Quantification and In Silico Toxicity Assessment of Tazarotene and its Impurities for a Quality and Safe Drug Product Development.

Tazarotene is internationally accepted common name for ethyl 6-[(4,4-dimethylthiochroman-6-yl)ethynyl]nicotinate. It is a synthetic retinoid used for the topical treatment of mild to moderate plaque psoriasis, acne vulgaris and photo aging. To ensure the quality of drug product and drug substance, a LC-MS compatible UHPLC method was developed for quantification of drug and its related substances. Stationary phase with fused core particle technology is used for the separation of impurities. Limit of quantification and limit of detection of the method are 0.1 and 0.03%, respectively. Precision of the method for Tazarotene and all its related substances is less than 2.2% RSD. The correlation coefficient is >0.999. Accuracy of method is ranged from 95.3% to 107.0%. Application of this method in stability analysis has been demonstrated by analyzing stressed samples. Experimental design is used for the verification of robustness of the method. To ensure the safety, an in silico toxicity of the drug and its related substances were determined using TOPKAT and DEREK toxicity predictions Both UHPLC and in silico methods were validated as per the ICH Q2 and ICH M7 guidelines, which will enable a rapid product development of Tazarotene topical formulations while ensuring the safety and quality of product.

Quantification and In Silico Toxicity Assessment of Nimesulide and its Related Substances.

Availability of analytical method which is compatible with both Ultra high performance liquid chromatography (UHPLC) and Liquid chromatography mass spectrometry to identify and quantify the impurities in pharmaceutical products is an advantage for drug product development; such analytical method was developed for Nimesulide using a fused core column. Ammonium acetate and methanol were used as mobile phase in the gradient elution at a detection wavelength 230 nm. Quality-by-design principles helped in identifying the critical method development parameters and design space. Limit of detection and the limit of quantification are 0.025 and 0.075%, respectively. Precision of the method is <0.4% RSD and correlation coefficient is >0.999 for Nimesulide and all related substances. Accuracy of Nimesulide ranged from 99.3 to 100.4% at assay concentration. Application of this method in stability analysis has been demonstrated by analyzing stressed samples. The method was validated for specificity, linearity, accuracy, precision and robustness in compliance with International Conference on Harmonization (ICH) Q2(R1). This is the first reported UHPLC method for the estimation of Nimesulide and its related substances. According to ICH M7 guidelines, all impurities were assessed for genotoxicity, LD50, skin sensitization, irritation potential and carcinogenicity using Derek and TOPKAT software. Combined knowledge of analytical and toxicology assessment will help in developing safe and quality product.

Insights from ligand and structure based methods in virtual screening of selective Ni-peptide deformylase inhibitors.

In recent years, there has been a growing interest in developing bacterial peptide deformylase (PDF) inhibitors as novel antibiotics. The purpose of the study is to generate a three-dimensional (3D) pharmacophore model by using diverse PDF inhibitors which is useful for designing of potential antibiotics. Twenty one structurally diverse compounds were considered for the generation of quantitative pharmacophore model using HypoGen of Catalyst, further model was validated using 78 compounds. Pharmacophore model demonstrated the importance of two acceptors, one donor and one hydrophobic feature toward the biological activity. The inhibitors were also docked into the binding site of PDF to comprehend the structural insights of the active site. Combination of ligand and structure based methods were used to find the potential antibiotics.

Strategies for generating less toxic P-selectin inhibitors: pharmacophore modeling, virtual screening and counter pharmacophore screening to remove toxic hits.

This paper describes the generation of ligand-based as well as structure-based models and virtual screening of less toxic P-selectin receptor inhibitors. Ligand-based model, 3D-pharmacophore was generated using 27 quinoline salicylic acid compounds and is used to retrieve the actives of P-selectin. This model contains three hydrogen bond acceptors (HBA), two ring aromatics (RA) and one hydrophobic feature (HY). To remove the toxic hits from the screened molecules, a counter pharmacophore model was generated using inhibitors of dihydrooratate dehydrogenase (DHOD), an important enzyme involved in nucleic acid synthesis, whose inhibition leads to toxic effects. Structure-based models were generated by docking and scoring of inhibitors against P-selectin receptor, to remove the false positives committed by pharmacophore screening. The combination of these ligand-based and structure-based virtual screening models were used to screen a commercial database containing 538,000 compounds.