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Journal articles on the topic 'Drug'

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Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2024

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1

Khanapure, Amol, Pem Chuki, and Avinash De Sousa. "Drug Repositioning : Old Drugs For New Indications." Indian Journal of Applied Research 4, no. 8 (October 1, 2011): 462–66. http://dx.doi.org/10.15373/2249555x/august2014/119.

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2

Bocharova, Inna Anatolevna, Vadim Agadzhanov, and Vadim Sagalaev. "Drug addiction. Drugs and their effects on man." Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Serija 11. Estestvennye nauki, no. 2 (December 1, 2013): 22–27. http://dx.doi.org/10.15688/jvolsu11.2013.2.3.

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3

S, Swathi V., and Joshna Rani S. "DRUG - DRUG INTERACTION: AN IMPORTANT DRUG RELATED PROBLEM." Indian Research Journal of Pharmacy and Science 6, no. 3 (September 2019): 2008–12. http://dx.doi.org/10.21276/irjps.2019.6.3.11.

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4

Staltari, Orietta, Christian Leporini, Benedetto Caroleo, Emilio Russo, Antonio Siniscalchi, Giovambattista De Sarro, and Luca Gallelli. "Drug-Drug Interactions: Antiretroviral Drugs and Recreational Drugs." Recent Patents on CNS Drug Discovery 9, no. 3 (March 6, 2015): 153–63. http://dx.doi.org/10.2174/1574889809666141127101623.

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5

Isnenia, Isnenia. "Penggunaan Non-Steroid Antiinflamatory Drug dan Potensi Interaksi Obatnya Pada Pasien Muskuloskeletal." Pharmaceutical Journal of Indonesia 6, no. 1 (December 1, 2020): 47–55. http://dx.doi.org/10.21776/ub.pji.2020.006.01.8.

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The main therapy on musculoskeletal patients is the use of non-steroidal anti-inflammatory drugs (NSAIDs) either as monotherapy or in combination with drugs of the same class or pain relievers from other groups. The use of more than one drugs have potentially caused drug-drug interactions that can affect to patient. This study was aimed to describe the patient's sociodemographic (sex, ages) and clinical (numbers of drugs, type of drugs and diagnose) characteristics, as well as to find the correlation between potential drug interactions with these variables. This research was a quantitative study with a cross sectional design. Data were taken from 100 medical records of patients who had diagnosed with top five musculoskeletal diseases. Data were analyzed descriptively for sex, ages, number of drugs, type of drugs, and potential drug interactions. Bivariate correlation with chi-square were conducted to find statistically significancy potential drug interactions with each variable consist of sex, ages, type of drugs and it’s diagnose. The result shows that the musculoskeletal patients were 44% male, 56% female. Most musculoskeletal patients were aged 18-65 years (78%). Patients who received drugs <5 were 68% and ≥ 5 were 32%. 54% of patients were taking the diclofenac and only 5% of patients were taking the two NSAIDs combination, diclofenac and ibuprofen. There was no significant correlation (p > 0,05) between potential drug interactions with age, sex, type of NSAID, and type of musculosceletal diseases.

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6

NIWA, Toshiro, Toshifumi SHIRAGA, and Akira TAKAGI. "Drug-Drug Interaction of Antifungal Drugs." YAKUGAKU ZASSHI 125, no. 10 (October 1, 2005): 795–805. http://dx.doi.org/10.1248/yakushi.125.795.

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7

Sriwijitalai, Won, and Viroj Wiwanitkit. "Drug–drug interaction analysis: Antituberculosis drugs versus antiretroviral drugs." Biomedical and Biotechnology Research Journal (BBRJ) 3, no. 2 (2019): 101. http://dx.doi.org/10.4103/bbrj.bbrj_52_19.

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8

Boffito, Marta, Edward Acosta, David Burger, Courtney V. Fletcher, Charles Flexner, Rodolphe Garaffo, Giorgio Gatti, et al. "Therapeutic Drug Monitoring and Drug–Drug Interactions Involving Antiretroviral Drugs." Antiviral Therapy 10, no. 4 (May 2005): 469–77. http://dx.doi.org/10.1177/135965350501000413.

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The consensus of current international guidelines for the treatment of HIV infection is that data on therapeutic drug monitoring (TDM) of non-nucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs) provide a framework for the implementation of TDM in certain defined scenarios in clinical practice. However, the utility of TDM is considered to be on an individual basis until more data are obtained from large clinical trials showing the benefit of TDM. In April 2004, a panel of experts met for the second time in Rome, Italy. This was following the inaugural meeting in Perugia, Italy, in October 2000, which resulted in the manuscript published in AIDS 2002, 16(Suppl 1):S5–S37. The objectives of this second meeting were to review and update the numerous questions surrounding TDM of antiretroviral drugs and discuss the clinical utility, current concerns and future prospects of drug concentration monitoring in the care of HIV-1-infected individuals. A major focus of the meeting was to discuss and critically analyse recent and precedent clinical drug–drug interaction data to provide a clear framework of the pharmacological basis of how one drug may impact the disposition of another. This report, which has been updated to include material published or presented at international conferences up to the end of December 2004, reviews recent pivotal pharmacokinetic interaction data and provides advice to clinical care providers on how some drug–drug interactions may be prevented, avoided or managed, and, when data are available, on what dose adjustments and interventions should be performed.

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9

Soodalter, Jesse, Marta Sousa, and Marta Boffito. "Drug–drug interactions involving new antiretroviral drugs and drug classes." Current Opinion in Infectious Diseases 22, no. 1 (February 2009): 18–27. http://dx.doi.org/10.1097/qco.0b013e328320d573.

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10

Ismatov, Farrukh Asliddinovich. "DRUG TREATMENT WITH NON-STEROIDAL ANTI-INFLAMMATORY DRUGS JAW ALVEOLITIS." Frontline Medical Sciences and Pharmaceutical Journal 02, no. 03 (March 1, 2022): 88–94. http://dx.doi.org/10.37547/medical-fmspj-02-03-09.

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Non-steroidal anti-inflammatory drugs are widely used to suppress inflammation in the body. NSAIDs are available in different forms: tablets, capsules, ointments. They have three main properties: antipyretic, anti-inflammatory and analgesic. The best non-steroidal anti-inflammatory drug can only be chosen by a doctor, based on the individual characteristics of the patient. Self-treatment in this case may be fraught with serious adverse reactions or overdose. We suggest reading the list of drugs. The rating is based on value for money, patient feedback and expert opinion.

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11

Jefferson, James W., John H. Greist, Judith Carroll, and Margaret Baudhuin. "Drug-drug and drug-disease interactions with nonsteroidal anti-inflammatory drugs." American Journal of Medicine 81, no. 5 (November 1986): 948. http://dx.doi.org/10.1016/0002-9343(86)90382-7.

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12

Craig Brater, D. "Drug-drug and drug-disease interactions with nonsteroidal anti-inflammatory drugs." American Journal of Medicine 80, no. 1 (January 1986): 62–77. http://dx.doi.org/10.1016/0002-9343(86)90933-2.

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13

More, Dr Sonali Ramakant. "Drug – Drug Interaction of Phenytoin and Isoniazid." Journal of Medical Science And clinical Research 05, no. 04 (April 8, 2017): 19927–30. http://dx.doi.org/10.18535/jmscr/v5i4.34.

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14

A. Amer, Sayed. "المخدرات الجديدة: العقاقير المصممة." Security Policy Paper 2, no. 2 (December 31, 2021): 01–03. http://dx.doi.org/10.26735/ndan7653.

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15

Aziz Ahmad, Kashif, Saleha Akram Nizami, and Muhammad Haroon Ghous. "Coronavirus - Drug Discovery and Therapeutic Drug Monitoring Options." Pharmaceutics and Pharmacology Research 5, no. 2 (January 6, 2022): 01–04. http://dx.doi.org/10.31579/2693-7247/044.

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COVID-19 is basically a medium size RNA virus and the nucleic acid is about 30 kb long, positive in sense, single stranded and polyadenylated. The RNA which is found in this virus is the largest known RNA and codes for a large polyprotein. In addition, coronaviruses are capable of genetic recombination if 2 viruses infect the same cell at the same time. SARS-CoV emerged first in southern China and rapidly spread around the globe in 2002–2003. In November 2002, an unusual epidemic of atypical pneumonia with a high rate of nosocomial transmission to health-care workers occurred in Foshan, Guangdong, China. In March 2003, a novel CoV was confirmed to be the causative agent for SARS, and was thus named SARS-CoV. Despite the report of a large number of virus-based and host-based treatment options with potent in vitro activities for SARS and MERS, only a few are likely to fulfil their potential in the clinical setting in the foreseeable future. Most drugs have one or more major limitations that prevent them from proceeding beyond the in vitro stage. First, many drugs have high EC50/Cmax ratios at clinically relevant dosages

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16

Obach, R. S. "Drug-drug interactions: An important negative attribute in drugs." Drugs of Today 39, no. 5 (2003): 301. http://dx.doi.org/10.1358/dot.2003.39.5.799456.

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17

Yoshiyama, Yuji, Takashi Sugiyama, Motoko Kanke, and Kanji Tsuchimoto. "Drug–Drug Interactions between Antiarrhythmic Drugs in Chick Embryos." Biological & Pharmaceutical Bulletin 27, no. 1 (2004): 128–30. http://dx.doi.org/10.1248/bpb.27.128.

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18

Hollingsworth, M. "Drugs and pregnancy maternal drug handling - fetal drug exposure." Early Human Development 11, no. 3-4 (September 1985): 345. http://dx.doi.org/10.1016/0378-3782(85)90091-x.

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19

Pelliccia, Francesco, Fabiana Rollini, Giuseppe Marazzi, Cesare Greco, Carlo Gaudio, and Dominick J. Angiolillo. "Drug–drug interactions between clopidogrel and novel cardiovascular drugs." European Journal of Pharmacology 765 (October 2015): 332–36. http://dx.doi.org/10.1016/j.ejphar.2015.08.059.

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20

Klatt, Sabine, Martin F. Fromm, and Jörg König. "Transporter-Mediated Drug–Drug Interactions with Oral Antidiabetic Drugs." Pharmaceutics 3, no. 4 (October 12, 2011): 680–705. http://dx.doi.org/10.3390/pharmaceutics3040680.

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21

Defoort, P. "Drugs and pregnancy. Maternal drug handling. Fetal drug exposure." European Journal of Obstetrics & Gynecology and Reproductive Biology 22, no. 5-6 (September 1986): 384. http://dx.doi.org/10.1016/0028-2243(86)90133-4.

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22

Barakat, Marina, Sara Wahdan, Azza Awad, and Ebtehal El-Demerdash Zaki. "Direct Acting Antiviral Drugs: Pharmacokinetics and Drug-Drug Interactions." Archives of Pharmaceutical Sciences Ain Shams University 6, no. 2 (December 1, 2022): 274–91. http://dx.doi.org/10.21608/aps.2023.189971.1106.

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23

Plant, Martin A. "Drugs and drug problems." Midwifery 1, no. 3 (September 1985): 133–34. http://dx.doi.org/10.1016/s0266-6138(85)80030-9.

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24

Allahgholi, Milad, Hossein Rahmani, Delaram Javdani, Zahra Sadeghi-Adl, Andreas Bender, Dezsö Módos, and Gerhard Weiss. "DDREL: From drug-drug relationships to drug repurposing." Intelligent Data Analysis 26, no. 1 (January 14, 2022): 221–37. http://dx.doi.org/10.3233/ida-215745.

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Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.

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25

Puwar, Dr Bhavna, Dr Vaibhavi Patel, and Dr Sheetal Vyas. "Injection Drug Users (Idus) and their Drug Injecting Behavior." Global Journal For Research Analysis 2, no. 1 (June 15, 2012): 160–61. http://dx.doi.org/10.15373/22778160/january2013/23.

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26

KONIECZNY, KAJA, and PAUL DORIAN. "Clinically Important Drug–Drug Interactions Between Antiarrhythmic Drugs and Anticoagulants." Journal of Innovations in Cardiac Rhythm Management, no. 3 (March 1, 2019): 3552–59. http://dx.doi.org/10.19102/icrm.2019.100304.

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27

Tatebe, Yasuhisa, Satoshi Esumi, Yoshihisa Kitamura, and Toshiaki Sendo. "Drug interaction (42. Drug interaction of new-generation antiepileptic drugs)." Okayama Igakkai Zasshi (Journal of Okayama Medical Association) 130, no. 2 (August 1, 2018): 85–89. http://dx.doi.org/10.4044/joma.130.85.

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28

Gennari, Alessandra. "DRUG TO DRUG INTERACTIONS FROM THE NEWER ANTI-CANCER DRUGS." Breast 59 (October 2021): S34. http://dx.doi.org/10.1016/s0960-9776(21)00514-2.

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29

Dickson, Michael, Thomas J. Bramley, Chris Kozma, Dilesh Doshi, and Marcia F. T. Rupnow. "Potential drug–drug interactions with antiepileptic drugs in Medicaid recipients." American Journal of Health-System Pharmacy 65, no. 18 (September 15, 2008): 1720–26. http://dx.doi.org/10.2146/ajhp070508.

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30

Hirano, Masaru, Kazuya Maeda, Yoshihisa Shitara, and Yuichi Sugiyama. "DRUG-DRUG INTERACTION BETWEEN PITAVASTATIN AND VARIOUS DRUGS VIA OATP1B1." Drug Metabolism and Disposition 34, no. 7 (April 4, 2006): 1229–36. http://dx.doi.org/10.1124/dmd.106.009290.

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31

Bhutani, Hemant, Saranjit Singh, and K. C. Jindal. "Drug-Drug Interaction Studies on First-Line Anti-tuberculosis Drugs." Pharmaceutical Development and Technology 10, no. 4 (January 2005): 517–24. http://dx.doi.org/10.1080/10837450500299982.

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32

Wishart, David S., Craig Knox, An Chi Guo, Dean Cheng, Savita Shrivastava, Dan Tzur, Bijaya Gautam, and Murtaza Hassanali. "DrugBank: a knowledgebase for drugs, drug actions and drug targets." Nucleic Acids Research 36, suppl_1 (November 29, 2007): D901—D906. http://dx.doi.org/10.1093/nar/gkm958.

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33

Akbulut, Muge, and Yuksel Urun. "Onco-cardiology: Drug-drug interactions of antineoplastic and cardiovascular drugs." Critical Reviews in Oncology/Hematology 145 (January 2020): 102822. http://dx.doi.org/10.1016/j.critrevonc.2019.102822.

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34

Sahasrabudhe, Vaishali, Tong Zhu, Alfin Vaz, and Susanna Tse. "Drug Metabolism and Drug Interactions: Potential Application to Antituberculosis Drugs." Journal of Infectious Diseases 211, suppl 3 (May 25, 2015): S107—S114. http://dx.doi.org/10.1093/infdis/jiv009.

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35

Liu, Fang, Yu Song, Ya-Nan Liu, Yan-Tuan Li, Zhi-Yong Wu, and Cui-Wei Yan. "Drug-Bridge-Drug Ternary Cocrystallization Strategy for Antituberculosis Drugs Combination." Crystal Growth & Design 18, no. 3 (January 23, 2018): 1283–86. http://dx.doi.org/10.1021/acs.cgd.7b01738.

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36

Iqbal, Syed Talat, Zainab Batool, Haseeba Amir, and Tamkenat Mansoor. "DRUG-DRUG INTERACTIONS;." Professional Medical Journal 21, no. 03 (June 10, 2014): 441–44. http://dx.doi.org/10.29309/tpmj/2014.21.03.2018.

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Introduction: This research paper is based on a study conducted on the in-doorpatients at a teaching hospital in Gujrat, Pakistan, in order to check for the frequency with whichPenicillins, Quinolones and Cephalosporins are being used together and in combinations withother drugs and the drug-drug interactions that occur due to these combinations and theirimpacts on the patients. Objectives: (1) To check the frequency with which Penicillins,Quinolone and Cephalosporins are being used in different combinations in patients. (2) Todetermine their drug-drug interactions. (3) Impact on patients due to these interactions. (4)Reasons for prescription of mismatched combinations by clinicians. Study Design: 270 randomprescriptions were collected from different wards of DHQ hospital, Gujrat. These prescriptionswere then analyzed for drug interactions among the above mentioned group of drugs, with thehelp of soft ware program named The Medical Letter Adverse Drug Interaction Program. Setting:Aziz Bhatti Shaheed Hospital (DHQ), Gujrat , Pakistan. Period: Prescriptions were collected overthe period of 3 months. Conclusions: Prescribing antibiotics for different indications in indoorpatients is unavoidable. However, it is the duty of the clinician to monitor the patient when he isusing two or more drugs together. This study recommends the use of drug-drug interactiondetecting software in hospitals, so that, the level of patients’ safety may be enhanced.

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37

Fernández de Palencia Espinosa, Ma Ángeles, Ma Sacramento Díaz Carrasco, Andrés Sánchez Salinas, Amelia de la Rubia Nieto, and Alberto Espuny Miró. "Potential drug–drug interactions in hospitalised haematological patients." Journal of Oncology Pharmacy Practice 23, no. 6 (August 10, 2016): 443–53. http://dx.doi.org/10.1177/1078155216664201.

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Background Frequently, haematological patients undergo highly complex and intensive treatment protocols, so a high risk of drug–drug interactions could be expected. Objectives To determine prevalence of clinically relevant drug–drug interactions, to identify the most frequent drug–drug interactions and associated risk factors. Methods A prospective, observational and descriptive study was carried out from November 2012 to February 2013. Twice a week, every patient’s treatment sheet was collected. Each medication list was screened through two databases: Thomson MicromedexTM and Drug Interaction FactsTM. All identified potential drug–drug interactions with a moderate or higher severity rating were recorded. Summary statistics were used to describe patient and disease characteristics, most often prescribed drugs, and frequency, types and classification of drug–drug interactions. Multiple logistic regression models were used to identify risk factors associated with drug–drug interactions. Results A total of 2061 drug–drug interactions were detected in 317 treatment sheets from 58 patients. The prevalence of treatment sheets with drug–drug interactions by Micromedex and Drug Interaction Facts databases were 74.1% and 56.8%, respectively. Azole antifungals, immunosuppressive drugs, antiemetics, antidepressants, acid suppressants and corticosteroids were the most frequent involved drugs. In multivariate analysis, the main risk factor associated with increased odds for drug–drug interactions was a higher number of non-antineoplastic drugs. Conclusions The prevalence of drug–drug interactions was common, with immunosuppressant and azole antifungal agents being the most commonly involved drugs. The factor having the greatest influence on drug–drug interactions was a higher number of non-antineoplastic drugs.

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38

Chawra, Himmat Singh, Y. S. Tanwar, Ritu M. Gilhotra, and S. K. Singh. "Gastroretentive drug delivery systems a potential approach for antihypertensive drugs: An updated review." Asian Pacific Journal of Health Sciences 5, no. 2 (June 2018): 217–23. http://dx.doi.org/10.21276/apjhs.2018.5.2.40.

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39

Singh, Uday, Gurjeet Singh, and Randhir Singh. "A STUDY ON DRUG UTILIZATION PATTERN OF ANTIHYPERTENSIVE DRUGS IN TERTIARY CARE HOSPITAL." INDIAN RESEARCH JOURNAL OF PHARMACY AND SCIENCE 7, no. 2 (June 2020): 2184–93. http://dx.doi.org/10.21276/irjps.2020.7.2.11.

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40

Zhang, L., F. Wu, S. C. Lee, H. Zhao, and L. Zhang. "pH-Dependent Drug–Drug Interactions for Weak Base Drugs: Potential Implications for New Drug Development." Clinical Pharmacology & Therapeutics 96, no. 2 (April 14, 2014): 266–77. http://dx.doi.org/10.1038/clpt.2014.87.

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41

Petrak, Karel. "The Difference between Targeted Drug Therapies and Targeted-Drug Therapies." Cancer Research and Cellular Therapeutics 2, no. 4 (September 8, 2018): 01–03. http://dx.doi.org/10.31579/2640-1053/032.

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42

EZEGBE, CHEKWUBE, Ogechukwu Umeh, and Sabinus Ofoefule. "Drug Carriers." Journal of Current Biomedical Research 2, no. 1 (February 28, 2022): 77–105. http://dx.doi.org/10.54117/jcbr.v2i1.3.

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In recent years, there has been an exponential interest in the development of novel drug delivery systems using drug carriers. Drug carriers offer significant advantages over the conventional drug delivery systems in terms of high stability, high specificity, high drug loading capacity, controlled release of drug and ability to deliver both hydrophilic and hydrophobic drugs. As a result of their unique behaviors, drug carriers have a wide range of biomedical and industrial applications. Nanospheres are associated with a lot of benefits such as ease of administration to target sites, reduction in toxicity level and ease of passage via the capillary vessels. Hydrogel nanoparticles are useful in the treatment of inflammatory diseases, as bioresponsive hydrogels in drug delivery system and as a carrier in controlled drug delivery system. Carbon nanotubes have a large surface area which has the ability to adsorb or conjugate with a wide variety of therapeutic and diagnostic agents. They are useful in the areas of gene delivery, tissue regeneration and biosensor diagnosis. Liposomes are known to target a drug to a specific site. They entrap drugs which are released for subsequent absorption. They are used to achieve active targeting, increase efficacy and therapeutic index of drugs. Niosomes improve the solubility and oral bioavailability of poorly soluble drugs. They protect drugs from biological environment, increase the stability of entrapped drugs and they can easily reach the site of action. Aquasomes are nanoparticulate carriers that can be characterized for structural analysis. They preserve conformational integrity and biochemical stability of drugs. Ethosomes are noninvasive delivery carriers that enable drugs to reach the deep skin layers and the systemic circulation. They contain phospholipids which could be in form of phosphatidyl choline (PC), hydrogenated PC, phosphatidic acid (PA), Phosphatidyl serine (PS) and phosphatidyl inositol (PI). Ethosomes are known to increase skin permeation of drugs, improve biological activity and pharmacodynamics profile of drugs. This review aims to emphasize the importance of drug carriers in drug delivery system, and applications of drug carriers in various areas of research, technology and treatment.

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43

Uwai, Yuichi. "Enantioselective Drug Recognition by Drug Transporters." Molecules 23, no. 12 (November 22, 2018): 3062. http://dx.doi.org/10.3390/molecules23123062.

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Drug transporters mediate the absorption, tissue distribution, and excretion of drugs. The cDNAs of P-glycoprotein, multidrug resistance proteins (MRPs/ABCC), breast cancer resistance protein (BCRP/ABCG2), peptide transporters (PEPTs/SLC15), proton-coupled folate transporters (PCFT/SLC46A1), organic anion transporting polypeptides (OATPs/SLCO), organic anion transporters (OATs/SLC22), organic cation transporters (OCTs/SLC22), and multidrug and toxin extrusions (MATEs/SLC47) have been isolated, and their functions have been elucidated. Enantioselectivity has been demonstrated in the pharmacokinetics and efficacy of drugs, and is important for elucidating the relationship with recognition of drugs by drug transporters from a chiral aspect. Enantioselectivity in the transport of drugs by drug transporters and the inhibitory effects of drugs on drug transporters has been summarized in this review.

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44

Taburet, Anne-Marie, and Eric Singlas. "Drug Interactions with Antiviral Drugs." Clinical Pharmacokinetics 30, no. 5 (May 1996): 385–401. http://dx.doi.org/10.2165/00003088-199630050-00005.

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45

Eadie, M. J. "Therapeutic drug monitoring - antiepileptic drugs." British Journal of Clinical Pharmacology 52, S1 (September 2001): 11–20. http://dx.doi.org/10.1111/j.1365-2125.2001.00394.x.

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46

Campbell, T. J., and K. M. Williams. "Therapeutic drug monitoring: antiarrhythmic drugs." British Journal of Clinical Pharmacology 52, S1 (September 2001): 21–33. http://dx.doi.org/10.1111/j.1365-2125.2001.00768.x.

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47

Le Couteur, D. G., A. J. McLachlan, and R. de Cabo. "Aging, Drugs, and Drug Metabolism." Journals of Gerontology Series A: Biological Sciences and Medical Sciences 67A, no. 2 (July 18, 2011): 137–39. http://dx.doi.org/10.1093/gerona/glr084.

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48

Eadie, M. J. "Therapeutic drug monitoring-antiepileptic drugs." British Journal of Clinical Pharmacology 46, no. 3 (September 1998): 185–93. http://dx.doi.org/10.1046/j.1365-2125.1998.00769.x.

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49

Campbell, T. J., and K. M. Williams. "Therapeutic drug monitoring: antiarrhythmic drugs." British Journal of Clinical Pharmacology 46, no. 4 (October 1998): 307–19. http://dx.doi.org/10.1046/j.1365-2125.1998.t01-1-00768.x.

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50

Eadie, M. J. "Therapeutic drug monitoring – antiepileptic drugs." British Journal of Clinical Pharmacology 52 (September 2001): 11–20. http://dx.doi.org/10.1046/j.1365-2125.2001.00394.x.

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