the sensitivity distribution across the five groups. Results: Lexi-Interact and Drugs.com had a sensitivity of 95% for the 20 tested drug interaction pairs. Epocrates had a sensitivity of 90%, and both Micromedex and Facts & Comparisons had a sensitivity of 70%. There was a statistically significant difference (P = .016) in the distribution across the databases in detecting clinically significant drug interactions. Conclusion: Commonly used databases for identifying drug interactions with oral antineoplastics vary significantly in their sensitivity. Clinicians should not rely on a single database and should consider using multiple resources as well as sound clinical judgment. Further work is needed in this area.

This article presents a study conducted on data containing drug concentrations. The data was obtained from femoral venous blood samples collected at medico legal autopsies in Sweden. Cases positive for antidepressant drugs were scrutinized and divided in to two groups for 15 antidepressant drugs: B‑cases, where the cause of death was intoxication with more than one drug detected in the blood sample. C‑cases, where the cause of death was NOT intoxication and at least one drug (the antidepressant) was detected in the blood sample. This data was then processed to find frequencies of concomitant drugs taken together with the antidepressant drugs. Frequencies of the most common concomitant drugs were then compared between B-cases and C-cases for each antidepressant drug. This revealed that the drugs dextropropoxyphene, ethanol, codeine, flunitrazepam, paracetamol, propiomazine and alimemazine were signifcantly more common as concomitant drugs in B-cases (intoxications) than in C‑cases (non‑intoxications). With regards to unknown interactions the most interesting combinations were: Propiomazine with mirtazapine, venlafaxine, citalopram or fluoxetine; Paracetamol with paroxetine; Flunitrazepam with mirtazapine, venlafaxine or citalopram; Codeine with mirtazapine or sertraline. These combinations should be further investigated.

In recent years, several new oral anticancer drugs have been approved, many via an accelerated approval process. These new agents have the potential for drug interactions, but lack of familiarity with these drugs by clinicians may increase the risk for drug interactions. We describe an interaction between the new anticancer agent idelalisib (CYP 3A4 inhibitor) and diazepam (CYP 3A4 substrate) that resulted in altered mental status and type II respiratory failure resulting in hospitalization. After discontinuation of both agents, the patient recovered quickly. Idelalisib was reinitiated after discharge. Lorazepam was substituted for diazepam since it is not metabolized via CYP 3A4. Both agents were tolerated well thereafter. This interaction was only flagged by two of four commonly used drug interaction databases. Clinicians should exercise caution with initiating new oral anticancer agents and consider the potential for drug interactions without solely relying on drug interaction databases.

This project applies structure-activity relationship (SAR), structure-based and database mining approaches to study ligand-protein interactions. To support these studies, we have developed a relational database system called EDinburgh University Ligand Selection System (EDULISS 2.0) which stores the structure-data files of +5.5 million commercially available small molecules (+4.0 million are recognised as unique) and over 1,500 various calculated molecular properties (descriptors) for each compound. A user-friendly web-based interface for EDULISS 2.0 has been established and is available at http://eduliss.bch.ed.ac.uk/. We have utilised PubChem bioassay data from an NMR based screen assay for a human FKBP12 protein (PubChem AID: 608). A prediction model using a Logistic Regression approach was constructed to relate the assay result with a series of molecular descriptors. The model reveals 38 descriptors which are found to be good predictors. These are mainly 3D-based descriptors, however, the presence of some predictive functional groups is also found to give a positive contribution to the binding interaction. The application of a neural network technique called Self Organising Maps (SOMs) succeeded in visualising the similarity of the PubChem compounds based on the 38 descriptors and clustering the 36 % of active compounds (16 out of 44) in a cluster and discriminating them from 95 % of inactive compounds. We have developed a molecular descriptor called the Atomic Characteristic Distance (ACD) to profile the distribution of specified atom types in a compound. ACD has been implemented as a pharmacophore searching tool within EDULISS 2.0. A structure-based screen succeeded in finding inhibitors for pyruvate kinase and the ligand-protein complexes have been successfully crystallised. This study also discusses the interaction of metal-binding sites in metalloproteins. We developed a database system and web-based interface to store and apply geometrical information of these metal sites. The programme is called MEtal Sites in Proteins at Edinburgh UniverSity (MESPEUS; http://eduliss.bch.ed.ac.uk/MESPEUS/). MESPEUS is an exceptionally versatile tool for the collation and abstraction of data on a wide range of structural questions. As an example we carried out a survey using this database indicating that the most common protein types which contain Mg-OATP-phosphate site are transferases and the most common pattern is linkage through the β- and γ-phosphate groups.

Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational prediction of drug-protein interactions and categorize them into protein structure-based and similarity-based methods. We evaluate three representative structure-based predictors and develop a Protein-Drug Interaction Database (PDID) that includes the putative drug targets generated by these three methods for the entire structural human proteome. To address the fact that only a limited set of proteins has known structures, we study the similarity-based methods that do not require this information. We review a comprehensive set of 35 high-impact similarity-based predictors and develop a novel, high-quality benchmark database. We group these predictors based on three types of similarities and their combinations that they use. We discuss and compare key architectural aspects of these methods including their source databases, internal databases and predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually or in all possible combinations. We assess predictive quality at the database-wide drug-protein interaction level and we are the first to also include evaluation across individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures AUC of 0.93. We offer a first-of-its-kind analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets.

Class of 2016 Abstract
Objectives: To identify common and serious drug-drug interactions involving novel anticoagulant drugs in cancer patients. Subjects: 60 patients who were treated at the Banner University of Arizona Cancer Center between November 1, 2013 and April 1, 2015 with rivaroxaban, dabigatran, or apixaban. Methods: A retrospective chart review was performed for patients who received a NOAC (novel oral anticoagulant) to determine if a medication regimen contained a drug-drug interaction involving the NOAC. Results: When analyzing the DDIs involving rivaroxaban, dabigatran, and apixaban, Micromedex® detected a total of 123 interactions, compared to Lexicomp®, which detected 111 interactions. When using Lexicomp®, there were 59 (32%) instances of no detected interactions, 19 (10%) moderate interactions, 27 (15%) major interactions, and 65 (36%) contraindicated DDIs with rivaroxaban. When using Micromedex®, there were 47 (26%) instances where no interaction was detected, 4 (2%) moderate interactions, and 119 (65%) major interactions, and no interactions were classified as contraindicated with rivaroxaban. Lexicomp® detected 3 (50%) interactions as major, and found no DDIs in 3 (50%) instances for dabigatran, and detected 1 (7%) moderate, 2 (14%) major and 6 (43%) contraindicated interactions for apixaban. Micromedex® detected 3 (50%) interactions as major, and found no DDIs in 3 (50%) instances for dabigatran, and detected 12 (86%) of interactions as major and found no DDIs in 2 (14%) instances for apixaban. Conclusions: There was significant variation in DDI detection between current literature4,5 and the drug information databases, Lexicomp® and Micromedex®, however most interactions detected were major or contraindicated.

Introdução. A varfarina tem sido considerada a principal terapêutica anticoagulante oral há aproximadamente 50 anos, estando entre os dez medicamentos mais envolvidos com reações adversas a medicamentos (RAM), apresenta estreita janela terapêutica e complexo regime posológico, exibe enorme variabilidade doseresposta e elevado risco de interações medicamentosas (IM). Objetivo. Identificar e avaliar as IM e RAM relacionadas com a administração da varfarina. Casuística e Métodos. Trata-se de um estudo transversal. Os dados foram coletados retrospectivamente através do banco de dados informatizado do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo pertencente ao Sistema Único de Saúde. As prescrições de janeiro/2004 a dezembro/2010 dos pacientes que utilizaram varfarina foram analisadas, sendo os pacientes divididos em dois grupos: estudo (uso de vitamina K até 168 horas após prescrição de varfarina) e controle. Posteriormente, as prescrições medicamentosas que não continham varfarina foram excluídas da análise. As informações coletadas incluíram idade, gênero, raça, unidade de atendimento, diagnóstico clínico, doses e medicamentos, e exames laboratoriais. As IM com varfarina foram classificadas em risco A, B, C, D e X de acordo a base de dados da Lexi-Interact(TM) Online. Foi realizada análise descritiva e analítica (p<0,05). Resultados e Discussão. Foram identificados 3048 pacientes, os quais receberam 154161 prescrições medicamentosas (42120 continham varfarina). A idade média foi de 55,8 (±19,3) anos, 53,2% do gênero feminino, prevalência de idosos (48,1%) e do diagnóstico outras doenças cerebrovasculares específicas (4,3%). Os valores médios da international normalized ratio (INR) (2,4±1,7) e dose de varfarina (5,1±1,8mg) encontraram-se dentro dos preconizados pelos protocolos terapêuticos. Foi observado que 66,4% dos pacientes realizaram uso de polifarmácia, o que pode elevar o risco de IM. Além disso, 63,2% dos pacientes apresentaram prescrição(ões) de medicamentos classificados como risco D e/ou X, com média de 1,4 (±0,4) medicamento/prescrição, destacando-se o ácido acetilsalicílico e amiodarona. Quando comparado grupo de estudo (n=429) versus controle (n=2619), houve diferença estatisticamente significativa na idade média (anos) (59,0±18,8 vs. 55,5±19,3; p<0,000), número médio de medicamentos/prescrição (7,1±2,8 vs. 6,2±2,8; p<0,000), número médio de medicamentos de Risco D e X de IM por prescrição (1,4±1,3 vs. 1,0±1,0; p<0,000), albumina sérica (g/dL) (3,4±0,6 vs. 3,7±0,6; p<0,000), aspartato aminotransferase (U/L) (60,7± 200,6 vs. 41,5±84,5; p<0,005) e INR (4,9±3,4 vs. 2,1±0,7; p<0,000), fatores estes que podem ter contribuído para ocorrência de RAM no grupo de estudo. Conclusão. Observou-se elevada ocorrência de possíveis IM e RAM nos usuários de varfarina, as quais podem comprometer a efetividade e segurança do tratamento farmacológico. Como possíveis fatores de risco para ocorrência de RAM, destacam-se elevados valores de idade, número de medicamentos/prescrição, prescrição de medicamentos classificados como risco D e/ou X, de INR e de aspartato aminotransferase, e valores diminuídos de albumina sérica.
Introduction. Warfarin has been considered the main oral anticoagulant therapy about 50 years ago and is among the ten drugs most commonly involved in adverse drug reactions (ADR), has a narrow therapeutic index and complex dosage regimen, exhibits enormous variability dose-response and high risk drug-drug interactions (DDI). Objective. To Identify and evaluate DDI and ADR related to the administration of warfarin. Casuistry and Methods. This was a cross sectional study. Data were collected retrospectively through the computerized database of the Faculty of Medicine of Ribeirao Preto Hospital, University of Sao Paulo linked to the Unified Health System. The prescriptions of the January/2004 to December/2010 of patients using warfarin were analyzed, and the patients were divided into two groups: study (utilization of vitamin K until 168 hours after prescribing warfarin) and control. Thereafter, the drug prescriptions that did not contain warfarin were excluded from analysis. Information collected included age, gender, race, patient service center, clinical diagnosis, dosages and drugs, and laboratory exams. The warfarin DDI were classified at risk A, B, C, D and X according to the database Lexi-Interact (TM) Online. Descriptive and analytical analysis were performed (p<0.05). Results and Discussion. We identified 3048 patients who received 154,161 drug prescriptions (42,120 contained warfarin). The mean age was 55.8 (±19.3) years, 53.2% female, prevalence of elderly (48.1%) and other cerebrovascular diseases specific diagnosis (4.3%). The average values of international normalized ratio (INR) (2.4±1.7) and warfarin dose (5.1±1.8mg) were within those recommended by therapeutic protocols. It was observed that 66.4% of patients received polypharmacy, which can raise the risk of DDI. In addition, 63.2% of patients had prescription(s) of drugs classified as D or X risk, with an average of 1.4 (±0.4) drugs per prescription, especially aspirin and amiodarone. Compared study group (n=429) versus control (n =2619), there was a statistically significant difference in mean age (years) (59.0±18.8 vs. 55.5±19.3; p<0.000), average number of medications/prescriptions (7.1±2.8 vs. 6.2±2.8; p<0.000), mean number of drugs with risk D and X DDI/prescription (1.4±1.3 vs. 1.0±1.0, p<0.000), serum albumin (g/dL) (3.4±0.6 vs. 3.7±0.6; p<0.000), aspartate aminotransferases (U/L) (60.7±200.6 vs. 41.5±84.5; p<0.005) and INR (4.9±3.4 vs. 2.1±0.7; p<0.000), factors that may have contributed to the occurrence of ADR in the study group. Conclusion. There was a high occurrence of possible DDI and ADR in patients treated with warfarin, which may compromise the effectiveness and safety of pharmacological treatment. Noteworthy is the high values of age, number of medications/prescriptions, prescription drugs classified as risk D or X, INR and aspartate aminotransferases, and lower values of serum albumin as potential risk factors for the occurrence of ADR.

The emergence of social media has impacted the way people think, communicate, behave, learn, and conduct research. In recent years, a large number of studies have analyzed and modeled this social phenomena. Driven by commercial and social interests, social media has become an attractive subject for researchers. Accordingly, new models, algorithms, and applications to address specific domains and solve distinct problems have erupted. In this thesis, we propose a novel network model and a path mining algorithm called HashnetMiner to discover implicit knowledge that is not easily exposed using other network models. Our experiments using HashnetMiner have demonstrated anecdotal evidence of drug-drug interactions when applied to a drug reaction context. The proposed research comprises three parts built upon the common theme of utilizing hashtags in tweets. 1 Digital Recruitment on Twitter. We build an expert system shell for two different studies: (1) a nicotine patch study where the system reads streams of tweets in real time and decides whether to recruit the senders to participate in the study, and (2) an environmental health study where the system identifies individuals who can participate in a survey using Twitter. 2 Does Social Media Big Data Make the World Smaller? This work provides an exploratory analysis of large-scale keyword-hashtag networks (K-H) generated from Twitter. We use two different measures, (1) the number of vertices that connect any two keywords, and (2) the eccentricity of keyword vertices, a well-known centrality and shortest path measure. Our analysis shows that K-H networks conform to the phenomenon of the shrinking world and expose hidden paths among concepts. 3 We pose the following biomedical web science question: Can patterns identified in Twitter hashtags provide clinicians with a powerful tool to extrapolate a new medical therapies and/or drugs? We present a systematic network mining method HashnetMiner, that operates on networks of medical concepts and hashtags. To the best of our knowledge, this is the first effort to present Biomedical Web Science models and algorithms that address such a question by means of data mining and knowledge discovery using hashtag-based networks.

碩士
臺北醫學大學
醫學資訊研究所
92
Background: Adverse drug interactions increase morbidity and mortality. There is no exact statistical data till now in Taiwan. This study quantifies prescriptions with potential adverse drug interactions in ambulatory care over a year period in Taiwan. Objectives: The purpose of this study was to explore the most common drug pairs of potential DDIs in a large claim database of ambulatory prescriptions in Taiwan. Methods: All prescriptions administered in 2000 were analysed to identify potential interactions amongst drugs appearing on the same prescription sheet. The data was from the database of National Health Insurance. Potential DDIs were defined according to the drug pairs in the “Drug Interaction Facts， 2000ed”. Potential DDIs were analyzed in relational database integrated with data mining methods. Results: There were 228,515,865 prescriptions including 989,349,362 medications during the study period. We found 63,798,362 potential DDIs. The prevalence rate of potential DDIs was 27.9%. The DDIs rates of 4 healthcare-classes in Taiwan were Medical Center， 44%， Metropolitan Hospital， 41%， Community Hospitals， 33%， Clinics， 23%。 Conclusions: The prevalence of potential DDIs in Taiwan’s was different to hospitals of different size. Digoxin was the most common medication found in potential DDIs. The Grid computing structure is more efficent than centerized structure in this study.

Trabalho Final de Mestrado Integrado, Ciências Farmacêuticas, Universidade de Lisboa, Faculdade de Farmácia, 2016
A polimedicação é um problema atual que coloca os doentes sobre um elevado risco de sofrerem interações medicamentosas. Isto verifica-se sobretudo na população idosa, que devido às comorbilidades e às mudanças fisiológicas associadas à idade, são especialmente vulneráveis a estas interações. Os mecanismos envolvidos nas interações entre fármacos podem afetar a farmacocinética ou a farmacodinâmica de um ou de ambos os fármacos em questão. O grande desafio da prática clínica é, portanto, conhecer as principais interações e saber como lidar com elas. Assim, os programas de screening de interações medicamentosas são uma mais valia para o clinico. Dois bons exemplos são os programas utilizados neste trabalho, Lexicomp Online e Drugs.com Interaction Checker, que permitiram avaliar as possíveis interações medicamentosas presentes num grupo de 30 doentes com idade superior a 65 anos, residentes de um lar de idosos. A utilização destes dois programas permitiu verificar algumas diferenças entre eles, que podem servir como fatores de escolha tendo em conta o objetivo pretendido.
Polipharmacy is a current problem that put patients under a serious risk of having drug interactions. This mostly occurs in elderly people because of their multimorbidity and age-related physiological changes, which make them especially vulnerable to interactions. The mechanisms involved in drugs interactions are associated with pharmacokinetic or pharmacodynamic alterations. The great challenge of clinical practice is to be aware of the most common interactions and know how to manage them. Therefore, drug-drug interactions screening programs are extremely useful for clinics. Two good examples are the programs used in this work, Lexicomp Online and Drugs.com Interaction Checker. They were used to evaluate the possible drug interactions in a group of 30 patients, aged more than 65 years old and living in a retirement home. The use of these two programs permitted to verify some differences between them that could be factors to help people choosing.

碩士
台北醫學院
醫學資訊研究所
89
Physicians often prescribed according to the combination of their expertise and the patient’s condition. These　prescriptions are usually safe, however, because of the differences and complicated degree of patients’ disease, physicians may use unusual drugs. If the Health Information System cannot provide proper alerts to reduce the error probabilities, then it is possible that some serious drug interactions will happen. Thus, patients may suffer from these contraindications. Therefore, even an expert for some complicated case, may make some mistakes. In this research we focused on outpatient prescriptions from a teaching hospital in two years. We categorize drug interactions into five different degrees. Then we screen all data thoroughly in each case. We find out there are a huge amount of severe drug interactions. Furthermore, we try to find the solution and reason of these drug interactions. We conclude that a drug interactions database is beneficial to clinical prescriptions. 　Drug interaction database was produced by generalizing conclusions from a collection of the clinical instances. The evidences depend on the clinical bibliography or scientific literature. However, in some cases, the physicians have to violate some rules for clinical reasons. Consequently, there are more drug interactions occurred in complicated regimens. It is not physician’s fault to ignore these drug interactions. Evaluations are needed to implement drug interactions into Medical Information System. Drug interactions are very important for qualities and safety of patient’s treatment. Drug interaction database will be a helpful decision support tool in the medical information system. 　We obtained a hospital’s outpatient prescriptions from October 1,1999 to April 30,2001. Totally, there are 1,644,894 prescription profiles were reviewed. By retrieving data, we made some preliminary analysis. The probability of the drug interaction for the first degree is 0.47%. After Comparing and calculating all data by the PC Cluster technology, we’ve chosen ten medical experts. Then, the clinical pharmacists select specific patients records according to their drug interactions, and design a questionnaire for clinical physicians to peer review. Finally, we use McNemar’s test to analyze the data. We found the prescriptions of clinical physicians have changed after drug interaction alerts. That is to say, the drug interaction database plays a indispensable role. The result appears that a few drug interactions will remain because clinical physicians consider they are valuable. In summary, the drug interactions database alert system will fairly affect the physician’s prescription in many aspects during their practice.

碩士
國立臺灣大學
臨床藥學研究所
93
It’s known that 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, or statins for short, are widely used for the treatment of hypercholesterolemia and have demonstrated efficacy for primary and secondary prevention of coronary heart disease (CHD). They are safe and usually well tolerated. The most frequently reported adverse events, including gastrointestinal disturbances, liver enzyme elevation, and muscle pain/weakness, are usually mild and there is almost no need to interrupt the therapy. Nevertheless, rhabdomyolysis is a quite severe adverse effect, though rare, and is related to the dosage of statins. Furthermore, statins are metabolized mainly by CYP system. The risk of rhabdomyolysis will be increased by drugs that share the same metabolic pathway. In this thesis, the prescribing patterns of statins and statin-drug interactions in outpatient are analyzed through the National Health Insurance Research Database in Taiwan. The relative risk of rhabdomyolysis and the incidence rate of mild muscular side effects are also examined. All cohort datasets within year 2002, total 200,000 patients, are included. The statin-drug interactions are analyzed not only on the same prescription but among different prescriptions of a person. The prescribing patterns are expressed in terms of person-time, cumulative using days per year, prescribed daily doses (PDD), the prevalence of potentially severe statin-drug interactions, and the duration of combination use. Using persons not taking statins as a control, a case-control study was performed to exam the relative risk of rhabdomyolysis in patients on statins. As for the incidence rates of mild muscular side effects, five different groups are selected and compared with each other. The rates are calculated by using case numbers as the numerator and time interval of drug used as the denominator. The study shows that atorvastatin was the most frequently prescribed statin. The average cumulative using days per year of statins are about 120 days. The prevalence of statin-drug interactions is 18.5 %, and 60 % of the total interactions are prescribed by the same doctors. Diltiazem and gemfibrozil were the two most frequently prescribed drugs that combined with statins, and cyclosporine is the drug that has the longest duration of combination. PDDs of statins in combination was not significant different from those in single use. Patients on statin therapy are much older than the general population and have higher incidence of underlying circulatory and endocrine diseases (p < 0.05). In this study, no of rhabdomyolysis was detected in statin users. After controlling the predisposing factors such as age, gender, duration for exposing to drug, DM and hypothyroidism, the incidence of mild muscular side effects for statin users was 1.6 times (95 % CI 1.3, 1.9; p < 0.0001) of those not using statins, and the odds ratio in fibrate users was even higher (OR 2.7; 95 % CI 1.9, 3.8; p < 0.0001). Drug-drug interactions easily occur for drugs metabolized via CYP3A isoenzyme under polypharmacy. Atorvastatin and simvastatin that are metabolized by CYP3A, are the two most frequently prescribed statins in our research. Around 40 % of interactions are difficult to detect due to patients’ habits of doctor shopping. Although there is no rhabdomyolysis was detected in our study in patients using statins, statin’s monotherapy increased the risk of muscular side effects. It is important to monitor the use of statins and provide proper patient education. Small sample size and short duration to follow up are the limitations of this study. That National Health Research Institutes (NHRI) set a new database of patients on lipid lowering drugs and further longitudinal studies on the use of statins are recommended.

Drug Information Centre service analysis IV. - drug interactions Author: Georg Michálek Supervisor: PharmDr. Kateřina Malá, Ph.D. Consultant: PharmDr. Petra Rozsívalová Department of Social and Clinical Pharmacy, Faculty of Pharmacy in Hradec Králové, Charles University, Czech Republic Introduction: Drug information centre (DIC) of the Faculty of Pharmacy in Hradec Králové, Charles University, and University Hospital Hradec Králové was established in 1994. It provides drug information to healthcare professionals in the form of timely and accurate answers to drug-related enquiries, including drug interactions (DI). Aim: This study aimed to analyse enquiries related to DI processed by DIC from 2015 to 2020 and to solve three drug enquiries related to drug interactions. Methods: Quantitative and qualitative analysis of drug enquiries was carried out. Data was collected from individual enquiries related, but not limited to drug-drug, drug-herbal, and drug-disease interactions. The quantitative analysis based on descriptive statistical methods was performed on two levels: enquiry level and drug interaction level. On the enquiry level, ATC codes found in the enquiries were analysed and on the drug interaction level various parameters assigned to each DI were analysed, such as interacting components,...

Indiana University-Purdue University Indianapolis (IUPUI)
Polypharmacy leads to increased risk of drug-drug interactions (DDI’s). In this dissertation, we create a database for quantifying fraction of metabolism (fm) of CYP450 isozymes for FDA approved drugs. A reproducible data collection protocol was developed to extract key information from publicly available in vitro selective CYP enzyme inhibition studies. The fm was then estimated from the curated data. Then, proposed a random control selection approach for nested case-control design for electronical health records (HER) and electronical medical records (EMR) databases. By relaxing the matching by case’s index time restriction, random control dramatically reduces the computational burden compared with traditional control selection approaches. Using the Observational Medical Outcomes Partnership gold standard and an EMR database, random control is demonstrated to have better performances as well. Finally, combining epidemiological studies and pharmacokinetic modeling with fm database, we detected and evaluated high-dimensional drug-drug interactions among thirty high frequency drugs. Multi-drug combinations that increased risk of myopathy were identified in the FAERS and EMR databases by a mixture drug-count response model (MDCM) model. Twenty-eight 3-way and 43 4-way DDI’s increased ratio of area under plasma concentration–time curve (AUCR) >2-fold and had significant myopathy risk in both databases. The predicted AUCR of omeprazole in the presence of fluconazole and clonidine was 9.35; and increased risk of myopathy was 6.41 (LFDR = 0.002) in FAERS and 18.46 (LFDR = 0.005) in EMR. We demonstrate that combining health record informatics and pharmacokinetic modeling is a powerful translational approach to detect high-dimensional DDI’s.
2 years

碩士
國立臺灣大學
臨床藥學研究所
93
Background: Drug-drug interactions (DDIs) may contribute to the occurance of adverse drug reactions, and unfortunately may cause death. It is one of the major concerns in clinical practice. It has been well-documented that some antipsychotics may cause QT interval prolongation or torsade de pointes leading to sudden death. The potential cardiotoxicity of thioridazine, in which drug interactions may play an important role, has caught lots of attention than other antipsychotics. The bulletins of the health regulatory agencies in many countries have continuously provided warnings with thioridazine to alert the potential cardiotoxicity of thioridazine since July, 2000. Drugs that may inhibit CYP 2D6 or prolong QT interval are considered as contraindications to combined use with thioridazine. Objective : The objective of this study is to evaluate potential significant DDIs with thioridazine in patients with schizophrenia from 1997 to 2001 in Taiwan using National Health Insurance Research Database – Psychiatric Inpatient Medical Claim Dataset (PIMC). Method:Criteria for DDIs regarding thioridazine was established based on the information from the MICROMEDEX® software program and Drug Interaction Facts. Patients with schizophrenia, who were prescribed thioridazine in the ambulatory settings from 1997 to 2001 based on the PIMC, were enrolled in this study. Concomitant administration is defined when the dates of prescriptions of drug interaction pairs were overlapped. Results: Our results showed that interactive drugs that most frequently combined with thioridazine (incidence >3.5%) were haloperidol, propranolol, chlorpromazine, lithium, risperidone and trifluoperazine., Of patients who were prescribed thioridazine from 1997 to 2001, 55.4% to 59.7% were exposed to potential risk of thioridazine related DDIs in the same prescription sheet, 65.2% to 68.9% were exposed to potential risk of thioridazine related DDIs from different prescription sheets. Overall, 75.0% to 77.4% were exposed to potential DDIs with thioridazine. According to thioridazine prescriptions, the rates of potential thioridazine-related DDIs in the same prescription sheet were 49.2% to 55.2%; and were 51.8% to 55.3% from different prescription sheets. Overall, the rates of potential DDIs with thioridazine were 59.3% to 65.0% per prescription. We found that polypharmacy is one of the factors that contribute to the high incidence of thioridazine related DDIs. The average daily dose of thioridazine (124.8∼143.2 mg) of the prescriptions with thioridazine related DDIs were lower than that of those without thioridazine related DDIs (150.3∼183.6 mg) (p<0.0001). Moreover, the average daily dose of thioridazine in combination with potential interactive antipsychotics (120.5∼134.3 mg) were lower than that of thioridazine without such combination (143.7∼178.1 mg) (p<0.0001). The dosage difference is most likely because physicians may prescribe thioridazine with low dose under antipsychotics polypharmacy or the thioridazine was used to as an adjunct treatment to other antipsychotics.

碩士
臺北醫學大學
醫學資訊研究所
102
Background: In 1995, Taiwan launched National Health Insurance (NHI), and traditional Chinese medicine was covered by NHI at 1996. People taking Chinese and western medicine at the same period increased, it is reasonable to propose that the adverse effects resulted from herb-drug interactions may increase too. It is imperative to build up the database of herb-drug interaction for monitoring the adverse effects from Traditional Chinese medicine compound formula with western medicine. Objective: The aim of this study is to (1): Establish the database for recorded drug interaction between traditional Chinese medicine compound formula and western medicine. (2): Analysis the interaction between traditional Chinese medicine and western drug status by using National Health Insurance Database. Method: The study is designed as 2 phases. Phase 1 is to establish the database of the interactions between traditional Chinese compound formula and western medicine. Phase 2 is divided into 2 parts; part 1 initiate the retrospective study, recruited the data from National Health Insurance Research Database in 1998 to 2011; to analyze the potential interaction between traditional Chinese medicine and western medicine, and investigated the incidence, demographic information and prescription hospital by this study. Part 2 is to set up auto alert system based on the traditional Chinese medicine and western medicine interaction database in a teaching hospital in the north of Taiwan. We analyse the incidence in this hospital and the doctors’ prescription pattern after them receiving the alerts. Result: Based on the above method to find traditional Chinese medicine which ingredients are mostly like western medicine: Ephedrae Herba (its extracts contain ephedrine ingredients) and there are 15 compound formula, Angelicae Sinensis Radix, Angelicae Dahuricae Radix (its extracts contain coumarin ingredients) and there are 9 compound formula. There were 586,318 potential herb-drug interactions documented in the traditional Chinese compound formula and western medicine interaction database. In period of 1998 to 2011, the prevalence of herb-drug interaction related to Ephedrae Herba was 0.18‰. The most common traditional Chinese compound formula were MA SHING GAN SHYR TANG(23.1%), SHEAU CHING LONG TANG(15.5%), DINQ CHUAN TANG(13.2%). The prevalence of herb-drug interaction related to Angelicae Sinensis Radix, Angelicae Dahuricae Radix was 4.59%. The most common traditional Chinese compound formula were TSANG EEL SAAN(32%), HUOH SHIANG JENQ CHIH SAAN(31.4%), SHY WUH TANG(10.7%). There were 480 potential herb-drug interactions documented in the teaching hospital alert system. Approximately 16.7% of doctors changed prescription medicine after receiving the alarm information. Conclusion: After implementation of National Health Insurance in Taiwan, the convenience of the clinic-visit makes the utilization of health care increased for both Chinese and western medicine. Different clinics prescription maybe cause drug interaction, the potential risks cannot be ignored. This herb-drug interaction database provides clinical physicians to determine when prescribing to avoid serious adverse drug reactions.

碩士
臺灣大學
臨床藥學研究所
98
Background: Warfarin exhibits narrow therapeutic range and high protein binding. It is metabolized via cytochrom P450 enzyme system. Therefore, patients treated with warfarin are prone to serious drug-drug interactions (DDIs) and major adverse events (AEs). DDI of warfarin is significantly associated with 1.2~4.5 times increased risk of major hemorrhage, hospital admission, and prolonged length of stay. However, the occurrence of DDIs and AEs during warfarin therapy has not been studied extensively among Asian population. Few studies used national database, and definitions and lists of DDIs varied in the literature. Objectives: Using Taiwan’s National Health Insurance Research Database to analyze the prescription pattern of warfarin, prevalence rate of potential significant DDIs, incidence rate (IR) of major AEs (i.e., hemorrhage and thromboembolism) among warfarin users in Taiwan. Methods: The National Health Insurance Research Database with one million randomly selected individuals in Taiwan was used in this retrospective cohort study. New users started warfarin between April 1997 and December 2004. Prescribing pattern, including prescribed daily warfarin dose and average PT/INR (prothrombin time/international normalized ratio) monitoring frequency, were analyzed. DDIs were defined as prescriptions from outpatient clinics with at least one day overlap with warfarin treatment episodes. Significance grade one DDIs were defined as DDIs with major severity and established documentation. A total of 56 grade one DDIs were evaluated in this study. In addition, dose of warfarin and PT/INR monitoring frequency when DDIs occurred were compared with those without grade one DDIs. Major adverse events were defined as the first emergency room visits and/or hospitalizations with a diagnosis of hemorrhage or thromboembolism, identified by International Classification of Disease, Ninth Revision, Clinical Modification (ICD9-CM), during the treatment episode. Moreover, all thromboembolism events were confirmed by antithrombotic agents and primary indications for warfarin. Results: Among 4047 new-users, mean age was 63.4 years old. More than half (56.3%) of new-users were female. Warfarin was prescribed mainly for ischemic heart disease (23.2%). The most common comobidity was hypertension (36.4%). Average daily dose of warfarin was 2.8 ±1.2 mg. There were 29.1% of patients never received PT/INR monitoring during warfarin treatment. Other patients monitored PT/INR every 40.5 days. A total of 2582 warfarin users (63.8%) were concurrently prescribed drugs with grade 1 DDIs. The most frequently prescribed medicines were aspirin (34.5%), noscapine (11.1%), amiodarone (9.4%), ketoprofen (7.4%), and naproxen (7.2%). Approximately 70% of these prescriptions were from the same physicians. About half of DDIs (54.4%) were from the medical centers. Doses of warfarin with or without DDIs were similar (p = 0.114). About half of patients (44.6%) had no PT/INR monitoring orders during DDIs. Fewer patients checked PT/INR when DDIs were involved different physicians. There were 412 patients developing hemorrhage with an incidence rate of 10.9% per person-year, and the majority was gastrointestinal hemorrhage (40.8%). On the other hand, 474 patients experiencing thromboembolism with an incidence rate of 12.8% per person-year, and the most common diagnosis was ischemic stroke (31.4%). Conclusions: Average daily warfarin dose was 2.8 mg. Approximately 30% of patients had no PT/INR monitoring during treatment. PT/INR was ordered about every 6 weeks. Prevalence of grade one DDIs were more than 60%. The most prevalent DDIs were aspirin, noscapine, amiodarone. Almost half of patients had no PT/INR monitoring orders during DDIs. Moreover, IRs of hemorrhage and thromboembolism were 10.9%, 12.8% per person-years, respectively. The most common major AEs were gastrointestinal hemorrhage and ischemic stroke. In Taiwan, medical records from different physicians have not yet been completely retrieved through medical insurance cards. Computerized DDI alert systems are uncommon in hospitals. It is essential to establish a computerized electronic alert system on DDIs along with continuing educations for physician and pharmacist to safeguard patients’ safety.

Ο φαρμακευτικός κλάδος μπορεί να επωφεληθεί, με την βοήθεια της σημερινής τεχνολογίας των ηλεκτρονικών υπολογιστών και συγκεκριμένα των βάσεων δεδομένων. Σκοπός της διπλωματικής εργασίας είναι η κάλυψη των αναγκών της Ελληνικής συνταγογραφίας των φαρμάκων. Συγκεκριμένα οι φαρμακευτικές βάσεις δεδομένων που ήδη υπάρχουν, δεν χρησιμοποιούν την τεχνολογία αναζήτησης των αλληλεπιδράσεων των φαρμάκων. Για το σκοπό αυτό σχεδιάστηκε και υλοποιήθηκε ένα μοντέλο βάσεων δεδομένων, που υποστηρίζει πλήρως τις αλληλεπιδράσεις των φαρμάκων, σύμφωνα με τις οδηγίες του Εθνικού Συνταγολογίου Φαρμάκων που εκδίδεται υπό την ευθύνη του ΕΟΦ. Έπειτα έγινε η εισαγωγή δείγματος φαρμάκων και δοκιμάστηκε η ακεραιότητα του συστήματος για την επιτυχή λειτουργία των αλληλεπιδράσεων μεταξύ φαρμάκων, καθώς μια εσφαλμένη αλληλεπίδραση μπορεί να αποβεί μοιραία για κάποιους ασθενείς. Τέλος αυτή η διπλωματική εργασία μπορεί να χρησιμοποιηθεί από φαρμακοποιούς και ιατρούς για την ασφαλέστερη και αποτελεσματικότερη συνταγογράφηση.
The pharmaceutical branch can profit, with the help of current technology of computers and concretely with the use of data bases. The purpose of this diplomatic work is to cover the needs of the Greek prescription on medicines. Particularly the pharmaceutical data bases, that already exist, do not use the search technology of interactions in medicines. For this aim it was drawn and implemented a model of data bases, that completely supports the interactions of medicines, according to the directives of national organization for medicines (N.O.M). The next step was the insertion of medicines and test the integrity of system for the successful operation of interactions between medicines, while a mistaken in interaction it can be turns out fatal for certain patients. Finally this diplomatic work can be used from pharmacists and doctors for secure and more effective prescription.