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Drug Kinetics

Drug kinetics, also known as pharmacokinetics, is the study of the movement and fate of drugs within the body.
It encompasses the absorption, distribution, metabolism, and excretion (ADME) of pharmaceutical agents.
Understanding drug kinetics is crucial for optimizing drug dosing, minimizing adverse effects, and improving therapeutic efficacy.
This field involves the application of mathematical models and statistical analysis to describe the time course of drug concentrations in the body.
Researchers in drug kinetics utilize various techniques, such as in vitro studies, animal models, and clinical trials, to evaulate the kinetic properties of new and existing drugs.
The goal is to develop safe and effective medication regimens that maximize the benefits for patients.
Pubcompare.ai's AI-powered platform can enhance drug kinetics research by helping scientists locate the best protocols from literature, preprints, and patents, as well as identify the most efective products to streamline their studies.

Most cited protocols related to «Drug Kinetics»

Detailed materials and methods are listed in the Supplementary Material (9 ). In brief, we analyzed genetic data from 15 GWAS of MS. For the autosomal non-MHC genome, we applied a partitioning approach to create regions of ±1Mbps around the most statistically significant SNP. Then we performed stepwise conditional analyses within each region to identify statistically independent effects (n=4,842). We replicated these effects in two large-scale replication cohorts: i) nine (9 ) data sets genotyped with the MS Replication Chip, and ii) eleven (11 (link)) data sets genotyped with the ImmunoChip. Chromosomes X and Y were analyzed jointly across all the data sets, i.e. the discovery and replication. The extended MHC region was also analyzed jointly across all data sets. We further imputed HLA class I and II alleles and corresponding amino acids. Statistically independent effects in the autosomal non-MHC genome were group into 4 categories post-replication: i) genome-wide effects (GW), ii) suggestive effects (S), iii) non-replicated (NR), and iv) no replication data (ND). Narrow sense heritability was estimated for various combinations of these effects, and the extended MHC region, to quantify the amount of the heritability our findings could explain. Next, we leveraged enrichment methods and tissue/cell reference data sets to characterize the potential involvement of the identified MS effects in the immune and central nervous system, at the tissue and cellular level. We developed an ensemble approach to prioritize genes putative associated with the identify effects, leveraging cell-specific eQTL studies, network approaches, and genomic annotations. Pathway analyses were performed to characterize canonical pathways statistically enriched for the putative causal genes. Finally, we leveraged protein-protein interaction networks to quantify the degree of connectivity of the putative causal genes and identify new mechanisms of action.
Publication 2019
Alleles Amino Acids Cells Central Nervous System DNA Chips DNA Replication Drug Kinetics Genes Genome Genome-Wide Association Study Reproduction Tissues X Chromosome
The literature from December 1, 2001 (the end of the previous panel's search) to March 30, 2011, was searched to identify published systematic reviews and meta-analyses that were relevant to the project. Search terms included adverse drug reactions, adverse drug events, medication problems, polypharmacy, inappropriate drug use, suboptimal drug therapy, drug monitoring, pharmacokinetics, drug interactions, and medication errors. Terms were searched alone and in combination. Search limits included human subjects, English language, and aged 65 and older. Data sources for the initial search included Medline, the Cochrane Library (Cochrane Database of Systematic Reviews), International Pharmaceutical abstracts, and references lists of selected articles that the panel co-chairs identified.
The initial search identified 25,549 citations, of which 6,505 were selected for preliminary review. The panel co-chairs reviewed 2,267 citations, of which 844 were excluded for not meeting the study purpose or not containing primary data. An additional search was conducted with the additional terms drug–drug and drug–disease interactions, pharmacoepidemiology, drug safety, geriatrics, and elderly prescribing. An additional search for randomized clinical trials and postmarketing and observational studies published between 2009 and 2011 was conducted using terms related to major drug classes and conditions, delimited by more-general topics (e.g., adverse drug reactions, Beers Criteria, suboptimal prescribing, and interventions). Previous searches were used to develop additional terms to be included in subsequent searches, such as a list of authors whose work was relevant to the goals of the project. When evidence was sparse on older medications, searches were conducted on drug class and individual medication names and included older search dates for these drugs. The co-chairs continually reviewed the updated search results for articles that might be relevant to the project. Panelists were also asked to forward pertinent citations that might be useful for revising the previous Beers Criteria or supporting additions to them.
At the time of the panel's face-to-face meeting, the co-chairs had selected 2,169 unduplicated citations for the full panel review. This total included 446 systematic reviews or meta-analyses, 629 randomized controlled trials, and 1,094 observational studies. Additional articles were found in a manual search of the reference lists of identified articles and the panelist's files, book chapter, and recent review articles, with 258 citations selected for the final evidence tables to support the list of drugs to avoid.
Publication 2012
Aged cDNA Library Drug Interactions Drug Kinetics Drug Reaction, Adverse Drugs, Non-Prescription Face Pharmaceutical Preparations Pharmacotherapy Polypharmacy Safety
To assess clinical actionability of mutations detected by MSK-IMPACT, we annotated sequence mutations, copy number alterations, and rearrangements according to OncoKB, a curated knowledge base of the oncogenic effects and treatment implications of somatic mutations (http://oncokb.org)40 . Mutations were classified in a tumor type-specific manner according to the level of evidence that the mutation is a predictive biomarker of drug response. Briefly, mutations were classified according to whether they are FDA-recognized biomarkers (Level 1), predict response to standard-of-care therapies (Level 2), or predict response to investigational agents in clinical trials (Level 3). Levels 2 and 3 were subdivided according to whether the evidence exists for the pertinent tumor type (2A, 3A) or a different tumor type (2B, 3B). Tumor samples were annotated according to the highest level of evidence for any mutation identified by MSK-IMPACT.
To determine the rate of enrollment to genomically matched clinical trials, we obtained a list of 850 clinical trials open at MSKCC on which any patient tested by MSK-IMPACT was ever enrolled up to September 2016. After reviewing the enrollment criteria and mechanism of action of each therapy, 197/850 clinical trials were deemed to have a target aberration. A patient was considered to be “matched” if he/she harbored at least one alteration considered to be a target for at least one clinical trial on which they were enrolled. Only patients whose tumors were sequenced during the first 18 months of the MSK-IMPACT sequencing initiative (prior to July 2015) were considered, given that utilization of molecular profiling results and changes to treatment regimens may not occur for many months (or longer) after testing. Of 5,009 patients tested by MSK-IMPACT prior to July 2015, 1,894 (38%) were enrolled on any clinical trial, 811 (16%) were enrolled on a clinical trial with a targeted agent, and 527 (11%) harbored genomic alterations matching the drug target. 72% of all matches occurred after the MSK-IMPACT reports were issued, with the remaining matches based on the results of prior molecular testing.
Clinical responses for patients receiving immunotherapy and targeted BRAF-directed therapy were assessed by detailed chart review. Response was defined as radiographic stable disease or tumor regression at or near 3 months from the initiation of therapy.
Publication 2017
Biological Markers BRAF protein, human Copy Number Polymorphism Diploid Cell Drug Delivery Systems Drug Kinetics Gene Rearrangement Genome Immunotherapy Mutation Neoplasms Oncogenes Patients Pharmaceutical Preparations Therapeutics Treatment Protocols X-Rays, Diagnostic
To assess clinical actionability of mutations detected by MSK-IMPACT, we annotated sequence mutations, copy number alterations, and rearrangements according to OncoKB, a curated knowledge base of the oncogenic effects and treatment implications of somatic mutations (http://oncokb.org)40 . Mutations were classified in a tumor type-specific manner according to the level of evidence that the mutation is a predictive biomarker of drug response. Briefly, mutations were classified according to whether they are FDA-recognized biomarkers (Level 1), predict response to standard-of-care therapies (Level 2), or predict response to investigational agents in clinical trials (Level 3). Levels 2 and 3 were subdivided according to whether the evidence exists for the pertinent tumor type (2A, 3A) or a different tumor type (2B, 3B). Tumor samples were annotated according to the highest level of evidence for any mutation identified by MSK-IMPACT.
To determine the rate of enrollment to genomically matched clinical trials, we obtained a list of 850 clinical trials open at MSKCC on which any patient tested by MSK-IMPACT was ever enrolled up to September 2016. After reviewing the enrollment criteria and mechanism of action of each therapy, 197/850 clinical trials were deemed to have a target aberration. A patient was considered to be “matched” if he/she harbored at least one alteration considered to be a target for at least one clinical trial on which they were enrolled. Only patients whose tumors were sequenced during the first 18 months of the MSK-IMPACT sequencing initiative (prior to July 2015) were considered, given that utilization of molecular profiling results and changes to treatment regimens may not occur for many months (or longer) after testing. Of 5,009 patients tested by MSK-IMPACT prior to July 2015, 1,894 (38%) were enrolled on any clinical trial, 811 (16%) were enrolled on a clinical trial with a targeted agent, and 527 (11%) harbored genomic alterations matching the drug target. 72% of all matches occurred after the MSK-IMPACT reports were issued, with the remaining matches based on the results of prior molecular testing.
Clinical responses for patients receiving immunotherapy and targeted BRAF-directed therapy were assessed by detailed chart review. Response was defined as radiographic stable disease or tumor regression at or near 3 months from the initiation of therapy.
Publication 2017
Biological Markers BRAF protein, human Copy Number Polymorphism Diploid Cell Drug Delivery Systems Drug Kinetics Gene Rearrangement Genome Immunotherapy Mutation Neoplasms Oncogenes Patients Pharmaceutical Preparations Therapeutics Treatment Protocols X-Rays, Diagnostic
To identify drugs and reagents that modulate the 332 host factors interacting with SARS-CoV-2-HEK293T/17 (MiST >= 0.70), we used two approaches: 1) a chemoinformatic analysis of open-source chemical databases and 2) a target- and pathway-specific literature search, drawing on specialist knowledge within our group. Chemoinformatically, we retrieved 2,472 molecules from the IUPHAR/BPS Guide to Pharmacology (2020–3-12)56 (link) (Supplementary Table 7) that interacted with 30 human “prey” proteins (38 approved, 71 in clinical trials), and found 10,883 molecules (95 approved, 369 in clinical trials) from the ChEMBL25 database77 (link) (Supplementary Table 8). For both approaches, molecules were prioritized on their FDA approval status, activity at the target of interest better than 1 μM, and commercial availability, drawing on the ZINC database78 (link). FDA approved molecules were prioritized except when clinical candidates or preclinical research molecules had substantially better selectivity or potency on-target. In some cases, we considered molecules with indirect mechanisms of action on the general pathway of interest based solely on literature evidence (e.g., captopril modulates ACE2 indirectly via its direct interaction with Angiotensin Converting Enzyme, ACE). Finally, we predicted 6 additional molecules (2 approved, 1 in clinical trials) for proteins with MIST scores between 0.7–0.6 to viral baits (Supplementary Tables 4 and 5). Complete methods can be found here (https://github.com/momeara/BioChemPantry/tree/master/vignette/COVID19).
Publication 2020
ACE2 protein, human Captopril COVID 19 Drug Kinetics Genetic Selection NR4A2 protein, human Peptidyl-Dipeptidase A Pharmaceutical Preparations Proteins SARS-CoV-2 Trees Zinc

Most recents protocols related to «Drug Kinetics»

Example 1

The sequence coding for the light chain variable region of the antibody was inserted into vector pFUSE2ss-CLIg-hK (Invivogen, Catalog Number: pfuse2ss-hclk) using EcoRI and BsiWI restriction sites to construct a light chain expression vector. The sequence coding for the heavy chain variable region of the antibody was inserted into vector pFUSEss-CHIg-hG2 (Invivogen, Catalog Number: pfusess-hchg2) or vector pFUSEss-CHIg-hG4 (Invivogen, Catalog Number: pfusess-hchg4) using EcoRI and NheI restriction sites to construct a heavy chain expression vector.

The culture and transfection of Expi293 cells were performed in accordance with the handbook of Expi293™ Expression System Kit from Invitrogen (Catalog Number: A14635). The density of the cells was adjusted to 2×106 cells/ml for transfection, and 0.6 μg of the light chain expression vector as described above and 0.4 μg of the heavy chain expression vector as described above were added to each ml of cell culture, and the supernatant of the culture was collected four days later.

The culture supernatant was subjected to non-reduced SDS-PAGE gel electrophoresis in accordance with the protocol described in Appendix 8, the Third edition of the “Molecular Cloning: A Laboratory Manual”.

Pictures were taken with a gel scanning imaging system from BEIJING JUNYI Electrophoresis Co., LTD and in-gel quantification was performed using Gel-PRO ANALYZER software to determine the expression levels of the antibodies after transient transfection. Results were expressed relative to the expression level of control antibody 1 (control antibody 1 was constructed according to U.S. Pat. No. 7,186,809, which comprises a light chain variable region as set forth in SEQ ID NO: 10 of U.S. Pat. No. 7,186,809 and a heavy chain variable region as set forth in SEQ ID NO: 12 of U.S. Pat. No. 7,186,809, the same below) (control antibody 2 was constructed according to U.S. Pat. No. 7,638,606, which comprises a light chain variable region as set forth in SEQ ID NO: 6 of U.S. Pat. No. 7,638,606 and a variable region as set forth in SEQ ID NO: 42 of U.S. Pat. No. 7,638,606, the same below). See Tables 2a-2c below for the results.

TABLE 2a
Expression levels of the antibodies of the present
invention after transient transfection (antibodies whose
expression levels are significantly higher than that of control antibody 1):
Number ofExpression level vsNumber of Expression level vs
the antibodycontrol antibody 1the antibodycontrol antibody 1
L1021H10002.08L1000H10281.27
L1020H10001.58L1000H10151.19
L1000H10271.56L1000H10321.18
L1000H10241.51L1000H10261.15
L1000H10251.48L1021H10291.12
L1001H10001.48L1000H10301.1
L1021H10161.43L1024H10311.08
L1000H10141.35L1000H10161.05

TABLE 2b
Expression levels of the antibodies of the present
invention after transient transfection (antibodies whose
expression levels are slightly lower than that of control antibody 1):
Number of Expression level vsNumber of Expression level vs
the antibodycontrol antibody 1the antibodycontrol antibody 1
L1000H10310.99L1017H10000.85
L1021H10310.99L1020H10160.84
L1020H10290.96L1000H10090.81
control anti-0.93L1000H10070.8
body 2
L1012H10000.89L1000H10230.8
L1019H10000.87L1020H10270.78
L1020H10310.87L1024H10070.77
L1021H10200.87L1000H10130.75
L1000H10290.86L1020H10070.74
L1008H10000.86L1021H10070.74
L1000H10010.85L1000H10210.71

TABLE 2c
Expression levels of the antibodies of the present
invention after transient transfection (antibodies whose
expression levels are significantly lower than that of control antibody 1):
Number ofExpression level vsNumber of Expression level vs
the antibodycontrol antibody 1the antibodycontrol antibody 1
L1000H10200.69L1024H10000.52
L1010H10000.69L1000H10080.51
L1000H10220.67L1000H10370.5
L1000H10120.64L1007H10000.49
L1022H10000.64L1016H10000.49
L1011H10000.63L1000H10170.47
L1000H10110.62L1000H10350.46
L1000H10330.62L1012H10270.46
L1020H10200.61L1018H10000.44
L1000H10360.6L1023H10000.43
L1021H10270.6L1012H10160.42
L1012H10070.59L1013H10000.41
L1009H10000.57L1000H10340.4
L1012H10200.57L1000H10180.35
L1012H10310.56L1000H10190.34
L1000H10380.54L1015H10000.27
L1012H10290.54L1014H10000.17
L1000H10100.53

Example 4

6-8 week-old SPF Balb/c mice were selected and injected subcutaneously with antibodies (the antibodies of the present invention or control antibody 2) in a dose of 5 mg/kg (weight of the mouse). Blood samples were collected at the time points before administration (0 h) and at 2, 8, 24, 48, 72, 120, 168, 216, 264, 336 h after administration. For blood sampling, the animals were anesthetized by inhaling isoflurane, blood samples were taken from the orbital venous plexus, and the sampling volume for each animal was about 0.1 ml; 336 h after administration, the animals were anesthetized by inhaling isoflurane and then euthanized after taking blood in the inferior vena cava.

No anticoagulant was added to the blood samples, and serum was isolated from each sample by centrifugation at 1500 g for 10 min at room temperature within 2 h after blood sampling. The collected supernatants were immediately transferred to new labeled centrifuge tubes and then stored at −70° C. for temporary storage. The concentrations of the antibodies in the mice were determined by ELISA:

1. Preparation of Reagents

sIL-4Rα (PEPRO TECH, Catalog Number: 200-04R) solution: sIL-4Rα was taken and 1 ml ddH2O was added therein, mixed up and down, and then a solution of 100 μg/ml was obtained. The solution was stored in a refrigerator at −20° C. after being subpacked.

Sample to be tested: 1 μl of serum collected at different time points was added to 999 μl of PBS containing 1% BSA to prepare a serum sample to be tested of 1:1000 dilution.

Standard sample: The antibody to be tested was diluted to 0.1 μg/ml with PBS containing 1% BSA and 0.1% normal animal serum (Beyotime, Catalog Number: ST023). Afterwards, 200, 400, 600, 800, 900, 950, 990 and 1000 μl of PBS containing 1% BSA and 0.1% normal animal serum were respectively added to 800, 600, 400, 200, 100, 50, 10 and 0 μl of 0.1 μg/ml antibodies to be tested, and thus standard samples of the antibodies of the present invention were prepared with a final concentration of 80, 60, 40, 20, 10, 5, 1, or 0 ng/ml respectively.

2. Detection by ELISA

250 μl of 100 μg/ml sIL-4Rα solution was added to 9.75 ml of PBS, mixed up and down, and then an antigen coating buffer of 2.5 μg/ml was obtained. The prepared antigen coating buffer was added to a 96-well ELISA plate (Corning) with a volume of 100 μl per well. The 96-well ELISA plate was incubated overnight in a refrigerator at 4° C. after being wrapped with preservative film (or covered). On the next day, the 96-well ELISA plate was taken out and the solution therein was discarded, and PBS containing 2% BSA was added thereto with a volume of 300 μl per well. The 96-well ELISA plate was incubated for 2 hours in a refrigerator at 4° C. after being wrapped with preservative film (or covered). Then the 96-well ELISA plate was taken out and the solution therein was discarded, and the plate was washed 3 times with PBST. The diluted standard antibodies and the sera to be detected were sequentially added to the corresponding wells, and three duplicate wells were made for each sample with a volume of 100 μl per well. The ELISA plate was wrapped with preservative film (or covered) and incubated for 1 h at room temperature. Subsequently, the solution in the 96-well ELISA plate was discarded and then the plate was washed with PBST for 3 times. Later, TMB solution (Solarbio, Catalog Number: PR1200) was added to the 96-well ELISA plate row by row with a volume of 100 μl per well. The 96-well ELISA plate was placed at room temperature for 5 minutes, and 2 M H2SO4 solution was added in immediately to terminate the reaction. The 96-well ELISA plate was then placed in flexstation 3 (Molecular Devices), the values of OD450 were read, the data were collected and the results were calculated with Winnonlin software. The pharmacokinetic results were shown in FIG. 1 and Table 6 below.

TABLE 6
Pharmacokinetic results of the antibodies of the present invention in mouse
Area
TimeUnder the
HalftoPeakdrug-timeVolume ofClearance
lifepeakconcentrationCurvedistributionrate
Numberhhμg/mlh*μg/mlml/kgml/h/kg
L1020H1031Mean269.347233.797679.28138.920.38
value
Standard105.730.000.42163.9122.480.09
deviation
L1012H1031Mean167.274845.59852.391.30.38
value
Standard8.520.001.86448.345.580.00
deviation
ControlMean56.67367.881132.68288.923.79
antibody 2value
Standard25.8416.970.2594.4249.451.12
deviation

Example 5

A series of pharmacokinetic experiments were carried out in Macaca fascicularises to further screen antibodies.

3-5 year-old Macaca fascicularises each weighting 2-5 Kg were selected and injected subcutaneously with antibodies (the antibodies of the present invention or control antibody 2) in a dose of 5 mg/kg (weight of the Macaca fascicularis). The antibody or control antibody 2 to be administered was accurately extracted with a disposable aseptic injector, and multi-point injections were made subcutaneously on the inner side of the thigh of the animal, and the injection volume per point was not more than 2 ml. Whole blood samples were collected from the subcutaneous vein of the hind limb of the animal at the time points before administration (0 h) and at 0.5, 2, 4, 8, 24, 48, 72, 120, 168, 240, 336 h, 432 h, 504 h, 600 h, 672 h after administration. The blood volume collected from each animal was about 0.1 ml each time.

No anticoagulant was added to the blood samples, and serum was isolated from each sample by centrifugation at 1500 g for 10 min at room temperature within 2 h after blood sampling. The collected supernatants were immediately transferred to new labeled centrifuge tubes and then stored at −70° C. for temporary storage. The concentrations of the antibodies in the Macaca fascicularises were determined according the method as described in Example 4. The pharmacokinetic results are shown in FIG. 2 and Table 7 below.

TABLE 7
Pharmacokinetic results of the antibodies of the present invention in macaca fascicularis
Area
TimeUnder the
HalftoPeakdrug-timeVolume ofClearance
lifepeakconcentrationCurvedistributionrate
Numberhhμg/mlh*μg/mlml/kgml/h/kg
L1020H1031Mean254.9548.0089.6522189.9175.940.22
value
Standard44.5733.9444.298557.1522.950.10
deviation
L1012H1031Mean185.75486516185.7373.410.28
value
Standard42.5433.944.52506.980.810.06
deviation
ControlMean37.031637.822773.2193.971.78
antibody 2value
Standard18.0311.316.75155.8442.470.07
deviation

Example 10

In vivo pharmacokinetics of the antibodies of the invention are further detected and compared in this Example, in order to investigate the possible effects of specific amino acids at specific positions on the pharmacokinetics of the antibodies in animals. The specific experimental method was the same as that described in Example 4, and the results are shown in Table 9 below.

TABLE 9
Detection results of in vivo pharmacokinetics of the antibodies of the present invention
Area
TimeUnder the
HalftoPeakdrug-timeVolume ofClearance
lifepeakconcentrationCurvedistributionrate
hhug/mlh*ug/mlml/kgml/h/kg
L1020H1031Mean185.494038.948188.8114.280.43
value
Standard18.5213.862.33510.476.50.05
deviation
L1012H1001Mean161.2648.0012.362491.19332.791.47
value
Standard54.300.002.26165.1676.910.20
deviation
L1001H1031Mean171.4156.0042.749273.7399.170.40
value
Standard6.1213.867.381868.6618.690.07
deviation
L1020H1001Mean89.0064.0020.113481.40164.141.30
value
Standard16.7013.862.14268.3922.860.20
deviation

From the specific sequence, the amino acid at position 103 in the sequence of the heavy chain H1031 (SEQ ID NO. 91) of the antibody (in CDR3) is Asp (103Asp), and the amino acid at position 104 is Tyr (104Tyr). Compared with antibodies that have no 103Asp and 104Tyr in heavy chain, the present antibodies which have 103Asp and 104Tyr have a 2- to 4-fold higher area under the drug-time curve and an about 70% reduced clearance rate.

The expression levels of the antibodies of the present invention are also detected and compared, in order to investigate the possible effects of specific amino acids at specific positions on the expression of the antibodies. Culture and transfection of Expi293 cells were conducted according to Example 1, and the collected culture supernatant was then passed through a 0.22 μm filter and then purified by GE MabSelect Sure (Catalog Number: 11003494) Protein A affinity chromatography column in the purification system GE AKTA purifier 10. The purified antibody was collected and concentrated using Amicon ultrafiltration concentrating tube (Catalog Number: UFC903096) and then quantified. The quantitative results are shown in Table 10 below.

TABLE 10
Detection results of the expression
levels of the antibodies of the present invention
Expression level
Antibody(×10−2 mg/ml culture medium)
L1020H10318.39
L1001H10311.79
L1020H10014.04
L1012H10015.00
L1023H10014.63
L1001H10011.75

From the specific sequence, the amino acid at position 31 in the sequence of the light chain L1012 (SEQ ID NO. 44), L1020 (SEQ ID NO. 55) or L1023 (SEQ ID NO. 51) of the antibody (in CDR1) is Ser (31Ser). Compared with antibodies that have no 31Ser in light chain, the present antibodies which have 31Ser have a 2- to 5-fold higher expression level.

The above description for the embodiments of the present invention is not intended to limit the present invention, and those skilled in the art can make various changes and variations according to the present invention, which are within the protection scope of the claims of the present invention without departing from the spirit of the same.

Patent 2024
Amino Acids Animals Antibodies Anticoagulants Antigens Asepsis BLOOD Blood Volume Buffers Cell Culture Techniques Cells Centrifugation Chromatography Chromatography, Affinity Cloning Vectors Culture Media Deoxyribonuclease EcoRI Drug Kinetics Electrophoresis Enzyme-Linked Immunosorbent Assay Hindlimb Human Body Immunoglobulin Heavy Chains Immunoglobulin Light Chains Immunoglobulins Interleukin-1 Isoflurane Light Macaca Macaca fascicularis Medical Devices Metabolic Clearance Rate Mice, Inbred BALB C Mus Open Reading Frames Pharmaceutical Preparations Pharmaceutical Preservatives SDS-PAGE Serum Staphylococcal Protein A Technique, Dilution Thigh Transfection Transients Ultrafiltration Veins Vena Cavas, Inferior

Example 6

Tg32 mice were homozygous, 8 week old, males. There were 4 mice per test article group. The test articles included CDA1-WT, CDA1-FcMut008, and CDA1-FcMut015. The mice were dosed at 10 mg/Kg by IV administration. Data were collected at thirteen time points (1 h, 8 h, 1 d, 2 d, 3 d, 4 d, 6 d, 8 d, 10 d, 13 d, 16 d, 19 d, and 22 d). Human IgG was quantified by ELISA using an anti-hIgG polyclonal antibody.

Tg32 is a human FcRn transgenic mouse model that can be used in drug discovery for early assessment and prediction of human pharmacokinetics of monoclonal antibodies. Monoclonal antibody clearance in Tg32 homozygous mice has the strongest correlation to monoclonal antibody clearance in humans (Avery et al. MAbs. 2016; 8(6):1064-78).

CDA1 (actoxumab) is known to have a half-life of >25 days in human. In vivo evaluation with additional mAbs in Tg32 model was performed. The different constructs can also be evaluated on Tg276 mice which are reported to have increased half-life differences between IgG variants. The results are shown in Table 2 and FIG. 10. FcMut015 increased the half-life of CDA1 in Tg32 mice.

TABLE 2
Half-Lives of Exemplary Antibody Molecules
in Tg32 Homozygous Mice
CmaxClastAUCinf
Groupt1/2 (hr)(ug/ml)(ug/ml)(hr * ug/ml)Rsq
WT261.17116.0315.4024108.030.99
FcMut008231.92131.3315.7425687.390.99
FCMut015436.69151.8227.6942735.90.93

Patent 2024
actoxumab Animals, Transgenic Antibodies, Anti-Idiotypic Drug Kinetics Enzyme-Linked Immunosorbent Assay hippuryl-glycyl-glycine Homo sapiens Homozygote Immunoglobulins Males Menopause Mice, House Mice, Laboratory Mice, Transgenic Monoclonal Antibodies
Not available on PMC !

Example 11

VEGF-A Protein Expression after Modified RNA Injection to the Heart with Citrate Saline Buffer is Saturable and has Similar Pharmacokinetics Across Multiple Species

To compare VEGF-A protein production, 150 μg of VEGF-A modified RNA in a citrate saline buffer and 100 μg of VEGF-A modified RNA using RNAiMax (a lipid-based formulation) as the delivery carrier were injected into a rat heart. After 24 hours, VEGF-A protein levels in the rats with the citrate saline buffer (NTB) was at a comparable level to rats injected with RNAiMax and the pharmacokinetic profile were similar (FIG. 12A). The protein expression was dose limited and saturable, which was seen across species (FIG. 12B). With a ten-fold increase in dose, there was only a 1.6-fold increase in the area under the curve (FIG. 12C).

Patent 2024
Buffers Citrate Drug Kinetics Heart Lipids Obstetric Delivery Polypeptides Proteins Rattus norvegicus Saline Solution Staphylococcal Protein A Transcription, Genetic vascular endothelial growth factor A, rat Vascular Endothelial Growth Factors Vision
Not available on PMC !

Example 3

To support the mechanism of action by which phenylbenzamides directly block AQP4, we perform in vitro binding studies using purified AQP4b and Compound 4 radiolabeled with 3H. Using a Hummel-Dryer style assay, a gel filtration column is equilibratrated with buffer containing detergent, to maintain solubility of AQP4b, and 1 μM [3H]-Compound 4. AQP4b is diluted to 250 μM in this column buffer and incubated at RT for 30 min. The sample is then applied to the column, fractions collected and the presence of [3H]-Compound 4 detected by liquid scintillation counting. FIG. 3 shows the elution profile of [3H]-Compound 4 from the gel filtration column with the elution positions of tetrameric and monomeric AQP4b indicated. The rise in [3H]-Compound 4 from a baseline value of 1 μM represents binding to each of these proteins. Although no monomeric AQP4b can be readily detected in our highly purified AQP4b by conventional means, this assay reveals the presence of a small, albiet vanishing, amount of monomer. The relative affinities for Compound 4 are ˜100 μM and less than 1 μM for tetramer and monomer, respectively. This assay shows relatively weak binding of Compound 4 to solubilized AQP4b; nevertheless, it clearly demonstrates that this phenylbenzamide directly interacts with AQP4b.

Patent 2024
Binding Proteins Biological Assay Buffers Cardiac Arrest Debility Detergents Drug Delivery Systems Drug Kinetics Gel Chromatography Tetrameres
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Example 2

Expressed and purified dihydropteroate synthase (DHPS) from S. aureus (saDHPS) was cloned. DHPS is the enzyme that installs PABA (p-aminobenzoic acid) in the folate biosynthesis pathway (Scheme 2). It has been demonstrated that the PABA analog PAS (2-aminosalicylate) is incorporated into folic acid in M. tuberculosis (Chakraborty, S. et al. 2013), suggesting that PAS is a substrate for DHPS. Using a coupled assay, it was determined that the kinetic parameters for saDHPS with PABA, PAS and F-PABA. Importantly, all three compounds have similar kcat and Km values indicating that F-PABA is an alternative substrate for saDHPS. Since PAS is an antibacterial compound whose mechanism of action may be related for the ability of this compound to compete with PABA for DHPS, we determined the antibacterial activity and cytotoxicity of F-PABA for several bacterial species as well as Vero cells. In each case no growth inhibition was observed up to 200 μg/ml. Unlike PAA, 2-F-PABA has no antibacterial activity (Table 1).

[Figure (not displayed)]

TABLE 1
MIC (μg/ml)
2-F-PABAPAS
M. tuberculosis>1000.08
S. aureus>200>200
E. coli>200>200

Patent 2024
4-Aminobenzoic Acid Anti-Bacterial Agents Bacteria Biological Assay Biosynthetic Pathways Cells Cytotoxin Dihydropteroate Synthase Drug Kinetics Enzymes Escherichia coli Folate Folic Acid Kinetics Mammals Mycobacterium tuberculosis Psychological Inhibition Vero Cells

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More about "Drug Kinetics"

Drug kinetics, also known as pharmacokinetics, is the study of the movement and fate of drugs within the body.
It encompasses the absorption, distribution, metabolism, and excretion (ADME) of pharmaceutical agents.
Understanding drug kinetics is crucial for optimizing drug dosing, minimizing adverse effects, and improving therapeutic efficacy.
This field involves the application of mathematical models and statistical analysis, such as those used in WinNonlin, Phoenix WinNonlin, GraphPad Prism 7, GraphPad Prism 5, and SAS version 9.4, to describe the time course of drug concentrations in the body.
Researchers in drug kinetics utilize various techniques, such as in vitro studies, animal models (e.g., Neuro-2a cells), and clinical trials, to evaluate the kinetic properties of new and existing drugs.
The goal is to develop safe and effective medication regimens that maximize the benefits for patients.
Pubcompare.ai's AI-powered platform can enhance drug kinetics research by helping scientists locate the best protocols from literature, preprints, and patents, as well as identify the most effecitve products to streamline their studies.
With features like Prism 8 and WinNonlin® version 3.2, the PubCompare.ai platform can assist researchers in optimizing their drug kinetics investigations and improving patient outcomes.