Bioanalytical assay development and validation for the pharmacokinetic study of gmc1, a novel fkbp52 co-chaperone inhibitor for castration resistant prostate cancer

Background: GMC1 (2-(1H-benzimidazol-2-ylsulfanyl)-N-[(Z)-(4-methoxyphenyl) methylideneamino] acetamide) effectively inhibits androgen receptor function by binding directly to FKBP52. This is a novel mechanism for the treatment of castration resistant prostate cancer (CRPC). Methods: an LC-MS/MS method was developed and validated to quantify GMC1 in plasma and urine from pharmacokinetics studies in rats. An ultra-high-performance liquid chromatography (UHPLC) system equipped with a Waters XTerra MS C18 column was used for chromatographic separation by gradient elution with 0.1% (v/v) formic acid in water and methanol. A Sciex 4000 QTRAP® mass spectrometer was used for analysis by multiple reaction monitoring (MRM) in positive mode; the specific ions [M+H]+ m/z 340.995 → m/z 191.000 and [M+H]+ m/z 266.013 → m/z 234.000 were monitored for GMC1 and internal standard (albendazole), respectively. Results: GMC1 and albendazole had retention times of 1.68 and 1.66 min, respectively. The calibration curves for the determination of GMC1 in rat plasma and urine were linear from 1–1000 ng/mL. The LC-MS/MS method was validated with intra-and inter-day accuracy and precision within the 15% acceptance limit. The extraction recovery values of GMC1 from rat plasma and urine were greater than 95.0 ± 2.1% and 97.6 ± 4.6%, respectively, with no significant interfering matrix effect. GMC1 is stable under expected sample handling, storage, preparation and LC-MS/MS analysis conditions. Conclusions: Pharmacokinetic evaluation of GMC1 revealed that the molecule has a biexponential disposition in rats, is distributed rapidly and extensively, has a long elimination half-life, and appears to be eliminated primarily by first order kinetics

Bioanalytical assay development and validation for the pharmacokinetic study of gmc1, a novel fkbp52 co-chaperone inhibitor for castration resistant prostate cancer

Background: GMC1 (2-(1H-benzimidazol-2-ylsulfanyl)-N-[(Z)-(4-methoxyphenyl) methylideneamino] acetamide) effectively inhibits androgen receptor function by binding directly to FKBP52. This is a novel mechanism for the treatment of castration resistant prostate cancer (CRPC). Methods: an LC-MS/MS method was developed and validated to quantify GMC1 in plasma and urine from pharmacokinetics studies in rats. An ultra-high-performance liquid chromatography (UHPLC) system equipped with a Waters XTerra MS C18 column was used for chromatographic separation by gradient elution with 0.1% (v/v) formic acid in water and methanol. A Sciex 4000 QTRAP® mass spectrometer was used for analysis by multiple reaction monitoring (MRM) in positive mode; the specific ions [M+H]+ m/z 340.995 → m/z 191.000 and [M+H]+ m/z 266.013 → m/z 234.000 were monitored for GMC1 and internal standard (albendazole), respectively. Results: GMC1 and albendazole had retention times of 1.68 and 1.66 min, respectively. The calibration curves for the determination of GMC1 in rat plasma and urine were linear from 1–1000 ng/mL. The LC-MS/MS method was validated with intra-and inter-day accuracy and precision within the 15% acceptance limit. The extraction recovery values of GMC1 from rat plasma and urine were greater than 95.0 ± 2.1% and 97.6 ± 4.6%, respectively, with no significant interfering matrix effect. GMC1 is stable under expected sample handling, storage, preparation and LC-MS/MS analysis conditions. Conclusions: Pharmacokinetic evaluation of GMC1 revealed that the molecule has a biexponential disposition in rats, is distributed rapidly and extensively, has a long elimination half-life, and appears to be eliminated primarily by first order kinetics

Bioanalytical assay development and validation for the pharmacokinetic study of gmc1, a novel fkbp52 co-chaperone inhibitor for castration resistant prostate cancer

Background: GMC1 (2-(1H-benzimidazol-2-ylsulfanyl)-N-[(Z)-(4-methoxyphenyl) methylideneamino] acetamide) effectively inhibits androgen receptor function by binding directly to FKBP52. This is a novel mechanism for the treatment of castration resistant prostate cancer (CRPC). Methods: an LC-MS/MS method was developed and validated to quantify GMC1 in plasma and urine from pharmacokinetics studies in rats. An ultra-high-performance liquid chromatography (UHPLC) system equipped with a Waters XTerra MS C18 column was used for chromatographic separation by gradient elution with 0.1% (v/v) formic acid in water and methanol. A Sciex 4000 QTRAP® mass spectrometer was used for analysis by multiple reaction monitoring (MRM) in positive mode; the specific ions [M+H]+ m/z 340.995 → m/z 191.000 and [M+H]+ m/z 266.013 → m/z 234.000 were monitored for GMC1 and internal standard (albendazole), respectively. Results: GMC1 and albendazole had retention times of 1.68 and 1.66 min, respectively. The calibration curves for the determination of GMC1 in rat plasma and urine were linear from 1–1000 ng/mL. The LC-MS/MS method was validated with intra-and inter-day accuracy and precision within the 15% acceptance limit. The extraction recovery values of GMC1 from rat plasma and urine were greater than 95.0 ± 2.1% and 97.6 ± 4.6%, respectively, with no significant interfering matrix effect. GMC1 is stable under expected sample handling, storage, preparation and LC-MS/MS analysis conditions. Conclusions: Pharmacokinetic evaluation of GMC1 revealed that the molecule has a biexponential disposition in rats, is distributed rapidly and extensively, has a long elimination half-life, and appears to be eliminated primarily by first order kinetics

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu

QSAR Modeling: Where Have You Been? Where Are You Going To?

Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making

Application of ‘Inductive’ QSAR Descriptors for Quantification of Antibacterial Activity of Cationic Polypeptides

On the basis of the inductive QSAR descriptors we have created a neural network-based solution enabling quantification of antibacterial activity in the series of 101 synthetic cationic polypeptides (CAMEL-s). The developed QSAR model allowed 80% correct categorical classification of antibacterial potencies of the CAMEL-s both in the training and the validation sets. The accuracy of the activity predictions demonstrates that a narrow set of 3D sensitive ‘inductive’ descriptors can adequately describe the aspects of intra- and intermolecular interactions that are relevant for antibacterial activity of the cationic polypeptides. The developed approach can be further expanded for the larger sets of biologically active peptides and can serve as a useful quantitative tool for rational antibiotic design and discovery