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Journal articles on the topic 'Drug effects'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

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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Ranasinghe, Leonard, Geetha Amarasinghe, and Amanda Lash. "The Effects of Childhood Trauma on Drug Use." Clinical Medical Reviews and Reports 2, no. 3 (June 22, 2020): 01–07. http://dx.doi.org/10.31579/2690-8794/019.

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Exposure to childhood physical abuse, sexual abuse, emotional abuse, and neglect have been found to have a positive association with drug use. Various studies have demonstrated how individuals may turn to drugs as a form of coping. However, this is a negative form of coping, which can take a toll on one’s mental state and even impact families and society. Studies have reported that a higher number of adverse childhood experiences (ACEs) have a stronger association with substance use later in life [1,3]. Recent research has also demonstrated those who experienced childhood neglect also displayed anxiety or depression, which might suggest a reason for the connection between the negligence and drug use [14]. There is also research that shows that childhood maltreatment could influence one’s susceptibility to drugs and the structural remodeling of the brain [4]. Various types of drugs have been used in association with childhood trauma; however, no single drug was reported to have a greater association than the others. Substance abuse has been a rising issue over the years. With the understanding that childhood trauma could contribute to the onset of drug use, it is essential to gain a better knowledge of the specific types of substance abuse linked to childhood trauma. Future examination of this topic can help raise awareness and educate society on the effects of childhood trauma and how to prevent drug use associated with it.

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3

Rees, Terry D. "Oral Effects of Drug Abuse." Critical Reviews in Oral Biology & Medicine 3, no. 3 (April 1992): 163–84. http://dx.doi.org/10.1177/10454411920030030101.

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Drug abuse is a major problem in the U.S. and most other countries of the world today. Many studies, surveys, and case reports have described the adverse social and medical effects of drug abuse; yet surprisingly little is known about the specific effects of many of these drugs in the oral cavity. This article reviews the current state of knowledge concerning the systemic and oral effects of drugs of abuse and the dental management of addicted patients.

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4

Kedyk, Antonina, and Oleksandr Kutsyn. "Metabolic effects of empagliflozine." Diabetes Obesity Metabolic Syndrome, no. 5 (2022): 38–56. http://dx.doi.org/10.57105/2415-7252-2022-5-04.

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Potentially beneficial metabolic effects of empagliflozin remain "overshadowed" by the undeniable benefits of this drug in terms of cardio- and renoprotection. Today, there is a large data array (meta-analyses, systematic reviews and separate cohort studies with empagliflozin) that confirm the beneficial effect of this drug on various metabolic processes, which was systematized in this scientific review. It is emphasized that the antihyperglycemic effect of the drug does not depend on the secretion of insulin by β-cells of the pancreas and insulin resistance, it is manifested only in conditions of glucosuria and limited by eGFR. Empagliflozin combines well with all oral and parenteral hypoglycemic drugs; combination with drugs that have a potential risk of hypoglycemia (insulin and sulfonylurea drugs) requires a dose reduction of the latter. The durability of empagliflozin allows to maintain the achieved levels of glycated hemoglobin for a long time and postpone the start of insulin therapy. Weight loss by drug using corrects blood pressure and insulin resistance. In addition to the ability to reduce the level of uric acid and postpone the appointment of antigout drugs, empagliflozin can be considered a drug that has a multi-vector effect on various component of the metabolic syndrome. Promising areas of the drug using are reducing the risk of nephrolithiasis, steatosis and slowing down the progression of liver fibrosis.

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5

Halliwell, Barry. "Drug Antioxidant Effects." Drugs 42, no. 4 (October 1991): 569–605. http://dx.doi.org/10.2165/00003495-199142040-00003.

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6

Terézhalmy, Géza T., and Marsha A. Pyle. "ADVERSE DRUG EFFECTS." Dental Clinics of North America 38, no. 4 (October 1994): 769–83. http://dx.doi.org/10.1016/s0011-8532(22)00189-6.

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7

Lamy, Peter P. "Adverse Drug Effects." Clinics in Geriatric Medicine 6, no. 2 (May 1990): 293–307. http://dx.doi.org/10.1016/s0749-0690(18)30618-9.

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8

Goudie, A. J. "Conditioned drug effects." European Journal of Pharmacology 183, no. 1 (July 1990): 98. http://dx.doi.org/10.1016/0014-2999(90)91357-h.

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9

Larson, Eric B. "Adverse drug effects." Journal of General Internal Medicine 8, no. 6 (June 1993): 342–43. http://dx.doi.org/10.1007/bf02600151.

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10

Derman, SG, and EY Adashi. "Erratum to: Adverse effects offertility drugs. Drug Saf." Drug Safety 12, no. 3 (March 1995): 208. http://dx.doi.org/10.1007/bf03257466.

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11

Ryu, Jae Yong, Hyun Uk Kim, and Sang Yup Lee. "Deep learning improves prediction of drug–drug and drug–food interactions." Proceedings of the National Academy of Sciences 115, no. 18 (April 16, 2018): E4304—E4311. http://dx.doi.org/10.1073/pnas.1803294115.

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Drug interactions, including drug–drug interactions (DDIs) and drug–food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. Several computational methods have been developed to better understand drug interactions, especially for DDIs. However, these methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction. Here, we report development of a computational framework DeepDDI that uses names of drug–drug or drug–food constituent pairs and their structural information as inputs to accurately generate 86 important DDI types as outputs of human-readable sentences. DeepDDI uses deep neural network with its optimized prediction performance and predicts 86 DDI types with a mean accuracy of 92.4% using the DrugBank gold standard DDI dataset covering 192,284 DDIs contributed by 191,878 drug pairs. DeepDDI is used to suggest potential causal mechanisms for the reported ADEs of 9,284 drug pairs, and also predict alternative drug candidates for 62,707 drug pairs having negative health effects. Furthermore, DeepDDI is applied to 3,288,157 drug–food constituent pairs (2,159 approved drugs and 1,523 well-characterized food constituents) to predict DFIs. The effects of 256 food constituents on pharmacological effects of interacting drugs and bioactivities of 149 food constituents are predicted. These results suggest that DeepDDI can provide important information on drug prescription and even dietary suggestions while taking certain drugs and also guidelines during drug development.

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12

Arshed, Muhammad Asad, Shahzad Mumtaz, Omer Riaz, Waqas Sharif, and Saima Abdullah. "A Deep Learning Framework for Multi Drug Side Effects Prediction with Drug Chemical Substructure." Vol 4 Issue 1 4, no. 1 (January 22, 2022): 19–31. http://dx.doi.org/10.33411/ijist/2022040102.

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Nowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of multiple side effects of drugs with the chemical structure of drugs. As it is a common experience that a single drug can cause multiple side effects, that’s why we have proposed a deep learning model that can predict multiple side effects for a single drug. We have considered three side effects (Dizziness, Allergy, Headache) in this study. We have collected the drug side effects information from the SIDER database. We have achieved an accuracy of ‘0.9494’ with our multi-label classification based proposed model. The proposed model can be used in different stages of the drug development process.

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13

Messiha, F. S. "Fluoxetine: Adverse Effects and Drug-Drug Interactions." Journal of Toxicology: Clinical Toxicology 31, no. 4 (January 1993): 603–30. http://dx.doi.org/10.3109/15563659309025765.

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14

Hardiana, Iyan, Ivans Panduwiguna, Aji Abdaul Mujaki, Jerry Jerry, and Taufani Taufani. "Evaluasi efek samping obat pada pasien rawat inap di Rumah Sakit “X” di Jakarta." Health Sciences and Pharmacy Journal 6, no. 1 (August 22, 2022): 35–41. http://dx.doi.org/10.32504/hspj.v6i1.670.

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One of the problems related to the use of drugs is the presence of side effects of drugs. Side Effects Drugs are responses to a drug that is detrimental and undesirable and that occur at doses typically used in humans for the prevention, diagnosis, or therapy of diseases or the modification of physiological functions. Side effects in drug administration are unexpected effects that can arise in treatment. This study aims to determine the number of Side Effects of Drug incidences and what drugs cause Side Effects of the Drug on inpatients at "X" Hospital Jakarta. The research method was descriptive non-experimental research by taking data using retrospective methods. The results of the study showed as many as 139 cases that were suspected of experiencing Adverse Drug Reactions with the number of drug side effects events with a Naranjo score scale of 9-13 must have occurred Adverse Drug Reactions obtained by one patient who experienced Adverse Drug Reactions, then the Naranjo score scale of 4 - 8 most likely Adverse Drug Reactions as many as 86 cases with a percentage of 61.87%, then with a Naranjo score scale 1 - 3 The possibility of Adverse Drug Reactions occurring as many as 52 points with a guarantee of 37.41%. The class of drugs suspected of causing drug side effects are antibiotics, with as many as 86 cases (61.87%). The drugs suspected of causing drug side effects are ciprofloxacin, as many as ten items (7.19%), and levofloxacin, ten items (7.19%), the form of manifestation of drug side effects was the highest form of Side Effects of the Drug, namely with redness symptoms as many as 33 cases (23.74%). The drugs given to overcome Side Effects of the Drug are dexamethasone 58 drug items (41.73%); these results showed that a high probability of Side Effects of the Drug in patients staying at X Jakarta hospital was still high. It needs to be re-evaluated for its use

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15

Dewulf, Pieter, Michiel Stock, and Bernard De Baets. "Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects." Pharmaceuticals 14, no. 5 (May 2, 2021): 429. http://dx.doi.org/10.3390/ph14050429.

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Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a limited number of combinations to predict for all. In this work, we identify various subtasks in predicting effects caused by drug–drug interaction. Predicting drug–drug interaction effects for drugs that already exist is very different from predicting outcomes for newly developed drugs, commonly called a cold-start problem. We propose suitable validation schemes for the different subtasks that emerge. These validation schemes are critical to correctly assess the performance. We develop a new model that obtains AUC-ROC =0.843 for the hardest cold-start task up to AUC-ROC =0.957 for the easiest one on the benchmark dataset of Zitnik et al. Finally, we illustrate how our predictions can be used to improve post-market surveillance systems or detect drug–drug interaction effects earlier during drug development.

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16

Crow, Stephen M., James W. Logan, and Lillian Y. Fok. "Illicit Drug Effects in Labor Arbitration Decision Making." Journal of Drug Issues 24, no. 3 (July 1994): 489–505. http://dx.doi.org/10.1177/002204269402400309.

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Two hundred twenty-six arbitration outcomes in alcohol and drug disciplinary cases were investigated to examine illicit drug effects on arbitral decision making. We examined these effects on arbitrators' final decisions and the standards or decision cues that arbitrators use to justify their decisions. We also examined interactions of illicit drugs and standards of proof and looked at decisions in two different time spans to determine if changing societal attitudes about alcohol and drugs might have an effect. In this study, arbitrators were less lenient with illicit drug users than with legal drug users. This finding suggests that an illicit drug effect may exist in other distributive justice scenarios.

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17

Hu, Baofang, Hong Wang, and Zhenmei Yu. "Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network." Molecules 24, no. 20 (October 11, 2019): 3668. http://dx.doi.org/10.3390/molecules24203668.

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Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug–target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential side-effects of drugs based on more comprehensive drug information in which the modes of action of drugs on target proteins are integrated. Drug information of multiple types is modeled as a signed heterogeneous information network. We propose a signed heterogeneous information network embedding framework for learning drug embeddings and predicting side-effects of drugs. We use two bias random walk procedures to obtain drug sequences and train a Skip-gram model to learn drug embeddings. We experimentally demonstrate the performance of the proposed method by comparison with state-of-the-art methods. Furthermore, the results of a case study support our hypothesis that modes of action of drugs on target proteins are meaningful in side-effect prediction.

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18

IQBAL, SYED TALAT, ZAINAB BATOOL, and SAJID MEHMOOD. "POSSIBLE TOXIC EFFECTS OF BENZODIAZEPINES;." Professional Medical Journal 20, no. 02 (February 7, 2013): 284–89. http://dx.doi.org/10.29309/tpmj/2013.20.02.654.

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Introduction: Benzodiazepines and its derivatives are used widely as anxiolytics, hypnotics, seizure control and as musclerelaxants. Design: The prescriptions of 270 patients were evaluated for moderate to severe drug interactions using drug interactiondetection software. Setting: Teaching hospital in Gujrat, Pakistan. Objective: This study is used to evaluate the possible toxic effects ofbenzodiazepine related drug-drug interactions in prescriptions of indoor patients. Material & Methods: The prescriptions wereprocessed through a software program named, The Medical Letter Adverse Drug Interaction program. The randomly collected patientchart profiles included both male and female patients ranging from age of few months old children to old aged patients. Result: Out of 270patients medication charts 210 medication charts were having at least one or more drug interactions ranging from moderate to severe.Out of 80 interacting drug combinations found, 15 were benzodiazepine related drug interactions. So, percentage of benzodiazepinesrelated drug interactions was 18.75%.Moreover, the data also showed that the percentage of DDIs increases as the prescription sizeincreases. Our results indicate that hospitalized patients in Pakistan are at risk of ADRs caused by potential DDIs. Moreover, there arechances that the safe therapeutic doses of benzodiazepines may become toxic or ineffective due to drug-drug interactions andpolypharmacy. Conclusions: So, the use of DDIs detection software programs in hospitals and pharmacies should be promoted in orderto minimize drugs especially benzodiazepines related injuries and to ensure patient safety.

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19

Li, Xiangyang, Jianxin Yang, Yijie Qiao, Yabin Duan, Yuanyao Xin, Yongqiong Nian, Lin Zhu, and Guiqin Liu. "Effects of Radiation on Drug Metabolism: A Review." Current Drug Metabolism 20, no. 5 (June 20, 2019): 350–60. http://dx.doi.org/10.2174/1389200220666190405171303.

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Background: Radiation is the fourth most prevalent type of pollution following the water, air and noise pollution. It can adversely affect normal bodily functions. Radiation alters the protein and mRNA expression of drugmetabolizing enzymes and drug transporters and the pharmacokinetic characteristics of drugs, thereby affecting drug absorption, distribution, metabolism, and excretion. Therefore, it is important to study the pharmacokinetic changes in drugs under radiation. Methods: To update data on the effects of ionizing radiation and non-ionizing radiation caused by environmental pollution or clinical treatments on the protein and mRNA expression of drug-metabolizing enzymes and drug transporters. Data and information on pharmacokinetic changes in drugs under radiation were analyzed and summarized. Results: The effect of radiation on cytochrome P450 is still a subject of debate. The widespread belief is that higherdose radiation increased the expression of CYP1A1 and CYP1B1 of rat, zebrafish or human, CYP1A2, CYP2B1, and CYP3A1 of rat, and CYP2E1 of mouse or rat, and decreased that of rat’s CYP2C11 and CYP2D1. Radiation increased the expression of multidrug resistance protein, multidrug resistance-associated protein, and breast cancer resistance protein. The metabolism of some drugs, as well as the clearance, increased during concurrent chemoradiation therapy, whereas the half-life, mean residence time, and area under the curve decreased. Changes in the expression of cytochrome P450 and drug transporters were consistent with the changes in the pharmacokinetics of some drugs under radiation. Conclusion: The findings of this review indicated that radiation caused by environmental pollution or clinical treatments can alter the pharmacokinetic characteristics of drugs. Thus, the pharmacokinetics of drugs should be rechecked and the optimal dose should be re-evaluated after radiation.

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20

Amara Badaoui Hafsia, Sahraoui. "Drug Addiction and its Effects on the Structure of Society." International Journal of Science and Research (IJSR) 12, no. 7 (July 5, 2023): 959–63. http://dx.doi.org/10.21275/sr23630024510.

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21

Anonymous. "Abusive Drug Effects Explored." Journal of Psychosocial Nursing and Mental Health Services 29, no. 7 (July 1991): 45. http://dx.doi.org/10.3928/0279-3695-19910701-13.

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22

Olijhoek, Ger, Jan Drukker, Ton van der Linden, and Els Terwindt-Rouwenhorst. "Drug effects on arthrosis." Acta Orthopaedica 59, no. 2 (April 1, 1988): 186–90. http://dx.doi.org/10.3109/17453678809169706.

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23

Amory, J. K. "Drug effects on spermatogenesis." Drugs of Today 43, no. 10 (2007): 717. http://dx.doi.org/10.1358/dot.2007.43.10.1131829.

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24

PRESTON, KENZIE L. "Drug abstinence effects: opioids." Addiction 86, no. 12 (December 1991): 1641–46. http://dx.doi.org/10.1111/j.1360-0443.1991.tb01759.x.

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25

Tarter, Ralph E. "Drug Abuse: Teratological Effects." Contemporary Psychology: A Journal of Reviews 36, no. 3 (March 1991): 205–6. http://dx.doi.org/10.1037/029508.

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26

Blume, Warren T. "Drug Effects on EEG." Journal of Clinical Neurophysiology 23, no. 4 (August 2006): 306–11. http://dx.doi.org/10.1097/01.wnp.0000229137.94384.fa.

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27

Butterworth, S., and B. Weaver. "Drug combination side effects." Veterinary Record 130, no. 12 (March 21, 1992): 251–52. http://dx.doi.org/10.1136/vr.130.12.251.

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Boydell, P. "Drug combination side effects." Veterinary Record 130, no. 14 (April 4, 1992): 307. http://dx.doi.org/10.1136/vr.130.14.307-c.

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Olijhoek, Ger, Jan Drukker, Ton J. van der Linden, and Els A. W. Terwindt-Rouwenhorst. "Drug effects on arthrosis." Acta Orthopaedica Scandinavica 59, no. 2 (January 1988): 186–90. http://dx.doi.org/10.1080/17453678809169706.

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30

Kinsella, A., and C. O'Boyle. "Testing Differential Drug Effects." Statistician 37, no. 4/5 (1988): 357. http://dx.doi.org/10.2307/2348759.

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31

Blume, Warren T. "Adverse Antiepileptic Drug Effects." Epilepsy Currents 10, no. 1 (January 2010): 11–12. http://dx.doi.org/10.1111/j.1535-7511.2009.01339.x.

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32

Little, Karley Y. "Detecting acute drug effects." Biological Psychiatry 25, no. 5 (March 1989): 645–47. http://dx.doi.org/10.1016/0006-3223(89)90229-1.

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33

Yeomans, David, Joanna Moncrieff, and Rhodri Huws. "Drug-centred psychopharmacology: a non-diagnostic framework for drug treatment†." BJPsych Advances 21, no. 4 (July 2015): 229–36. http://dx.doi.org/10.1192/apt.bp.114.013094.

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SummaryWe propose a ‘drug-centred’ framework for understanding the nature of drug treatment in psychiatry. In contrast to the prevailing ‘disease-centred’ model, which suggests that drugs work by targeting underlying abnormalities, the drug-centred model maintains that drugs exert their effects through their psychoactive properties. According to this view, distinctive drug-induced alterations to normal cognition, emotion and behaviour can modify the manifestations of mental disorders independent of diagnosis or aetiological theory. The drug-centred approach already forms the basis of some current practice, particularly off-label prescribing. Within this framework, the matching of drug-induced effects to symptoms or difficulties, taking into account the unwanted aspects of the drug-induced state, becomes the focus of a collaborative endeavour between doctor and patient, consistent with the principles of the recovery model. More research into the full range of effects that psychiatric drugs produce is required to ground a judicious drug-centred practice and inform psychiatric training.

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Shoshi, Alban, Venus Ogultarhan, Tobias Hoppe, Benjamin Kormeier, Ulrich Müller, and Ralf Hofestädt. "Identifying adverse drug reactions and drug-induced diseases using network-based drug mapping." Journal of Bioinformatics and Computational Biology 13, no. 01 (February 2015): 1540007. http://dx.doi.org/10.1142/s0219720015400077.

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Drugs are essential for the prevention and treatment of diseases. However, co-administration of multiple drugs may cause serious adverse drug reactions, which are usually known but sometimes unknown. Package inserts of prescription drugs are supposed to contain risks and side effects, but such information is not necessarily complete. At the core of efforts to improve prescription quality, there is reliance on the extent and quality of information used for decision of a medical doctor. To address this on-going need, GraphSAW provides users a comprehensive view on drug-related pharmacological and molecular information. The features of GraphSAW allow users to analyze drug cocktails for adverse drug reactions and drug-induced diseases. Network visualization by drug mapping enables exploring associative networks of drugs, pathways, and diseases to fully understand effects of drugs in an intuitive way. GraphSAW is meant to be a platform and starting point for health professionals and researchers for educational and scientific research in order to achieve substantial improvements in patient safety.

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Seo, Sukyung, Taekeon Lee, Mi-hyun Kim, and Youngmi Yoon. "Prediction of Side Effects Using Comprehensive Similarity Measures." BioMed Research International 2020 (February 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/1357630.

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Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.

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Lukas, Marina, Britta Velten, Leopold Sellner, Katarzyna Tomska, Jennifer Hüellein, Tatjana Walther, Lena Wagner, et al. "Survey of ex vivo drug combination effects in chronic lymphocytic leukemia reveals synergistic drug effects and genetic dependencies." Leukemia 34, no. 11 (May 13, 2020): 2934–50. http://dx.doi.org/10.1038/s41375-020-0846-5.

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Abstract Drug combinations that target critical pathways are a mainstay of cancer care. To improve current approaches to combination treatment of chronic lymphocytic leukemia (CLL) and gain insights into the underlying biology, we studied the effect of 352 drug combination pairs in multiple concentrations by analysing ex vivo drug response of 52 primary CLL samples, which were characterized by “omics” profiling. Known synergistic interactions were confirmed for B-cell receptor (BCR) inhibitors with Bcl-2 inhibitors and with chemotherapeutic drugs, suggesting that this approach can identify clinically useful combinations. Moreover, we uncovered synergistic interactions between BCR inhibitors and afatinib, which we attribute to BCR activation by afatinib through BLK upstream of BTK and PI3K. Combinations of multiple inhibitors of BCR components (e.g., BTK, PI3K, SYK) had effects similar to the single agents. While PI3K and BTK inhibitors produced overall similar effects in combinations with other drugs, we uncovered a larger response heterogeneity of combinations including PI3K inhibitors, predominantly in CLL with mutated IGHV, which we attribute to the target’s position within the BCR-signaling pathway. Taken together, our study shows that drug combination effects can be effectively queried in primary cancer cells, which could aid discovery, triage and clinical development of drug combinations.

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John, M. L., I. J. J. Otene, and G. E. Antenyi. "An Overview of Drug Abuse: Causes, Effects, and Control Measures." Asian Journal of Medicine and Health 21, no. 11 (November 14, 2023): 263–68. http://dx.doi.org/10.9734/ajmah/2023/v21i11945.

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This study was conducted to assess the causes, effects, and control measures of drug abuse. The review was composed of literature search from databases (Google Scholar, Science Direct, Springer, Scopus and PubMed). Major findings from this study includes: a) Causes of Drug Abuse: The causes of drug abuse varies from social, interpersonal, cultural, environmental, and family factors. People abuse drugs due to pleasure derived from it. Drug abuse can be socially learned through drug use by peer group members, exposure to offers to use, and easy access to drugs. Pressure from friends that abuse drugs including their frequent escalation of drug experience may appeal others to start the use of drugs. Curiosity arising from recurrent references to drugs by public media generate curiosity for having a personal experience of the drugs. Growing up in a single-parent family, lack of parental support or supervision as well as low involvement with the child, and exposure of children to elders in the family who take drugs can promote drug use. Frustration and depression could make some people to take drugs to experience relief or relief from pain mostly from a prolonged use of pain-relieving drugs prescribed by a doctor. b) Effects of Drug Abuse: The signs or harmful effects of drug abuse could be physical, emotional, family dynamics, school behaviours, and social problems. They include cardiovascular disease; abnormalities in brain structure and function; respiratory problems; weakened immune system; insomnia; reduction in libido or sexual dysfunction; anxiety and irritability; loss or increase in appetite; and poor judgment. Different crimes such as armed robbery, kidnapping, and rape have been identified with young people under the influence of drugs. Family dynamics will reflect in the form of secretiveness, withdrawing from family, starting arguments, and breaking rules. For the school behaviours, the teenager will begin to play truancy, display discipline problems, decline in grades, decreased interest, many absences, and subsequently withdrawal from school. In terms of social problems, the teenager will begin to have problems with the law, have new friends, abnormal request for money, changes to less conventional styles in dress and music. c) Control Measures for Drug Abuse: Effective drug prevention programs should involve the family, schools, communities, and the media. This includes creating healthy home environment (functional family communication or interaction, parents taking extra measures to monitor the activities of their children including their associations, reduce child’s exposure to drug users in the family). Government should provide easy and affordable access to rehabilitation centres, implement effective addiction counselling and prevention programmes, provide policies that would address the wider availability of drugs in the society, create job opportunities for youth to become self-reliant; develop effective awareness/campaign programs on drug abuse; establish recreational centres; and finally religiosity can prevent people from using drugs even if they are exposed to drugs in the environment.

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Evans, William E., and Howard L. McLeod. "Pharmacogenomics — Drug Disposition, Drug Targets, and Side Effects." New England Journal of Medicine 348, no. 6 (February 6, 2003): 538–49. http://dx.doi.org/10.1056/nejmra020526.

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39

Lyle, William M. "DRUG-INDUCED OCULAR SIDE EFFECTS AND DRUG INTERACTIONS." Optometry and Vision Science 66, no. 5 (May 1989): 325–26. http://dx.doi.org/10.1097/00006324-198905000-00014.

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40

Sears, Marvin. "Drug-Induced Ocular Side Effects and Drug Interactions." Archives of Ophthalmology 107, no. 12 (December 1, 1989): 1730. http://dx.doi.org/10.1001/archopht.1989.01070020812014.

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Chen, Lei, Ying Yang, Mi Luo, Borui Hu, Shicheng Yin, and Zongfu Mao. "The Impacts of National Centralized Drug Procurement Policy on Drug Utilization and Drug Expenditures: The Case of Shenzhen, China." International Journal of Environmental Research and Public Health 17, no. 24 (December 15, 2020): 9415. http://dx.doi.org/10.3390/ijerph17249415.

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In 2019, the Chinese government implemented the first round of the National Centralized Drug Procurement (NCDP) pilot (so-called “4 + 7” policy) in mainland China, in which 25 drugs were included. We conducted this study to examine the impacts of NCDP policy on drug utilization and expenditures, and to clarify the main factors contributing to drug expenditure changes. This study used drug purchasing order data from the Centralized Drug Procurement Survey in Shenzhen 2019. Drugs related to the “4 + 7” policy were selected as study samples, including 23 “4 + 7” policy-related varieties and 15 basic alternative drugs. Driving factors for drug expenditures changes were analyzed using A.M. index system analysis (Addis A. & Magrini N.’ method). After the implementation of the NCDP policy, the volume of “4 + 7” policy-related varieties increased by 73.8%, among which winning products jumped by 1638.2% and non-winning products dropped by 70.8%; the expenditures of “4 + 7” policy-related varieties decreased by 36.9%. Structure effects (0.47) and price effects (0.78) negatively contributed to the increase in drug expenditures of “4 + 7” policy-related varieties, while volume effects (1.73) had positive influence. NCDP policy successfully decreased drug expenditures of “4 + 7” policy-related varieties with structure effects playing a leading role. However, total drug expenditures were not effectively controlled due to the increasing use of alternative drugs.

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Little, P., A. Trafford, G. Girling, A. Hasler, and A. Craven. "Drug treatment in hypertension ? metabolic side effects of drugs." European Journal of Clinical Pharmacology 38, no. 4 (April 1990): 407–8. http://dx.doi.org/10.1007/bf00315587.

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43

Gill, Andrew M., and Robert J. Michaels. "Does Drug Use Lower Wages?" ILR Review 45, no. 3 (April 1992): 419–34. http://dx.doi.org/10.1177/001979399204500301.

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This study, using microdata from the 1980 and 1984 waves of the National Longitudinal Survey of Youth, examines the effects of drug use on wages and employment. Contrary to most previous researchers' findings that illegal drug use negatively affects earnings, this analysis suggests that, once an allowance is made for self-selection effects (that is, unobservable factors simultaneously affecting wages and the decision to use drugs), drug users actually received higher wages than non-drug users. A similar analysis of employment effects shows that the sample of all drug users (which included users of “hard” and “soft” drugs) had lower employment levels than non-drug users, but the smaller sample consisting only of users of hard drugs, surprisingly, did not.

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Fang, H., S. J. Stanhope, and H. Wu. "Exploiting Online Discussions to Discover Unrecognized Drug Side Effects." Methods of Information in Medicine 52, no. 02 (2013): 152–59. http://dx.doi.org/10.3414/me12-02-0004.

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SummaryBackground: Drugs can treat human diseases through chemical interactions between the ingredients and intended targets in the human body. However, the ingredients could unexpectedly interact with off-targets, which may cause adverse drug side effects. Notifying patients and physicians of potential drug effects is an important step in improving healthcare quality and delivery.Objective: With the increasing popularity of Web 2.0 applications, more and more patients start discussing drug side effects in many online sources. These online discussions form a valuable source for mining interesting knowledge about side effects. The main goal of this paper is to investigate the feasibility of exploiting these discussions to discover unrecognized drug side effects.Methods: We propose methods that can 1) build a knowledge base for drug side effects by automatically integrating the in -formation related to drug side effects from different sources; and 2) monitor online discussions about drugs and discover potential unrecognized drug side effects.Results: Experiment results show that the online discussions indeed provide useful information discovering unrecognized drug side effects. We find that the integrated knowledge base contains more information than individual online sources. Moreover, both proposed detection methods can identi -fy the side effects related to the four recently recalled drugs, and the information from online discussions makes it possible to make the detection much earlier than official announcements. Finally, the proposed genera -tive modeling method is shown to be more effective than the discriminative method.Conclusions: We find that it is possible to monitor online discussions to detect un -recognized drug side effects. The developed system is expected to serve as a com -plementary tool for drug companies and FDA to receive feedbacks from the patients, and it has the potentials to expedite the discovery process of unrecognized drug side effects and to improve the quality of healthcare.

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Joiner, Jordan B., Alka Prasher, Isabella C. Young, Jessie Kim, Roopali Shrivastava, Panita Maturavongsadit, and Soumya Rahima Benhabbour. "Effects of Drug Physicochemical Properties on In-Situ Forming Implant Polymer Degradation and Drug Release Kinetics." Pharmaceutics 14, no. 6 (June 1, 2022): 1188. http://dx.doi.org/10.3390/pharmaceutics14061188.

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In-situ forming implants (ISFIs) represent a simple, tunable, and biodegradable polymer-based platform for long-acting drug delivery. However, drugs with different physicochemical properties and physical states in the polymer-solvent system exhibit different drug release kinetics. Although a few limited studies have been performed attempting to elucidate these effects, a large, systematic study has not been performed until now. The purpose of this study was to characterize the in vitro drug release of 12 different small molecule drugs with differing logP and pKa values from ISFIs. Drug release was compared with polymer degradation as measured by lactic acid (LA) release and change in poly(DL-lactide-co-glycolide) (PLGA) molecular weight (MW) measured by size exclusion chromatography with multi-angle laser light scattering (SEC-MALS). Drug physical state and morphology were also measured using differential scanning calorimetry (DSC) and scanning electron microscopy (SEM). Together, these results demonstrated that hydrophilic drugs have higher burst release at 24 h (22.8–68.4%) and complete drug release within 60 days, while hydrophobic drugs have lower burst release at 24 h (1.8–18.9%) and can sustain drug release over 60–285 days. Overall, drug logP and drug physical state in the polymer–solvent system are the most important factors when predicting the drug release rate in an ISFI for small-molecule drugs. Hydrophilic drugs exhibit high initial burst and less sustained release due to their miscibility with the aqueous phase, while hydrophobic drugs have lower initial burst and more sustained release due to their affinity for the hydrophobic PLGA. Additionally, while hydrophilic drugs seem to accelerate the degradation of PLGA, hydrophobic drugs on the other hand seem to slow down the PLGA degradation process compared with placebo ISFIs. Furthermore, drugs that were in a crystalline state within the ISFI drugs exhibited more sustained release compared with amorphous drugs.

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Hasnain, Hina, Huma Ali, Farya Zafar, Ali Akbar Sial, Kamran Hameed, Huma Shareef, Neelam Mallick, Anum Tariq, and Rasheeda Fatima. "DRUG-DRUG INTERACTION;." Professional Medical Journal 24, no. 03 (March 7, 2017): 357–65. http://dx.doi.org/10.29309/tpmj/2017.24.03.1551.

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Drug-drug interaction (DDI) is a specific type of adverse event, which developsdue to multiple regimen therapy, and that may lead to significant hospitalization and death.Clinical and economic impact of drug interactions are increasingly accredited as a chiefconcern in critical care. Potentiating effects of DDIs in intensive care units are far more criticaldue to complex medications regimen, high risk severely ill population and associated metabolicand physiological disturbances which can impede drug effects. Pharmacist contribution isclassified as clarification of drug order, appropriate drug information provision, and advice forsubstitute treatment. A multidisciplinary approach is very necessary in developing a pharmacotherapeuticregimen designed to optimize patient outcome and minimize any potential dugdrug interactions. This review encompasses the prevalence, categorization, significance interm of patient safety and prescription efficacy, clinical and economic burdens, national andinternational data comparisons related to drug-drug interactions.

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Joo, Seung-Jae. "Side-effects of antihypertensive drugs." Journal of Medicine and Life Science 7, no. 1 (June 1, 2010): 60–65. http://dx.doi.org/10.22730/jmls.2010.7.1.60.

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Each antihypertensive drug has different major side-effects. and practice guidelines describe the absolute and relative contraindications of antihypertensive drugs. Sometimes patients taking antihypertensive drugs complain of minor side- effects, resulting in the obstacle to the adequate control of blood pressure. and poor compliance. Although old drugs such as diuretics or beta-blockers have unavoidable. dose-related side-eflects，new drugs such as calcium antagonists，angiotensin-converting enzyme inhibitors，or angiotensin receptor blockers are relatively well-tolerated. Not only short-term side-effects ，but tong-term safety of antihypertensive drug must be considered in individual prescription. Low-dose combination therapy is a recommended strategy to minimize the side-effects of anti-hypertensive drugs. Short-term and long-term side-effects of diuretics ，beta-blockers ，calcium antagonists，angiotensin converting enzyme inhibitors ，and angiotensin receptor blockers with be reviewed.

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Smith, Robert. "Covid-19 Effects on the Medications Used to Treat Diabetes." Pharmaceutics and Pharmacology Research 3, no. 1 (October 5, 2020): 01–05. http://dx.doi.org/10.31579/2693-7247/008.

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Minority patients taking prescription drugs for diabetes mellitus and hypertension are sick and vulnerable. They should not be subjected to poor quality medications that can make them worse. The purpose of this review is to present data fortifying an argument that Covid-19 will continue to disproportionally effect minorities who suffer with Type 2 Diabetes Mellitus, Hypertension, and GI ulcer disease by limiting drug safety for pharmaceuticals that are imported from oversea manufacturers even after the pandemic is over. As a foundation an overview of the Food and Drug Administration (FDA) overseas inspection process that have been validated by previously published reports will be offered to accent the procedural process. Limitations and challenges to the overseas drug inspection process as documented by congressional leadership and the FDA are offered. An accurate scientific description of N-nitrosodimethylamine (NDMA) will be presented to emphasize its danger to these patients. Five key points or initiatives recently published by the FDA to assist in food and drug safety from overseas manufacturers during the Covid-19 pandemic will be presented

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Batur, Eslim, Samet Özdemir, Meltem Ezgi Durgun, and Yıldız Özsoy. "Vesicular Drug Delivery Systems: Promising Approaches in Ocular Drug Delivery." Pharmaceuticals 17, no. 4 (April 16, 2024): 511. http://dx.doi.org/10.3390/ph17040511.

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Ocular drug delivery poses unique challenges due to the complex anatomical and physiological barriers of the eye. Conventional dosage forms often fail to achieve optimal therapeutic outcomes due to poor bioavailability, short retention time, and off-target effects. In recent years, vesicular drug delivery systems have emerged as promising solutions to address these challenges. Vesicular systems, such as liposome, niosome, ethosome, transfersome, and others (bilosome, transethosome, cubosome, proniosome, chitosome, terpesome, phytosome, discome, and spanlastics), offer several advantages for ocular drug delivery. These include improved drug bioavailability, prolonged retention time on the ocular surface, reduced systemic side effects, and protection of drugs from enzymatic degradation and dilution by tears. Moreover, vesicular formulations can be engineered for targeted delivery to specific ocular tissues or cells, enhancing therapeutic efficacy while minimizing off-target effects. They also enable the encapsulation of a wide range of drug molecules, including hydrophilic, hydrophobic, and macromolecular drugs, and the possibility of combination therapy by facilitating the co-delivery of multiple drugs. This review examines vesicular drug delivery systems, their advantages over conventional drug delivery systems, production techniques, and their applications in management of ocular diseases.

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Hudzik, Thomas J., and D. E. McMillan. "Drug effects on response duration differentiation I: differential effects of drugs of abuse." Psychopharmacology 114, no. 4 (May 1994): 620–27. http://dx.doi.org/10.1007/bf02244993.

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