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Journal articles on the topic 'Drug screening'

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Author: Grafiati

Published: 4 June 2021

Last updated: 22 June 2024

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1

DUPONT, ROBERT L. "Drug Screening." Pediatrics 85, no. 2 (February 1, 1990): 233. http://dx.doi.org/10.1542/peds.85.2.233.

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To the Editor.— The joint report of the committee on Adolescence, the Committee on Bioethics, and the Provisional Committee on Substance Abuse (Pediatrics 1989;84:396-398) appears to miss the mark by a wide margin. Drugs and kids are a bad combination. Those of us concerned about children and youth need to work to help them grow up drug free. Screening for drug use is no more a violation of privacy than is screening for diabetes or tuberculosis.

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2

SCHWARTZ, RICHARD H., and VLADIMIR TSESIS. "Drug Screening." Pediatrics 85, no. 2 (February 1, 1990): 232–33. http://dx.doi.org/10.1542/peds.85.2.232.

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To the Editor.— We are writing to express our strong opposition to the recently published American Academy of Pediatrics' (AAP) position paper concerning drug screening of adolescents (Pediatrics 1989;84:396-398). The improved accuracy and widespread use of urine tests for drugs of abuse has been one of the most significant advances in the diagnosis and management of diseases of addiction. Tests for drugs of abuse furnish objective evidence of exposure to illicit drugs—evidence that is difficult or impossible to obtain by any other means.

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3

TOLMAS, HYMAN C. "Drug Screening." Pediatrics 85, no. 2 (February 1, 1990): 233. http://dx.doi.org/10.1542/peds.85.2.233a.

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To the Editor.— I was dismayed and disappointed by the recommendations of the three committees responsible for the policy statement regarding drug screening, and I believe that they have failed to take into consideration the feelings of the general membership. The small print disclaimer on page 396 hardly equalizes the variations from stated policy for those who feel differently. Although I agree with many of the facts as stated, I believe the Committees have been remiss in not weighing some of the realistic aspects of this most important problem that plagues our youth and society in general.

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4

FOST, NORMAN, S. KENNETH SCHONBERG, and MANUEL SCHYDLOWER. "Drug Screening." Pediatrics 85, no. 2 (February 1, 1990): 233–34. http://dx.doi.org/10.1542/peds.85.2.233b.

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In Reply.— The writers raise a number of issues involving process, fact, and ethics. We can only respond to a few of the main points. Regarding process, a major impetus for the statement was requests from private practitioners seeking the Academy's support to help them resist demands, from school districts and parents, that they screen athletes and patients surreptitiously. There was extensive involvement by private practitioners in the writing, review, and approval of the statement by three committees, the Council on Child and Adolescent health, and the Executive Board, which is comprised predominantly of private practitioners.

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MILMAN, DORIS H. "Drug Screening." Pediatrics 85, no. 2 (February 1, 1990): 231–32. http://dx.doi.org/10.1542/peds.85.2.231.

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To the Editor.— The Committee on Adolescence and its adjunctive committees' recently published statement (Pediatrics 1989;84:396-398) concerning drug screening, of signal importance in its subject and obviously earnest in its intent, is somewhat disappointing in its content. With respect to the points made as recommendations, only the first is unexceptionable; the rest appear to be illogical, or self-contradictory, or poorly stated, or aimed at reconciling irreconcilable differences among the various participants, or all of the above.

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6

&NA;. "Drug Screening." Journal of Occupational and Environmental Medicine 30, no. 10 (October 1988): 760. http://dx.doi.org/10.1097/00043764-198810000-00001.

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7

Liang, Qiying, Peng Ma, Qi Zhang, Youjie Yin, Ping Wang, Saifei Wang, Yao Zhang, Ruolei Han, and Hansong Deng. "A gum Arabic assisted sustainable drug delivery system for adult Drosophila." Biology Open 9, no. 6 (June 2, 2020): bio052241. http://dx.doi.org/10.1242/bio.052241.

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ABSTRACTLarge-scale compound screening in adult flies is hampered by the lack of continuous drug delivery systems and poor solubility of numerous compounds. Here we found that gum Arabic (Acacia/Senegal gum), a widely used stabilizer, can also emulsify lipophilic compounds and profoundly increase their accessibility to target tissues in Drosophila and mice. We further developed a gum Arabic-based drug delivery system, wherein the drug was ground into gum Arabic and emulsified in liquid food fed to flies by siphoning through a U-shape glass capillary. This system did not affect food intake nor cell viability. Since drugs were continuously delivered by siphoning, minimal compound waste and less frequent food changes make this system ideal for large-scale long-term screenings. In our pilot screening for antitumor drugs in the NCI DTP library, we used a Drosophila model of colorectal cancer and identified two drugs that are especially hydrophobic and were not identified in previous screenings. Our data demonstrated that gum Arabic facilitates drug delivery in animal models and the system is suitable for long-term high-throughput drug screening in Drosophila. This system would accelerate drug discovery for chronic and cognitive conditions.

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8

Sundar, Kothandapani. "Quorum Sensing Based Drug Screening Against Vibrio Cholerae." Journal of Microbes and Research 1, no. 1 (November 28, 2022): 01–05. http://dx.doi.org/10.58489/2836-2187/001.

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The QS method is a means of bacterial cell-to-cell communication, which uses extracellular signal molecules called autoinducers to transmit information between cells.Bacteria can use QS to collaborate on tasks. The pathogen Vibrio cholerae uses QS to inhibit the development of virulence factors and the formation of biofilms. Cholera is caused by the Gram-negative, curved bacteria Vibrio cholerae (Clemens et al., 2017). There are also a number of virulence components produced by this disease, including cholera hemolysin (CH), toxin-co-regulated pilus (TCP), flagellum, etc. By constraining the target protein, HapR, with adequate bioactive compounds, the pathogenic activity of in vibrio cholerae can be suppressed.Bioactive substances from various natural food sources were chosen and analysed for their quorum quenching effect against HapR protein utilising bioinformatics methods. The in-silico analysis produced notable results for thirteen of the 25 substances evaluated, with the best docking score. These chemicals could be employed for QSI-based therapeutics against vibrio cholerae infections and could be suggested for in vitro and in vivo investigations.

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9

Schulberg, Mark, and Dimitri Gerostamoulos. "Urinary drug screening." Australian Prescriber 36, no. 4 (August 1, 2013): 111–12. http://dx.doi.org/10.18773/austprescr.2013.051.

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10

Jekelis, Albert. "Urine Drug Screening." Diagnostic Innovation 11, no. 3 (November 2001): 18???22. http://dx.doi.org/10.2165/00024666-200111000-00004.

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11

Enna, SJ. "Phenotypic drug screening." Journal of the Peripheral Nervous System 19, S2 (September 30, 2014): S4—S5. http://dx.doi.org/10.1111/jns.12079_2.

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12

Flight, Monica Hoyos. "Tailored drug screening." Nature Reviews Drug Discovery 8, no. 10 (October 2009): 774. http://dx.doi.org/10.1038/nrd3023.

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13

ADAMS, D. J. "Antineoplastic Drug Screening." JNCI Journal of the National Cancer Institute 84, no. 16 (August 19, 1992): 1288–89. http://dx.doi.org/10.1093/jnci/84.16.1288.

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14

DuBow, MichaelS. "Antituberculosis drug screening." Lancet 342, no. 8869 (August 1993): 448–49. http://dx.doi.org/10.1016/0140-6736(93)91586-b.

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15

Engleking, P. Renee. "Employee Drug Screening." AAOHN Journal 34, no. 9 (September 1986): 416–19. http://dx.doi.org/10.1177/216507998603400901.

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16

Wish, E. D. "Preemployment drug screening." JAMA: The Journal of the American Medical Association 264, no. 20 (November 28, 1990): 2676–77. http://dx.doi.org/10.1001/jama.264.20.2676.

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17

Goldweber, R. T. "Preemployment drug screening." JAMA: The Journal of the American Medical Association 267, no. 1 (January 1, 1992): 52b—52. http://dx.doi.org/10.1001/jama.267.1.52b.

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18

Goldweber, Robert T. "Preemployment Drug Screening." JAMA: The Journal of the American Medical Association 267, no. 1 (January 1, 1992): 52. http://dx.doi.org/10.1001/jama.1992.03480010060010.

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19

Wish, Eric D. "Preemployment Drug Screening." JAMA: The Journal of the American Medical Association 264, no. 20 (November 28, 1990): 2676. http://dx.doi.org/10.1001/jama.1990.03450200084036.

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20

Bell, Susan Givens. "Drug Screening in Neonates." Neonatal Network 35, no. 5 (2016): 321–26. http://dx.doi.org/10.1891/0730-0832.35.5.321.

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AbstractGestational substance exposure continues to be a significant problem. Neonates may be exposed to various substances including illicit drugs, prescription drugs, and other legal substances that are best not used during pregnancy because of their potential deleterious effects as possible teratogens or their potential to create dependence and thus withdrawal in the neonate. Screening the newborn for gestational substance exposure is important for both acute care and early intervention to promote the best possible long-term outcomes. This column provides insight into what is known about the extent of substance use by pregnant women, an overview of neonatal biologic matrices for drug testing, and a discussion of the legal implications of neonatal substance screening.

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21

Wilkinson, Graeme F., and Kevin Pritchard. "In Vitro Screening for Drug Repositioning." Journal of Biomolecular Screening 20, no. 2 (December 19, 2014): 167–79. http://dx.doi.org/10.1177/1087057114563024.

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Drug repositioning or repurposing has received much coverage in the scientific literature in recent years and has been responsible for the generation of both new intellectual property and investigational new drug submissions. The literature indicates a significant trend toward the use of computational- or informatics-based methods for generating initial repositioning hypotheses, followed by focused assessment of biological activity in phenotypic assays. Another viable method for drug repositioning is in vitro screening of known drugs or drug-like molecules, initially in disease-relevant phenotypic assays, to identify and validate candidates for repositioning. This approach can use large compound libraries or can focus on subsets of known drugs or drug-like molecules. In this short review, we focus on ways to generate and validate repositioning candidates in disease-related in vitro and phenotypic assays, and we discuss specific examples of this approach as applied to a variety of disease areas. We propose that in vitro screens offer several advantages over biochemical or in vivo methods as a starting point for drug repositioning.

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22

Guillet, Ronnie, Jennifer M. Kwon, SiXaio Chen, and Michael P. McDermott. "Urine Phenobarbital Drug Screening." Journal of Child Neurology 27, no. 2 (September 27, 2011): 200–203. http://dx.doi.org/10.1177/0883073811416665.

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23

Heemskerk, Jill. "High throughput drug screening." Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders 5, sup1 (September 2004): 19–21. http://dx.doi.org/10.1080/17434470410019735.

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24

Sage, Linda. "Microarrays for drug screening." Analytical Chemistry 77, no. 7 (April 2005): 135 A. http://dx.doi.org/10.1021/ac0533523.

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25

Boyd, James R. "Nonprescription Drug Screening-Pseudoephedrine." American Pharmacy 26, no. 11 (November 1986): 22–24. http://dx.doi.org/10.1016/s0160-3450(16)33105-1.

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26

Sharman, J. R. "Screening for drug poisoning." Pathology 23 (1991): 15. http://dx.doi.org/10.1016/s0031-3025(16)36207-9.

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27

Lafont, Olivier. "Handbook of Drug Screening." European Journal of Medicinal Chemistry 37, no. 7 (July 2002): 617. http://dx.doi.org/10.1016/s0223-5234(02)01367-3.

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28

Gold, Mark S., Kimberly Frost-Pineda, Bruce A. Goldberger, and Robert L. DuPont. "Physicians and Drug Screening." Journal of Adolescent Health 39, no. 2 (August 2006): 154–55. http://dx.doi.org/10.1016/j.jadohealth.2006.05.001.

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29

Bojanic, Dejan. "Handbook of Drug Screening." Drug Discovery Today 6, no. 24 (December 2001): 1266. http://dx.doi.org/10.1016/s1359-6446(01)02101-8.

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30

Kuehn, Bridget M. "Screening for Drug Use." JAMA 301, no. 20 (May 27, 2009): 2085. http://dx.doi.org/10.1001/jama.2009.714.

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31

Fukunishi, Yoshifumi. "Structure-Based Drug Screening and Ligand-Based Drug Screening with Machine Learning." Combinatorial Chemistry & High Throughput Screening 12, no. 4 (May 1, 2009): 397–408. http://dx.doi.org/10.2174/138620709788167890.

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32

Malinovská, Jana, Barbora Švarcová, Ludmila Brunerová, Sabina Pálová, and Jan Brož. "Screening and brief intervention in illicit drug users." Vnitřní lékařství 66, no. 7 (November 3, 2020): 450–54. http://dx.doi.org/10.36290/vnl.2020.127.

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33

Valler, Martin J., and Darren Green. "Diversity screening versus focussed screening in drug discovery." Drug Discovery Today 5, no. 7 (July 2000): 286–93. http://dx.doi.org/10.1016/s1359-6446(00)01517-8.

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34

Kalpana Seelam and Daisy Rani A. "Microfluidic based Platform for drug screening-A review." International Journal of Research in Pharmaceutical Sciences 11, no. 3 (July 6, 2020): 2999–3004. http://dx.doi.org/10.26452/ijrps.v11i3.2394.

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There is a lot of requirement to develop a preclinical rapid drug screening devices to treat the throat cancerous patients in effective manner. Very High cost models like animal based, 2d and 3d type models are static drug screening models and they are unable to mimic the human body dynamic condition. So microfluidic platform based drug screening devices will give predetermined and prominent result in rapid drug screening by mimic dynamic body conditions. Now it is required to review what are the developments from last five years in screening the drug for cancer. Recently research is going on microfluidic platforms to screen the efficacy of mixed drugs, drug tolerance, and drug susceptibility. So this study presents the review on what are the advancements in microfluidic platforms for rapid drug screening and for different cancer patients. This study also presents what are the different sensing methodologies of microfluidic devices exists to screen the drug for various cancerous tissues

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35

Callahan, H. L., A. C. Portal, R. Devereaux, and M. Grogl. "An axenic amastigote system for drug screening." Antimicrobial Agents and Chemotherapy 41, no. 4 (April 1997): 818–22. http://dx.doi.org/10.1128/aac.41.4.818.

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Currently available primary screens for selection of candidate antileishmanial compounds are not ideal. The choices include screens that are designed to closely reflect the situation in vivo but are labor-intensive and expensive (intracellular amastigotes and animal models) and screens that are designed to facilitate rapid testing of a large number of drugs but do not use the clinically relevant parasite stage (promastigote model). The advent of successful in vitro culture of axenic amastigotes permits the development of a primary screen which is quick and easy like the promastigote screen but still representative of the situation in vivo, since it uses the relevant parasite stage. We have established an axenic amastigote drug screening system using a Leishmania mexicana strain (strain M379). A comparison of the 50% inhibitory concentration (IC50) drug sensitivity profiles of M379 promastigotes, intracellular amastigotes, and axenic amastigotes for six clinically relevant antileishmanial drugs (sodium stibogluconate, meglumine antimoniate, pentamidine, paromomycin, amphotericin B, WR6026) showed that M379 axenic amastigotes are a good model for a primary drug screen. Promastigote and intracellular amastigote IC50s differed for four of the six drugs tested by threefold or more; axenic amastigote and intracellular amastigote IC50s differed by twofold for only one drug. This shows that the axenic amastigote susceptibility to clinically used reference drugs is comparable to the susceptibility of amastigotes in macrophages. These data also suggest that for the compounds tested, susceptibility is intrinsic to the parasite stage. This contradicts previous hypotheses that suggested that the activities of antimonial agents against intracellular amastigotes were solely a function of the macrophage.

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Booij, Tijmen, Sandro Nuciforo, David Keller, Eva Riegler, Diego Calabrese, and Markus Heim. "Drug screening for hepatocellular carcinoma: automating and miniaturizing organoid assays for drug screening." Journal of Hepatology 78 (June 2023): S532—S533. http://dx.doi.org/10.1016/s0168-8278(23)01297-7.

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37

Kim, Jungeun, Hoe Suk Kim, Ga Yeon Kim, Kyung hyeun Park, Seung yeon Ryu, Sangeun Lee, Dong Woo Lee, Bosung Ku, Han-Byoel Lee, and Wonshik Han. "Abstract P5-02-02: Development of automated 3D high-throughput drug screening platform for patient-derived breast cancer organoids." Cancer Research 82, no. 4_Supplement (February 15, 2022): P5–02–02—P5–02–02. http://dx.doi.org/10.1158/1538-7445.sabcs21-p5-02-02.

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Abstract Background Patient-derived cancer organoids, which reliably conserve original features of tumors, are emerging as an excellent model for predicting therapy response and drug screening. Developing optimized 3D high-throughput drug screening platform to establish patient-derived cancer organoids and simultaneously perform drug screening is essential for personalized medicine. Methods We established normal breast organoids (n=4) and breast cancer organoids (n=10) from 20 fresh surgical specimen (normal 7, tumor 13 cases). A number (500-2000) of normal and cancer cells were automatically dispensed with the ASFA™ Spotter ST and organoids were generated by hydrogel hanging-drop culture on Cellvitro™ Pillar platform (Medical & Bio Decision, South Korea). Organoids were subjected to drug screening for 17 anticancer drugs including chemoreagents and targeted drugs in the 3D HTS system. Drug sensitivity was tested in triplicate in different concentration ranges for 5 days. Drug cytotoxic effect was assessed by calcein AM staining. Acquisition and analysis of high-content 3D organoid images were peformed using ASFA™ SCANNER (Medical & Bio Decision, South Korea). The IC50 for each drug was calculated by a sigmoidal dose-response curve, using the GraphPad Prism 9 program. We analyzed a drug response index (DRI) using a prediction alogorithm to evalute drug sensitivity (DRI<-0.5) and resistance (DRI>0.5). Results We summarized the DRI value of patient-derived breast organoids of 7 drugs (Table 1). 6 tumor organoids (2T, 6T, 8T, 10T, 13T, 15T) showed high sensitivity to Docetaxel, Doxorubicin, Paclitaxel, and Gemcitabin while 4 tumor organoids (1T, 9T, 14T, 16T) were less sensitive and resistant. 2 tumor organoids (10T, 16T) were sensitive to Tamoxifen and 2 tumor organoids (6T, 8T) show high sensitivity to palbociclib and erlotinib. Normal organoids show less sensitivity and resistance to chemotherapeutic drugs. Drug response index >0.5 : resistancy top 30%, Drug response index <-0.5 : sensitivity top 30%. Conclusions Herein, we developed the hydrogel hanging-drop culture on Cellvitro™ Pillar platform for easily and rapidly high-throughput drug screening in patient-derived organoids using a small number of cells by testing clinically actionable drugs at different concentrations. There were different drug response indeces for each individual organoids to chemoreagents and targeted drugs. We anticipate that 3D high-throughput drug screenings platform based on patient-derived organoids can provide the information to predict drug response and allow for finding more appropriate therapy for individual patients. Table 1.DRI values of patient-derived breast organoids of 7 drugs.Patient No.Tumor/NormalDocetaxelPaclitaxelDoxorubicinTamoxifenGemcitabinePalbociclibErlotinib1TTumor1.051.261.080.060.590.26-2TTumor-0.64-0.72-0.60-0.08-0.79--6TTumor-0.98-0.99-2.330.00-1.44-2.31-1.608TTumor-0.32-0.50-0.250.64-1.07-1.47-0.669TTumor1.050.980.610.870.430.610.4410TTumor-0.84-0.77-0.78-1.680.790.610.0913TTumor-0.87-0.430.560.70-0.510.611.1714TTumor0.861.261.180.481.81--15TTumor-1.59-1.45-1.41--0.71-0.13-16TTumor1.051.261.18-1.70-1.22-2NNormal1.051.260.650.741.810.610.7810NNormal1.05-0.910.810.870.880.610.084NNormal-0.31-0.25-0.530.25-0.190.61-1.575NNormal1.051.130.380.75-0.630.610.10 Citation Format: Jungeun Kim, Hoe Suk Kim, Ga Yeon Kim, Kyung hyeun Park, Seung yeon Ryu, Sangeun Lee, Dong Woo Lee, Bosung Ku, Han-Byoel Lee, Wonshik Han. Development of automated 3D high-throughput drug screening platform for patient-derived breast cancer organoids [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-02-02.

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Rodrigues, A. David, and Jiunn H. Lin. "Screening of drug candidates for their drug–drug interaction potential." Current Opinion in Chemical Biology 5, no. 4 (August 2001): 396–401. http://dx.doi.org/10.1016/s1367-5931(00)00220-9.

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39

Xu, Xue, Chao Zhang, PiDong Li, FeiLong Zhang, Kuo Gao, JianXin Chen, and HongCai Shang. "Drug-symptom networking: Linking drug-likeness screening to drug discovery." Pharmacological Research 103 (January 2016): 105–13. http://dx.doi.org/10.1016/j.phrs.2015.11.015.

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40

Ovics, Paz, Danielle Regev, Polina Baskin, Mor Davidor, Yuval Shemer, Shunit Neeman, Yael Ben-Haim, and Ofer Binah. "Drug Development and the Use of Induced Pluripotent Stem Cell-Derived Cardiomyocytes for Disease Modeling and Drug Toxicity Screening." International Journal of Molecular Sciences 21, no. 19 (October 3, 2020): 7320. http://dx.doi.org/10.3390/ijms21197320.

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Over the years, numerous groups have employed human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a superb human-compatible model for investigating the function and dysfunction of cardiomyocytes, drug screening and toxicity, disease modeling and for the development of novel drugs for heart diseases. In this review, we discuss the broad use of iPSC-CMs for drug development and disease modeling, in two related themes. In the first theme—drug development, adverse drug reactions, mechanisms of cardiotoxicity and the need for efficient drug screening protocols—we discuss the critical need to screen old and new drugs, the process of drug development, marketing and Adverse Drug reactions (ADRs), drug-induced cardiotoxicity, safety screening during drug development, drug development and patient-specific effect and different mechanisms of ADRs. In the second theme—using iPSC-CMs for disease modeling and developing novel drugs for heart diseases—we discuss the rationale for using iPSC-CMs and modeling acquired and inherited heart diseases with iPSC-CMs.

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41

Jungwirth, Gerhard, Adrian Paul, Cao Junguo, Andreas Unterberg, Amir Abdollahi, and Christel Herold-Mende. "DDRE-42. TOWARDS PRECISION MEDICINE: AUTOMATED DRUG SCREENING PLATFORM UTILIZING TUMOR-ORGANOIDS TO IDENTIFY PATIENT-SPECIFIC DRUG-RESPONSES IN MENINGIOMA." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi83. http://dx.doi.org/10.1093/neuonc/noab196.326.

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Abstract Tumor-organoids (TO) are mini-tumors generated from tumor tissue preserving its genotype and phenotype by maintaining the cellular heterogeneity and important components of the tumor microenvironment. We recently developed a protocol to reliably establish TOs from meningioma (MGM) in large quantities. The use of TOs in combination with lab automation holds great promise for drug discovery and screening of comprehensive drug libraries. This might help to tailor patient-specific therapy in the future. The aim of our study was to establish an automated drug screening platform utilizing TOs. For this purpose, we established TOs by controlled reaggregation of freshly prepared single cell suspension of MGM tissue samples in the high-throughput format of 384-well plates. The drug screening was performed fully automated by utilizing the robotic liquid handler Hamilton Microlab STAR and a drug library containing 166 FDA-approved oncology agents. Viability was assessed with CellTiterGlo3D. In total, we performed the drug screening with 166 drugs on TOs from 11 patients suffering from MGM (n=8 WHO°I, n=2 WHO°II, n=1 WHO°III). The top five most effective drugs resulted in a decrease of TO viability ranging from 84.6–63.3%. K-means clustering analysis resulted in groupings of drugs with similar modes of action. One cluster consisted of epigenetic drugs while another cluster consisted of several proteasome inhibitors. However, when looking at a patient-individual level, in 11 patients 44 of 166 drugs, were among the top 10 most effective drugs, providing strong evidence for heterogeneous drug-responses in MGM patients. Taken together, we successfully developed an automated drug screening platform pipeline utilizing TOs from MGM to identify patient-specific drug-responses. The observed intra-individual differences of drug responses mandate for a personalized testing of comprehensive drug libraries in TOs to tailor more effective therapies in MGM patients.

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42

Crespi, Charles L., and David M. Stresser. "Fluorometric screening for metabolism-based drug–drug interactions." Journal of Pharmacological and Toxicological Methods 44, no. 1 (July 2000): 325–31. http://dx.doi.org/10.1016/s1056-8719(00)00112-x.

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43

Monjezi, Mojdeh, Milad Rismanian, Hamidreza Jamaati, and Navid Kashaninejad. "Anti-Cancer Drug Screening with Microfluidic Technology." Applied Sciences 11, no. 20 (October 11, 2021): 9418. http://dx.doi.org/10.3390/app11209418.

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The up-and-coming microfluidic technology is the most promising platform for designing anti-cancer drugs and new point-of-care diagnostics. Compared to conventional drug screening methods based on Petri dishes and animal studies, drug delivery in microfluidic systems has many advantages. For instance, these platforms offer high-throughput drug screening, require a small number of samples, provide an in vivo-like microenvironment for cells, and eliminate ethical issues associated with animal studies. Multiple cell cultures in microfluidic chips could better mimic the 3D tumor environment using low reagents consumption. The clinical experiments have shown that combinatorial drug treatments have a better therapeutic effect than monodrug therapy. Many attempts have been made in this field in the last decade. This review highlights the applications of microfluidic chips in anti-cancer drug screening and systematically categorizes these systems as a function of sample size and combination of drug screening. Finally, it provides a perspective on the future of the clinical applications of microfluidic systems for anti-cancer drug development.

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Lee, I.-Chi. "Cancer-on-a-chip for Drug Screening." Current Pharmaceutical Design 24, no. 45 (April 16, 2019): 5407–18. http://dx.doi.org/10.2174/1381612825666190206235233.

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: The oncology pharmaceutical research spent a shocking amount of money on target validation and drug optimization in preclinical models because many oncology drugs fail during clinical trial phase III. One of the most important reasons for oncology drug failures in clinical trials may due to the poor predictive tool of existing preclinical models. Therefore, in cancer research and personalized medicine field, it is critical to improve the effectiveness of preclinical predictions of the drug response of patients to therapies and to reduce costly failures in clinical trials. Three dimensional (3D) tumor models combine micro-manufacturing technologies mimic critical physiologic parameters present in vivo, including complex multicellular architecture with multicellular arrangement and extracellular matrix deposition, packed 3D structures with cell–cell interactions, such as tight junctions, barriers to mass transport of drugs, nutrients and other factors, which are similar to in vivo tumor tissues. These systems provide a solution to mimic the physiological environment for improving predictive accuracy in oncology drug discovery. : his review gives an overview of the innovations, development and limitations of different types of tumor-like construction techniques such as self-assemble spheroid formation, spheroids formation by micro-manufacturing technologies, micro-dissected tumor tissues and tumor organoid. Combination of 3D tumor-like construction and microfluidic techniques to achieve tumor on a chip for in vitro tumor environment modeling and drug screening were all included. Eventually, developmental directions and technical challenges in the research field are also discussed. We believe tumor on chip models have provided better sufficient clinical predictive power and will bridge the gap between proof-of-concept studies and a wider implementation within the oncology drug development for pathophysiological applications.

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Alodeani, Essa Ajmi, Mohammad Asrar Izhari, and Mohammad Arshad. "Antileishmanial screening, physicochemical properties and drug likeness of pyrazole carbaldehyde derivatives." Asian Pacific Journal of Health Sciences 2, no. 2 (April 2015): 41–47. http://dx.doi.org/10.21276/apjhs.2015.2.2.8.

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Williams, Petal Petersen, Catherine Mathews, Esmé Jordaan, Yukiko Washio, Mishka Terplan, and Charles DH Parry. "Validation of simple dichotomous self-report on prenatal alcohol and other drug use in women attending midwife obstetric units in the Cape Metropole, South Africa." Clinical Ethics 15, no. 4 (May 31, 2020): 181–86. http://dx.doi.org/10.1177/1477750920928885.

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Background This paper examines the degree of agreement among simple dichotomous self-report, validated screening results, and biochemical screening results of prenatal alcohol and other drug use among pregnant women. Method Secondary analysis was conducted on a cohort of pregnant women 16 years or older, presenting for prenatal care in the greater Cape Town, South Africa. Dichotomous verbal screening is a standard of care, and pregnant patients reporting alcohol and other drug use in dichotomous verbal screenings were asked to engage in screening using the Alcohol Smoking and Substance Involvement Screening Test (ASSIST) and urinalysis. Results Significant agreements between dichotomous and ASSIST scores were observed (K = 0.73–0.76). A higher rate of self-reported (36.9%) alcohol use was detected, relative to urine screening (19.6%) with a predictive value of 34.9; while underreporting of illicit substance use was observed (3.6% self-report vs. 8.8% urine screening) with an overall predictive value of 50.0. Conclusion Dichotomous verbal screening was considered valid after comparison with the ASSIST; however, combined use with urine screenings can be recommended especially for identifying illicit substance use in order to accurately detect alcohol and other drug use in pregnancy, so that women can be identified and referred for appropriate interventions where needed.

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Swanson, Jennifer. "Urine Drug Screening for Opioids." Journal of Pain & Palliative Care Pharmacotherapy 16, no. 1 (January 2002): 111–14. http://dx.doi.org/10.1080/j354v16n01_10.

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Dove, Alan. "Drug screening—beyond the bottleneck." Nature Biotechnology 17, no. 9 (September 1999): 859–63. http://dx.doi.org/10.1038/12845.

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Hallas, Jesper, and Peter Bytzer. "Screening for drug related dyspepsia." European Journal of Gastroenterology & Hepatology 10, no. 1 (January 1998): 27–32. http://dx.doi.org/10.1097/00042737-199801000-00006.

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Wootton, Robert C. R., and Andrew J. deMello. "Analog-to-digital drug screening." Nature 483, no. 7387 (February 29, 2012): 43–44. http://dx.doi.org/10.1038/483043a.

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