Computational Structural Correlation and Spectrophotometric Method Development for the Quantification of Drug Impurities

Abstract

The discovery, identification, and quantification of unidentified impurity molecules during the commercial drug manufacturing have already been necessitated by the regulatory bodies worldwide. The existence of impurities in the drug formulation could impact therapeutical value and also compromise the potency and efficiency of the drug systems. Many times, the exploratory studies of these compounds can direct us to better drug options. Impurity profiling also tells us about the toxicity impacts due to the possible carry-over of such molecules into drug formulations. The impurity molecules are nothing but minor products, intermediate products, or even penultimate step products formed during the manufacturing of drugs. Some of these impurity molecules might also be produced inside the receiver s body as a result of the metabolic degradation of the drug upon oral administration. In certain cases, the active pharmaceutical ingredient (API) of the drugs and their corresponding impurities or metabolites might have analogous structures. Also, the impurities or metabolites may not always influence the drug action adversely. However, the therapeutic compliance and toxicity profile might vary depending on the concentration of these molecules. Hence, any foreign substance - notwithstanding whether inactive, noxious or therapeutically better - has to be scrutinized and recorded. Though the structural and chemical properties of most of the drug systems have been studied well and reported, there is a dearth of information concerning the impurity systems. In the present study, we have selected four therapeutically important impurity molecules for structural, spectral, and quantitative analysis. A perusal of literature revealed that the structural and spectral exploration of these impurities has not been done yet. newlineIn the current study, the optimized structure, geometrical parameters, and vibrational frequencies of the selected impurity molecules were generated insilico using Gaussian 09 software.