Journal articles: 'Drug screening model'

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ANENE-NZELU, CHUKWUEMEKA, YAN WANG, HANRY YU, and LEO HWA LIANG. "LIVER TISSUE MODEL FOR DRUG TOXICITY SCREENING." Journal of Mechanics in Medicine and Biology 11, no. 02 (April 2011): 369–90. http://dx.doi.org/10.1142/s0219519411004083.

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Understanding the mechanisms involved in the biotransformation of new drugs and their toxicological implications is important for drug development. In this regard, a lot of effort has been put into research to recreate the liver tissue in the laboratory for the purpose of drug screening. This has also helped to minimize the use of laboratory animal and reduce incidence of post-market withdrawal of drugs. Despite the progress made so far, cell source remains a major limitation since primary human hepatocytes are scarce and the various cell alternatives do not express all the genes found in the normal liver. In terms of tissue construct, there is a current shift to 3D models since the cell–cell interactions found in the 3D configuration enhance the morphology and function of hepatocytes. Furthermore, the engineered tissue's performance can be optimized by cocultures, perfusion-based systems, and the use of scaffolds. Nanotechnology seems promising in the field of tissue engineering, as it has been proven that cell–matrix interactions at the nano level can influence greatly on the outcome of the tissue. The review explores the various cell sources, the 3D model, flow-based systems, cocultures, and nanoscaffolds use in hepatocytes in vitro drug testing

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Genri, Kawahara, and Yukiko Hayashi. "Drug screening using transgenic zebrafish model." Proceedings for Annual Meeting of The Japanese Pharmacological Society 94 (2021): 2—S17–3. http://dx.doi.org/10.1254/jpssuppl.94.0_2-s17-3.

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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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Kondo, Jumpei, and Masahiro Inoue. "Application of Cancer Organoid Model for Drug Screening and Personalized Therapy." Cells 8, no. 5 (May 17, 2019): 470. http://dx.doi.org/10.3390/cells8050470.

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Drug screening—i.e., testing the effects of a number of drugs in multiple cell lines—is used for drug discovery and development, and can also be performed to evaluate the heterogeneity of a disease entity. Notably, intertumoral heterogeneity is a large hurdle to overcome for establishing standard cancer treatment, necessitating disease models better than conventional established 2D cell lines for screening novel treatment candidates. In the present review, we outline recent progress regarding experimental cancer models having more physiological and clinical relevance for drug screening, which are important for the successful evaluation of cellular response to drugs. The review is particularly focused on drug screening using the cancer organoid model, which is emerging as a better physiological disease model than conventional established 2D cell lines. We also review the use of cancer organoids to examine intertumor and intratumor heterogeneity, and introduce the perspective of the clinical use of cancer organoids to enable precision medicine.

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Cagan, Ross. "Drug screening using model systems: some basics." Disease Models & Mechanisms 9, no. 11 (November 1, 2016): 1241–44. http://dx.doi.org/10.1242/dmm.028159.

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Liu, Chen, Tianyu Qin, Yuhan Huang, Yuan Li, Gang Chen, and Chaoyang Sun. "Drug screening model meets cancer organoid technology." Translational Oncology 13, no. 11 (November 2020): 100840. http://dx.doi.org/10.1016/j.tranon.2020.100840.

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Parng, Chuenlei, Wen Lin Seng, Carlos Semino, and Patricia McGrath. "Zebrafish: A Preclinical Model for Drug Screening." ASSAY and Drug Development Technologies 1, no. 1 (November 2002): 41–48. http://dx.doi.org/10.1089/154065802761001293.

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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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Dixit, Vaibhav A. "A simple model to solve a complex drug toxicity problem." Toxicology Research 8, no. 2 (2019): 157–71. http://dx.doi.org/10.1039/c8tx00261d.

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Decker, S., M. Hollingshead, C. A. Bonomi, J. P. Carter, and E. A. Sausville. "The hollow fibre model in cancer drug screening." European Journal of Cancer 40, no. 6 (April 2004): 821–26. http://dx.doi.org/10.1016/j.ejca.2003.11.029.

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Ahmed, Shamima Nasreen, Biswajit Das, and Jashabir Chakraborty. "PROSPECTIVE AND RETROSPECTIVE ANIMAL MODEL USED IN THE PHARMACOLOGICAL SCREENING OF ANTI-CANCER DRUG." International Journal of Current Pharmaceutical Research 10, no. 4 (July 16, 2018): 13. http://dx.doi.org/10.22159/ijcpr.2018v10i4.28472.

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Cancer is a disease characterized by uncontrolled proliferation of cells that have transformed from the normal cells of the body. The widely used cancer drugs suffers from the drawback of high toxicity not within the reach of a common man. This urgently necessitating the screening of these compounds. This review focuses on the major contributions of preclinical screening models to anticancer drug development over the years till recent times, from the empirical drug screening of cytotoxic agents against uncharacterized tumor models to the target-orientated drug screening of agents with defined mechanisms of action,, a general transition has been observed. The newer approaches to anticancer drug development involve the molecular characterization of models along with an appreciation of the pharmacodynamics and pharmacokinetic properties of compounds [e. g., the US National Cancer Institute (NCI) in vitro 60-cell line panel, hollow fibre assay, and s. c. xenograft]. In vivo tumor models including orthotopic, metastatic, and genetically engineered mouse models are also reviewed. The preclinical screening efforts of the European are also included. In 2015 with the rapid development of cancer modeling in zebrafish, great opportunities exist for chemical screens to find anticancer drug since 1970 the European Organisation for Research and Treatment of Cancer and Cancer Research UK, have been collaborating with the NCI in the acquisition and screening of compounds.

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Puscas, Ina, Florian Bernard-Patrzynski, Martin Jutras, Marc-André Lécuyer, Lyne Bourbonnière, Alexandre Prat, Grégoire Leclair, and V. Gaëlle Roullin. "IVIVC Assessment of Two Mouse Brain Endothelial Cell Models for Drug Screening." Pharmaceutics 11, no. 11 (November 8, 2019): 587. http://dx.doi.org/10.3390/pharmaceutics11110587.

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Since most preclinical drug permeability assays across the blood-brain barrier (BBB) are still evaluated in rodents, we compared an in vitro mouse primary endothelial cell model to the mouse b.End3 and the acellular parallel artificial membrane permeability assay (PAMPA) models for drug screening purposes. The mRNA expression of key feature membrane proteins of primary and bEnd.3 mouse brain endothelial cells were compared. Transwell® monolayer models were further characterized in terms of tightness and integrity. The in vitro in vivo correlation (IVIVC) was obtained by the correlation of the in vitro permeability data with log BB values obtained in mice for seven drugs. The mouse primary model showed higher monolayer integrity and levels of mRNA expression of BBB tight junction (TJ) proteins and membrane transporters (MBRT), especially for the efflux transporter Pgp. The IVIVC and drug ranking underlined the superiority of the primary model (r2 = 0.765) when compared to the PAMPA-BBB (r2 = 0.391) and bEnd.3 cell line (r2 = 0.019) models. The primary monolayer mouse model came out as a simple and reliable candidate for the prediction of drug permeability across the BBB. This model encompasses a rapid set-up, a fair reproduction of BBB tissue characteristics, and an accurate drug screening.

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Cheng, Lijun, Abhishek Majumdar, Daniel Stover, Shaofeng Wu, Yaoqin Lu, and Lang Li. "Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine." Genes 11, no. 3 (February 28, 2020): 263. http://dx.doi.org/10.3390/genes11030263.

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Background: Large-scale screening of drug sensitivity on cancer cell models can mimic in vivo cellular behavior providing wider scope for biological research on cancer. Since the therapeutic effect of a single drug or drug combination depends on the individual patient’s genome characteristics and cancer cells integration reaction, the identification of an effective agent in an in vitro model by using large number of cancer cell models is a promising approach for the development of targeted treatments. Precision cancer medicine is to select the most appropriate treatment or treatments for an individual patient. However, it still lacks the tools to bridge the gap between conventional in vitro cancer cell models and clinical patient response to inhibitors. Methods: An optimal two-layer decision system model is developed to identify the cancer cells that most closely resemble an individual tumor for optimum therapeutic interventions in precision cancer medicine. Accordingly, an optimal grid parameters selection is designed to seek the highest accordance for treatment selection to the patient’s preference for drug response and in vitro cancer cell drug screening. The optimal two-layer decision system model overcomes the challenge of heterology data comparison between the tumor and the cancer cells, as well as between the continual variation of drug responses in vitro and the discrete ones in clinical practice. We simulated the model accuracy using 681 cancer cells’ mRNA and associated 481 drug screenings and validated our results on 315 breast cancer patients drug selection across seven drugs (docetaxel, doxorubicin, fluorouracil, paclitaxel, tamoxifen, cyclophosphamide, lapitinib). Results: Comparing with the real response of a drug in clinical patients, the novel model obtained an overall average accordance over 90.8% across the seven drugs. At the same time, the optimal cancer cells and the associated optimal therapeutic efficacy of cancer drugs are recommended. The novel optimal two-layer decision system model was used on 1097 patients with breast cancer in guiding precision medicine for a recommendation of their optimal cancer cells (30 cancer cells) and associated efficacy of certain cancer drugs. Our model can detect the most similar cancer cells for each individual patient. Conclusion: A successful clinical translation model (optimal two-layer decision system model) was developed to bridge in-vitro basic science to clinical practice in a therapeutic intervention application for the first time. The novel tool kills two birds with one stone. It can help basic science to seek optimal cancer cell models for an individual tumor, while prioritizing clinical drugs’ recommendations in practice. Tool associated platform website: We extended the breast cancer research to 32 more types of cancers across 45 therapy predictions.

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Liao, Shuyi, Wenmin Yang, Ting Yu, Lu Dai, Xiaoliang Liu, Jiangping Zhang, Jinghong Zhao, and Chi Liu. "Establishment of a Drug Screening Model for Cardiac Complications of Acute Renal Failure." Biomolecules 11, no. 9 (September 16, 2021): 1370. http://dx.doi.org/10.3390/biom11091370.

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Acute renal failure (ARF) is a clinical critical syndrome with rapid and severe decline of renal function. Complications of ARF, especially its cardiac complications (cardiorenal syndrome type 3, CRS-3), are the main causes of death in patients with ARF. However, the shortage and limited efficacy of therapeutic drugs make it significant to establish new large-scale drug screening models. Based on the Nitroreductase/Metronidazole (NTR/MTZ) cell ablation system, we constructed a Tg(cdh17:Dendra2-NTR) transgenic zebrafish line, which can specifically ablate renal tubular epithelial cells. The absence of renal tubular epithelial cells can lead to ARF in zebrafish larvae. The ARF symptoms, such as heart enlargement, slow heart rate and blood stasis, are similar to the clinical manifestations of human CRS-3. Furthermore, two therapeutic drugs (digoxin and enalapril) commonly used in the clinical treatment of heart failure were also effective in alleviating the symptoms of CRS-3 in zebrafish, which proved the effectiveness of this model. Drug screening further discovered a potential drug candidate, α-lipoic acid, which can effectively alleviate the symptoms of CRS-3 through its antioxidant function. Accordingly, we established a new ARF model of zebrafish, which laid a foundation for large-scale screening of new therapeutic drugs for its complications.

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Noguchi, Yoshihiro, Tomoya Tachi, and Hitomi Teramachi. "Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database." Pharmaceutics 12, no. 8 (August 12, 2020): 762. http://dx.doi.org/10.3390/pharmaceutics12080762.

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Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.

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Moya, Elisa L. J., Elodie Vandenhaute, Eleonora Rizzi, Marie-Christine Boucau, Johan Hachani, Nathalie Maubon, Fabien Gosselet, and Marie-Pierre Dehouck. "Miniaturization and Automation of a Human In Vitro Blood–Brain Barrier Model for the High-Throughput Screening of Compounds in the Early Stage of Drug Discovery." Pharmaceutics 13, no. 6 (June 16, 2021): 892. http://dx.doi.org/10.3390/pharmaceutics13060892.

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Central nervous system (CNS) diseases are one of the top causes of death worldwide. As there is a difficulty of drug penetration into the brain due to the blood–brain barrier (BBB), many CNS drugs treatments fail in clinical trials. Hence, there is a need to develop effective CNS drugs following strategies for delivery to the brain by better selecting them as early as possible during the drug discovery process. The use of in vitro BBB models has proved useful to evaluate the impact of drugs/compounds toxicity, BBB permeation rates and molecular transport mechanisms within the brain cells in academic research and early-stage drug discovery. However, these studies that require biological material (animal brain or human cells) are time-consuming and involve costly amounts of materials and plastic wastes due to the format of the models. Hence, to adapt to the high yields needed in early-stage drug discoveries for compound screenings, a patented well-established human in vitro BBB model was miniaturized and automated into a 96-well format. This replicate met all the BBB model reliability criteria to get predictive results, allowing a significant reduction in biological materials, waste and a higher screening capacity for being extensively used during early-stage drug discovery studies.

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Veit, Joachim G. S., Bhaskar Birru, Ruby Singh, Elizabeth M. Arrigali, and Monica A. Serban. "An In Vitro Model for Characterization of Drug Permeability across the Tympanic Membrane." Pharmaceuticals 15, no. 9 (September 7, 2022): 1114. http://dx.doi.org/10.3390/ph15091114.

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Otic disorders, such as otitis media and hearing loss, affect a substantial portion of the global population. Despite this, oto-therapeutics, in particular those intended to treat hearing loss, have seen limited development and innovation. A significant factor to this is likely a result of the inherent costs and complexities of drug discovery and development. With in vitro 3D tissue models seeing increased utility for the rapid, high-throughput screening of drug candidates, it stands to reason that the field of otology could greatly benefit from such innovations. In this study, we propose and describe an in vitro 3D model, designed using a physiologically based approach, which we suggest can be used to estimate drug permeability across human tympanic membranes (TM). We characterize the permeability properties of several template drugs in this model under various growth and storage conditions. The availability of such cost-effective, rapid, high-throughput screening tools should allow for increased innovation and the discovery of novel drug candidates over the currently used animal models. In the context of this TM permeation model, it may promote the development of topical drugs and formulations that can non-invasively traverse the TM and provide tissue-targeted drug delivery as an alternative to systemic treatment, an objective which has seen limited study until present.

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Kato, Yuki, Yutaka Tonomura, Hiroyuki Hanafusa, Kyohei Nishimura, Tamio Fukushima, and Motonobu Ueno. "Adult Zebrafish Model for Screening Drug-Induced Kidney Injury." Toxicological Sciences 174, no. 2 (February 10, 2020): 241–53. http://dx.doi.org/10.1093/toxsci/kfaa009.

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Abstract Drug-induced kidney injury is a serious safety issue in drug development. In this study, we evaluated the usefulness of adult zebrafish as a small in vivo system for detecting drug-induced kidney injury. We first investigated the effects of typical nephrotoxicants, gentamicin and doxorubicin, on adult zebrafish. We found that gentamicin induced renal tubular necrosis with increased lysosome and myeloid bodies, and doxorubicin caused foot process fusion of glomerular podocytes. These findings were similar to those seen in mammals, suggesting a common pathogenesis. Second, to further evaluate the performance of the model in detecting drug-induced kidney injury, adult zebrafish were treated with 28 nephrotoxicants or 14 nonnephrotoxicants for up to 4 days, euthanized 24 h after the final treatment, and examined histopathologically. Sixteen of the 28 nephrotoxicants and none of the 14 nonnephrotoxicants caused drug-induced kidney injury in zebrafish (sensitivity, 57%; specificity, 100%; positive predictive value, 100%; negative predictive value, 54%). Finally, we explored genomic biomarker candidates using kidneys isolated from gentamicin- and cisplatin-treated zebrafish using microarray analysis and identified 3 candidate genes, egr1, atf3, and fos based on increased expression levels and biological implications. The expression of these genes was upregulated dose dependently in cisplatin-treated groups and was > 25-fold higher in gentamicin-treated than in the control group. In conclusion, these results suggest that the adult zebrafish has (1) similar nephrotoxic response to those of mammals, (2) considerable feasibility as an experimental model for toxicity studies, and (3) applicability to pathological examination and genomic biomarker evaluation in drug-induced kidney injury.

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Hamilton, Lloyd, Dirk Sieger, Belen Rubio Ruiz, and Asier Unciti-broceta. "A novel zebrafish xenograft model for immunotherapeutic drug screening." Neuro-Oncology 20, suppl_1 (January 2018): i14. http://dx.doi.org/10.1093/neuonc/nox238.063.

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Stilwell, Geoff E., Sudipta Saraswati, J. Troy Littleton, and Scott W. Chouinard. "Development of aDrosophilaseizure model forin vivohigh-throughput drug screening." European Journal of Neuroscience 24, no. 8 (October 2006): 2211–22. http://dx.doi.org/10.1111/j.1460-9568.2006.05075.x.

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Kaarj, Kattika, Jennifer Ngo, Christina Loera, Patarajarin Akarapipad, Soohee Cho, and Jeong-Yeol Yoon. "Simple Paper-based Liver Cell Model for Drug Screening." BioChip Journal 14, no. 2 (June 2020): 218–29. http://dx.doi.org/10.1007/s13206-020-4211-6.

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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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Nakarin, Fahsai, Kajjana Boonpalit, Jiramet Kinchagawat, Patcharapol Wachiraphan, Thanyada Rungrotmongkol, and Sarana Nutanong. "Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation." Molecules 27, no. 4 (February 11, 2022): 1226. http://dx.doi.org/10.3390/molecules27041226.

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A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure–activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model’s applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.

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Lee, Sung Hak, Jung Won Kang, Tao Lin, Jae Eun Lee, and Dong Il Jin. "Teratogenic Potential of Antiepileptic Drugs in the Zebrafish Model." BioMed Research International 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/726478.

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The zebrafish model is an attractive candidate for screening of developmental toxicity during early drug development. Antiepileptic drugs (AEDs) arouse concern for the risk of teratogenicity, but the data are limited. In this study, we evaluated the teratogenic potential of seven AEDs (carbamazepine (CBZ), ethosuximide (ETX), valproic acid (VPN), lamotrigine (LMT), lacosamide (LCM), levetiracetam (LVT), and topiramate (TPM)) in the zebrafish model. Zebrafish embryos were exposed to AEDs from initiation of gastrula (5.25 hours post-fertilization (hpf)) to termination of hatching (72 hpf) which mimic the mammalian teratogenic experimental design. The lethality and teratogenic index (TI) of AEDs were determined and the TI values of each drug were compared with the US FDA human pregnancy categories. Zebrafish model was useful screening model for teratogenic potential of antiepilepsy drugs and was in concordance within vivomammalian data and human clinical data.

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Xie, Yujiang, Genpei Shi, Jie Sun, Si Li, Wei Gao, Yimin Hu, Chang Zu, Weiwei Tang, and Junbo Gong. "Computational Screening and Experimental Validation on Multicomponent Crystals of a New Class of Janus Kinase (JAK) Inhibitor Drug with Improved Solubility." Crystals 12, no. 12 (November 27, 2022): 1722. http://dx.doi.org/10.3390/cryst12121722.

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Developing multicomponent crystal forms, especially cocrystals and salts, is becoming a promising pathway to improve the solubility and bioavailability of drugs. Herein, new multicomponent crystals of SHR0302, a new generation of Janus Kinase (JAK) inhibitor that suffers from poor solubility, were developed based on a cooperative approach of computational and experimental coformer screenings. Virtual screening methods, including the conductor-like screening model for realistic solvents (COSMO-RS) and molecular complementary (MC) analysis, were employed to predict the binding affinity between SHR0302 and selected coformers. The developed screening method was capable of reducing the screening database to 30 coformers from a total of 42 proposed coformers. The proof-of-concept experimental screening study was performed to demonstrate the efficiency of computational screening, wherein three new multicomponent crystalline forms were found and fully characterized by powder X-ray diffraction, thermal analysis, and IR and 1H-NMR spectroscopy. Further, the measurements of the solubility property of these new multicomponent crystal forms reveal an apparent promotion compared with the drug alone. Finally, the receiver operator characteristic (ROC) curve was used to assess the prediction performance of the COSMO-RS model. It was found that the established screening model can effectively shorten the experimental screening time and efforts.

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Wang, Jianzheng, Jinxi Huang, Hui Wang, Wei Yang, Qiwen Bai, Zhentao Yao, Qingli Li, et al. "Personalized Treatment of Advanced Gastric Cancer Guided by the MiniPDX Model." Journal of Oncology 2022 (January 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/1987705.

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Background. The morbidity and mortality of gastric cancer are high in China. There are challenges to develop precise and individualized drug regimens for patients with gastric cancer after a standard treatment. Choosing the most appropriate anticancer drug after a patient developing drug resistance is very important to improve the patient’s prognosis. MiniPDX has been widely used as a new and reliable preclinical research model to predict the sensitivity of anticancer drugs. Methods. The OncoVee® MiniPDX system developed by Shanghai LIDE Biotech Co., Ltd. was used to establish the MiniPDX models using specimens of patients with gastric cancer. The cancer tissues were biopsied under endoscopy, and then, the tumor cell suspension was prepared for a drug sensitivity test by subcutaneously implanting into Balb/c-nude mice. The selected optimal regimen obtained from the MiniPDX assay was used to treat patients with drug-resistant gastric cancer. Results. We successfully established an individualized and sensitive drug screening system for four patients from January 2021 to July 2021. MiniPDX models identified potentially effective drugs for these four patients, with partial remission in two of the patients after treatment and disease progression in the remaining of two patients. Severe side effects from chemotherapy or targeted therapy were not observed in all patients. Conclusion. Establishing a personalized drug screening system for patients with drug-resistant gastric cancer can guide the selection of clinical drugs, improve the clinical benefit of patients, and avoid ineffective treatments. It can be an effective supplement for treatment options.

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Singh, Ruby, Bhaskar Birru, Joachim G. S. Veit, Elizabeth M. Arrigali, and Monica A. Serban. "Development and Characterization of an In Vitro Round Window Membrane Model for Drug Permeability Evaluations." Pharmaceuticals 15, no. 9 (September 5, 2022): 1105. http://dx.doi.org/10.3390/ph15091105.

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Hearing loss and balance disorders are highly common disorders, and the development of effective oto-therapeutics remains an area of intense research. Drug development and screening in the hearing research field heavily rely on the use of preclinical models with often ambiguous translational relevance. This often leads to failed advancement in the market of effective therapeutics. In this context, especially for inner ear-specific pathologies, the availability of an in vitro, physiologically relevant, round window membrane (RWM) model could enable rapid, high-throughput screening of potential topical drugs for inner ear and cochlear dysfunctions and could help accelerate the advancement to clinic and market of more viable drug candidates. In this study, we report the development and evaluation of an in vitro model that mimics the native RWM tissue morphology and microenvironment as shown via immunostaining and histological analyses. The developed three-dimensional (3D) in vitro model was additionally assessed for barrier integrity by transepithelial electrical resistance, and the permeability of lipophilic and hydrophilic drugs was determined. Our collective findings suggest that this in vitro model could serve as a tool for rapid development and screening of topically deliverable oto-therapeutics.

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Wang, Zhaohui, Emilio Cortes-Sanchez, Chieh-Hsiang Yang, Daniel Nelson, Daniel Delubac, Shiaowen David Hsu, Alana Welm, and Xiling Shen. "Micro-organospheres: An automated patient-derived model platform for rapid drug screening of breast cancer." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e12628-e12628. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e12628.

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e12628 Background: Patient-derived breast cancer (BC) organoids are valuable preclinical models to study patient drug responses, demonstrating good correlations with patients’ clinical outcomes. However, establishment and expansion of such organoids from patient tumors for drug screening is currently a time-consuming and labor-intensive process. A more rapid and high-throughput method will enable broader utility in diagnostics and drug development. Methods: An automated, rapid and scalable microfluidic platform was used to process and develop BC micro-organospheres. Drug sensitivities studies on BC micro-organospheres were performed on day 3 and day 6 using 10-FDA approved drugs, including palbociclib, adriamycin, 5-FU, gemcitabine, methotrexate, everolimus, paclitaxel, docetaxel, ixabepilone, and vinblastine. The responses of micro-organospheres and organoids to the drugs were assessed by CellTiter 3D Glo assay on day 6 after the drug treatment. The growth and establishment of the micro-organospheres by imaging. The drug sensitivity and resistance of the micro-organospheres were analyzed by calculating the percentage cell viability and normalized growth rate inhibition (GRI) and compared to organoids. Results: We successfully established micro-organospheres from eight patient-derived BC organoids with a 100% success rate. The micro-organospheres preserved similar cell morphologies to the bulk organoids. 7/8 micro-organosphere models had similar drug response patterns to organoids between day 3 and day 6 as evident by the GRI heatmap. Specifically, we treated matching micro-organospheres and conventional organoids from two patients with 10 frontline BC chemotherapy drugs, and both showed similar response patterns with GRI heatmap. For the other 6 patient-derived models, the responses of micro-organospheres to docetaxel and everolimus also matched the historical drug responses of in bulk organoid culture with similar GRI heatmap. Conclusions: We have now shown the feasibility of establishing micro-organospheres as a rapid, scalable, and cost-effective platform to study patient-derived BC drug response. This technology has the potential to be used for both diagnostics to guide patient treatment and as a screening platform for new BC drug discovery.

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Sofela, Samuel, Sarah Sahloul, and Yong-Ak Song. "Biophysical analysis of drug efficacy on C. elegans models for neurodegenerative and neuromuscular diseases." PLOS ONE 16, no. 6 (June 11, 2021): e0246496. http://dx.doi.org/10.1371/journal.pone.0246496.

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Caenorhabditis elegans has emerged as a powerful model organism for drug screening due to its cellular simplicity, genetic amenability and homology to humans combined with its small size and low cost. Currently, high-throughput drug screening assays are mostly based on image-based phenotyping with the focus on morphological-descriptive traits not exploiting key locomotory parameters of this multicellular model with muscles such as its thrashing force, a critical biophysical parameter when screening drugs for muscle-related diseases. In this study, we demonstrated the use of a micropillar-based force assay chip in combination with a fluorescence assay to evaluate the efficacy of various drugs currently used in treatment of neurodegenerative and neuromuscular diseases. Using this two-dimensional approach, we showed that the force assay was generally more sensitive in measuring efficacy of drug treatment in Duchenne Muscular Dystrophy and Parkinson’s Disease mutant worms as well as partly in Amyotrophic Lateral Sclerosis model. These results underline the potential of our force assay chip in screening of potential drug candidates for the treatment of neurodegenerative and neuromuscular diseases when combined with a fluorescence assay in a two-dimensional analysis approach.

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Ordas, Anita, Robert-Jan Raterink, Fraser Cunningham, Hans J. Jansen, Malgorzata I. Wiweger, Susanne Jong-Raadsen, Sabine Bos, et al. "Testing Tuberculosis Drug Efficacy in a Zebrafish High-Throughput Translational Medicine Screen." Antimicrobial Agents and Chemotherapy 59, no. 2 (November 10, 2014): 753–62. http://dx.doi.org/10.1128/aac.03588-14.

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ABSTRACTThe translational value of zebrafish high-throughput screens can be improved when more knowledge is available on uptake characteristics of potential drugs. We investigated reference antibiotics and 15 preclinical compounds in a translational zebrafish-rodent screening system for tuberculosis. As a major advance, we have developed a new tool for testing drug uptake in the zebrafish model. This is important, because despite the many applications of assessing drug efficacy in zebrafish research, the current methods for measuring uptake using mass spectrometry do not take into account the possible adherence of drugs to the larval surface. Our approach combines nanoliter sampling from the yolk using a microneedle, followed by mass spectrometric analysis. To date, no single physicochemical property has been identified to accurately predict compound uptake; our method offers a great possibility to monitor how any novel compound behaves within the system. We have correlated the uptake data with high-throughput drug-screening data fromMycobacterium marinum-infected zebrafish larvae. As a result, we present an improved zebrafish larva drug-screening platform which offers new insights into drug efficacy and identifies potential false negatives and drugs that are effective in zebrafish and rodents. We demonstrate that this improved zebrafish drug-screening platform can complement conventional models ofin vivoMycobacterium tuberculosis-infected rodent assays. The detailed comparison of two vertebrate systems, fish and rodent, may give more predictive value for efficacy of drugs in humans.

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Jungwirth, Gerhard, Tao Yu, Cao Junguo, Catharina Lotsch, Andreas Unterberg, and Christel Herold-Mende. "TMOD-29. STANDARDIZED GENERATION OF TUMOR-ORGANOIDS AS NOVEL DRUG SCREENING MODEL IN MENINGIOMA." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi221—vi222. http://dx.doi.org/10.1093/neuonc/noab196.890.

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Abstract Tumor-organoids (TOs) are novel, complex three-dimensional ex vivo tissue cultures that under optimal conditions accurately reflect genotype and phenotype of the original tissue with preserved cellular heterogeneity and morphology. They may serve as a new and exciting model for studying cancer biology and directing personalized therapies. The aim of our study was to establish TOs from meningioma (MGM) and to test their usability for large-scale drug screenings. We were capable of forming several hundred TO equal in size by controlled reaggregation of freshly prepared single cell suspension of MGM tissue samples. In total, standardized TOs from 60 patients were formed, including eight grade II and three grade III MGMs. TOs reaggregated within 3 days resulting in a reducted diameter by 50%. Thereafter, TO size remained stable throughout a 14 days observation period. TOs consisted of largely viable cells, whereas dead cells were predominantly found outside of the organoid. H&E stainings confirmed the successful establishment of dense tissue-like structures. Next, we assessed the suitability and reliability of TOs for a robust large-scale drug testing by employing nine highly potent compounds, derived from a drug screening performed on several MGM cell lines. First, we tested if drug responses depend on TO size. Interestingly, drug responses to these drugs remained identical independent of their sizes. Based on a sufficient representation of low abundance cell types such as T-cells and macrophages an overall number of 25.000 cells/TO was selected for further experiments revealing FDA-approved HDAC inhibitors as highly effective drugs in most of the TOs with a mean z-AUC score of -1.33. Taken together, we developed a protocol to generate standardized TO from MGM containing low abundant cell types of the tumor microenvironment in a representative manner. Robust and reliable drug responses suggest patient-derived TOs as a novel drug testing model in meningioma research.

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Nii, Teruki. "Strategies Using Gelatin Microparticles for Regenerative Therapy and Drug Screening Applications." Molecules 26, no. 22 (November 10, 2021): 6795. http://dx.doi.org/10.3390/molecules26226795.

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Gelatin, a denatured form of collagen, is an attractive biomaterial for biotechnology. In particular, gelatin particles have been noted due to their attractive properties as drug carriers. The drug release from gelatin particles can be easily controlled by the crosslinking degree of gelatin molecule, responding to the purpose of the research. The gelatin particles capable of drug release are effective in wound healing, drug screening models. For example, a sustained release of growth factors for tissue regeneration at the injured sites can heal a wound. In the case of the drug screening model, a tissue-like model composed of cells with high activity by the sustained release of drug or growth factor provides reliable results of drug effects. Gelatin particles are effective in drug delivery and the culture of spheroids or cell sheets because the particles prevent hypoxia-derived cell death. This review introduces recent research on gelatin microparticles-based strategies for regenerative therapy and drug screening models.

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Montanari, Floriane, Anna Cseke, Katrin Wlcek, and Gerhard F. Ecker. "Virtual Screening of DrugBank Reveals Two Drugs as New BCRP Inhibitors." SLAS DISCOVERY: Advancing the Science of Drug Discovery 22, no. 1 (July 11, 2016): 86–93. http://dx.doi.org/10.1177/1087057116657513.

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The breast cancer resistance protein (BCRP) is an ABC transporter playing a crucial role in the pharmacokinetics of drugs. The early identification of substrates and inhibitors of this efflux transporter can help to prevent or foresee drug-drug interactions. In this work, we built a ligand-based in silico classification model to predict the inhibitory potential of drugs toward BCRP. The model was applied as a virtual screening technique to identify potential inhibitors among the small-molecules subset of DrugBank. Ten compounds were selected and tested for their capacity to inhibit mitoxantrone efflux in BCRP-expressing PLB985 cells. Results identified cisapride (IC50 = 0.4 µM) and roflumilast (IC50 = 0.9 µM) as two new BCRP inhibitors. The in silico strategy proved useful to prefilter potential drug-drug interaction perpetrators among a database of small molecules and can reduce the amount of compounds to test.

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Deng, Lei, Yibiao Huang, Xuejun Liu, and Hui Liu. "Graph2MDA: a multi-modal variational graph embedding model for predicting microbe–drug associations." Bioinformatics 38, no. 4 (November 23, 2021): 1118–25. http://dx.doi.org/10.1093/bioinformatics/btab792.

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Abstract Motivation Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have become novel targets for the development of antibacterial agents. Therefore, screening of microbe–drug associations can benefit greatly drug research and development. With the increase of microbial genomic and pharmacological datasets, we are greatly motivated to develop an effective computational method to identify new microbe–drug associations. Results In this article, we proposed a novel method, Graph2MDA, to predict microbe–drug associations by using variational graph autoencoder (VGAE). We constructed multi-modal attributed graphs based on multiple features of microbes and drugs, such as molecular structures, microbe genetic sequences and function annotations. Taking as input the multi-modal attribute graphs, VGAE was trained to learn the informative and interpretable latent representations of each node and the whole graph, and then a deep neural network classifier was used to predict microbe–drug associations. The hyperparameter analysis and model ablation studies showed the sensitivity and robustness of our model. We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods. We also explored the meaning of the learned latent representations of drugs and found that the drugs show obvious clustering patterns that are significantly consistent with drug ATC classification. Moreover, we conducted case studies on two microbes and two drugs and found 75–95% predicted associations have been reported in PubMed literature. Our extensive performance evaluations validated the effectiveness of our proposed method. Availability and implementation Source codes and preprocessed data are available at https://github.com/moen-hyb/Graph2MDA. Supplementary information Supplementary data are available at Bioinformatics online.

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Doh, Il, Yong-Jun Kwon, Bosung Ku, and Dong Woo Lee. "Drug Efficacy Comparison of 3D Forming and Preforming Sphere Models with a Micropillar and Microwell Chip Platform." SLAS DISCOVERY: Advancing the Science of Drug Discovery 24, no. 4 (February 12, 2019): 476–83. http://dx.doi.org/10.1177/2472555218821292.

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Hepatocellular carcinoma (HCC), a major histological subtype of liver cancer, is the third most common cause of cancer-related death worldwide. Currently, many curative standard treatments using target-specific chemotherapeutic agents are being developed. However, drug efficacy tests based on the 2D monolayer cell culture model do not effectively screen the best drug candidates because they do not accurately reflect in vivo tumor microenvironments. Thus, to select the best drug candidates or repositioning drugs, we developed new 3D in vitro hepatic tumor models, including 3D forming and preformed sphere models. A micropillar and microwell chip platform was used for the 3D in vitro liver cell-based model for high-throughput screening. We measured the efficacy of 60 drugs and sorted the most efficacious drugs by comparing the drug response of the 2D monolayer model with the 3D forming and preformed sphere models. Among the 60 drugs, 17 drugs (28.3%) showed a significant high efficacy in the 3D preformed sphere model, while 45 drugs (75%) showed an efficacy in the 2D model. We also calculated the IC50 values of the 17 drugs and found that 7 drugs exhibited a high sensitivity in HCC, which was in agreement with previous studies.

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Stackhouse, Christian T., James R. Rowland, Rachael S. Shevin, Raj Singh, G. Yancey Gillespie, and Christopher D. Willey. "A Novel Assay for Profiling GBM Cancer Model Heterogeneity and Drug Screening." Cells 8, no. 7 (July 11, 2019): 702. http://dx.doi.org/10.3390/cells8070702.

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Accurate patient-derived models of cancer are needed for profiling the disease and for testing therapeutics. These models must not only be accurate, but also suitable for high-throughput screening and analysis. Here we compare two derivative cancer models, microtumors and spheroids, to the gold standard model of patient-derived orthotopic xenografts (PDX) in glioblastoma multiforme (GBM). To compare these models, we constructed a custom NanoString panel of 350 genes relevant to GBM biology. This custom assay includes 16 GBM-specific gene signatures including a novel GBM subtyping signature. We profiled 11 GBM-PDX with matched orthotopic cells, derived microtumors, and derived spheroids using the custom NanoString assay. In parallel, these derivative models underwent drug sensitivity screening. We found that expression of certain genes were dependent on the cancer model while others were model-independent. These model-independent genes can be used in profiling tumor-specific biology and in gauging therapeutic response. It remains to be seen whether or not cancer model-specific genes may be directly or indirectly, through changes to tumor microenvironment, manipulated to improve the concordance of in vitro derivative models with in vivo models yielding better prediction of therapeutic response.

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Choi, Tae-Woo, Jeong Cho, Joohong Ahnn, and Hyun-Ok Song. "Novel Findings of Anti-Filarial Drug Target and Structure-Based Virtual Screening for Drug Discovery." International Journal of Molecular Sciences 19, no. 11 (November 13, 2018): 3579. http://dx.doi.org/10.3390/ijms19113579.

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Lymphatic filariasis and onchocerciasis caused by filarial nematodes are important diseases leading to considerable morbidity throughout tropical countries. Diethylcarbamazine (DEC), albendazole (ALB), and ivermectin (IVM) used in massive drug administration are not highly effective in killing the long-lived adult worms, and there is demand for the development of novel macrofilaricidal drugs affecting new molecular targets. A Ca2+ binding protein, calumenin, was identified as a novel and nematode-specific drug target for filariasis, due to its involvement in fertility and cuticle development in nematodes. As sterilizing and killing effects of the adult worms are considered to be ideal profiles of new drugs, calumenin could be an eligible drug target. Indeed, the Caenorhabditis elegans mutant model of calumenin exhibited enhanced drug acceptability to both microfilaricidal drugs (ALB and IVM) even at the adult stage, proving the roles of the nematode cuticle in efficient drug entry. Molecular modeling revealed that structural features of calumenin were only conserved among nematodes (C. elegans, Brugia malayi, and Onchocerca volvulus). Structural conservation and the specificity of nematode calumenins enabled the development of drugs with good target selectivity between parasites and human hosts. Structure-based virtual screening resulted in the discovery of itraconazole (ITC), an inhibitor of sterol biosynthesis, as a nematode calumenin-targeting ligand. The inhibitory potential of ITC was tested using a nematode mutant model of calumenin.

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Di Veroli, Giovanni Y., Mark R. Davies, Henggui Zhang, Najah Abi-Gerges, and Mark R. Boyett. "High-throughput screening of drug-binding dynamics to HERG improves early drug safety assessment." American Journal of Physiology-Heart and Circulatory Physiology 304, no. 1 (January 1, 2013): H104—H117. http://dx.doi.org/10.1152/ajpheart.00511.2012.

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The use of computational models to predict drug-induced changes in the action potential (AP) is a promising approach to reduce drug safety attrition but requires a better representation of more complex drug-target interactions to improve the quantitative prediction. The blockade of the human ether-a-go-go-related gene (HERG) channel is a major concern for QT prolongation and Torsade de Pointes risk. We aim to develop quantitative in-silico AP predictions based on a new electrophysiological protocol (suitable for high-throughput HERG screening) and mathematical modeling of ionic currents. Electrophysiological recordings using the IonWorks device were made from HERG channels stably expressed in Chinese hamster ovary cells. A new protocol that delineates inhibition over time was applied to assess dofetilide, cisapride, and almokalant effects. Dynamic effects displayed distinct profiles for these drugs compared with concentration-effects curves. Binding kinetics to specific states were identified using a new HERG Markov model. The model was then modified to represent the canine rapid delayed rectifier K+ current at 37°C and carry out AP predictions. Predictions were compared with a simpler model based on conductance reduction and were found to be much closer to experimental data. Improved sensitivity to concentration and pacing frequency variables was obtained when including binding kinetics. Our new electrophysiological protocol is suitable for high-throughput screening and is able to distinguish drug-binding kinetics. The association of this protocol with our modeling approach indicates that quantitative predictions of AP modulation can be obtained, which is a significant improvement compared with traditional conductance reduction methods.

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Maeng, Ju Eun, Ha-Young Seo, Soon-Chan Kim, and Ja-Lok Ku. "Novel Drug Screening Platform: Tumor Organoid." Korean Journal of Pancreas and Biliary Tract 26, no. 4 (October 31, 2021): 233–40. http://dx.doi.org/10.15279/kpba.2021.26.4.233.

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Pancreatic ductal adenocarcinoma (PDAC) is known to be one of the most lethal cancers among all cancer types, with a relative 5-year survival rate of less than 8%. Currently, surgery is the only probable curative treatment for PDAC which is available for only 10-15% of the patients diagnosed with the cancer. Organoids resemble the original tissue in morphology and function with self-organizing capacity. Organoids can be cultured with high effectiveness from individual patient derived tumor tissue which makes them an extremely fitting model for translational uses and the improvement of personalized cancer medicine. Before personalized medicine based on organoids can be applied in the clinic, the improvement of drug screening platforms in terms of sensitivity and robustness is necessary.

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Nie, Xialin, Zhixing Liang, Kun Li, Haoyuan Yu, Yuhan Huang, Linsen Ye, and Yang Yang. "Novel organoid model in drug screening: Past, present, and future." Liver Research 5, no. 2 (June 2021): 72–78. http://dx.doi.org/10.1016/j.livres.2021.05.003.

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Wang, Yan-Ying, Wei-Wei Ma, and I.-Feng Peng. "Screening of sleep assisting drug candidates with a Drosophila model." PLOS ONE 15, no. 7 (July 29, 2020): e0236318. http://dx.doi.org/10.1371/journal.pone.0236318.

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Kawahara, G., J. A. Karpf, J. A. Myers, M. S. Alexander, J. R. Guyon, and L. M. Kunkel. "Drug screening in a zebrafish model of Duchenne muscular dystrophy." Proceedings of the National Academy of Sciences 108, no. 13 (March 14, 2011): 5331–36. http://dx.doi.org/10.1073/pnas.1102116108.

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Tims, F. M., A. M. Horton, and B. F. Fletcher. "Screening for Cognitive impairment with drug addicts: The DATOS model." Archives of Clinical Neuropsychology 7, no. 4 (January 1, 1992): 367. http://dx.doi.org/10.1093/arclin/7.4.367.

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Sharma, Pallavi, Supriya Sharma, Vikram Patial, Damanpreet Singh, and Yogendra Shantaram Padwad. "Zebrafish (Danio rerio): A potential model for nephroprotective drug screening." Clinical Queries: Nephrology 3, no. 2-4 (April 2014): 97–105. http://dx.doi.org/10.1016/j.cqn.2014.11.002.

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Jamieson, Laura, Donna Meckoll-Brinck, and Nancy Keller. "Characterized and predictable rabbit uveitis model for antiinflammatory drug screening." Journal of Pharmacological Methods 21, no. 4 (July 1989): 329–38. http://dx.doi.org/10.1016/0160-5402(89)90070-3.

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Lee, Myoung Woo, Hye Jin Kim, Dae Seong Kim, Meong Hi Son, Soo Hyun Lee, Hye Lim Jung, Keon Hee Yoo, Ki Woong Sung, and Hong Hoe Koo. "Establishment of Bioluminescence Imaging Based Leukemia In Vivo Model for Preclinical Testing." Blood 118, no. 21 (November 18, 2011): 4879. http://dx.doi.org/10.1182/blood.v118.21.4879.4879.

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Abstract Abstract 4879 Background. A hematological malignant animal model is an essential tool for evaluating efficacy of anti-cancer drugs and elucidating underlying mechanism of leukemogenesis. Intraperitoneal (IP) and intravenous (IV) xenograft of acute lymphoblastic leukemia (ALL) cells have limited capacity as in vivo anti-cancer drug screening system. Purpose. In this study, we aimed to establish an ALL animal model using NOD/SCID mouse and evaluate efficiency and sensitivity of the model as a preclinical drug screening system. Materials and Methods. Firefly luciferase (fLuc)-gene introduced ALL (ALL/fLuc) cell line and patient-originated ALL cells were transplanted into a tibia of NOD/SCID mouse. We conducted a comparative analysis of intra-bone marrow (IBMT) transplanted leukemia model with IP and IV transplantation of leukemic cells. Results. IBMT of ALL/fLuc cells effectively established a bioluminescent leukemia NOD/SCID mouse model. Upon comparison of IBMT model with IP and IV transplantation models, infusing identical number of ALL/fLuc cells into NOD/SCID mice resulted in IBMT model with evaluable bioluminescent signal, but not in IP and IV models. In IBMT model, bioluminescent signals of ALL/fLuc cells emitted from peripheral blood, tibia and infiltrated organs indicated that leukemia model was established. The changes in these signals' strength reflected dose-dependent cytotoxic effects of vincristine, which allowed leukemia model with evaluable bioluminescent signal to be utilized as a preclinical drug screening system. IBMT leukemia model was also established using primary ALL cells that can provide additional insights for the development of leukemia therapeutics. Conclusion. IBMT of ALL/fLuc cells enables development of leukemia mouse model with the greater bioluminescent sensitivity than IP and IV in NOD/SCID to evaluate candidate for development of anti-cancer drug. Disclosures: No relevant conflicts of interest to declare.

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Gaikwad, Pooja Popat, Vishal S. Adak, and Rajkumar V. Shete. "The Screening models for antiepileptic drugs: A Review." Journal of Drug Delivery and Therapeutics 11, no. 2-S (April 15, 2021): 175–78. http://dx.doi.org/10.22270/jddt.v11i2-s.4809.

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Considering the prevalence of epilepsy and the problems associated with currently available antiepileptic drugs like side effects, resistance, safety issue and high cost, herbal medicine with fewer complications could be very appropriate alternative. Therefore in the present study, we have examined the antiepileptic properties of ethanolic extract of leaves in mice using maximal electroshock seizers (MES)test, Pentylenetetrazole (PTZ), induced seizures, strychnine induced convulsion, Isoniazid-induced convulsions, Picrotoxin-induced convulsions, Kainic acid (KA) model etc.There is increased concern on agents for epilepsy disease modification and prevention. To solve these unmet needs, the research scientist must have a thorough knowledge of available animal models of epilepsy so that he can pick up the best model for his research. In this article, we are reviewing the diversity of animal models of epilepsy and their implications in antiepileptic drug discovery. Keywords: Epilepsy, animal model, seizures,

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Antonia, Ricardo J., Kan Toriguchi, Eveliina Karelehto, Dania Annuar, Luika Timmerman, Noura Tbeileh, Aras N. Mattis, et al. "Patient-derived organoids for personalized drug screening in intrahepatic cholangiocarcinoma." Journal of Clinical Oncology 38, no. 4_suppl (February 1, 2020): 581. http://dx.doi.org/10.1200/jco.2020.38.4_suppl.581.

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581 Background: Despite standard treatment with gemcitabine and cisplatin, median survival for unresectable Intrahepatic Cholangiocarcinoma (ICC) is < 1 year. Clearly, novel therapeutic strategies are urgently needed. The paucity of targetable mutations in ICC and the as yet unproven benefit of genetically targeted drugs led us to ask whether a reliable clinical benefit may be revealed by patient-specific therapeutic testing in novel models of ICC. Here we describe our ability to establish patient-derived three-dimensional organoid cultures (PDO) that enable individualized identification of active single agents or drug combinations in surrogate models of ICC. Methods: To model patient-specific drug responses, we used the freshly resected ICCs from small samples of single patient tumors to generate PDXs and PDOs, small spheroidal clusters of tumor cells grown in vitro. We have employed a high-throughput drug screening platform using AI-enhanced robotics (Yamaha Motor Corporation) to identify and distribute single, uniformly sized PDOs into 384-well ultra-low adherent plates. This is coupled with a TECAN D300e drug dispenser that rapidly delivers nanoliter volumes of a 34-drug panel, thereby facilitating rapid, reliable drug response analyses. Results: Our data show that PDOs retain characteristic genomic and histological features of the patients’ tumors. Drug responses were specific to each patient tumor, but PDOs from all patients responded to a greater or lesser degree to mTOR inhibition, suggesting that this pathway is important in ICC. The responses of PDO to the mTOR inhibitor Sapanisertib (INK128), was recapitulated in the same patient’s PDX. Further, INK128 was synergistic with gemcitabine in patient 970 PDOs as well as in vivo in PDX also from patient 970. Conclusions: As it is believed that PDX can predict patient responses to drugs, our results suggest that PDO may also predict patient drug responses. The establishment of PDO may allow economical patient-specific, high throughput drug screens that could ultimately inform clinical practice. [Table: see text]

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Ren, Ji-Xia, Rui-Tao Zhang, and Hui Zhang. "Identifying Novel ATX Inhibitors via Combinatory Virtual Screening Using Crystallography-Derived Pharmacophore Modelling, Docking Study, and QSAR Analysis." Molecules 25, no. 5 (March 2, 2020): 1107. http://dx.doi.org/10.3390/molecules25051107.

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Autotaxin (ATX) is considered as an interesting drug target for the therapy of several diseases. The goal of the research was to detect new ATX inhibitors which have novel scaffolds by using virtual screening. First, based on two diverse receptor-ligand complexes, 14 pharmacophore models were developed, and the 14 models were verified through a big test database. Those pharmacophore models were utilized to accomplish virtual screening. Next, for the purpose of predicting the probable binding poses of compounds and then carrying out further virtual screening, docking-based virtual screening was performed. Moreover, an excellent 3D QSAR model was established, and 3D QSAR-based virtual screening was applied for predicting the activity values of compounds which got through the above two-round screenings. A correlation coefficient r2, which equals 0.988, was supplied by the 3D QSAR model for the training set, and the correlation coefficient r2 equaling 0.808 for the test set means that the developed 3D QSAR model is an excellent model. After the filtering was done by the combinatory virtual screening, which is based on the pharmacophore modelling, docking study, and 3D QSAR modelling, we chose nine potent inhibitors with novel scaffolds finally. Furthermore, two potent compounds have been particularly discussed.

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Nainu, Firzan, Muh Akbar Bahar, Sartini Sartini, Reski Amalia Rosa, Nur Rahmah, Reski Amelia Kamri, Nur Rahma Rumata, Risfah Yulianty, and Elly Wahyudin. "Proof-of-Concept Preclinical Use of Drosophila melanogaster in the Initial Screening of Immunomodulators." Scientia Pharmaceutica 90, no. 1 (February 8, 2022): 11. http://dx.doi.org/10.3390/scipharm90010011.

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Drug discovery is a complex process, and the use of a comprehensive approach is deemed necessary to discover new chemical entities with novel mechanisms of action. This research was carried out to determine whether Drosophila melanogaster can serve as an appropriate model organism in the initial screening of drug candidates with immunomodulatory activities. To test this, we performed phenotypic assay and molecular analysis to investigate the immunomodulatory activities of aspirin, dexamethasone, curcumin, and epigallocatechin gallate (EGCG), that have been reported to yield such effects in the mammalian model system. In vivo survival analysis demonstrated that all drugs/compounds were relatively safe at the tested concentrations. In the infection assay, curcumin and EGCG showed a protective signature to bacterial infection in flies lacking Toll-mediated immune responses. Furthermore, dexamethasone and aspirin, drugs with immunosuppressive activity, could improve the survival of PGRP-LBΔ mutant flies with hyperactivated immune system. These phenotypes were supported by RT-qPCR-based molecular analysis, revealing that drugs/compounds used in this study could modulate the expression level of genes related to the immune system. In conclusion, while curcumin and EGCG could promote the improvement of fly survival against infection, aspirin and dexamethasone were able to suppress overactivation of immune responses in D. melanogaster. These results are in line with the ones observed in the mammalian model system, further emphasizing the notion that flies would serve as a prospective model organism in the initial screening of drug candidates for their immunomodulatory activities prior to further checking in the mammalian animal models. In the end, this will reduce the use of mammalian animal models for preliminary experiments in an effort to discover/repurpose drugs with immunomodulatory activity.

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Journal articles on the topic 'Drug screening model'

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