Deep eutectic solvents as green and sustainable diluents in headspace gas chromatography for the determination of trace level genotoxic impurities in pharmaceuticals

Genotoxic impurities (GTIs) are potential carcinogens that need to be controlled down to ppm or lower concentration levels in pharmaceuticals under strict regulations. The static headspace gas chromatography (HS-GC) coupled with electron capture detection (ECD) is an effective approach to monitor halogenated and nitroaromatic genotoxins. Deep eutectic solvents (DESs) possess tunable physico-chemical properties and low vapor pressure for HS-GC methods. In this study, zwitterionic and non-ionic DESs have been used for the first time to develop and validate a sensitive analytical method for the analysis of 24 genotoxins at sub-ppm concentrations. Compared to non-ionic diluents, zwitterionic DESs produced exceptional analytical performance and the betaine : 7 (1,4- butane diol) DES outperformed the betaine : 5 (1,4-butane diol) DES. Limits of detection (LOD) down to the 5-ppb concentration level were achieved in DESs. Wide linear ranges spanning over 5 orders of magnitude (0.005-100 µg g-1) were obtained for most analytes with exceptional sensitivities and high precision. The method accuracy and precision were validated using 3 commercially available drug substances and excellent recoveries were obtained. This study broadens the applicability of HS-GC in the determination of less volatile GTIs by establishing DESs as viable diluent substitutes for organic solvents in routine pharmaceutical analysis.

Impurity study of tecovirimat

This article delineates the systematic identification, synthesis, and impurity control methods used during the manufacturing process development of tecovirimat, an antiviral drug that treats monkeypox. Critical impurities were synthesized, and their chemical structure was confirmed through NMR analysis, GC, and HPLC mass spectrometry. The results established a thorough approach to identify, address, and control impurities to produce high-quality tecovirimat drug substance in accordance with International Conference on Harmonization (ICH)-compliant standards. This study is the first of its kind to evaluate both process and genotoxic impurities in tecovirimat, demonstrating effective control measures during commercial sample investigations and scaling up to a 60-kg batch size. The findings highlight the importance of critical impurity characterization and control in pharmaceutical development and production to ensure the safety and efficacy of the final product.

Determination of genotoxic impurities of aromatic aldehydes in pharmaceutical preparations by high performance liquid chromatography after derivatization with N-Cyclohexyl-4-hydrazino-1,8-naphthalenediimide

The detection of aromatic aldehydes, considered potential genotoxic impurities, holds significant importance during drug development and production. Current analytical methods necessitate complex pre-treatment processes and exhibit insufficient specificity and sensitivity. This study presents the utilization of naphthalenediimide as a pre-column derivatisation reagent to detect aromatic aldehyde impurities in pharmaceuticals via high-performance liquid chromatography (HPLC). We screened a series of derivatisation reagents through density functional theory (DFT) and investigated the phenomenon of photoinduced electron transfer (PET) for both the derivatisation reagents and the resulting products. Optimal experimental conditions for derivatisation were achieved at 40 °C for 60 min. This approach has been successfully applied to detect residual aromatic aldehyde genotoxic impurities in various pharmaceutical preparations, including 4-Nitrobenzaldehyde, 2-Nitrobenzaldehyde, 1,4-Benzodioxane-6-aldehyde, and 5-Hydroxymethylfurfural. The pre-column derivatisation method significantly enhanced detection sensitivity and reduced the limit of detection (LOD), which ranged from 0.002 to 0.008 μg/ml for the analytes, with relative standard deviations < 3 %. The correlation coefficient (R2) >0.998 demonstrated high quality. In chloramphenicol eye drops, the concentration of 4-Nitrobenzaldehyde was measured to be 8.6 µg/mL below the specified concentration, with recoveries ranging from 90.0 % to 119.2 %. In comparison to existing methods, our work simplifies the pretreatment process, enhances the sensitivity and specificity of the analysis, and offers comprehensive insights into impurity detection in pharmaceutical preparations.

Analytical Method Capable of Quantifying Eight Nitrosamine Impurities from Five Different Commercially Available Metformin Formulations with Glipizide, Glibenclamide, Gliclazide, Evogliptin, and Glimepiride by Ultra High Performance Liquid Chromatography Tripple Quadrupole Mass Spectrometry

Metformin and its combinations are widely used to treat type 2 diabetes. The drugs commonly used in combination with Metformin are Glipizide, Glibenclamide, Gliclazide, Evogliptin, and Glimepiride. Combination therapy is preferred over monotherapy of Metformin in most diabetics. About eighteen pharmaceutical manufacturers have lately recalled metformin formulation batches from the U.S. market due to N-nitrosodimethylamine (NDMA) impurities based on the food and drug administration (USFDA) guideline "Control of Nitrosamine in Human Drugs." European Medicines Agency (EMA) and Health Canada have also established guidelines for nitrosamine impurities. Nitrosamines are well-known mutagenic impurities and probable human carcinogens found in pharmaceutical formulations. Thus, global regulatory agencies require pharmaceutical and formulation manufacturers to complete risk assessments for nitrosamine impurities for patient safety. Therefore, drug manufacturers must develop analytical techniques for monitoring trace nitrosamine impurities. Quantifying nitrosamine impurities in formulations requires modern equipment like LC-MS/MS and great intellect. The present study intends to give a single pre-packaged LC-MS/MS method parameters, including liquid chromatography and triple quadrupole mass spectrometer configuration. This method could quantify eight nitrosamine impurities from five different Metformin combinations (Metformin with Glipizide, Glibenclamide, Gliclazide, Evogliptin, and Glimepiride). The atmospheric pressure chemical ionisation (APCI) was used as an ionisation source, and the mass spectrometer was set to multiple reaction monitoring (MRM) mode for all eight nitrosamine impurities. A unified pre-packaged analytical setup allows analytical chemists to develop a reliable, sensitive, robust, and precise method for quantifying eight nitrosamine impurities from five different Metformin formulations of varying manufacturers. This analytical method saves time, money, and the environment using fewer pharmaceutical chemicals.

A sensitive LC-MS/MS method for the determination of potential genotoxic impurities in Cinnarizine

A highly sensitive LC-MS/MS method for the trace level determination of genotoxic impurities, Cinnamyl chloride and Benzhydryl chloride, in Cinnarizine drug substance was developed and validated. Chromatographic separation was successfully achieved on Atlantis d C18 column with dimensions 150× 4.6mm and particle size: 5μm. 0.1% Trifluoroacetic acid in water and 100% acetonitrile was used as mobile phases with gradient mode of elution at 1.0mL/min flow rate. Mass spectroscopic detection was carried out with selective ion monitoring (SIM) technique in positive mode at m/z 117 and 167 for Cinnamyl chloride and Benzhydryl chloride respectively. Developed method was proven to be selective, sensitive, and precise for the quantification of potential genotoxic impurities in Cinnarizine by validating as per the regulatory guidelines. The LOD and LOQ values observed for Cinnamyl chloride were 0.49 and 1.47ppm and for Benzhydryl chloride 0.55 and 1.67ppm respectively. Precision of the method at LOQ level was shown with good % RSD of 4.21. Method was proven linear from LOQ to 150% level with a correlation of 0.996 and accurate with a range of recovery from 86.4 to 100.8%. This highly sensitive method can be used to control both the genotoxic impurities in Cinnarizine drug substance by LC-MS/MS.

Use of In Silico Methods for Regulatory Toxicological Assessment of Pharmaceutical Impurities

The use of novel non-testing methodologies to support the toxicological assessment of drug impurities is having a growing impact in the regulatory framework for pharmaceutical development and marketed products. For DNA reactive (mutagenic) impurities specific recommendations for the use of in silico structure-based approaches (namely (Q)SAR methodologies) are provided in the ICH M7 guideline. In 2018 a draft reflection paper has been published by EMA addressing open issues in the qualification approach of non-genotoxic impurities (NGI) according to the ICH Q3A/Q3B guidelines, and proposing the use of alternative testing strategies, including TTC, (Q)SAR, read-across, and in vitro approaches, to gather impurity-specific safety information.In the present chapter we describe a workflow to perform the safety assessment of drug impurities based on non-testing in silico methodologies. The proposed approach consists of a stepwise decision scheme including three key phases: PHASE 1: assessment of bacterial mutagenicity and consequent classification of impurities according to ICH M7; PHASE 2: risk characterization of mutagenic impurities (Classes 1, 2 or 3); PHASE 3: qualification of non-mutagenic impurities (Classes 4 or 5). The proposed decision scheme offers the possibility to acquire impurity-specific data, also if testing is not feasible, and to decide on further in vitro testing, besides meeting 3R's principle.

Liquid Chromatography-Mass Spectrometric Methods for Trace Quantification of Potential Genotoxic Impurities in Ivacaftor and Lumacaftor

OBJECTIVE

The objective of the current study was to develop and validate the sensitive LC-MS methods for trace analysis of genotoxic impurities in Ivacaftor and Lumacaftor. The first method is for the trace analysis of 2,4-di-tert-butyl-5-nitrophenol in ivacaftor and the second method is for the trace analysis of 1-(2,2-difluoro-1,3-benzodioxol-5yl)-cyclopropane carboxylic acid and 3-carboxyphenyl boronic acid in lumacaftor.

MATERIALS AND METHODS

High pure analytical grade solvents and reagents were used for this study. The chromatographic separation was performed on Luna C18 (250 × 4.6 mm, 5.0 µm) at a column temperature of 25 °C using eluent consisting of acetonitrile and 0.1% v/v formic acid in water in a gradient elution mode. The eluent was run at a flow of 1.0 mL/min and injection volume of 20 µL.

RESULTS

The linearity, precision and accuracy of the developed methods was validated over the concentration range of 0.35 - 15.0 ppm for 2,4-di-tert-butyl-5-nitrophenol, 0.30 - 15.0 ppm for 1-(2,2-difluoro-1,3-benzodioxol-5yl)-cyclopropane carboxylic acid and 0.23 - 15.0 ppm for 3-carboxyphenyl boronic acid. In both methods, interference was not observed at the retention time of analyte peaks. All the analytes were found to be stable in solution for a period of 48 h.

CONCLUSION

The proposed methods are reliable, sensitive, precise, accurate, and robust for the trace level quantification of genotoxic impurities in Ivacaftor and Lumacaftor. These methods can be successfully implemented in the quality control lab for routine analysis.

A reversed phase HPLC-UV method for the simultaneous determination of residual formaldehyde and Triton X-100 in vaccine products

Many of the inactivated viral vaccines for human and animal use are manufactured using formaldehyde as an inactivating agent. Apart from formaldehyde, Triton X-100 is also one of the chemicals commonly used in viral vaccine manufacturing. Triton X-100 is typically used to extract the cell-associated viruses and / or components during manufacturing process. The concentration of formaldehyde and Triton X-100 in the final bulks are also reduced during vaccine purification process. Here we report a simple RP-HPLC-UV based method for the quantification of residual Triton X-100 and formaldehyde as process impurities in viral vaccines. This method is also adopted for the residual impurity determination of either formaldehyde or Triton X-100 in other non-viral vaccines, multivalent as well as sub-unit vaccines, such as liquid pentavalent, includes TT, DT, Hepatitis B (rDNA) and Haemophilus type b conjugate vaccine (adsorbed). This method is rapid and can quantify both Triton X-100 and formaldehyde in a single preparation with improved peak asymmetry. This new assay has a linearity range starting from 0.0625 to 1 µg/mL for formaldehyde and 0.625-10 µg/mL for Triton X-100. This method would be very useful for viral vaccine manufacturing and release.

Machine learning guided prediction of liquid chromatography-mass spectrometry ionization efficiency for genotoxic impurities in pharmaceutical products

The limitation and control of genotoxic impurities (GTIs) has continued to receive attention from pharmaceutical companies and authorities for several decades. Because GTIs have the ability to damage deoxyribonucleic acid (DNA) and the potential to cause cancer, low-level quantitation is required to protect patients. A quick and easy method of determining the liquid chromatography-mass spectrometry (LC/MS) conditions for high-sensitivity analysis of GTIs may prospectively accelerate pharmaceutical development. In this study, a quantitative structure-property relationship (QSPR) model was developed for predicting the ionization efficiency of compounds using liquid-chromatography-mass spectrometry (LC/MS) parameters and molecular descriptors. Before implementing the QSPR prediction model, linear regression analysis was performed to model the relationship between the ionization efficiency and the LC/MS parameters for each compound. Comparison of the predicted peak areas with the experimentally observed peak areas showed good agreement based on the coefficient of determination (R² > 0.96). The machine learning-based QSPR approach begins with computation of the molecular descriptors expressing the physicochemical properties of a compound, followed by a genetic algorithm-based feature selection. Linear and nonlinear regression were performed, and support vector machine (SVM) was selected as the best machine learning algorithm for the prediction. The SVM algorithm was developed and optimized using 1031 experimental data points for nine compounds, including well-known GTIs. Validation of the model by comparison of the predicted and observed relative ionization efficiencies (RIE) showed a high coefficient of determination (R² = 0.96) and low root mean squared error value (RMSE = 0.118). Finally, this established prediction model was applied to hydrophilic interaction liquid chromatography coupled with MS for a new compound in new mobile phase compositions and new MS parameter settings. The RMSE of the predicted versus observed RIE was 0.203. This prediction accuracy was sufficient to determine the starting point of the LC/MS method development. The methodology demonstrated in this study can be used to determine the LC/MS conditions for high sensitivity analysis of GTIs.

In silico prediction of genotoxicity

The in silico prediction of genotoxicity has made considerable progress during the last years. The main driver for the pharmaceutical industry is the ICH M7 guideline about the assessment of DNA reactive impurities. An important component of this guideline is the use of in silico models as an alternative approach to experimental testing. The in silico prediction of genotoxicity provides an established and accepted method that defines the first step in the assessment of DNA reactive impurities. This was made possible by the growing amount of reliable Ames screening data, the attempts to understand the activity pathways and the subsequent development of computer-based prediction systems. This paper gives an overview of how the in silico prediction of genotoxicity is performed under the ICH M7 guideline.

The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities

The toxicological assessment of DNA-reactive/mutagenic or clastogenic impurities plays an important role in the regulatory process for pharmaceuticals; in this context, in silico structure-based approaches are applied as primary tools for the evaluation of the mutagenic potential of the drug impurities. The general recommendations regarding such use of in silico methods are provided in the recent ICH M7 guideline stating that computational (in silico) toxicology assessment should be performed using two (Q)SAR prediction methodologies complementing each other: a statistical-based method and an expert rule-based method.Based on our consultant experience, we describe here a framework for in silico assessment of mutagenic potential of drug impurities. Two main applications of in silico methods are presented: (1) support and optimization of drug synthesis processes by providing early indication of potential genotoxic impurities and (2) regulatory evaluation of genotoxic potential of impurities in compliance with the ICH M7 guideline. Some critical case studies are also discussed.

Determination of the impurities in drug products containing montelukast and in silico/in vitro genotoxicological assessments of sulfoxide impurity

Impurities affecting safety, efficacy, and quality of pharmaceuticals are of increasing concern for regulatory agencies and pharmaceutical industries, since genotoxic impurities are understood to play important role in carcinogenesis. The study aimed to analyse impurities of montelukast chronically used in asthma theraphy and perform genotoxicological assessment considering regulatory approaches. Impurities (sulfoxide, cis-isomer, Michael adducts-I&II, methylketone, methylstyrene) were quantified using RP-HPLC analysis on commercial products available in Turkish market. For sulfoxide impurity, having no toxicity data and found to be above the qualification limit, in silico mutagenicity prediction analysis, miniaturized bacterial gene mutation test, mitotic index determination and in vitro chromosomal aberration test w/wo metabolic activation system were conducted. In the analysis of different batches of 20 commercial drug products from 11 companies, only sulfoxide impurity exceeded qualification limit in pediatric tablets from 2 companies and in adult tablets from 7 companies. Leadscope and ToxTree programs predicted sulfoxide impurity as nonmutagenic. It was also found to be nonmutagenic in Ames MPF Penta I assay. Sulfoxide impurity was dose-dependent cytotoxic in human peripheral lymphocytes, however, it was found to be nongenotoxic. It was concluded that sulfoxide impurity should be considered as nonmutagenic and can be classified as ordinary impurity according to guidelines.