Dissertations / Theses on the topic 'Drug Side Effect Prediction'
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1
Wang, Chen. "High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5509.
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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.
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Bellón, Molina Víctor. "Prédiction personalisée des effets secondaires indésirables de médicaments." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEM023/document.
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Les effets indésirables médicamenteux (EIM) ont des répercussions considérables tant sur la santé que sur l'économie. De 1,9% à 2,3% des patients hospitalisés en sont victimes, et leur coût a récemment été estimé aux alentours de 400 millions d'euros pour la seule Allemagne. De plus, les EIM sont fréquemment la cause du retrait d'un médicament du marché, conduisant à des pertes pour l'industrie pharmaceutique se chiffrant parfois en millions d'euros.De multiples études suggèrent que des facteurs génétiques jouent un rôle non négligeable dans la réponse des patients à leur traitement. Cette réponse comprend non seulement les effets thérapeutiques attendus, mais aussi les effets secondaires potentiels. C'est un phénomène complexe, et nous nous tournons vers l'apprentissage statistique pour proposer de nouveaux outils permettant de mieux le comprendre.Nous étudions différents problèmes liés à la prédiction de la réponse d'un patient à son traitement à partir de son profil génétique. Pour ce faire, nous nous plaçons dans le cadre de l'apprentissage statistique multitâche, qui consiste à combiner les données disponibles pour plusieurs problèmes liés afin de les résoudre simultanément.Nous proposons un nouveau modèle linéaire de prédiction multitâche qui s'appuie sur des descripteurs des tâches pour sélectionner les variables pertinentes et améliorer les prédictions obtenues par les algorithmes de l'état de l'art. Enfin, nous étudions comment améliorer la stabilité des variables sélectionnées, afin d'obtenir des modèles interprétablesAdverse drug reaction (ADR) is a serious concern that has important health and economical repercussions. Between 1.9%-2.3% of the hospitalized patients suffer from ADR, and the annual cost of ADR have been estimated to be of 400 million euros in Germany alone. Furthermore, ADRs can cause the withdrawal of a drug from the market, which can cause up to millions of dollars of losses to the pharmaceutical industry.Multiple studies suggest that genetic factors may play a role in the response of the patients to their treatment. This covers not only the response in terms of the intended main effect, but also % according toin terms of potential side effects. The complexity of predicting drug response suggests that machine learning could bring new tools and techniques for understanding ADR.In this doctoral thesis, we study different problems related to drug response prediction, based on the genetic characteristics of patients.We frame them through multitask machine learning frameworks, which combine all data available for related problems in order to solve them at the same time.We propose a novel model for multitask linear prediction that uses task descriptors to select relevant features and make predictions with better performance as state-of-the-art algorithms. Finally, we study strategies for increasing the stability of the selected features, in order to improve interpretability for biological applications
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Amanzadi, Amirhossein. "Predicting safe drug combinations with Graph Neural Networks (GNN)." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446691.
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Many people - especially during their elderly - consume multiple drugs for the treatment of complex or co-existing diseases. Identifying side effects caused by polypharmacy is crucial for reducing mortality and morbidity of the patients which will lead to improvement in their quality of life. Since there is immense space for possible drug combinations, it is infeasible to examine them entirely in the lab. In silico models can offer a convenient solution, however, due to the lack of a sufficient amount of homogenous data it is difficult to develop both reliable and scalable models in its ability to accurately predict Polypharmacy Side Effect. Recent advancement in the field of representational learning has utilized the power of graph networks to harmonize information from the heterogeneous biological databases and interactomes. This thesis takes advantage of those techniques and incorporates them with the state-of-the-art Graph Neural Network algorithms to implement a Deep learning pipeline capable of predicting the Adverse Drug Reaction of any given paired drug combinations.
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Villafranca, Steven Wayne. "The effect of early psychostimulant treatment on abuse liability and dopamine receptors." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2824.
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Examines whether the reinforcing properties of drugs of abuse were altered in adulthood by methylphenidate, more commonly known as Ritalin. Subjects were 108 rats of Sprague-Dawley descent (Harlan). Methylphenidate, or saline was administered daily to the subjects from the postnatal period (11-20 days old). The rats preference for morphine during early adulthood was measured using conditioned place preference. The number of dopamine D₂ receptors was measured in each rat and the correlation between receptor number and morphine preference was determined. Results indicate that rats pretreated with methylphenidate showed greater preference for morphine than saline pretreated rats and suggests that exposure to methylphenidate during the postnatal period increases the rewarding value of morphine.
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Diaz, Boada Juan Sebastian. "Polypharmacy Side Effect Prediction with Graph Convolutional Neural Network based on Heterogeneous Structural and Biological Data." Thesis, KTH, Numerisk analys, NA, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288537.
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The prediction of polypharmacy side effects is crucial to reduce the mortality and morbidity of patients suffering from complex diseases. However, its experimental prediction is unfeasible due to the many possible drug combinations, leaving in silico tools as the most promising way of addressing this problem. This thesis improves the performance and robustness of a state-of-the-art graph convolutional network designed to predict polypharmacy side effects, by feeding it with complexity properties of the drug-protein network. The modifications also involve the creation of a direct pipeline to reproduce the results and test it with different datasets.För att minska dödligheten och sjukligheten hos patienter som lider av komplexa sjukdomar är det avgörande att kunna förutsäga biverkningar från polyfarmaci. Att experimentellt förutsäga biverkningarna är dock ogenomförbart på grund av det stora antalet möjliga läkemedelskombinationer, vilket lämnar in silico-verktyg som det mest lovande sättet att lösa detta problem. Detta arbete förbättrar prestandan och robustheten av ett av det senaste grafiska faltningsnätverken som är utformat för att förutsäga biverkningar från polyfarmaci, genom att mata det med läkemedel-protein-nätverkets komplexitetsegenskaper. Ändringarna involverar också skapandet av en direkt pipeline för att återge resultaten och testa den med olika dataset.
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Zayed, Aref. "Development of ICP-MS assays for the study and prediction of the efficacy and side effects of Pt-based drugs in cancer chemotherapy." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/9446.
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Pt-based drugs are important cytotoxic agents that are used in the chemotherapeutic regimes of ~50% of all cancer patients. However, the efficacy of these drugs is often limited by drug toxicity and tumour resistance. Determination of the cellular pharmacokinetics and pharmacodynamics of Pt-drugs is important for understanding their molecular mechanisms of action and toxicity, and may be used, therefore, to predict the outcome of the treatment. ICP-MS is the most sensitive technique for the determination of Pt in biological samples and can offer robust, fast and accurate quantitations for studying pharmacokinetics and pharmacodynamics of Pt-drugs in patients. This thesis describes the development of a set of ICP-MS based assays for the determination of Pt-DNA adducts and Pt sub-cellular distribution in leukocytes of cancer patients and human cancer cell lines following treatment with Pt-based chemotherapy. It is ultimately aimed to use these assays in the clinic to predict the effectiveness and toxicity of Pt-based chemotherapy in individual patients, and offer those who would respond to the treatment personalised drug doses. Alternatively, patients who would not benefit from these drugs would be offered other forms of treatment. Pt DNA adduct formation was determined in leukocytes from patients undergoing Pt-based chemotherapy demonstrating significant inter-patient variability and excellent reproducibility of the assay. The sensitivity of the technique enabled quantitation of as little as 0.2 Pt adducts per 106 nucleotides using 10 µg of patient DNA. It was shown that Pt/P ratio was robust against DNA matrix effects, and was considered more reliable approach, with Eu as internal standard, for estimating Pt adducts per nucleotide compared to using Pt data in combination with DNA concentration measured by UV. Comparison of in vivo Pt-DNA adduct formation with the patients clinical notes suggested possible correlation between the adduct formation in leukocytes and toxicity. Speciation methods employing HPLC with complementary ICP-MS and ESI-Ion Trap-MS detection were developed and used for characterisation of oxaliplatin bi-functional adducts with mono-nucleotides and di-nucleotides. Further, a fast and sensitive LC-ICP-MS assay was developed and used for the quantification of oxaliplatin GG intra-strand adducts in human cancer cell lines. The assay, which has a detection limit of 0.22 Pt adduct per 106 nucleotides based on a 10 μg DNA sample, is suitable for in vivo assessment of the adducts in patients undergoing oxaliplatin chemotherapy. Combining the ICP-MS quantitation with a cell fractionation procedure allowed, for the first time, the detailed quantitation of entire sub-cellular Pt-drug partitioning in patient leukocytes in vivo, and in human cancer cell lines in vitro, following exposure to variety of Pt-drugs. The studies showed that Pt broadly follows the total protein content of the individual sub-cellular compartments with the majority being scavenged in the cytosol compartment.
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Gauthier, Kelly J. "Length of Hospital Stay, Delirium and Discharge Status Outcomes Associated With Anticholinergic Drug Use in Elderly Hospitalized Dementia Patients." VCU Scholars Compass, 2006. http://hdl.handle.net/10156/1704.
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Winter, Lara. "Characterisation of the neurosteroid analgesic alphadolone." Monash University, Dept. of Anaesthesia, 2004. http://arrow.monash.edu.au/hdl/1959.1/9669.
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Kucher, Kellie Lynn. "Effect of preweanling methylphenidate exposure on the induction, extinction and reinstatement of morphine-Induced conditioned place preference in rats." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2892.
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This study examined the effect of preweanling methyphenidate exposure on later drug reward. We examined the induction, extinction, and reinstatement of morphine induced conditioned place preference (CPP) in rats that received methylphenidate pretreatment during the preweanling period.
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Gouws, Stephanus Andries. "The impact of hospital surveillance programmes on the incidence of adverse drug reaction reporting in a South African teaching hospital." Master's thesis, University of Cape Town, 1989. http://hdl.handle.net/11427/27186.
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Post-marketing surveillance refers to any non-experimental or observational study, method, or monitoring strategy that is applied to obtain information on drug experience (primarily adverse) after a drug has been approved for clinical use. One of the major problems in post-marketing surveillance studies is the lack or under-reporting of drug experiences by health care professionals. This study was developed to describe the impact of three different prescription event monitoring programmes on the reporting of adverse drug reactions (ADR's) in the hospital situation. The intensive ADR monitoring programme and two voluntary ADR monitoring programmes which followed were conducted in the medical wards of an urban teaching and referral hospital. All patients admitted to the designated wards were monitored by a dedicated pharmacist in the intensive programme, ward pharmacists in the first voluntary programme and by medical and nursing staff in the second voluntary programme. The pharmacist monitored a cohort of patients prospectively in two medical wards for a period of three months. The patient's record was linked with any suspected ADR. All details, i.e. patient drug orders, characteristics and ADR description, were recorded and then reported. From 228 patients monitored, 25 cases have been reported. The impact of the intensive ADR monitoring programme was a reporting rate of 11 percent. Reports were received on ADR's of a particularly mild, common and pharmacologically predictable (type A) nature. The first voluntary ADR monitoring programme comprised the reporting of suspected AD R's and the recording of drug orders for the patients and the patient characteristics. The ward pharmacists monitored for suspected AD R's in all patients during their regular ward rounds. Six cases were reported in a population of 1506 patients monitored during the three months. The reports were mainly on moderate to severe suspected AD R's of pharmacologically unpredictable (type B) nature. The rate of reports received by the surveillance unit in this study was 4 reports per ward pharmacist per annum. The second voluntary ADR monitoring programme comprised the prospective monitoring of 1555 patients by medical and nursing staff during their stay at the designated medical wards during the three month period. Patients were monitored for any ADR and when an ADR was suspected, the patient characteristics and drug orders were recorded and reported to the surveillance unit. Ten cases were reported represented by six reports from doctors and four by sisters. The reporting rate was 2 reports per doctor in four years and 3 reports for each member of the nursing team in 5 years. Reports were mainly received on moderate to severe suspected ADR's of a pharmacologically unpredictable (type B) nature.
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Isreb, Abdullah. "The use of solubility parameters to predict the behaviour of a co-crystalline drug dispersed in a polymeric vehicle : approaches to the prediction of the interactions of co-crystals and their components with hypromellose acetate succinate and the characterization of that interaction using crystallographic, microscopic, thermal, and vibrational analysis." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5525.
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Dispersing co-crystals in a polymeric carrier may improve their physicochemical properties such as dissolution rate and solubility. Additionally co-crystal stability may be enhanced. However, such dispersions have been little investigated to date. This study focuses on the feasibility of dispersing co-crystals in a polymeric carrier and theoretical calculations to predict their stability. Acetone/chloroform, ethanol/water, and acetonitrile were used to load and grow co-crystals in a HPMCAS film. Caffeine-malonic acid and ibuprofennicotinamide co-crystals were prepared using solvent evaporation method. The interactions between each of the co-crystals components and their mixtures with the polymer were studied. A solvent evaporation approach was used to incorporate each compound, a mixture, and co-crystals into HPMCAS films. Differential scanning calorimetry data revealed a higher affinity of the polymer to acidic compounds than their basic counterparts as noticed by the depression of the glass transition temperature (Tg). Moreover, the same drug loading produced films with different Tgs when different solvents were used. Solubility parameter values (SP) of the solvents were employed to predict that effect on the depression of polymer Tg with relative success. SP values were more successful in predicting the preferential affinity of two acidic compounds to interact with the polymer. This was confirmed using binary mixtures of naproxen, flurbiprofen, malonic acid, and ibuprofen. On the other hand, dispersing basic compounds such as caffeine or nicotinamide with malonic acid in HPMCAS film revealed the growth of co-crystals. A dissolution study showed that the average release of caffeine from films containing caffeine-malonic acid was not significantly different to that of films containing similar caffeine concentration. The stability of the caffeine-malonic acid co-crystals in HPMC-AS was prolonged to 8 weeks at 95% relative humidity and 45°C. The theory developed in this project, that an acidic drug with a SP value closer to the polymer will dominate the interaction process and prevent the majority of the other material from interacting with the polymer, may have utility in designing co-crystal systems in polymeric vehicles
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Prague, Mélanie. "Utilisation des modèles dynamiques pour l'optimisation des traitements des patients infectés par le VIH." Thesis, Bordeaux 2, 2013. http://www.theses.fr/2013BOR22056.
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La plupart des patients infectés par le VIH ont une charge virale qui peut être rendue indétectable par des combinaisons antirétrovirales hautement actives (cART); cependant, il existe des effets secondaires aux traitements. L'utilisation des modèles mécanistes dynamiques basés sur des équations différentielles ordinaires (ODE) a considérablement amélioré les connaissances de la dynamique HIV-système immunitaire et permet d'envisager une personnalisation du traitement. L'objectif de ces travaux de thèse est d'améliorer les techniques statistiques d'estimation de paramètres dans les modèles mécanistes dynamiques afin de proposer des stratégies de surveillance et d'optimisation des traitements. Après avoir introduit NIMROD un algorithme d'estimation bayésienne basé sur une maximisation de la vraisemblance pénalisée, nous montrons la puissance des approches mécanistes dynamiques pour l'évaluation des effets traitements par rapport aux méthodes descriptives d'analyse des trajectoires des biomarqueurs. Puis, nous définissons le « modèle à cellules cibles », un système ODE décrivant la dynamique du VIH et des CD4. Nous montrons qu'il possède de bonnes capacités prédictives. Nous proposons une preuve de concept de la possibilité de contrôler individuellement la dose de traitement. Cette stratégie adaptative réajuste la dose du patient en fonction de sa réaction à la dose précédente par une procédure bayésienne. Pour finir, nous introduisons la possibilité de l’'individualisation des changements de cART. Ce travail passe par la quantification in vivo d'effets de cART en utilisant des indicateurs d'activité antivirale in vitro. Nous discutons la validité des résultats et les étapes méthodologiques nécessaires pour l'intégration de ces méthodes dans les pratiques cliniquesMost HIV-infected patients viral loads can be made undetectable by highly active combination of antiretroviral therapy (cART), but there are side effects of treatments. The use of dynamic mechanistic models based on ordinary differential equations (ODE) has greatly improved the knowledge of the dynamics of HIV and of the immune system and can be considered for personalization of treatment. The aim of these PhD works is to improve the statistical techniques for estimating parameters in dynamic mechanistic models so as to elaborate strategies for monitoring and optimizing treatments. We present an algorithm and program called NIMROD using Bayesian inference based on the maximization of the penalized likelihood. Then, we show the power of dynamic mechanistic approaches for the evaluation of treatment effects compared to methods based on the descriptive analysis of the biomarkers trajectories. Next, we build the “target cells model “, an ODE system of the dynamics between the HIV and CD4. We demonstrate it has good predictive capabilities. We build a proof of concept for drug dose individualization. It consists in tuning the dose of the patient based on his reaction to the previous doses using a Bayesian update procedure. Finally, we introduce the possibility of designing an individualized change of cART. This work involves the quantification of in vivo effects of cART using in vitro antiviral activity indicators. We discuss the validity of the results and the further steps needed for the integration of these methods in clinical practice
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Bongini, Pietro. "Graph Neural Networks for Molecular Data." Doctoral thesis, 2022. http://hdl.handle.net/2158/1274291.
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Tesi di dottorato di Pietro Bongini, Dottorato in Smart Computing, XXXIV Ciclo, discussa in data: 17/06/2022.
Pietro Bongini's Ph.D. Thesis, Ph.D. program in Smart Computing, Cycle XXXIV, discussed on 17/06/2022.
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Chi, Chih Chien, and 紀旨倩. "Predicting Drug Side Effects and Targets Using Machine Learning Approaches - A Case Study on Antidepressants." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/r34a32.
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碩士國立清華大學
資訊系統與應用研究所
104
Depression is a life-threatening mental health disorder which is expected to be the second leading cause of psychosocial disability throughout the world by 2020 and will become the largest contributor to lost work productivity by 2030 as reported by World Health Organization (WHO, 2012). Despite the availability of various therapeutic options, the underlying pathological mechanisms remain unclear. The important concerns with antidepressants are delayed therapeutic response and insufficient efficacy. With a wide range of adverse effects, there is no doubt a large unmet need for better pharmaceutical treatment. The purpose of our study is to develop a computational approach to investigate potential side effects and targets of antidepressants, hoping to provide support for better strategies for the future of drug development and therapy. We presented an aggregation framework to predict unknown side effects and hidden targets from 816 drugs by adopting 653 chemical, 984 biological and 6,111 phenotypic features. Among four machine learning-based algorithms, we found that the aggregation random forest model achieved best in overall performance. Hence, we used this computational approach to predict the potential candidates for antidepressants. We conducted the case study using 15 depression-related drugs, including 9 first generation, 5 second generation antidepressants and 1 muscle relaxant that has a structure similar to tricyclic antidepressant (TCA). The in silico model obtained promising results with AUROC score of 0.9140834, AUPR score of 0.5185952 for side effects prediction and AUROC score of 0.9513566, AUPR score of 0.3101223 for targets prediction.
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Yun-Chu-Chen and 陳韻竹. "Effects of quercetin on the antitumor and side effect of chemotherapy drug-cisplatin." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/29268310115564743895.
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碩士中山醫學大學
營養學研究所
103
Part 1. Cisplatin (CIS) is a widely used chemotherapy drug for human cancers, including lung cancer. Despite its significant antitumor activity, the side effects limit its use. Quercetin (Q), a flavonoid present abundantly in plants, has been shown to may increase the antitumor effects of some anti-cancer drugs and to decrease their harmful effects. In this study, we first used a xenograft model to investigate the effects of quercetin on the antitumor and side effects of CIS. Male nude mice were injected with A549 cells (human lung cancer cell line) into the flank. After 4 weeks, the tumor-bearing mice were randomly treated with cisplatin (2 or 5 mg/kg, once a week; CIS2 and CIS5, respectively) alone, or CIS2 in combination with quercetin for 12 weeks. Quercetin was given by a quercetin containing diet (0.1% or 1% quercetin diet; LQ and HQ, respectively) or by intraperitoneal injection (10 mg/kg, 3 times a week; IQ). The results showed that CIS2+HQ and CIS2+IQ rather than CIS2 alone or CIS2+LQ significantly inhibited tumor growth. The effects of CIS2+IQ and CIS2+HQ were similar to that of CIS5. We found that CIS2 in combination a Q containing diet or IQ tended to decrease plasma TBARs levels as well as proinflammatory cytokines in plasma and in tumors, especially IQ. Quercetin containing diets and IQ also significantly increased the total quercetin concentration in tumor tissues in an order HQ>LQ and IQ. In addition, quercetin containing diets and IQ tended to increase gastrocnemius muscle, epididymal fat weight and bone marrow cell number compared to CIS2. Most the efficiencies of IQ were the best. CIS2 decreased the neutrophil count and increased the lymphocyte count. Quercetin tended to suppress these effects of cisplatin. In addition, CIS in combination with quercetin treatment significantly increase the platelet count, while CIS alone had no effect. However, CIS significantly reduced the red blood cell count and quercetin did not recover such an effect of CIS. The results of the present study demonstrated that quercetin not only increase the antitumor effect of cisplatin, but also reduce some of the side effects of cisplatin in vivo. Part 2. We found that the enhancing effect of IQ on the antitumor effect of CIS was better than that of quercetin from diet. We then compared the effect of quercetin and its metabolites, quercetin-3-glucuronide (G), at 2 μM and 5 μM on the anti-growth effect of CIS (1 μM) in A549 cells and explored the possible mechanisms. The results showed that CIS+Q and CIS+G enhanced CIS-induced cells growth arrest in A549 cells in a dose- and time-dependent manner. The combined inhibition efficiency of CIS+Q on cell growth was greater than that of the CIS+G at the same dose. Furthermore, we found that CIS induced cell cycle arrest in G2/M phase, and quercetin-3-glucuronide rather than quercetin significantly increased such an effect of CIS. However, CIS+Q significantly increased the cells in sub-G1 phase, indicating CIS+Q inducing apoptosis. CIS+Q also significantly increase caspase-3 activity, while cisplatin or CIS+G had no significant effect. We determined the expression of p21 and p53, which are associated with cell cycle arrest and apoptosis, in treated cells. The result showed that CIS in combination with Q quercetin or quercetin-3-glucuronide significantly increased p21 and p53 protein expression earlier than CIS alone. The effect of CIS+Q was better than that of CIS+G. In conclusion, quercetin has a better efficiency than quercetin-3-glucuronide to enhance the suppressed effect of CIS on the growth of A549 cells, which is in agreement with our in vivo findings. The results also suggest that quercetin and quercetin-3-glucuronide exert their effect may through increasing the expression of p21 and p53, which in turn inducing cell cycle arrest and apoptosis.
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Tu, Wei-Ming, and 涂偉銘. "Investigation of doxorubicin-loaded redox-responsive silica-Au drug nanocarrier for cancer inhibition and side effect mitigation in a zebrafish tumor model." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/28015184474832617105.
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碩士國立交通大學
應用化學系碩博士班
105
Despite the advancement in medicine, cancer still ranked first in the top ten causes of death. Chemotherapy has been the most common strategy to treat cancer. However, its non-specificity led to severe side effects in patients, as a result, the tolerant dosage was limited and the therapeutic effect could be reduced. The drug-loaded nanocarriers thus were employed to enable targeted delivery by selective ligand functionalization, improving the therapeutic efficacy. Doxorubicin is a well-known anti-cancer drug which is effective to several types of carcinoma but with severe and irreversible cardiotoxicity thereby hindering its usage. In this study, we developed a novel silica-gold nanocomposite Tf-DOX-ReSi-Au NPs in which the disulfide-linked redox-responsive silica (ReSi) was functionalized on the surface of gold nanoparticles (Au NPs, 13 nm), followed by the electrostatic adsorption of doxorubicin (DOX). Transferrin (Tf) served as selective ligand with high affinity to its receptor expressed predominantly in carcinomas. The release of DOX was then triggered upon high concentration of glutathione stimuli to reduce the disulfide bond embedded in the nanocomposite. The zebrafish tumor model was established in the study to evaluate the biocompatibility of nanocomposite in vivo. Tf-DOX-ReSi-Au NPs has found to be more effective in the inhibition of tumor growth than that of free DOX. Moreover, using the laser scanning confocal microscope, a 3D zebrafish heart model was constructed to investigate the cardiovascular functions after free drug and the nanocomposites treatment. Tf-DOX-ReSi-Au NPs revealed insignificant cardiotoxicity and side effects compared with that in free DOX.
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Han, Xu. "Identification and mechanistic investigation of clinically important myopathic drug-drug interactions." Thesis, 2014. http://hdl.handle.net/1805/5275.
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Indiana University-Purdue University Indianapolis (IUPUI)Drug-drug interactions (DDIs) refer to situations where one drug affects the pharmacokinetics or pharmacodynamics of another. DDIs represent a major cause of morbidity and mortality. A common adverse drug reaction (ADR) that can result from, or be exacerbated by DDIs is drug-induced myopathy. Identifying DDIs and understanding their underlying mechanisms is key to the prevention of undesirable effects of DDIs and to efforts to optimize therapeutic outcomes. This dissertation is dedicated to identification of clinically important myopathic DDIs and to elucidation of their underlying mechanisms. Using data mined from the published cytochrome P450 (CYP) drug interaction literature, 13,197 drug pairs were predicted to potentially interact by pairing a substrate and an inhibitor of a major CYP isoform in humans. Prescribing data for these drug pairs and their associations with myopathy were then examined in a large electronic medical record database. The analyses identified fifteen drug pairs as DDIs significantly associated with an increased risk of myopathy. These significant myopathic DDIs involved clinically important drugs including alprazolam, chloroquine, duloxetine, hydroxychloroquine, loratadine, omeprazole, promethazine, quetiapine, risperidone, ropinirole, trazodone and simvastatin. Data from in vitro experiments indicated that the interaction between quetiapine and chloroquine (risk ratio, RR, 2.17, p-value 5.29E-05) may result from the inhibitory effects of quetiapine on chloroquine metabolism by cytochrome P450s (CYPs). The in vitro data also suggested that the interaction between simvastatin and loratadine (RR 1.6, p-value 4.75E-07) may result from synergistic toxicity of simvastatin and desloratadine, the major metabolite of loratadine, to muscle cells, and from the inhibitory effect of simvastatin acid, the active metabolite of simvastatin, on the hepatic uptake of desloratadine via OATP1B1/1B3. Our data not only identified unknown myopathic DDIs of clinical consequence, but also shed light on their underlying pharmacokinetic and pharmacodynamic mechanisms. More importantly, our approach exemplified a new strategy for identification and investigation of DDIs, one that combined literature mining using bioinformatic algorithms, ADR detection using a pharmacoepidemiologic design, and mechanistic studies employing in vitro experimental models.
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Huang, Hui. "System biology modeling : the insights for computational drug discovery." Thesis, 2014. http://hdl.handle.net/1805/5612.
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Indiana University-Purdue University Indianapolis (IUPUI)Traditional treatment strategy development for diseases involves the identification of target proteins related to disease states, and the interference of these proteins with drug molecules. Computational drug discovery and virtual screening from thousands of chemical compounds have accelerated this process. The thesis presents a comprehensive framework of computational drug discovery using system biology approaches. The thesis mainly consists of two parts: disease biomarker identification and disease treatment discoveries. The first part of the thesis focuses on the research in biomarker identification for human diseases in the post-genomic era with an emphasis in system biology approaches such as using the protein interaction networks. There are two major types of biomarkers: Diagnostic Biomarker is expected to detect a given type of disease in an individual with both high sensitivity and specificity; Predictive Biomarker serves to predict drug response before treatment is started. Both are essential before we even start seeking any treatment for the patients. In this part, we first studied how the coverage of the disease genes, the protein interaction quality, and gene ranking strategies can affect the identification of disease genes. Second, we addressed the challenge of constructing a central database to collect the system level data such as protein interaction, pathway, etc. Finally, we built case studies for biomarker identification for using dabetes as a case study. The second part of the thesis mainly addresses how to find treatments after disease identification. It specifically focuses on computational drug repositioning due to its low lost, few translational issues and other benefits. First, we described how to implement literature mining approaches to build the disease-protein-drug connectivity map and demonstrated its superior performances compared to other existing applications. Second, we presented a valuable drug-protein directionality database which filled the research gap of lacking alternatives for the experimental CMAP in computational drug discovery field. We also extended the correlation based ranking algorithms by including the underlying topology among proteins. Finally, we demonstrated how to study drug repositioning beyond genomic level and from one dimension to two dimensions with clinical side effect as prediction features.
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Zottmann, Claudia. "EKT und unerwünschte Ereignisse – eine retrospektive Analyse an der Universitätsmedizin Göttingen." Doctoral thesis, 2017. http://hdl.handle.net/11858/00-1735-0000-002B-7D51-3.
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Prague, Melanie. "Utilisation des modèles dynamiques pour l'optimisation des traitements des patients infectés par le VIH." Thesis, 2013. http://www.theses.fr/2013BOR22056/document.
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La plupart des patients infectés par le VIH ont une charge virale qui peut être rendue indétectable par des combinaisons antirétrovirales hautement actives (cART); cependant, il existe des effets secondaires aux traitements. L'utilisation des modèles mécanistes dynamiques basés sur des équations différentielles ordinaires (ODE) a considérablement amélioré les connaissances de la dynamique HIV-système immunitaire et permet d'envisager une personnalisation du traitement. L'objectif de ces travaux de thèse est d'améliorer les techniques statistiques d'estimation de paramètres dans les modèles mécanistes dynamiques afin de proposer des stratégies de surveillance et d'optimisation des traitements. Après avoir introduit NIMROD un algorithme d'estimation bayésienne basé sur une maximisation de la vraisemblance pénalisée, nous montrons la puissance des approches mécanistes dynamiques pour l'évaluation des effets traitements par rapport aux méthodes descriptives d'analyse des trajectoires des biomarqueurs. Puis, nous définissons le « modèle à cellules cibles », un système ODE décrivant la dynamique du VIH et des CD4. Nous montrons qu'il possède de bonnes capacités prédictives. Nous proposons une preuve de concept de la possibilité de contrôler individuellement la dose de traitement. Cette stratégie adaptative réajuste la dose du patient en fonction de sa réaction à la dose précédente par une procédure bayésienne. Pour finir, nous introduisons la possibilité de l’'individualisation des changements de cART. Ce travail passe par la quantification in vivo d'effets de cART en utilisant des indicateurs d'activité antivirale in vitro. Nous discutons la validité des résultats et les étapes méthodologiques nécessaires pour l'intégration de ces méthodes dans les pratiques cliniquesMost HIV-infected patients viral loads can be made undetectable by highly active combination of antiretroviral therapy (cART), but there are side effects of treatments. The use of dynamic mechanistic models based on ordinary differential equations (ODE) has greatly improved the knowledge of the dynamics of HIV and of the immune system and can be considered for personalization of treatment. The aim of these PhD works is to improve the statistical techniques for estimating parameters in dynamic mechanistic models so as to elaborate strategies for monitoring and optimizing treatments. We present an algorithm and program called NIMROD using Bayesian inference based on the maximization of the penalized likelihood. Then, we show the power of dynamic mechanistic approaches for the evaluation of treatment effects compared to methods based on the descriptive analysis of the biomarkers trajectories. Next, we build the “target cells model “, an ODE system of the dynamics between the HIV and CD4. We demonstrate it has good predictive capabilities. We build a proof of concept for drug dose individualization. It consists in tuning the dose of the patient based on his reaction to the previous doses using a Bayesian update procedure. Finally, we introduce the possibility of designing an individualized change of cART. This work involves the quantification of in vivo effects of cART using in vitro antiviral activity indicators. We discuss the validity of the results and the further steps needed for the integration of these methods in clinical practice
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McCanna, David. "Development of Sensitive In Vitro Assays to Assess the Ocular Toxicity Potential of Chemicals and Ophthalmic Products." Thesis, 2009. http://hdl.handle.net/10012/4338.
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The utilization of in vitro tests with a tiered testing strategy for detection of mild ocular irritants can reduce the use of animals for testing, provide mechanistic data on toxic effects, and reduce the uncertainty associated with dose selection for clinical trials. The first section of this thesis describes how in vitro methods can be used to improve the prediction of the toxicity of chemicals and ophthalmic products. The proper utilization of in vitro methods can accurately predict toxic threshold levels and reduce animal use in product development. Sections two, three and four describe the development of new sensitive in vitro methods for predicting ocular toxicity. Maintaining the barrier function of the cornea is critical for the prevention of the penetration of infections microorganisms and irritating chemicals into the eye. Chapter 2 describes the development of a method for assessing the effects of chemicals on tight junctions using a human corneal epithelial and canine kidney epithelial cell line. In Chapter 3 a method that uses a primary organ culture for assessing single instillation and multiple instillation toxic effects is described. The ScanTox system was shown to be an ideal system to monitor the toxic effects over time as multiple readings can be taken of treated bovine lenses using the nondestructive method of assessing for the lens optical quality. Confirmations of toxic effects were made with the utilization of the viability dye alamarBlue. Chapter 4 describes the development of sensitive in vitro assays for detecting ocular toxicity by measuring the effects of chemicals on the mitochondrial integrity of bovine cornea, bovine lens epithelium and corneal epithelial cells, using fluorescent dyes.
The goal of this research was to develop an in vitro test battery that can be used to accurately predict the ocular toxicity of new chemicals and ophthalmic formulations. By comparing the toxicity seen in vivo animals and humans with the toxicity response in these new in vitro methods, it was demonstrated that these in vitro methods can be utilized in a tiered testing strategy in the development of new chemicals and ophthalmic formulations.