Category: 72509-76-3

Zarybnicky, Tomas published the artcileThe selection and validation of reference genes for mRNA and microRNA expression studies in human liver slices using RT-qPCR, Product Details of C18H19Cl2NO4, the main research area is insulin YWHAZ ACTB B2M CYP3A4 CYP1A2 miR165p biomarker steatosis; RT-qPCR; human liver; mRNA; miRNA; precision-cut liver slices; reference gene.

The selection of a suitable combination of reference genes (RGs) for data normalization is a crucial step for obtaining reliable and reproducible results from transcriptional response anal. using a reverse transcription-quant. polymerase chain reaction. This is especially so if a three-dimensional multicellular model prepared from liver tissues originating from biol. diverse human individuals is used. The mRNA and miRNA RGs stability were studied in thirty-five human liver tissue samples and twelve precision-cut human liver slices (PCLS) treated for 24 h with DMSO (controls) and PCLS treated with β-naphthoflavone (10μM) or rifampicin (10μM) as cytochrome P 450 (CYP) inducers. Validation of RGs was performed by an expression anal. of CYP3A4 and CYP1A2 on rifampicin and β-naphthoflavone induction, resp. Regarding mRNA, the best combination of RGs for the controls was YWHAZ and B2M, while YWHAZ and ACTB were selected for the liver samples and treated PCLS. Stability of all candidate miRNA RGs was comparable or better than that of generally used short non-coding RNA U6. The best combination for the control PCLS was miR-16-5p and miR-152-3p, in contrast to the miR-16-5b and miR-23b-3p selected for the treated PCLS. Our results showed that the candidate RGs were rather stable, especially for miRNA in human PCLS.

Genes published new progress about Biomarkers. 72509-76-3 belongs to class pyridine-derivatives, name is 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, and the molecular formula is C18H19Cl2NO4, Product Details of C18H19Cl2NO4.

Referemce:
Pyridine – Wikipedia,
Pyridine | C5H5N – PubChem

Holt, Kimberly published the artcilePrediction of tissue-plasma partition coefficients using microsomal partitioning: incorporation into physiologically based pharmacokinetic models and steady-state volume of distribution predictions, Formula: C18H19Cl2NO4, the main research area is plasma partition coefficient protein binding microsomal partitioning PBPK model.

Drug distribution is a necessary component of models to predict human pharmacokinetics. A new membrane-based tissue-plasma partition coefficient (Kp) method (Kp,mem) to predict unbound tissue to plasma partition coefficients (Kpu) was developed using in vitro membrane partitioning [fraction unbound in microsomes (fum)], plasma protein binding, and log P. The resulting Kp values were used in a physiol. based pharmacokinetic (PBPK) model to predict the steady-state volume of distribution (Vss) and concentration-time (C-t) profiles for 19 drugs. These results were compared with Kp predictions using a standard method [the differential phospholipid Kp prediction method (Kp,dPL)], which differentiates between acidic and neutral phospholipids. The Kp,mem method was parameterized using published rat Kpu data and tissue lipid composition The Kpu values were well predicted with R2 = 0.8. With one outlier removed for Kp,mem and two for Kp,dPL, the Vss predictions for R2 were 0.80 and 0.79 for the Kp,mem and Kp,dPL methods, resp. The C-t profiles were also predicted and compared. Overall, the Kp,mem method predicted the Vss and C-t profiles equally or better than the Kp,dPL method. An advantage of using fum to parameterize membrane partitioning is that fum data are used for clearance prediction and are, therefore, generated early in the discovery/development process. Also, the method provides a mechanistically sound basis for membrane partitioning and permeability for further improving PBPK models.

Drug Metabolism & Disposition published new progress about Adipose tissue. 72509-76-3 belongs to class pyridine-derivatives, name is 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, and the molecular formula is C18H19Cl2NO4, Formula: C18H19Cl2NO4.

Referemce:
Pyridine – Wikipedia,
Pyridine | C5H5N – PubChem

Li, Congwei published the artcileSpectroscopic methodology and molecular docking studies on changes in binding interaction of felodipine with bovine serum albumin induced by cocrystallization with β-resorcylic acid, Safety of 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, the main research area is felodipine bovine serum albumin beta resorcylic acid mol docking; binding interaction; bovine serum albumin; cocrystal; felodipine; molecular docking; β-resorcylic acid.

In the present study, a novel cocrystal of felodipine (FEL) and β-resorcylic acid (βRA) was developed. We specially focused on the change of binding pattern with bovine serum albumin (BSA) induced by cocrystn. of FEL with βRA. The solid characterizations and d. functional theory (DFT) simulation verified that FEL-βRA cocrystal formed in equimolar ratio (1 : 1 M ratio) through C=O···H-O hydrogen bond between C=O group in FEL and O-H group in βRA. The binding interactions between FEL-βRA system and BSA were studied using fluorescence spectral and mol. docking methods. Two guest mol. systems, including a phys. mixture of FEL and βRA and FEL-βRA cocrystal were performed binding to BSA in mol. docking. Mol. docking simulation suggested that FEL and its cocrystal inserted into the subdomain IIIA (site II’) of BSA. The interactions between FEL and BSA including hydrogen bonding with ASN390 residue and intermol. hydrophobic interactions with LEU429 and LEU452 residues. However, the size of supramol. FEL-βRA better matched that of active cavity of BSA; the cocrystal is closely bound to BSA through hydrogen bonding with ASN390 residue and intermol. hydrophobic interactions with LEU429, VAL432, LEU452 and ILE387 residues. This change on binding affinity of FEL to BSA induced by cocrystn. with βRA provided theor. basis to evaluate the transportation, distribution and metabolism of cocrystal drug.

Chemical & Pharmaceutical Bulletin published new progress about Density functional theory. 72509-76-3 belongs to class pyridine-derivatives, name is 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, and the molecular formula is C18H19Cl2NO4, Safety of 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate.

Referemce:
Pyridine – Wikipedia,
Pyridine | C5H5N – PubChem

Zhou, Wei published the artcileInfluence of batch effect correction methods on drug induced differential gene expression profiles, Recommanded Product: 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, the main research area is batch effect correction method drug differential gene expression; Batch effect; Drug repositioning; Microarray.

Batch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods. Differences in average signature size were observed with different methods applied. The gene signatures identified by the Latent Effect Adjustment after Primary Projection (LEAPP) method and the methods fitted with Linear Models for Microarray Data (limma) software demonstrated little agreement. The external validity of the gene signatures was evaluated by connectivity mapping between the CMAP database and the Library of Integrated Network-based Cellular Signatures (LINCS) database. The results of connectivity mapping indicate that the genes identified were not reliable for drugs with total sample size (drug + control samples) smaller than 40, irresp. of the batch effect correction method applied. With total sample size larger than 40, the methods correcting for batch effects produced significantly better results than the method with no batch effect correction. In a simulation study, the power was generally low for simulated data with sample size smaller than 40. We observed best performance when using the limma method correcting for two principal components. Batch effect correction methods strongly impact differential gene expression anal. when the sample size is large enough to contain sufficient information and thus the downstream drug repositioning. We recommend including two or three principal components as covariates in fitting models with limma when sample size is sufficient (larger than 40 drug and controls combined).

BMC Bioinformatics published new progress about Computational biology. 72509-76-3 belongs to class pyridine-derivatives, name is 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, and the molecular formula is C18H19Cl2NO4, Recommanded Product: 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate.

Referemce:
Pyridine – Wikipedia,
Pyridine | C5H5N – PubChem

Wang, Dongdong published the artcilePopulation pharmacokinetics of tacrolimus in pediatric refractory nephrotic syndrome and a summary of other pediatric disease models, Quality Control of 72509-76-3, the main research area is tacrolimus pediatric refractory nephrotic syndrome pharmacokinetics; nonlinear mixed-effects modeling; pediatric refractory nephrotic syndrome; population pharmacokinetics; tacrolimus; therapeutic drug monitoring.

Different tacrolimus (TAC) population pharmacokinetic (PPK) models have been established in various pediatric disease populations. However, a TAC PPK model for pediatric refractory nephrotic syndrome (PRNS) has not been well characterized. The current study aimed to establish a TAC PPK model in Chinese PRNS and provide a summary of previous literature concerning TAC PPK models in different pediatric diseases. A total of 147 TAC conventional therapeutic drug monitoring (TDM) data from multiple blood samples obtained from 65 Chinese patients with PRNS were characterized using nonlinear mixed-effects modeling. The impacts of demog. features, biol. characteristics and drug combination were evaluated. Model validation was assessed using the bootstrap method. A one-compartment model with first-order absorption and elimination was determined to be the most suitable model for TDM data in PRNS. The absorption rate constant (Ka) was set at 4.48 h-1. The typical values of apparent oral clearance (CL/F) and apparent volume of distribution (V/F) in the final model were 5.46 l/h and 57.1 l, resp. The inter-individual variability of CL/F and V/F were 22.2 and 0.2%, resp. The PPK equation for TAC was: CL/F = 5.46 × exponential function (EXP)(0.0323 × age) × EXP(-0.359 × cystatin-C) × EXP(0.148 × daily dose of TAC). No significant effects of covariates on V/F were observed In conclusion, the current study developed and validated the first TAC PPK model for patients with PRNS. The study also provided a summary of previous literature concerning other TAC PPK models in different pediatric diseases.

Experimental and Therapeutic Medicine published new progress about Homo sapiens. 72509-76-3 belongs to class pyridine-derivatives, name is 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, and the molecular formula is C18H19Cl2NO4, Quality Control of 72509-76-3.

Referemce:
Pyridine – Wikipedia,
Pyridine | C5H5N – PubChem

Lee, A. Reum published the artcileLercanidipine synergistically enhances bortezomib cytotoxicity in cancer cells via enhanced endoplasmic reticulum stress and mitochondrial Ca2+ overload, Application of 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, the main research area is breast cancer lercanidipine bortezomib cytotoxicity endoplasmic reticulum mitochondria calcium; ER stress; bortezomib; lercanidipine; mitochondrial Ca2+ overload; paraptosis.

The proteasome inhibitor (PI), bortezomib (Btz), is effective in treating multiple myeloma and mantle cell lymphoma, but not solid tumors. In this study, we show for the first time that lercanidipine (Ler), an antihypertensive drug, enhances the cytotoxicity of various PIs, including Btz, carfilzomib, and ixazomib, in many solid tumor cell lines by inducing paraptosis, which is accompanied by severe vacuolation derived from the endoplasmic reticulum (ER) and mitochondria. We found that Ler potentiates Btz-mediated ER stress and ER dilation, possibly due to misfolded protein accumulation, in MDA-MB 435S cells. In addition, the combination of Btz and Ler triggers mitochondrial Ca2+ overload, critically contributing to mitochondrial dilation and subsequent paraptotic events, including mitochondrial membrane potential loss and ER dilation. Taken together, our results suggest that a combined regimen of PI and Ler may effectively kill cancer cells via structural and functional perturbations of the ER and mitochondria.

International Journal of Molecular Sciences published new progress about Antitumor agents. 72509-76-3 belongs to class pyridine-derivatives, name is 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, and the molecular formula is C18H19Cl2NO4, Application of 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate.

Referemce:
Pyridine – Wikipedia,
Pyridine | C5H5N – PubChem

Williams, Dominic P. published the artcilePredicting Drug-Induced Liver Injury with Bayesian Machine Learning, HPLC of Formula: 72509-76-3, the main research area is machine learning drug liver toxicity prediction risk assessment.

Drug induced liver injury (DILI) can require significant risk management in drug development, and on occasion can cause morbidity or mortality, leading to drug attrition. Optimizing candidates preclinically can minimise hepatotoxicity risk but it is difficult to predict due to multiple etiologies encompassing DILI, often with multifactorial and overlapping mechanisms. In addition to epidemiol. risk factors, physicochem. properties, dose, disposition, lipophilicity, and hepatic metabolic function are also relevant for DILI risk. Better human relevant, predictive models are required to improve hepatotoxicity risk assessment in drug discovery. The authors’ hypothesis is that integrating mechanistically relevant hepatic safety assays with Bayesian machine learning will improve hepatic safety risk prediction. The authors present a quant. and mechanistic risk assessment for candidate nomination using data from in vitro assays (hepatic spheroids, BSEP, mitochondrial toxicity and bioactivation), together with physicochem. (cLogP) and exposure (Cmaxtotal) variables from a chem. diverse compound set (33 no/low-, 40 medium- and 23 high-severity DILI compounds). The Bayesian model predicts the continuous underlying DILI severity and uses a data-driven prior distribution over the parameters to prevent overfitting. The model quantifies the probability that a compound falls into either no/low, medium, or high-severity categories, with a balanced accuracy of 63% on held-out samples, and a continuous prediction of DILI severity along with uncertainty in the prediction. For a binary yes/no DILI prediction, the model has a balanced accuracy of 86%, a sensitivity of 87%, a specificity of 85%, a pos. predictive value of 92%, and a neg. predictive value of 78%. Combining physiol. relevant assays, improved alignment with FDA recommendations, and optimal statistical integration of assay data, leads to improved DILI risk prediction.

Chemical Research in Toxicology published new progress about Biotransformation. 72509-76-3 belongs to class pyridine-derivatives, name is 3-Ethyl 5-methyl 4-(2,3-dichlorophenyl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarboxylate, and the molecular formula is C18H19Cl2NO4, HPLC of Formula: 72509-76-3.

Referemce:
Pyridine – Wikipedia,
Pyridine | C5H5N – PubChem