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Antineoplastic Agents

Antineoplastic Agents are a diverse group of pharmaceutical compounds used in the treatment of cancer.
These agents work by interfering with cellular processes essential for tumor growth and proliferation, such as DNA replication, cell division, and metabolic pathways.
Antineoplastic Agents may be derived from natural sources, synthetically produced, or engineered through molecular biology techniques.
They are an essential component of modern cancer therapies, offering hope and improved outcomes for patients.
Rigorous research and clinical trials continue to expand the arsenal of Antineoplastic Agents, optimizing their efficacy and safety.
Experince the future of antineoplastic agent research today.

Most cited protocols related to «Antineoplastic Agents»

1
Correlating Drug Response with Cancer Functional Events
Cited 160 times
For each drug an ANOVA model was fitted to correlate drug response with the status of Cancer Functional Events (CFEs), as described in Garnett et al. (2012) (link), implemented in GDSCtools (http//gdsctools.readthedocs.io) and detailed in the the Supplemental Experimental Procedures. The downsampling ANOVA simulation studies are detailed in the Supplemental Experimental Procedures. We applied the LOBICO (Knijnenburg et al., 2016 ) framework as detailed in the Supplemental Experimental Procedures. Machine learning models were computed as detailed in the Supplemental Experimental Procedures.
Iorio F., Knijnenburg T.A., Vis D.J., Bignell G.R., Menden M.P., Schubert M., Aben N., Gonçalves E., Barthorpe S., Lightfoot H., Cokelaer T., Greninger P., van Dyk E., Chang H., de Silva H., Heyn H., Deng X., Egan R.K., Liu Q., Mironenko T., Mitropoulos X., Richardson L., Wang J., Zhang T., Moran S., Sayols S., Soleimani M., Tamborero D., Lopez-Bigas N., Ross-Macdonald P., Esteller M., Gray N.S., Haber D.A., Stratton M.R., Benes C.H., Wessels L.F., Saez-Rodriguez J., McDermott U, & Garnett M.J. (2016). A Landscape of Pharmacogenomic Interactions in Cancer. Cell, 166(3), 740-754.
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Publication 2016
Antineoplastic Agents neuro-oncological ventral antigen 2, human Pharmaceutical Preparations
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Predicting Chemotherapeutic Response Using GDSC
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Publication 2019
Antineoplastic Agents Cisplatin Gemcitabine Gene Expression Histocompatibility Testing Hypersensitivity Pharmacotherapy Psychological Inhibition
3
Predicting Drug Sensitivities Using Genomic Features
Cited 55 times
An elastic net regression analysis similar to the one used previously1 (link) was run to find genomic features that predict drug sensitivities as measured by AUC. The feature set included mutations, DNA copy number, mRNA expression and RPPA protein data. These features were used to predict sensitivities to 24 compounds profiled in the CCLE and 138 compounds from Genomics of Drug Sensitivity in Cancer (GDSC) project.
Features with an absolute Pearson correlation of greater than 0.1 with the target drug sensitivity profile were selected. Optimal values for the alpha and lambda parameters were found by a tenfold cross validation using cv.glmnet function in the glmnet R package64 (link). A 200-fold bootstrapping was then performed using the optimal parameter values. We calculated the frequency of selection and average weight for each feature.
The above analysis was performed twice for each drug, once using all features and another time using all features with the exclusion of RPPA values. The model prediction errors for the two models were compared to estimate the accuracy gained by adding the RPPA data.
Ghandi M., Huang F.W., Jané-Valbuena J., Kryukov G.V., Lo C.C., McDonald ER I.I.I., Barretina J., Gelfand E.T., Bielski C.M., Li H., Hu K., Andreev-Drakhlin A.Y., Kim J., Hess J.M., Haas B.J., Aguet F., Weir B.A., Rothberg M.V., Paolella B.R., Lawrence M.S., Akbani R., Lu Y., Tiv H.L., Gokhale P.C., de Weck A., Mansour A.A., Oh C., Shih J., Hadi K., Rosen Y., Bistline J., Venkatesan K., Reddy A., Sonkin D., Liu M., Lehar J., Korn J.M., Porter D.A., Jones M.D., Golji J., Caponigro G., Taylor J.E., Dunning C.M., Creech A.L., Warren A.C., McFarland J.M., Zamanighomi M., Kauffmann A., Stransky N., Imielinski M., Maruvka Y.E., Cherniack A.D., Tsherniak A., Vazquez F., Jaffe J.D., Lane A.A., Weinstock D.M., Johannessen C.M., Morrissey M.P., Stegmeier F., Schlegel R., Hahn W.C., Getz G., Mills G.B., Boehm J.S., Golub T.R., Garraway L.A, & Sellers W.R. (2019). Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature, 569(7757), 503-508.
Publication 2019
Antineoplastic Agents Drug Delivery Systems Genome Hypersensitivity Mutation N-dodecyl-L-lysine amide Pharmaceutical Preparations RNA, Messenger type III polyketide synthase
4
Longitudinal Evaluation of Pediatric Cancer Survivors
Cited 33 times
The ongoing institutional review board-approved SJLIFE study was initiated in late 2007 with the aim of facilitating longitudinal evaluation of health outcomes among individuals surviving pediatric cancer.(13 (link)) Eligibility criteria for participation in SJLIFE initially included: diagnosis of pediatric cancer treated or followed at St. Jude Children’s Research Hospital (SJCRH), attained age of 18 years or older, and survival of 10 or more years from diagnosis. In 2015, eligibility criteria were expanded to include five-year survivors of any age. The SJLIFE study design involves a retrospective cohort with prospective follow-up and ongoing accrual (Figure 1). The retrospective component of SJLIFE utilizes (3 (link)–9 (link)) data from surviving cancer patients treated at SJCRH since its opening in 1962. During and following treatment of pediatric malignancy, cancer remission status and treatment-related toxicities are routinely monitored by the primary oncology team and/or the long-term follow-up (After Completion of Therapy) clinic until the survivor is 10 years from diagnosis and at least 18 years of age. Data obtained from medical record review of all participants include demographic details, the cumulative doses of specific chemotherapeutic agents, the fields and doses of radiation, information on surgical interventions, primary cancer recurrences and subsequent neoplasms, and acute and late organ-specific toxicity.
In addition to longitudinal evaluations undertaken as part of SJLIFE, all oncology patients transitioned from SJCRH long-term follow-up care to community providers are followed by the institutional review board-approved St. Jude Long-Term Follow-Up Study (SJLTFU) study. All SJCRH patients are invited to participate in SJLTFU study at diagnosis. Health and vital status of SJLTFU participants are monitored by the St. Jude Cancer Registry and supplemented by periodic National Death Index searches.
Following provision of informed consent, participants in the SJLIFE cohort are invited to return to SJCRH at least once every five years for follow-up using protocol-based medical evaluations and assessments of patient-reported outcomes, neurocognitive function, and physical performance status. Permission for release of medical records is requested at each evaluation to validate interim, survivor-reported medical events. Data available through both retrospective health record review and prospective, standardized clinical assessment provides detailed information about symptoms, physical findings, laboratory/diagnostic study results, and clinical interventions to consider in the severity grading of chronic and late health events experienced by cohort members.
Hudson M.M., Ehrhardt M.J., Bhakta N., Baassiri M., Eissa H., Chemaitilly W., Green D.M., Mulrooney D.A., Armstrong G.T., Brinkman T.M., Klosky J.L., Krull K.R., Sabin N.D., Wilson C.L., Huang I.C., Bass J.K., Hale K., Kaste S., Khan R.B., Srivastava D.K., Yasui Y., Joshi V.M., Srinivasan S., Stokes D., Hoehn M.E., Wilson M., Ness K.K, & Robison L.L. (2016). Approach for Classification and Severity-grading of Long-term and Late-onset Health Events among Childhood Cancer Survivors in the St. Jude Lifetime Cohort. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 26(5), 666-674.
Publication 2016
Antineoplastic Agents Diagnosis Eligibility Determination Ethics Committees, Research Long-Term Care Malignant Neoplasms Neoplasms Operative Surgical Procedures Patient Outcome Assessment Patients Performance, Physical Physical Examination Radiotherapy Recurrence Survivors Tests, Diagnostic Therapeutics
5
Plasma-Derived cfDNA Profiling for Cancer
Cited 30 times
344 plasma samples from 200 patients with multiple cancer types were collected along with plasma from 65 healthy controls. Among the patients, 172 individuals, and notably the OV04 samples, were recruited through prospective clinical studies at Addenbrooke’s Hospital, Cambridge, UK, approved by the local research ethics committee (REC reference numbers: 07/Q0106/63; and NRES Committee East of England - Cambridge Central 03/018). Written informed consent was obtained from all patients, and blood samples were collected before and after initiation of treatment with surgery or chemotherapeutic agents. DNA was extracted from 2 mL of plasma using the QIAamp circulating nucleic acid kit (Qiagen) or QIAsymphony (Qiagen) according to the manufacturer’s instructions. In addition, 28 patients were recruited as part of the Copenhagen Prospective Personalized Oncology (CoPPO) program (Ref: PMID: 25046202) at Rigshospitalet, Copenhagen, Denmark, approved by the local research ethics committee. Baseline tumor tissue biopsies were available from all 28 patients, together with re-biopsies collected at relapse from two patients, and matched plasma samples. Brain tumor patients were recruited at Addenbrooke’s Hospital, Cambridge, UK, as part of the BLING study (REC – 15/EE/0094). Bladder cancer patients were recruited at the Netherlands Cancer Institute, Amsterdam, The Netherlands, and approval according to national guidelines was obtained (N13KCM/CFMPB250) (47 ). 65 plasma samples were obtained from healthy control individuals using a similar collection protocol (Seralab). Plasma samples have not been freeze-thawed more than 2 times to reduce artifactual fragmentation of cfDNA. A flowchart of the study is presented in fig. S1.
Mouliere F., Chandrananda D., Piskorz A.M., Moore E.K., Morris J., Ahlborn L.B., Mair R., Goranova T., Marass F., Heider K., Wan J.C., Supernat A., Hudecova I., Gounaris I., Ros S., Jimenez-Linan M., Garcia-Corbacho J., Patel K., Østrup O., Murphy S., Eldridge M.D., Gale D., Stewart G.D., Burge J., Cooper W.N., van der Heijden M.S., Massie C.E., Watts C., Corrie P., Pacey S., Brindle K., Baird R.D., Mau-Sørensen M., Parkinson C.A., Smith C.G., Brenton J.D, & Rosenfeld N. (2018). Enhanced detection of circulating tumor DNA by fragment size analysis. Science translational medicine, 10(466), eaat4921.
Publication 2018
Antineoplastic Agents Biopsy BLOOD Brain Neoplasms Cancer of Bladder Cell-Free DNA Cell-Free Nucleic Acids Ethics Committees, Research Freezing Malignant Neoplasms Neoplasms Operative Surgical Procedures Patients Plasma Relapse Tissues

Most recents protocols related to «Antineoplastic Agents»

1
Cytotoxicity of Plant Stem Cell Extracts
Not available on PMC !

Example 7

The MTT Cell Proliferation assay determines cell survival following apple stem cell extract treatment. The purpose was to evaluate the potential anti-tumor activity of apple stem cell extracts as well as to evaluate the dose-dependent cell cytotoxicity.

Principle: Treated cells are exposed to 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT). MTT enters living cells and passes into the mitochondria where it is reduced by mitochondrial succinate dehydrogenase to an insoluble, colored (dark purple) formazan product. The cells are then solubilized with DMSO and the released, solubilized formazan is measured spectrophotometrically. The MTT assay measures cell viability based on the generation of reducing equivalents. Reduction of MTT only occurs in metabolically active cells, so the level of activity is a measure of the viability of the cells. The percentage cell viability is calculated against untreated cells.

Method: A549 and NCI-H520 lung cancer cell lines and L132 lung epithelial cell line were used to determine the plant stem cell treatment tumor-specific cytotoxicity. The cell lines were maintained in Minimal Essential Media supplemented with 10% FBS, penicillin (100 U/ml) and streptomycin (100 μg/ml) in a 5% CO2 at 37 Celsius. Cells were seeded at 5×103 cells/well in 96-well plates and incubated for 48 hours. Triplicates of eight concentrations of the apple stem cell extract were added to the media and cells were incubated for 24 hours. This was followed by removal of media and subsequent washing with the phosphate saline solution. Cell proliferation was measured using the MTT Cell Proliferation Kit I (Boehringer Mannheim, Indianapolis, IN) New medium containing 50 μl of MTT solution (5 mg/ml) was added to each well and cultures were incubated a further 4 hours. Following this incubation, DMSO was added and the cell viability was determined by the absorbance at 570 nm by a microplate reader.

In order to determine the effectiveness of apple stem cell extracts as an anti-tumor biological agent, an MTT assay was carried out and IC50 values were calculated. IC50 is the half maximal inhibitory function concentration of a drug or compound required to inhibit a biological process. The measured process is cell death.

Results: ASC-Treated Human Lung Adenocarcinoma Cell Line A549.

TABLE 7
Results of cytotoxicity of apple stem cell extract on lung cancer cell
line A549 as measured by MTT assay (performed in triplicate).
Values of replicates are % of cell death.
Concentration*replicatereplicatereplicateMean of% Live
(μg/ml)123replicatesSDSEMCells
25093.1890.8690.3491.461.510.878.54
10086.8885.1885.6985.920.870.5014.08
5080.5879.4981.0480.370.800.4619.63
2574.2873.8176.3974.831.380.7925.17
12.567.9868.1371.7569.282.131.2330.72
6.2561.6762.4567.1063.742.931.6936.26
3.12555.3756.7762.4558.203.752.1641.80
1.56249.0751.0857.8052.654.572.6447.35
0.78142.7745.4053.1547.115.403.1252.89

Results: ASC-Treated Human Squamous Carcinoma Cell Line NCI-H520.

TABLE 8
Results of cytotoxicity of apple stem cell extract on lung cancer
cell line NCI-H520 measured by MTT assay (performed in triplicate).
Values of replicates are % of cell death.
Concen-%
tration*replicatereplicatereplicateMean ofLive
(μg/ml)123replicatesSDSEMcell
25088.2889.2987.7388.430.790.4611.57
10078.1379.1978.1378.480.610.3521.52
5067.9869.0968.5468.540.560.3231.46
2557.8358.9958.9458.590.660.3841.41
12.547.6848.8949.3448.640.860.5051.36
6.2537.5338.7939.7538.691.110.6461.31
3.12527.3728.6930.1528.741.390.8071.26
1.56217.2218.5920.5618.791.680.9781.21
0.781 7.07 8.4810.96 8.841.971.1491.16

Results: ASC-treated Lung Epithelial Cell Line L132.

TABLE 9
Results of cytotoxicity of apple stem cell extract on
lung epithelial cell line L132 as measured by MTT assay
(performed in triplicate). Values of replicates are % of cell death.
Concen-rep-rep-rep-Mean%
tration*licatelicatelicateofLive
(μg/ml)123replicatesSDSEMcell
25039.5142.5244.0342.022.301.3357.98
10032.9334.4433.6933.690.750.4466.31
5030.6028.9430.5230.020.940.5469.98
2527.9627.8127.1327.630.440.2572.37
12.525.6225.5525.4025.520.120.0774.48
6.2523.1320.8718.6120.872.261.3179.13
3.12513.3411.0811.8312.081.150.6687.92
1.562 6.56 7.31 9.57 7.811.570.9192.19
0.781 8.06 4.30 3.54 5.302.421.4094.70

Summary Results: Cytotoxicity of Apple Stem Cell Extracts.

TABLE 10
IC50 values of the apple stem cell extracts on the on the target
cell lines as determined by MTT assay.
Target Cell
LineIC50
A54912.58
NCI-H52010.21
L132127.46

Apple stem cell extracts killed lung cancer cells lines A549 and NCI-H520 at relatively low doses: IC50s were 12.58 and 10.21 μg/ml respectively as compared to 127.46 μg/ml for the lung epithelial cell line L132. Near complete anti-tumor activity was seen at a dose of 250 μg/ml in both the lung cancer cell lines. This same dose spared more than one half of the L132 cells. See Tables 7-10. The data revealed that apple stem cell extract is cytotoxic to lung cancer cells while sparing lung epithelial cells. FIG. 6 shows a graphical representation of cytotoxicity activity of apple stem cell extracts on lung tumor cell lines A549, NCIH520 and on L132 lung epithelial cell line (marked “Normal”). The γ-axis is the mean % of cells killed by the indicated treatment compared to unexposed cells. The difference in cytotoxicity levels was statistically significant at p≤05.

Example 9

The experiment of Example 7 was repeated substituting other plant materials for ASC. Plant stem cell materials included Dandelion Root Extract (DRE), Aloe Vera Juice (AVJ), Apple Fiber Powder (AFP), Ginkgo Leaf Extract (GLE), Lingonberry Stem Cells (LSC), Orchid Stem Cells (OSC) as described in Examples 1 and 2. The concentrations of plant materials used were nominally 250, 100, 50, 25, 6.25, 3.125, 1.562, and 0.781 μg/mL. These materials were tested only for cells the human lung epithelial cell line L132 (as a proxy for normal epithelial cells) and for cells of the human lung adenocarcinoma cell line A549 (as a proxy for lung cancer cells).

A549 cells lung cancer cell line cytotoxicity results for each of the treatment materials.

DRE-Treated Lung Cancer Cell Line A549 Cells.

TABLE 11
Triplicate results of cell death of DRE-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-DRE-Live
treated A549% of cell deathMeanSDSEMcell
25080.4376.4074.8477.232.891.6722.77
10067.6075.2663.7768.885.853.3831.12
5065.3262.9459.9462.732.701.5637.27
2556.8357.9748.1454.315.383.1145.69
6.2555.5949.6949.1751.483.572.0648.52
3.12551.7648.4545.3448.523.211.8551.48
1.56243.6944.0036.0241.244.522.6158.76
0.78137.4726.1919.5727.749.055.2372.26

AVJ-Treated Lung Cancer Cell line A549 Cells.

TABLE 12
Triplicate results of cell death of AVJ-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-AVJ-treatedLive
A549% of cell deathMeanSDSEMcell
25076.8178.1675.8876.951.140.6623.05
10076.4075.2673.7175.121.350.7824.88
5065.3266.1559.9463.803.371.9536.20
2550.1048.4556.6351.734.322.5048.27
6.2547.5246.3846.1746.690.720.4253.31
3.12539.8638.6143.7940.752.701.5659.25
1.56232.4019.7730.5427.576.823.9472.43
0.78120.5015.6332.1922.778.514.9277.23

AFP-Treated Lung Cancer Cell line A549 Cells.

TABLE 13
Triplicate results of cell death of AFP-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-AFP-treatedLive
A549% of cell deathMeanSDSEMcell
25086.1387.9986.6586.920.960.5613.08
10079.5081.0682.0980.881.300.7519.12
5073.6072.4671.3372.461.140.6627.54
2568.0167.7066.9867.560.530.3132.44
6.2560.8762.1160.7761.250.750.4338.75
3.12549.4851.7650.7250.661.140.6649.34
1.56240.0641.7247.0042.933.622.0957.07
0.78139.2337.7836.8537.961.200.6962.04

GLE-treated Lung Cancer Cell line A549 Cells.

TABLE 14
Triplicate results of cell death of GLE-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration%
(μg/mL)-GLE-treatedLive
A549% of cell deathMeanSDSEMcell
25088.4291.4990.4490.121.560.909.88
10084.3983.7783.1683.770.610.3516.23
5079.4781.5876.7579.272.421.4020.73
2573.6072.5471.4072.511.100.6327.49
6.2562.8963.6859.9162.161.991.1537.84
3.12550.1854.4751.8452.162.171.2547.84
1.56246.9344.3043.3344.851.861.0755.15
0.78139.5639.3940.9639.970.870.5060.03

LSC-treated lung cancer cell lines A549 cells.

TABLE 15
Triplicate results of cell death of LSC-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration
(μg/mL)% Live
LSC treated A549% of cell deathMeanSDSEMcell
25077.5478.8578.2078.200.650.3821.80
10077.1476.0476.5976.590.550.3223.41
5066.4268.5266.8267.251.120.6532.75
2559.8067.2264.1663.733.732.1536.27
6.2550.5348.8248.0749.141.260.7350.86
3.12541.1443.6042.7242.491.240.7257.51
1.56239.4739.7440.6139.940.600.3460.06
0.78138.5531.8336.7935.723.482.0164.28

OSC-treated Lung Cancer Cell line A549 Cells.

TABLE 16
Triplicate results of cell death of OSC-treated
A549 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration
(μg/mL)% Live
OSC-treated A549% of cell deathMeanSDSEMcell
25070.8465.5771.4969.303.251.8730.70
10048.8150.9157.2852.334.412.5547.67
5046.5949.6053.3349.843.381.9550.16
2538.7740.8136.5838.722.111.2261.28
6.2535.7440.7941.0539.193.001.7360.81
3.12534.5533.6837.0235.081.731.0064.92
1.56233.8633.4427.6331.643.482.0168.36
0.78121.3220.0034.8225.388.214.7474.62

L132 cells (“normal” lung epithelial cell line) cytotoxicity results for each of the treatment materials.

DRE-Treated Lung Epithelial Cell Line L132 cells.

TABLE 17
Triplicate results of cell death of DRE-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates.
Concentration% of %
(μg/mL)cellLive
DRE-treated L132deathMeanSDSEMcell
25086.6686.6186.6686.640.030.0213.36
10076.2977.3976.8476.840.550.3223.16
5065.9268.1767.0167.031.130.6532.97
2555.5458.9557.1957.231.700.9842.77
6.2545.1749.7347.3747.422.281.3252.58
3.12534.8040.5037.5437.612.851.6562.39
1.56224.4231.2827.7227.813.431.9872.19
0.78114.0522.0617.8918.004.012.3182.00

AVJ-Treated Lung Epithelial Cell Line L132 cells.

TABLE 18
Triplicate results of cell death of AVJ-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration % of %
(μg/mL)cellLive
AVJ-treated L132deathMeanSDSEMcell
25057.0355.9353.6255.531.741.0044.47
10050.9949.7847.0449.272.031.1750.73
5044.9543.6340.4543.012.311.3456.99
2538.9137.4933.8636.752.601.5063.25
6.2532.8831.3427.2830.502.891.6769.50
3.12526.8425.1920.6924.243.181.8475.76
1.56220.8019.0514.1117.983.472.0082.02
0.78114.7612.90 7.5211.733.762.1788.27

AFP-Treated Lung Epithelial Cell Line L132 cells.

TABLE 19
Triplicate results of cell death of AFP-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
AFP-treated L132% of cell deathMeanSDSEMcell
25056.1555.4357.1956.260.880.5143.74
10049.9548.2447.6448.611.200.6951.39
5043.7441.0538.0940.962.831.6359.04
2537.5433.8628.5433.324.532.6166.68
6.2531.3426.6718.9925.676.243.6074.33
3.12525.1419.489.4418.027.954.5981.98
1.56218.9412.2910.8714.034.312.4985.97
0.78112.73 5.10 6.81 8.214.002.3191.79

GLE-Treated Lung Epithelial Cell Line L132 cells.

TABLE 20
Triplicate results of cell death of GLE-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
GLE-treated L132% of cell deathMeanSDSEMcell
25084.4283.2083.0883.570.740.4316.43
10080.0579.2978.5979.310.730.4220.69
5072.7571.5974.1072.811.260.7227.19
2580.0581.8679.9980.631.060.6119.37
6.2568.2670.1368.2668.881.080.6231.12
3.12560.6263.0760.6261.441.410.8238.56
1.56248.0748.7748.8348.560.420.2451.44
0.78146.2745.5746.6746.170.560.3253.83

LSC-Treated Lung Epithelial Cell Line L132 cells.

TABLE 21
Triplicate results of cell death of LSC-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration
(μg/mL)% Live
LSC-treated L132% of cell deathMeanSDSEMcell
25086.4185.8285.7686.000.350.2014.00
10081.2181.2779.9980.820.720.4219.18
5075.9674.7473.5174.741.230.7125.26
2574.7472.7571.4772.991.650.9527.01
6.2570.1368.3268.2668.901.060.6131.10
3.12554.0358.0553.4455.172.511.4544.83
1.56253.9751.9851.9852.641.150.6647.36
0.78146.7945.6244.9245.78 0.940.54 54.22

OSC-Treated Lung Epithelial Cell Line L132 cells.

TABLE 22
Triplicate results of cell death of OSC-treated
L132 cells measured by MTT assay.
Percentage of live cells calculated as 100% − Mean of triplicates
AFP-treated lung epithelial cell line L132 cells.
Concentration %
(μg/mL)Live
OSC-treated L132% of cell deathMeanSDSEMcell
25061.8462.3760.4461.551.000.5738.45
10054.1453.4452.1053.231.040.6046.77
5042.9442.3040.3241.851.370.7958.15
2535.9434.4833.3134.581.320.7665.42
6.2533.9632.6732.0332.890.980.5767.11
3.12527.4826.2026.7226.800.650.3773.20
1.562 9.80 7.29 7.35 8.151.430.8391.85
0.781 7.29 8.98 8.05 8.110.850.4991.89

Calculated values.

TABLE 23
Calculated IC50 doses (ug/mL) and therapeutic ratios
(IC50 for L132 cells/IC50 for A549 cells) for each
treatment material. Values greater than one indicate
that a material would be more selective in killing cancer
cells than normal cells. ASC results imported from
Example 8. These studies indicate that at least
some of the materials may be effective anti-cancer agents.
ASC has outstanding selectivity compared to other materials.
ASCDREAVJAFPGLELSCOSC
A549 12.589.82211.4811.9811.1 13.733.9 
IC50
L132 127.4656.88 62.6682.6577.6369.26715.38
IC50
Ther.10.15.8 5.56.97.0 0.70.5
Ratio

US11872258B2. Oral cavity treatments (2024-01-16). None [US]. Inventors: Maryam Rahimi [US].
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Patent 2024
14-3-3 Proteins 43-63 61-26 A549 Cells Action Potentials Adenocarcinoma of Lung Aloe Aloe vera Antineoplastic Agents Biological Assay Biological Factors Biological Processes Bromides Cardiac Arrest Cell Death Cell Extracts Cell Lines Cell Proliferation Cells Cell Survival Cytotoxin diphenyl DNA Replication Epistropheus Epithelial Cells Fibrosis Formazans Genetic Selection Ginkgo biloba Ginkgo biloba extract Homo sapiens Lingonberry Lung Lung Cancer Lung Neoplasms Malignant Neoplasms Mitochondria Mitochondrial Inheritance Neoplasms Neoplastic Stem Cells Oral Cavity PEG SD-01 Penicillins Pharmaceutical Preparations Phosphates Plant Cells Plant Leaves Plant Roots Plants Powder Psychological Inhibition Saline Solution SD 31 SD 62 SEM-76 Squamous Cell Carcinoma Stem, Plant Stem Cells Streptomycin Succinate Dehydrogenase Sulfoxide, Dimethyl Taraxacum Tetrazolium Salts
2
Anti-tumor Effects of Colchicine on Tumor Cell Lines
Not available on PMC !

Example 6

A suitable in vitro model for studying the anti-tumor effects of drugs is utilized for this test. Such a test may include commonly used tumor cell lines with or without combination with cultivated PBMCs. Colchicine inhibits or reduces growth of tumor cell lines upon incubation with therapeutically effective amount of colchicine and/or cultivation with PBMCs pretreated with a therapeutically effective amount of colchicine.

Many modifications and other embodiments of the embodiments set forth herein will come to mind to one skilled in the art to which these embodiments pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the presently disclosed features are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims and list of embodiments disclosed herein. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

US11877991B2. Use of colchicine to inhibit tumor growth and metastases (2024-01-23). MURRAY AND POOLE ENTERPRISES LTD [GI]. Inventors: Michael Riel [AE].
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Patent 2024
Antineoplastic Agents Cardiac Arrest Cell Line, Tumor Colchicine Generic Drugs Neoplasms Teaching
3
Venous Ulcer Management in Primary Care
The study will be carried out in 16 primary care health centres in the Region of Madrid.
It will include participants aged 18 or older, with a diagnosis of venous ulcer recorded in the electronic medical record (ABI greater than 0.8 and less than 1.3; diameter of the lesion greater than or equal to 1 cm) and under treatment in primary care nursing consultations. The individuals must be able to walk with or without the aid of devices, understand and answer the questionnaires autonomously, be accessible throughout the duration of the study and have expressed their agreement to participate and signed the consent form.
Those who are unable to commute to the health centre, or who reside outside the area where the research is carried out for more than 6 months per year, will be excluded. People with mixed ulcers, deep vein thrombosis (DVT) in acute phase, decompensated heart failure, dermatitis in acute phase, rheumatoid arthritis, undergoing treatment with antineoplastic drugs or with some absolute contraindication for physical exercise will also be excluded.
Withdrawal criteria are set for patients who, during the course of the trial, present a change in their clinical condition that prevents them from further participation, such as inflammation of the locomotor system (with heat, flushing, pain and functional impotence) or trauma due to a fall during the course of program with or without fracture and/or haematoma at both joint and soft tissue level (muscle and tendons) [24 ], must drop out of the study.
Herraiz-Ahijado B., Folguera-Álvarez C., Verdú-Soriano J., Mori-Vara P, & Rico-Blázquez M. (2023). Active legs: Impact of physical activity as an adjuvant treatment in the healing of venous ulcers in primary care: a RCT protocol study. BMC Nursing, 22, 65.
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Publication 2023
Antineoplastic Agents Congestive Heart Failure Deep Vein Thrombosis Dermatitis Diagnosis Erectile Dysfunction Fracture, Bone Hematoma Inflammation Joints Medical Devices Muscle Tissue Musculoskeletal System Pain Patients Primary Health Care Rheumatoid Arthritis Tendons Tissues Ulcer Varicose Ulcer Wounds and Injuries
4
Predicting Immunotherapy Response with IPS and TIDE
In the immunotherapy response analyses, immunophenoscore (IPS) was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (PD-1) antibodies [25 (link)]. IPS, available through The Cancer Immunome Atlas (TCIA) (https://tcia.at/), is developed from four categories: effector cells (activated CD4 + T cells, activated CD8 + T cells, effector memory CD4 + T cells, and effector memory CD8 + T cells), suppressive cells (Tregs and MDSCs), MHC-related molecules, and checkpoints or immunomodulators. Tumor Immune Dysfunction and Exclusion (TIDE) was calculated online (http://tide.dfci.harvard.edu/) and had potential clinical efficacy to assess the responsiveness of patients in different risk groups to immune checkpoint inhibitors (ICIs) therapy. The TIDE score is superior to recognized immunotherapy biomarkers (PD-L1 level, and interferon γ) for assessing anti-PD1 and anti-CTLA4 effectiveness. The responses to chemotherapy and target therapy were assessed using the “pRRophetic” package based on the Genomics of Drug Sensitivity in Cancer (GDSC) website (https://www.cancerrxgene.org/). A lower half-maximal inhibitory concentration (IC50) referred to a higher sensitivity to the drug treatment.
Liu Q., Li R., Wu H, & Liang Z. (2023). A novel cuproptosis-related gene model predicts outcomes and treatment responses in pancreatic adenocarcinoma. BMC Cancer, 23, 226.
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Publication 2023
Antibodies Antineoplastic Agents Biological Markers Biological Response Modifiers CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes CD274 protein, human Cell Cycle Checkpoints Cells CTLA4 protein, human Effector Memory T Cells Group Therapy Hypersensitivity Immune Checkpoint Inhibitors Immune System Diseases Immunotherapy Interferon Type II Malignant Neoplasms Myeloid-Derived Suppressor Cells Neoplasms Patients Pharmaceutical Preparations Pharmacotherapy Psychological Inhibition
5
CRISPR-based Drug Sensitivity Analysis
The CRISPR-screen of the Genomics of Drug Sensitivity in Cancer (GDSC) data repository was used to analyze IMMT expression-based drug sensitivity [34 (link)]. Data regarding sensitivity to pyridostatin was retrieved from the CCLE [35 (link)].
Lin H.Y., Wu H.J, & Chu P.Y. (2023). Multi-omics and experimental analysis unveil theragnostic value and immunological roles of inner membrane mitochondrial protein (IMMT) in breast cancer. Journal of Translational Medicine, 21, 189.
Full text: Click here
Publication 2023
Antineoplastic Agents Cancer Screening Clustered Regularly Interspaced Short Palindromic Repeats Hypersensitivity Malignant Neoplasms Pharmaceutical Preparations pyridostatin Substance Abuse Detection

Top products related to «Antineoplastic Agents»

1
Fetal bovine serum (fbs) by Thermo Fisher Scientific
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
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Cisplatin by Merck Group
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Cisplatin is a platinum-based medication used as a chemotherapeutic agent. It is a crystalline solid that can be dissolved in water or saline solution for administration. Cisplatin functions by interfering with DNA replication, leading to cell death in rapidly dividing cells.
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Dimethyl sulfoxide (dmso) by Merck Group
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DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.
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Methyl thiazolyl tetrazolium (mtt) by Merck Group
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Paclitaxel by Merck Group
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Doxorubicin by Merck Group
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Doxorubicin is a cytotoxic medication that is commonly used in the treatment of various types of cancer. It functions as an anthracycline antibiotic, which works by interfering with the DNA replication process in cancer cells, leading to their destruction. Doxorubicin is widely used in the management of different malignancies, including leukemia, lymphoma, and solid tumors.
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Rpmi 1640 medium by Thermo Fisher Scientific
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RPMI 1640 medium is a commonly used cell culture medium developed at Roswell Park Memorial Institute. It is a balanced salt solution that provides essential nutrients, vitamins, and amino acids to support the growth and maintenance of a variety of cell types in vitro.
8
Streptomycin by Thermo Fisher Scientific
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Streptomycin is a broad-spectrum antibiotic used in laboratory settings. It functions as a protein synthesis inhibitor, targeting the 30S subunit of bacterial ribosomes, which plays a crucial role in the translation of genetic information into proteins. Streptomycin is commonly used in microbiological research and applications that require selective inhibition of bacterial growth.
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Celltiter glo by Promega
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Penicillin by Thermo Fisher Scientific
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Penicillin is a type of antibiotic used in laboratory settings. It is a broad-spectrum antimicrobial agent effective against a variety of bacteria. Penicillin functions by disrupting the bacterial cell wall, leading to cell death.

More about "Antineoplastic Agents"

Antineoplastic agents, also known as cancer-fighting drugs or chemotherapeutic agents, are a diverse collection of pharmaceutical compounds used in the treatment of malignant neoplasms, or cancers.
These medicinal agents work by interfering with various cellular processes that are essential for tumor growth and proliferation, such as DNA replication, cell division, and metabolic pathways.
Antineoplastic agents can be derived from natural sources, synthetically produced, or engineered through advanced molecular biology techniques.
They are a crucial component of modern, comprehensive cancer therapies, providing hope and improved outcomes for patients battling this devastating disease.
Rigorous research and meticulous clinical trials continue to expand the arsenal of antineoplastic agents, optimizing their efficacy and safety profiles.
Compounds like cisplatin, paclitaxel, and doxorubicin have become staples in chemotherapy regimens, while newer agents like targeted therapies and immunotherapies are revolutionizing the field.
Effective in vitro evaluation of antineoplastic agents often involves the use of cell lines, such as those cultured in RPMI 1640 medium, and assessment of cytotoxicity through assays like MTT and CellTiter-Glo.
The use of solvents like DMSO can facilitate compound solubility and experimentation.
Antibiotics like penicillin and streptomycin are commonly used to prevent microbial contamination in cell culture systems.
Experince the future of antineoplastic agent research today by exploring the capabilities of AI-powered tools like PubCompare.ai, which can optimize research protocols, enhance reproducibility, and provide intelligent analysis of the latest advancements in this dynamic field of cancer therapeutics.