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Biopharmaceuticals

Biopharmaceuticals are a class of medicinal products derived from biological sources, such as living cells or organisms.
These specialized therapeutics, including proteins, nucleic acids, and vaccines, have revolutionized the treatment of a wide range of diseases, from cancer and autoimmune disorders to rare genetic conditions.
Biopharaceuticals offer targeted, often more effective, and potentially safer alternatives to traditional small-molecule drugs.
Their development and production involve complex biological processes, requirng specialized expertise and cutting-edge technologies.
Researchers and developers in the biopharmaceutical field are at the forefront of medical innovation, harnessing the power of biology to address unmet medical needs and improve patient outcomes.

Most cited protocols related to «Biopharmaceuticals»

Output sequences were first compared to the known 16S rRNA gene reference sequences of the members of each mock community. If an output sequence matched a reference sequences, it was classified as Reference, and if it had one mismatch or gap to a reference sequence it was classified as One Off. Output sequences that were at least Hamming distance 2 from any reference sequence were then BLASTed against the nr/nt database. If the best hit was an exact match covering the full output sequence, it was classified Exact. If there was a single mismatch or indel, it was classified One Off. Output sequences that remained unclassified to this point were classified Other.
We included the BLAST against nr/nt step because even amplicon sequencing data from communities with a putatively known reference composition will contain contaminant sequences. Contaminants are real, albeit unwanted, biological variation, and should be identified when correcting amplicon errors. While the nr/nt database is imperfect, it is reasonable to expect that Exact matches are far more likely to be real variants than are Others. Output sequences classified as Other, and output sequences classified as One Off that differed by one substitution from a more abundant output sequence, were considered a proxy for false positives. Output sequences classified as Reference or Exact were considered true positives.
Publication 2016
Biopharmaceuticals INDEL Mutation Ribosomal RNA Genes
Output sequences were first compared to the known 16S rRNA gene reference sequences of the members of each mock community. If an output sequence matched a reference sequences, it was classified as Reference, and if it had one mismatch or gap to a reference sequence it was classified as One Off. Output sequences that were at least Hamming distance 2 from any reference sequence were then BLASTed against the nr/nt database. If the best hit was an exact match covering the full output sequence, it was classified Exact. If there was a single mismatch or indel, it was classified One Off. Output sequences that remained unclassified to this point were classified Other.
We included the BLAST against nr/nt step because even amplicon sequencing data from communities with a putatively known reference composition will contain contaminant sequences. Contaminants are real, albeit unwanted, biological variation, and should be identified when correcting amplicon errors. While the nr/nt database is imperfect, it is reasonable to expect that Exact matches are far more likely to be real variants than are Others. Output sequences classified as Other, and output sequences classified as One Off that differed by one substitution from a more abundant output sequence, were considered a proxy for false positives. Output sequences classified as Reference or Exact were considered true positives.
Publication 2016
Biopharmaceuticals INDEL Mutation Ribosomal RNA Genes
Three test datasets were used in this work. (i) SIM2 is a selected subset of the simulated bimeras and control sequences used to train and evaluate ChimeraSlayer. (ii) MOCK is the Uneven datasets used to evaluate Perseus (Quince et al., 2011 (link)). They are derived from pyrosequencing reads of ‘mock’ communities, i.e. experimentally mixed DNAs of known composition. These reads were processed by AmpliconNoise (Quince et al., 2011 (link)), which attempts to remove sequencing error and generates a set of predicted sequences for the amplicons. Sequences in this set were classified as biological or chimeric by comparing them to reference sequences for the species in each community, and chimera detection algorithms were assessed by their success in reproducing this classification. (iii) SIMM is a new set of simulated m-meras created for this work. SIM2 and SIMM were used to compare the performance of the reference database mode of UCHIME with ChimeraSlayer, MOCK was used to compare the de novo mode of UCHIME with Perseus. The parameters of UCHIME were trained on SIM2; the score threshold h was set to a value giving an average error rate over the whole SIM2 dataset lower than the error rate of ChimeraSlayer on the same data. UCHIME was trained by an exhaustive search over manually selected pairs (β,n). The optimal pair (β+,n+) was identified by maximizing the area under a receiver operating characteristic curve (Mason and Graham, 2002 ). Given β+ and n+, an optimal score threshold h+ is determined by (i) specifying a maximum desired error rate or minimum desired sensitivity and (ii) maximizing sensitivity or minimizing error rate, respectively. After training, the sensitivity of UCHIME averaged over all SIM2 sets was 70.6% with an error rate of 0.49%, compared with 54.6% sensitivity and 0.62% errors for ChimeraSlayer.
Publication 2011
Biopharmaceuticals Chimera DNA Hypersensitivity Quince
By default, the GeneMANIA prediction server uses one of two different adaptive network weighting methods. For longer gene lists, GeneMANIA uses the basic weighting method [called GeneMANIAEntry-1 in (10 (link)) and called ‘assigned based on query genes’ on the web site] and weights each network so that after the networks are combined, the query genes interact as much as possible with each other while interacting as little as possible with genes not in the list. GeneMANIA learns from longer gene lists, allowing a gene list-specific network weighting to be calculated. Shorter gene lists do not contain enough information for GeneMANIA to learn which networks mediate the underlying functional relationship among the genes. For short gene lists, GeneMANIA uses a similar principle to weight networks, but tries to reproduce Gene Ontology (GO) biological process co-annotation patterns rather than the gene list. This method is described in detail in (11 ). The user may choose other adaptive and non-adaptive weighting methods in the advanced options panel, found directly under the gene query text box. The two non-adaptive methods are the most conservative options and work well on small gene lists (10 (link)). These methods allow users to choose either to weight every individual network equally, or weight each class (e.g. co-expression and protein interaction) of network equally. Network weights can also be assigned based on how well they reproduce GO co-annotation patterns for that organism in the molecular function, biological process or cellular component hierarchies. Note that the annotation-based weighting may slightly inflate weights for networks on which current annotations are based or for networks that were derived based on co-annotation patterns of genes. The networks most affected by this inflation are the older, smaller scale protein and genetic interaction studies and networks classified as ‘predicted’. However, this inflation does not seem to have a large impact on weights and may be largely avoided by only using networks derived from high-throughput assays with the annotation-based schemes.
Publication 2010
Acclimatization Biological Processes Biopharmaceuticals Cellular Structures Gene Annotation Gene Regulatory Networks Genes High-Throughput Screening Proteins Reproduction
Large-scale, arrayed format RNAi screens to identify genes essential for proliferation/viability were performed as described3 (link),14 (link). The effect of introducing each of the 5002 shRNAs (targeting 957 genes) was determined in 19 cell lines, and normalized using the B-score metric4 (link). Feature selection of shRNA B-score data was performed using the Comparative Marker Application Suite in GenePattern5 (link) and was independently analyzed using RIGER analysis6 (link) to compute NES for each gene. Secondary screen viability data was normalized using a percent of control statistic, given the biased nature of the candidate shRNA plate. Expression profiling was used to generate a signature that correlates with KRAS activation and implicated NF-κB signaling in cell lines and tumors dependent on KRAS. Regulation of NF-κB by TBK1 was shown using biochemical and cell biological approaches. Details of the analytical methods are provided in the Full Methods.
Publication 2009
Biopharmaceuticals Cell Lines Cells Genes K-ras Genes Neoplasms RELA protein, human RNA Interference Short Hairpin RNA TBK1 protein, human

Most recents protocols related to «Biopharmaceuticals»

Example 7

Synthetic urine is prepared by dissolving 14.1 g of NaCl, 2.8 g KCl, 17.3 g of urea, 19 ml ammonia water (25%), 0.60 g CaCl2 and 0.43 g MgSO4 in 0.02 mole/L of HCl. The final pH of synthetic urine is adjusted to 6.04 by using HCl and ammonia water.

40 mg Sigma creatinine is dissolved in 10 ml of synthetic urine solution. 3 mg of human albumin is dissolved in 10 ml of synthetic urine solution to prepare the micro albumin solution.

4 mg Sigma hemin is dissolved in 20 ml of synthetic urine, 20 μL Hemin solution is used as a receptor for urine albumin detection at different creatinine concentration.

A desired volume of the biological sample (synthetic urine) is taken and dispensed on the electrode of the biosensor device and the corresponding cyclic voltammogram is obtained by the CHI-Electrochemical workstation using the potential window, that varies from 0 V to −1 V with scan rate of 0.1 V/sec.

The albumin content in the urine sample binds hemin thereby demonstrates a linear decrease in peak redox current with urine albumin concentration as shown in FIG. 15(a) for different creatinine concentrations. If the concentration of albumin in urine sample is increased, then the albumin increasingly binds with hemin thereby reducing the free hemin concentration on the electrode resulting in the decrease in peak redox current of free hemin. FIG. 16 shows the urine albumin concentrations, urine creatinine concentrations and calculated ACR for different samples.

The values of concentrations of the urine albumin (mg/L) and creatinine for different samples is shown in Table 4.

TABLE 4
SampleUrine albuminUrine CreatinineACR
Number(mg/L)(mg/dL)(mg/g)
1526.719
22026.775
35026.7187
410026.7375
515026.7562
65133.34
720133.315
850133.338
9100133.375
10150133.3113

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Patent 2024
Albumins Ammonium Hydroxide Biopharmaceuticals Biosensors Creatinine Hemin Moles Oxidation-Reduction Radionuclide Imaging Receptors, Albumin Serum Albumin, Human Sodium Chloride Sulfate, Magnesium Urea Urine

Example 49

The functional activity of compounds was determined in a cell line where p70S6K is constitutively activated. Test article was dissolved in DMSO to make a 10 μM stock. PathScan® Phospho-S6 Ribosomal Protein (Ser235/236) Sandwich ELISA Kit was purchased from Cell Signaling Technology. A549 lung cancer cell line, was purchased from American Type Culture Collection. A549 cells were grown in F-12K Medium supplemented with 10% FBS. 100 μg/mL penicillin and 100 μg/mL streptomycin were added to the culture media. Cultures were maintained at 37° C. in a humidified atmosphere of 5% CO2 and 95% air. 2.0×105 cells were seeded in each well of 12-well tissue culture plates for overnight. Cells were treated with DMSO or test article (starting at 100 μM, 10-dose with 3 fold dilution) for 3 hours. The cells were washed once with ice cold PBS and lysed with 1× cell lysis buffer. Cell lysates were collected and samples were added to the appropriate wells of the ELISA plate. Plate was incubated for overnight at 4° C. 100 μL of reconstituted Phospho-S6 Ribosomal Protein (Ser235/236) Detection Antibody was added to each well and the plate was incubated at 37° C. for 1 hour. Wells were washed and 100 μl of reconstituted HRP-Linked secondary antibody was added to each well. The plate was incubated for 30 minutes at 37° C. Wash procedure was repeated and 100 μL of TMB Substrate was added to each well. The plate was incubated for 10 minutes at 37° C. 100 μL of STOP Solution was added to each well and the absorbance was read at 460 nm using Envision 2104 Multilabel Reader (PerkinElmer, Santa Clara, CA). IC50 curves were plotted and IC50 values were calculated using the GraphPad Prism 4 program based on a sigmoidal dose-response equation.

TABLE 2
In vitro biological data for representative compounds of Formula
I-IX Unless otherwise noted, compounds that were tested had an IC50
of less than 50 μM in the S6K binding assay.
Example NumberS6K Binding Activity
1A
2B
3B
4A
5A
6A
7A
8A
9B
10B
11B
12C
13C
14C
15A
16A
17B
18A
19A
20A
21A
22C
23B
24A
25A
26C
27A
28C
29C
30C
31A
32A
33C
34C
35C
36C
37C
38A
39A
40A
41A

Unless otherwise noted, compounds that were tested had an IC50 of less than 50 μM in the S6K Binding assay. A=less than 0.05 μM; B=greater than 0.05 μM and less than 0.5 μM; C=greater than 0.5 μM and less than 10 μM;

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Patent 2024
A549 Cells Atmosphere Biological Assay Biopharmaceuticals Buffers Cell Lines Cells Cold Temperature Culture Media Enzyme-Linked Immunosorbent Assay Immunoglobulins Lung Cancer Penicillins prisma Psychological Inhibition Ribosomal Proteins Ribosomal Protein S6 Ribosomal Protein S6 Kinases, 70-kDa Streptomycin Sulfoxide, Dimethyl Technique, Dilution Tissues

Example 19

To confirm bioactivity of 3 and 7, experiments were performed with the HH cell line, a mature T cell line derived from peripheral blood of a patient with aggressive cutaneous T cell leukemia/lymphoma (ATCC® CRL-2105™) which been demonstrated to only express the IL-2Rβ/γ. One of the earliest events in cytokine mediated activation of lymphocytes such as CD8+ T cells and NK cells is Janus Associated Kinase mediated phosphorylation and activation of Signal transducer and activator of transcription (pSTAT5). Thus, pSTAT5 was used to measure biological activity of 3 and 7 alongside 12. 3 demonstrated clear bioactivity in IL-2Rβ/γ expressing HH cells (EC50: 773 ng/ml) that was approximately 3.5 fold lower than 12 (EC50: 233 ng/ml). Additionally, 7 induced bioactivity (EC50: 756 ng/ml) very similar to 3, demonstrating that 7 retains bioactivity after being released from prodrug 5 even after accelerated (stress) conditions.

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Patent 2024
Biopharmaceuticals BLOOD CD8-Positive T-Lymphocytes Cell Lines Cells Cytokine IL19 protein, human Kinase, Janus Leukemia Lymphocyte Activation Lymphoma, T-Cell, Cutaneous Natural Killer Cells Patients Phosphorylation Prodrugs Transcription, Genetic Transducers

Example 4

To determine enzymatic activities, 500 mg of tobacco leaf tissue collected from leaf 23 of three biological replicates of was ground in 1 ml HEPES extraction buffer (100 mM HEPES, 2 mM dithiothreitol (DTT), 1 mM EDTA, pH 7.5) and 100 mg of polyvinylpyrrolidone was added during grinding. Following centrifugation (13,000 g, 10 min, 4° C.), the enzyme activities were measured using an isotopic method as described by Capell et al. (1998) by measuring the release of 14CO2. L-[1-14C]Arg and L-[1-14C]Orn were used as radioactive substrates.

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Patent 2024
Biopharmaceuticals Buffers Centrifugation Dithiothreitol Edetic Acid enzyme activity HEPES Isotopes Nicotiana Plant Leaves Povidone Radioactivity Tissues

Example 5

Probes of the invention include a porous material, such as paper, that can function to both separate chemicals in biological fluids before in situ ionization by mass spectrometry. In this Example, the porous material for the probe was chromatography paper. As shown in FIG. 24, a mixture of two dyes was applied to the paper as a single spot. The dyes were first separated on the paper by TLC (thin layer chromatograph) and the separated dyes were examined using MS analysis by methods of the invention with the paper pieces cut from the paper media (FIG. 24). Data show the separate dyes were detected by MS analysis (FIG. 24).

The chromatography paper thus allowed for sample collection, analyte separation and analyte ionization. This represents a significant simplification of coupling chromatography with MS analysis. Chromatography paper is a good material for probes of the invention because such material has the advantage that solvent movement is driven by capillary action and there is no need for a syringe pump. Another advantage is that clogging, a serious problem for conventional nanoelectrospray sources, is unlikely due to its multi-porous characteristics. Therefore, chromatography paper, a multi-porous material, can be used as a microporous electrospray ionization source.

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Patent 2024
Biopharmaceuticals Capillary Action Chromatography Dyes Mass Spectrometry Movement Solvents Specimen Collection Syringes Thin Layer Chromatography

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More about "Biopharmaceuticals"

Biopharmaceuticals are a specialized class of medicinal products derived from biological sources, such as living cells or organisms.
These innovative therapeutics, including proteins, nucleic acids, and vaccines, have revolutionized the treatment of a wide range of diseases, from cancer and autoimmune disorders to rare genetic conditions.
Biopharaceuticals offer targeted, often more effective, and potentially safer alternatives to traditional small-molecule drugs.
Their development and production involve complex biological processes, requiring specialized expertise and cutting-edge technologies like TRIzol reagent, FBS, Prism 8, RNeasy Mini Kit, TRIzol, GraphPad Prism 7, Agilent 2100 Bioanalyzer, Prism 9, and Prism 6.
Researchers and developers in the biopharmaceutical field are at the forefront of medical innovation, harnessing the power of biology to address unmet medical needs and improve patient outcomes.
Biopharmaceuticals, also known as biologics or biotech drugs, have become increasingly important in the treatment of a wide range of conditions, from cancer and autoimmune diseases to rare genetic disorders.
These specialized therapeutics, including recombinant proteins, monoclonal antibodies, gene therapies, and cell-based therapies, offer targeted and often more effective treatments compared to traditional small-molecule drugs.
The development and production of biopharmaceuticals involve complex biological processes, requiring specialized expertise and state-of-the-art technologies.
Researchers in the biopharmaceutical field are at the forefront of medical innovation, leveraging advanced tools and techniques to advance the field and improve patient outcomes.
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