Regulation of genotoxic and carcinogenic impurities in drug substances and products

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.

Long term online desalting analysis of MS/LC-MS using thermal assisted recrystallization ionization

Mass spectrometric analysis of non-volatile salts containing samples remains challenging due to salt-induced ion suppression and contamination. This challenge is even more pronounced for a liquid chromatography-mass spectrometry analysis, where the accumulation of salts in the transmission system poses an ongoing problem. In this study, a novel thermal assisted recrystallization ionization mass spectrometry (TARI-MS) device was developed to achieve efficient on-line desalting and prolonged analysis of saline samples. The core component of this device was a heated plate positioned between the electrospray unit and the MS inlet. The desalting mechanism was demonstrated as the spontaneous separation of target molecules from salts during the "crystallization" process. After optimization, the angle between the nebulizer and the heated plate was 45°; the distance between the front end of the heated plate and the MS inlet was 2 mm; the distance between the front edge of the heated plate and the center of the sample spray projected onto the heating plate was 3 mm; the distance between the emitter of nebulizer and the heated plate was 3 mm. TARI-MS realized direct analysis of eight drugs dissolved in eight commonly used non-volatile salts solutions (up to 0.5 mol/L). The high sensitivity, repeatability, linearity, accuracy, and intra- and inter-day precision of TARI-MS confirm its reliability as a robust tool for the analysis of saline samples. Furthermore, TARI-MS allowed continuous analysis of salty eluates of LC for up to nearly 1 h without maintenance and verified the feasibility of LC-MS analysis through detecting a five-drug mixture and a crude aripiprazole product. Finally, six impurities in the crude aripiprazole product were successfully detected by LC-TARI-MS. The established method holds promise for applications across academic and pharmaceutical domains.

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.

The comprehensive strategy in the human health risk assessment of total chromium impurities in cough syrups with Marshmallow Root (Althaea officinalis) available in Polish pharmacies: regulatory aspects and special emphasis on Cr(VI) mode of action

Chromium, which can currently only be considered pharmacologically active and not an essential element, is a very intriguing elemental impurity in final pharmaceutical products, especially traditional herbal medicinal products. This kind of traditional herbal medicinal product with Marshmallow root (Althaea officinalis L., radix) registered in the EU is widely used among the European population. The aim of this article is to propose a double regulatory strategy in assessing the human health risk of total chromium impurities in cough syrups with Marshmallow Root (Althaea officinalis) available in Polish pharmacies. We applied the strategy based on the requirements of the ICH Q3D (R1) guideline for the assessment of Cr impurities in final traditional herbal medicinal products with Marshmallow Root registered in the EU. Furthermore, we applied the strategy based on the concept of margin of exposure (MoE) considering Cr(VI) genotoxicity mode of action (MOA) and based on BMD10 for Cr(VI) as a point of departure (PoD). The total Cr content was in the range: 1.12-9.61 µg/L (in comparison with the ICH Q3D R1 guidelines: 1100 µg/g). Total Cr levels in a single dose were relatively high compared to raw results, but were not a threat to patients. Comparison of estimated results with oral PDE value for Cr in final drugs suggested by the ICH Q3D R1 guideline (10,700 µg/day) show that all the products analyzed were below this value (the highest result was 278.40 ng/day). Despite conservative assumptions, the MoE values obtained for Cr in daily dose for each Marshmallow Root cough syrup were above 10,000; therefore, exposure to Cr would not cause a health risk for specific population groups (children and adults). It can be summarized that each of the phytopharmaceuticals analysed with Marshmallow root available in Polish pharmacies does not represent a health hazard to patients. We confirm the safety of Cr impurities by applying a double regulatory strategy without the application of an expansive and demanding HPLC-ICP-MS technique for Cr speciation.

A Green-and-White Integrative Analytical Strategy Combining Univariate and Chemometric Techniques for Quantifying Recently Approved Multi-Drug Eye Solution and Potentially Cancer-Causing Impurities: Application to the Aqueous Humor

BACKGROUND

Drug impurities are now seen as a major threat to the production of pharmaceuticals around the world and a major part of the global contamination problem, especially when it comes to carcinogenic impurities.

OBJECTIVE

We present the first spectrophotometric strategy based on a combination of univariate and multivariate methods as impurity profiling methods for the estimation of lignocaine (LIG) and fluorescein (FLS) with their carcinogenic impurities: 2,6-xylidine (XYL) and benzene-1,3-diol (BZD).

METHOD

The data processing strategy depends on overcoming unresolved bands by employing five affordable, accurate, selective, and sensitive methods. The methods applied were a direct UV univariate spectrophotometric analysis (D0) and four multivariate chemometric methods, including classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm (GA-PLS). FLS analysis (1-16 μg/mL) was performed using the D0 method at 478 nm; then, the application of the ratio subtraction method (RSM) allowed the removal of interference caused by the FLS spectrum. From the resulting ratio spectra, LIG, XYL, and BZD can be efficiently determined by chemometrics. The calibration set was carefully selected at five concentration levels using a partial factorial training design, resulting in 25 mixtures with central levels of 160, 40, and 3 μg/mL for LIG, XYL, and BZD, respectively. Another 13 samples were applied to validate the predictive ability.

RESULTS

The statistical parameters demonstrated exceptional recoveries and smaller prediction errors, confirming the experimental model's predictive power.

CONCLUSIONS

The proposed approach was effectively tested using newly FDA-approved LIG and FLS pharmaceutical preparation and aqueous humor. Additionally, it was effectively assessed for whiteness, greenness, and sustainability using five assessment tools.

HIGHLIGHTS

With its remarkable analytical performance, sustainability, affordability, simplicity, and cost-efficiency, the proposed strategy is an indispensable tool for quality control and in situ analysis in little-equipped laboratories, increasing the proposed approach's surveillance ability.

DNA Repair-Responsive Engineered Whole Cell Microbial Sensors for Sensitive and High-Throughput Screening of Genotoxic Impurities

Genotoxic impurities (GTIs) occurred in drugs, and food and environment pose a threat to human health. Accurate and sensitive evaluation of GTIs is of significance. Ames assay is the existing gold standard method. However, the pathogenic bacteria model lacks metabolic enzymes and requires mass GTIs, leading to insufficient safety, accuracy, and sensitivity. Whole-cell microbial sensors (WCMSs) can use normal strains to simulate the metabolic environment, achieving safe, sensitive, and high-throughput detection and evaluation for GTIs. Here, based on whether GTIs causing DNA alkylation required metabolic enzymes or not, two DNA repair-responsive engineered WCMS systems were constructed including Escherichia coli-WCMS and yeast-WCMS. A DNA repair-responsive promoter as a sensing element was coupled with an enhanced green fluorescent protein as a reporter to construct plasmids for introduction into WCMS. The ada promoter was screened out in the E. coli-WCMS, while the MAG1 promoter was selected for the yeast-WCMS. Different E. coli and yeast strains were modified by gene knockout and mutation to eliminate the interference and enhance the GTI retention in cells and further improved the sensitivity. Finally, GTI consumption of WCMS for the evaluation of methyl methanesulfonate (MMS) and nitrosamines was decreased to 0.46-8.53 μg and 0.068 ng-2.65 μg, respectively, decreasing 2-3 orders of magnitude compared to traditional methods. This study provided a novel approach to measure GTIs with different DNA damage pathways at a molecular level and facilitated the high-throughput screening and sensitive evaluation of GTIs.

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.

Heat-Assisted Dual Neutral Spray Ionization for High-Performance Online Desalting in Mass Spectrometric Analysis

In mass spectrometry (MS), nonvolatile salts contaminate the transmission system and cause ion suppression, hampering MS analysis. When MS is combined with liquid chromatography (LC) that uses a salty mobile phase, the problems become more intractable due to long analysis time. Here, a novel heat-assisted dual neutral spray ionization (HADSI) method was developed, which projected sample solution spray and solvent spray onto a heated plate to achieve online desalting and high ionization. The experimental parameters of HADSI were optimized, which indicated that the plate temperature was crucial for ionization and desalination. Eight drug compounds dissolved in various commonly used buffers were directly analyzed using HADSI-MS, even though the concentration of PBS buffer reached 500 mmol/L. The established method showed considerable sensitivity in the positive ion mode with the limits of detection at the level of nmol/L, and good linearity (R² > 0.99) was achieved for all the analyzed compounds. The repeatability and intra- and interday precisions of the method were evaluated, demonstrating the feasibility and reliability of the analysis of salty samples by HADSI-MS. Further, the method was demonstrated to tolerate the long-time analysis of high-salt LC eluates and the device was easy to maintain. Finally, a crude roxithromycin product was separated by LC and then analyzed by HADSI-MS, and seven unknown impurities and nine known impurities were successfully detected. Our results indicated that HADSI-MS may have potential applications in academic and industrial fields.

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.

Liquid chromatography-tandem mass spectrometric method for trace quantification of ethyl methanesulfonate: a genotoxic impurity in dapoxetine hydrochloride

Background

Dapoxetine hydrochloride is a selective serotonin reuptake inhibitor drug for treating premature ejaculation. This study was designed to develop and validate a sensitive and selective LC–MS/MS method for trace analysis of genotoxic impurity ethyl methanesulfonate in Dapoxetine hydrochloride.

Results

Chromatographic separation was achieved on the Shodex RSpak DS-413 column, 150 × 4.6 mm, 3.0 µm using eluent containing a equal volumes of acetonitrile and 0.1% v/v formic acid in water was used in the isocratic elution mode at a pump flow of 1.0 mL/min. No interference was observed at the retention time of ethyl methanesulfonate, indicating that the developed method is specific and selective for trace level quantification.The developed method was found to be linear in the concentration range of 1–50 ppm with coefficient of regression of 0.9997. Detection limit and quantification limit were determined to be 0.6 ppm and 1.0 ppm respectively. Acceptable RSD values (< 10.0%) and recovery results (> 90%) obtained from the accuracy and precison experiments indicate that the developed method is precise and accurate in the concentration range of 1–50 ppm. Ethyl methanesulfonate solutions were stable for two days when stored at room and refrigerated temperatures.

Conclusion

The developed method has the ability to quantify ethyl methanesulfonate in dapoxetine hydrochloride. Thus, the anticipated method has high probability to adopt in the quality testing laboratories of pharmaceutical industry.

An efficient HILIC-MS/MS method for the trace level determination of three potential genotoxic impurities in aripiprazole active drug substance

A sensitive and selective hydrophilic interaction liquid chromatography-tandem mass spectrometry (HILIC-MS/MS) method was developed and validated for trace analysis of potential genotoxic impurities (PGIs): 2,3-dichloroaniline (PGI-1), bis(2-chloroethyl) amine (PGI-2), and 2-chloroethylamine (PGI-3), in aripiprazole (APZ) active drug substance. Separation of analytes was achieved on ACE HILIC–N Column (HILN-5-1046U, 100 × 4.6 mm, 5 μm) in gradient elution mode with mobile phase A [acetonitrile:ammonium formate buffer (95:5 v/v)] and mobile phase B [acetonitrile:ammonium formate buffer (50:50 v/v)] at a flow rate of 0.8 mL/min. Developed method was linear in the concentration range of 8–100 ppm for PGI-1, 11–100 ppm for PGI-2, and 12.5–100ppm for PGI-3 with R2 > 0.996. The developed method was accurate for quantification of each PGI with percent recoveries greater than 96% and RSD (%) not more than 5%. The developed method was precise for quantification of PGIs in aripiprazole with RSD (%) of not more than 4% for any of the PGIs. There was no interference of diluent peaks at the retention time of the PGIs and APZ in the method. All the PGIs and sample solutions were found to be stable at ambient laboratory temperature (25 ± 5 °C) and refrigerated condition (2–8 °C) for a period of 48 h. The developed HILIC-MS/MS method can be used for trace quantification of PGIs in aripiprazole drug in quality control laboratories of the pharmaceutical industry.

Critical Analysis of Drug Product Recalls due to Nitrosamine Impurities

A product recall is the outcome of a careful pharmacovigilance; and it is an integral part of drug regulation. Among various reasons for product recall, the detection of unacceptable levels of carcinogenic impurities is one of the most serious concerns. The genotoxic and carcinogenic potential of N-nitrosamines raises a serious safety concern, and in September 2020, the FDA issued guidance for the pharmaceutical industry regarding the control of nitrosamines in drug products. The FDA database shows that >1400 product lots have been recalled from the market due to the presence of carcinogenic N-nitrosamine impurities at levels beyond the acceptable intake limit of 26.5 ng/day. The drugs that were present in recalled products include valsartan, irbesartan, losartan, metformin, ranitidine, and nizatidine. This perspective provides a critical account of these product recalls with an emphasis on the source and mechanism for the formation of N-nitrosamines in these products.

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.

Dispersive liquid-liquid microextraction with high-performance liquid chromatography for the analysis of 1,4-benzodioxane-6-aldehyde in eliglustat tartrate active pharmaceutical ingredient

Potential genotoxic impurities (PGIs) are a series of compounds that could potentially damage DNA. Therefore, a sensitive method is needed for detection and quantification. The present work described and validated a method for the quantification of one PGI (namely 1,4-benzodioxane-6-aldehyde) in Eliglustat tartrate (EGT) active pharmaceutical ingredient (API) substances using dispersive liquid-liquid microextraction (DLLME) as sample preparation to remove matrix effect and detected by HPLC-UV. Parameters influencing the microextraction efficiency were systematically investigated. The combined application of DLLME and HPLC-UV provided the sensitivity of the method. The achieved limit of detection (LOD) and the limit of quantification (LOQ) were adequate for the specific purpose and found to be 1.29 μg g^-1 and 2.58 μg g^-1, respectively. This simple and effective methodology offers a key advantage in the ease of removing matrix effect and improves sensitivity obviously. In addition, no costly instrumentation and skilled personnel are needed when using this method, which is available and can be successfully implemented in routine factory drug quality control analysis.

Determination of genotoxic epoxide at trace level in drug substance by direct injection GC/MS

A novel direct injection gas chromatography method coupled with selective ion monitoring mass spectrometry (GC/SIM-MS) was developed for quantitation of trace levels of high boiling point (HBP) epoxide genotoxic impurity (GTI) in drug substance. The injector temperature was optimized with the aims to minimize matrix effects and enhance SIM signal response. The final injector temperature 160°C was selected after balancing between these two factors. The column screening was conducted as well and MN OPTIMA delta-3 silica capillary column was selected since it showed good peak symmetry without column bleeding. The good linearity was established for the concentration in the range from 0.0045μg/mL to 0.5μg/mL with a R²=0.9999. The limit of detection (LOD) and the limit of quantitation (LOQ) were 0.0014μg/mL and 0.0045μg/mL, respectively. The recovery which ranged from 95.0% to 112.5% could meet the ICH acceptance criteria. The validation results demonstrated the good linearity, precision and accuracy of the method which can be further adopted as an adequate quality control tool for quantitation of epoxide impurity at trace levels in drug substance and drug product.

A photochemical kinetic model for solid dosage forms

Photochemical kinetic models to describe the solution phase degradation of pharmaceutical compounds have been extensively reported, but formalisms applicable to the solid phase under polychromatic light have not received as much attention. The objective of this study was to develop a mathematical model to describe the solid state photodegradation of pharmaceutical powder materials under different area/volumetric scales and light exposure conditions. The model considered the previous formalism presented for photodegradation kinetics in solution phase with important elements applied to static powder material being irradiated with a polychromatic light source. The model also included the influence of optical phenomena (i.e. reflectance, scattering factors, etc.) by applying Beer-Lambert law to light attenuation, including effects of powder density. Drug substance and drug product intermediates (blends and tablet cores) were exposed to different light sources and intensities. The model reasonably predicted the photodegradation levels of powder beds of drug substance and drug product intermediates under white and yellow lights with intensities around 5-11kLux. Importantly, the model estimates demonstrated that the reciprocity law for photoreactions was held. Further model evaluation showed that, due to light attenuation, the powder bed is in virtual darkness at cake depths greater than 500μm. At 100μm, the photodegradation of the investigated compound is expected to be close to 100% in 10days under white fluorescent halophosphate light at 9.5kLux. For tablets, defining the volume over exposed surface area ratio is more challenging. Nevertheless, the model can consider a bracket between worst and best cases to provide a reasonable photodegradation estimate. This tool can be significantly leveraged to simulate different light exposure scenarios while assessing photostability risk in order to define appropriate control strategy in manufacturing.

Comparative evaluation of 11 in silico models for the prediction of small molecule mutagenicity: role of steric hindrance and electron-withdrawing groups

The goal of this investigation was to perform a comparative analysis on how accurately 11 routinely-used in silico programs correctly predicted the mutagenicity of test compounds that contained either bulky or electron-withdrawing substituents. To our knowledge this is the first study of its kind in the literature. Such substituents are common in many pharmaceutical agents so there is a significant need for reliable in silico programs to predict precisely whether they truly pose a risk for mutagenicity. The predictions from each program were compared to experimental data derived from the Ames II test, a rapid reverse mutagenicity assay with a high degree of agreement with the traditional Ames assay. Eleven in silico programs were evaluated and compared: Derek for Windows, Derek Nexus, Leadscope Model Applier (LSMA), LSMA featuring the in vitro microbial Escherichia coli-Salmonella typhimurium TA102 A-T Suite (LSMA+), TOPKAT, CAESAR, TEST, ChemSilico (±S9 suites), MC4PC and a novel DNA docking model. The presence of bulky or electron-withdrawing functional groups in the vicinity of a mutagenic toxicophore in the test compounds clearly affected the ability of each in silico model to predict non-mutagenicity correctly. This was because of an over reliance on the part of the programs to provide mutagenicity alerts when a particular toxicophore is present irrespective of the structural environment surrounding the toxicophore. From this investigation it can be concluded that these models provide a high degree of specificity (ranging from 71% to 100%) and are generally conservative in their predictions in terms of sensitivity (ranging from 5% t o 78%). These values are in general agreement with most other comparative studies in the literature. Interestingly, the DNA docking model was the most sensitive model evaluated, suggesting a potentially useful new mode of screening for mutagens. Another important finding was that the combination of a quantitative structure-activity relationship and an expert rules system appeared to offer little advantage in terms of sensitivity, despite of the requirement for such a screening paradigm under the ICH M7 regulatory guideline.

Prediction of Non-Genotoxic Carcinogenicity Based on Genetic Profiles of Short Term Exposure Assays

Non-genotoxic carcinogens are substances that induce tumorigenesis by non-mutagenic mechanisms and long term rodent bioassays are required to identify them. Recent studies have shown that transcription profiling can be applied to develop early identifiers for long term phenotypes. In this study, we used rat liver expression profiles from the NTP (National Toxicology Program, Research Triangle Park, USA) DrugMatrix Database to construct a gene classifier that can distinguish between non-genotoxic carcinogens and other chemicals. The model was based on short term exposure assays (3 days) and the training was limited to oxidative stressors, peroxisome proliferators and hormone modulators. Validation of the predictor was performed on independent toxicogenomic data (TG-GATEs, Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System, Osaka, Japan). To build our model we performed Random Forests together with a recursive elimination algorithm (VarSelRF). Gene set enrichment analysis was employed for functional interpretation. A total of 770 microarrays comprising 96 different compounds were analyzed and a predictor of 54 genes was built. Prediction accuracy was 0.85 in the training set, 0.87 in the test set and increased with increasing concentration in the validation set: 0.6 at low dose, 0.7 at medium doses and 0.81 at high doses. Pathway analysis revealed gene prominence of cellular respiration, energy production and lipoprotein metabolism. The biggest target of toxicogenomics is accurately predict the toxicity of unknown drugs. In this analysis, we presented a classifier that can predict non-genotoxic carcinogenicity by using short term exposure assays. In this approach, dose level is critical when evaluating chemicals at early time points.

An evaluation of in-house and off-the-shelf in silico models: implications on guidance for mutagenicity assessment

The evaluation of impurities for genotoxicity using in silico models are commonplace and have become accepted by regulatory agencies. Recently, the ICH M7 Step 4 guidance was published and requires two complementary models for genotoxicity assessments. Over the last ten years, many companies have developed their own internal genotoxicity models built using both public and in-house chemical structures and bacterial mutagenicity data. However, the proprietary nature of internal structures prevents sharing of data and the full OECD compliance of such models. This analysis investigated whether using in-house internal compounds for training models is needed and substantially impacts the results of in silico genotoxicity assessments, or whether using commercial-off-the-shelf (COTS) packages such as Derek Nexus or Leadscope provide adequate performance. We demonstrated that supplementation of COTS packages with a Support Vector Machine (SVM) QSAR model trained on combined in-house and public data does, in fact, improve coverage and accuracy, and reduces the number of compounds needing experimental assessment, i.e., the liability load. This result indicates that there is added value in models trained on both internal and public structures and incorporating such models as part of a consensus approach improves the overall evaluation. Lastly, we optimized an in silico consensus decision-making approach utilizing two COTS models and an internal (SVM) model to minimize false negatives.

Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities

The toxicological assessment of genotoxic impurities is important in the regulatory framework for pharmaceuticals. In this context, the application of promising computational methods (e.g. Quantitative Structure-Activity Relationships (QSARs), Structure-Activity Relationships (SARs) and/or expert systems) for the evaluation of genotoxicity is needed, especially when very limited information on impurities is available. To gain an overview of how computational methods are used internationally in the regulatory assessment of pharmaceutical impurities, the current regulatory documents were reviewed. The software recommended in the guidelines (e.g. MCASE, MC4PC, Derek for Windows) or used practically by various regulatory agencies (e.g. US Food and Drug Administration, US and Danish Environmental Protection Agencies), as well as other existing programs were analysed. Both statistically based and knowledge-based (expert system) tools were analysed. The overall conclusions on the available in silico tools for genotoxicity and carcinogenicity prediction are quite optimistic, and the regulatory application of QSAR methods is constantly growing. For regulatory purposes, it is recommended that predictions of genotoxicity/carcinogenicity should be based on a battery of models, combining high-sensitivity models (low rate of false negatives) with high-specificity ones (low rate of false positives) and in vitro assays in an integrated manner.