Prediction Of Toxicity Research Articles

Prediction of the drug intestinal absorption and drug-induced intestinal toxicity with the use of cultured human/animal crypt-derived intestinal stem cells

Prediction of intestinal drug absorption and drug-induced intestinal toxicity is critical for the development of orally-administered drugs. However, it is difficult to accurately predict these events because of large species differences and a lack of appropriate in vitro assay. Then, we proposed the use of human crypt-derived intestinal cells for the prediction of intestinal absorption and the risk of intestinal toxicity. 3D human intestinal spheroids were established from fresh surgical specimens of proximal jejunum and terminal ileum using the conditioned media containing Wnt3a, R-spondin 3, and noggin. To generate 2D monolayer, spheroids were enzymatically dissociated into single cells and plated onto Matrigel-precoated culture plates/inserts. We have confirmed the activities of typical drug-metabolizing enzymes and uptake/efflux transporters in human jejunal spheroid-derived differentiated cells. Intestinal availability (Fg) estimated from the apical-to-basal permeation clearance across the jejunal monolayer showed a good correlation with in vivo human Fg values for five CYP3A substrate drugs. As for the prediction of intestinal toxicity, we found that the degree of ATP decreases in intestinal spheroids incubated with different EGFR-TKIs varied greatly depending on the drugs and the rank order of the extent of ATP decrease corresponded with that of frequency of clinically-observed diarrhea. We also constructed enterochromaffin (EC) cell-rich spheroids and quantified serotonin release from EC cells upon exposure to drugs for the prediction of drug-induced nausea and vomiting. As a result, we found that the serotonin release was related to the high/low risk of nausea and vomiting of each ALK/ROS1 kinase inhibitors.

LP-53 Combining statistical approaches with MIE Analysis for interpretable developmental toxicity prediction

LP-53 Combining statistical approaches with MIE Analysis for interpretable developmental toxicity prediction

Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models.

Machine Learning (ML) has demonstrated potential in enhancing care in adult oncology. However, its application in paediatric haematological malignancies is still emerging, necessitating a comprehensive review of its capabilities and limitations in this area. A literature search was conducted through Ovid. Studies included focused on ML models in paediatric patients with haematological malignancies. Studies were categorised into thematic groups for analysis. Twenty studies, primarily on leukaemia, were included in this review. Studies were organised into thematic categories such as prognoses, treatment responses and toxicity predictions. Prognostic studies showed AUC scores between 0.685 and 0.929, indicating moderate-high predictive accuracy. Treatment response studies demonstrated AUC scores between 0.840 and 0.875, reflecting moderate accuracy. Toxicity prediction studies reported high accuracy with AUC scores from 0.870 to 0.927. Only five studies (25%) performed external validation. Significant heterogeneity was noted in ML tasks, reporting formats, and effect measures across studies, highlighting a lack of standardised reporting and challenges in data comparability. The clinical applicability of these ML models remains limited by the lack of external validation and methodological heterogeneity. Addressing these challenges through standardised reporting and rigorous external validation is needed to translate ML from a promising research tool into a reliable clinical practice component. Key message: Machine Learning (ML) significantly enhances predictive models in paediatric haematological cancers, offering new avenues for personalised treatment strategies. Future research should focus on developing ML models that can integrate with real-time clinical workflows. Addition to literature: Provides a comprehensive overview of current ML applications and trends. It identifies limitations to its applicability, including the limited diversity in datasets, which may affect the generalisability of ML models across different populations. Encourages standardisation and external validation in ML studies, aiming to improve patient outcomes through precision medicine in paediatric haematological oncology.

Potential endocrine-disrupting effects of iprodione via estrogen and androgen receptors: evaluation using in vitro assay and an in silico model

This study was conducted to provide evidence, using in vitro and in silico testing methods, regarding the adverse effects of iprodione, a representative dichlorophenyl dicarboxamide fungicide, on the endocrine system. In the present study, we used the HeLa9903 stably transfected transactivation assay (OECD TG 455), 22Rv1/MMTV_GR‒KO androgen receptor transcriptional activation assay (OECD TG 458), and toxicity prediction using VEGA QSAR. Our results showed that iprodione had no estrogen receptor antagonistic or androgen receptor agonistic effects; however, iprodione was determined to be an estrogen receptor agonist (log PC10 value is less than − 9) and androgen receptor antagonist (log IC30 value is − 4.58) without intrinsic toxicity against the human cell lines used in this study. VEGA QSAR was used to evaluate five substances with structures similar to that of iprodione. Among them, four chemicals were found to have positive androgen receptor and aromatase activities and have been observed to be developmental toxicants. These results suggest that iprodione regulates steroid hormone receptor interactions and is a potential reproductive toxicant.

Synthesis, crystal structure, Hirshfeld surface, computational and biological studies of spiro-oxindole derivatives as MDM2-p53 inhibitors.

The spiro-oxindole derivatives were synthesized via a 1,3-dipolar cycloaddition approach and characterized by FT-IR, 1H, 13C NMR and mass spectral techniques. The single crystal XRD of 6d further validates the formation of compounds. DFT calculations indicated the reactive nature of compound 6d. Docking studies with 5LAW disclosed the minimum binding energy of - 10.83kcal/mol for 6d. Furthermore, safe oral bioavailability was ensured by the physicochemical, pharmacokinetic, and toxicity predictions. The anticancer analysis of synthesized compounds showed substantial activity against A549 cells, notably with an IC50 value of 8.13 ± 0.66µM for 6d compared to standard doxorubicin. 6d was also evaluated for cytotoxicity against L929 healthy cells and A549, showing selectivity towards A549 than healthy cells. AO/EB staining method showed early apoptotic cellular death in the A549 cell line in a dose-dependent manner.

Comparative case study on NAMs: towards enhancing specific target organ toxicity analysis

Traditional risk assessment methodologies in toxicology have relied upon animal testing, despite concerns regarding interspecies consistency, reproducibility, costs, and ethics. New Approach Methodologies (NAMs), including cell culture and multi-level omics analyses, hold promise by providing mechanistic information rather than assessing organ pathology. However, NAMs face limitations, like lacking a whole organism and restricted toxicokinetic interactions. This is an inherent challenge when it comes to the use of omics data from in vitro studies for the prediction of organ toxicity in vivo. One solution in this context are comparative in vitro–in vivo studies as they allow for a more detailed assessment of the transferability of the respective NAM data. Hence, hepatotoxic and nephrotoxic pesticide active substances were tested in human cell lines and the results subsequently related to the biology underlying established effects in vivo. To this end, substances were tested in HepaRG and RPTEC/tERT1 cells at non-cytotoxic concentrations and analyzed for effects on the transcriptome and parts of the proteome using quantitative real-time PCR arrays and multiplexed microsphere-based sandwich immunoassays, respectively. Transcriptomics data were analyzed using three bioinformatics tools. Where possible, in vitro endpoints were connected to in vivo observations. Targeted protein analysis revealed various affected pathways, with generally fewer effects present in RPTEC/tERT1. The strongest transcriptional impact was observed for Chlorotoluron in HepaRG cells (increased CYP1A1 and CYP1A2 expression). A comprehensive comparison of early cellular responses with data from in vivo studies revealed that transcriptomics outperformed targeted protein analysis, correctly predicting up to 50% of in vivo effects.

Insilico toxicity prediction by using ProTox-II computational tools

Insilico toxicity prediction by using ProTox-II computational tools

The impact of organic carbon mineralization on pollution and toxicity of toxic metal in sediments: Yellow Sea and East China Sea study

Organic carbon mineralization is the main driving force of metal migration and transformation in sediments, greatly influencing the distribution, pollution degree, and toxicity of toxic metals. However, relevant research on this subject is still limited. In this study, the concentration of toxic metals (Cr, Cd, Cu, Pb, Zn, Co, Fe, Mn, Ni, As) in the solid and liquid phase (porewater) of sediments were measured, toxic metal pollution degree and toxicity of the Yellow Sea (YS) and the East China Sea (ECS) were assessed. Combined with the rate of organic carbon mineralization, the impact of organic carbon mineralization was analyzed. The results showed that Ni was slightly enriched and posed a certain ecological risk, and As was moderately enriched in the studied area, Pb was at a moderate pollution level in the studied area. Zn, Co, Mn, and Fe were at a moderate pollution level in the mud area of SYS and the west coastal area of ECS. Additionally, the total organic carbon mineralization rate (TCMR) in the ECS (5.12–18.04 mmol C m−2 d−1) was slightly higher than that in the YS (3.29–14.46 mmol C m−2 d−1) during spring. Moreover, organic carbon mineralization promotes metal enrichment, and the TCMR was significantly correlated with the pollution load index. Thus, TCMR can be used as an indicator to predict the degree of metal pollution. Furthermore, organic carbon mineralization promotes the mobilization of Cu from the solid phase to the liquid phase, while facilitating the transfer of Cr, Pb, Co, Ni, and Fe from the liquid phase to the solid phase. This process increases the potential risks of Cu and reduces the toxicity of Cr, Pb, Co, Ni, and Fe. Therefore, the impact of organic carbon mineralization should be considered in future assessments and predictions of toxic metal pollution and toxicity.

Callus of Mundulea sericea as potential anti-diabetic agents; an in silico approach

Mundulea sericea, a rare plant of the fabaceae family, has high medicinal value but a low regeneration rate in its natural habitat. The present study aims to conduct in vitro propagation and screen the potential bioactive compounds in the callus through in silico analysis. The explants were collected, sterilized, and inoculated in a standardized MS media with growth regulators in vitro to obtain sterile plants. The experiment resulted in the formation of a greenish-brown callus at a rate of 82.41 % on MS medium supplemented with BAP (6.66 µM L−1). The callus was powdered and subjected to GC–MS analysis. A total of 31 compounds were identified in the methanolic extract, along with their corresponding peak values. The compounds underwent ADME analysis for toxicity predictions. Eight compounds were chosen further based on pharmacokinetic and pharmacodynamic properties. The selected ligands were docked with anti-diabetic targets α-amylase and α-glucosidase. As a result, the compound CID 5283663 exhibited a strong binding affinity of −9.3 with the α-amylase receptor. The ligand CID 33010 exhibits a strong interaction of −7.5 with the α-glucosidase receptor. Glibenclamide and acarbose were used as standards. This study is the first attempt to identify compounds in the callus extract that could inhibit α-amylase and α-glucosidase enzymes. The findings from molecular docking analysis suggest these compounds could become potent treatments for diabetics, showcasing the promise of natural sources in drug discovery.

Identification of Optimal Machine Learning Algorithms and Molecular Fingerprints for Explainable Toxicity Prediction Models Using ToxCast/Tox21 Bioassay Data.

Recent studies have primarily focused on introducing novel frameworks to enhance the predictive power of toxicity prediction models by refining molecular representation methods and algorithms. However, these methods are inherently complex and often pose challenges in understanding and explaining, leading to barriers in their regulatory adoption and validation. Therefore, it is necessary to select the optimal model, considering not only model performance but also interpretability. This study aimed to identify the optimal combination of molecular fingerprints (pattern-based versus algorithm-based) and machine learning algorithms (simple versus complex) for developing explainable toxicity prediction models through an comprehensive investigation of the ToxCast/Tox21 bioassay data set. For 1092 ToxCast/Tox21 assays, five molecular fingerprints (MACCS, Morgan, RDKit, Layered, and Patterned) and six algorithms (MLP, GBT, Random Forest, kNN, Logistic Regression, and Naïve Bayes) were used to train the models. Results showed that 35 models revealed acceptable performance (F1 score or accuracy is 0.8 or higher). Among the combinations, either MACCS or Morgan, paired with Random Forest, demonstrated robust performance compared with other molecular fingerprints and algorithms. MACCS and Random Forest are valuable, even when prioritizing interpretability. Consequently, the MACCS-Random Forest combination model based on four assays, targeting G protein-coupled receptor and kinase, were identified and they can be used to discern specific structural features or patterns in chemical compounds, offering explainable insights into toxicity-related chemical structures. This study indicates the importance of not disregarding the utilization of simple models when assessing both predictivity and interpretability within the context of chemical feature-based Tox21 data analysis.

In Silico Analysis of Bioactive Compounds from Leaves of Abrus precatorius L. (Rosary Pea) Against Breast Cancer.

Introduction Breast cancer is one of the most common causes of cancer among women. Since the administration of chemotherapy drugs can cause several adverse effects, thus it leads to research on effective treatment from natural sources. Leaves of Abrus precatorius L., a member of the Fabaceae family,contain several medicinal properties. It has drawn interest as an anti-cancer agent since its leaves contain different phytochemicals that can cause apoptosis in a variety of cancer types. Methods A total of 97 compounds were identified from the ethyl acetate extract of A. precatorius leaves by gas chromatography/mass spectrometry (GC/MS) analysis. Of those, eight compounds were selected based on the percentage area above 2. Cheminformatics software such as Molinspiration (Molinspiration Cheminformatics free web services, Slovensky Grob, Slovakia) was used to predict the molecular properties and bioactivity. PASS software (NCSS LLC, Kaysville, Utah, United States) was used to predict the scores of anticancer properties such as antioxidant, anti-inflammatory, immunosuppressant, antineoplastic, and cytoprotective. Osiris Property Explorer software was used to determine pharmacokinetic profile and toxicity prediction, and molecular docking was performed to determine the binding affinity towards the receptors. Results Out of eight compounds, one was selected based on the scores of the above software, then docking studies were done by using AutoDock Vina 4.2.6 (Center for Computational Structural Biology, La Jolla, California, United States). The results were compared with the reference compound, 5-fluorouracil, and 1,4-dimethyl-4 pentenyl acetate was identified as the most promising active compound found in this study. It shows better binding affinity towards the progesterone receptor (-6.0)when compared to the reference compound. Conclusion Based on the results, it has been proved that 1,4-dimethyl-4 pentenyl acetate maybe used as an alternative for the management of breast cancer.

ECOTOXr: An R package for reproducible and transparent retrieval of data from EPA's ECOTOX database

The US EPA ECOTOX database provides key ecotoxicological data that are crucial in environmental risk assessment. It can be used for computational predictions of toxicity or indications of hazard in a wide range of situations. There is no standardised or formalised method for extracting and subsetting data from the database for these purposes. Consequently, results in such meta-analyses are difficult to reproduce. The present study introduces the software package ECOTOXr, which provides the means to formalise data retrieval from the ECOTOX database in the R scripting language. Three cases are presented to evaluate the performance of the package in relation to earlier data extractions and searches on the website. These cases demonstrate that the package can reproduce data sets relatively well. Furthermore, they illustrate how future studies can further improve traceability and reproducibility by applying the package and adhering to some simple guidelines. This contributes to the FAIR principles, credibility and acceptance of research that uses data from the ECOTOX database.

Identification of Marine-Derived SLC7A11 Inhibitors: Molecular Docking, Structure-Based Virtual Screening, Cytotoxicity Prediction, and Molecular Dynamics Simulation.

The search for anticancer drugs that target ferroptosis is a promising avenue of research. SLC7A11, a key protein involved in ferroptosis, has been identified as a potential target for drug development. Through screening efforts, novel inhibitors of SLC7A11 have been designed with the aim of promoting ferroptosis and ultimately eliminating cancer cells. We initially screened 563 small molecules using pharmacophore and 2D-QSAR models. Molecular docking and ADMET toxicity predictions, with Erastin as a positive control, identified the small molecules 42711 and 27363 as lead compounds with strong inhibitory activity against SLC7A11. Further optimization resulted in the development of a new inhibitor structure (42711_11). Molecular docking and ADMET re-screening demonstrated successful fragment substitution for this small molecule. Final molecular dynamics simulations also confirmed its stable interaction with the protein. These findings represent a significant step towards the development of new therapeutic strategies for ferroptosis-related diseases.

Complete dehalogenation of chloramphenicol by bimetallic alloy Pd-Au nanoparticles in a H2-Based membrane Catalyst-Film reactor

An effective treatment method to achieve complete dehalogenation of halogenated antibiotics is an urgent affair, because partially dehalogenated products remain persistent pollutants and sometimes having higher toxicity than the original antibiotics. This study is the first to achieve complete reductive dechlorination of chloramphenicol (CAP) using bimetallic alloy Pd-Au nanoparticles (Pd-AuNPs) in a hydrogen (H2)-based membrane catalyst-film reactor (H2-MCfR). Compared to mono-Pd nanoparticles (PdNPs), bimetallic alloy Pd-AuNPs gave faster removal and more complete dechlorination of CAP in batch and continuous-flow experiments. Notably, complete CAP dechlorination was achieved with a catalyst film of Pd-AuNPs (mole ratio 1:1) that had negligible catalyst loss in a 20-day continuous operation. LC-MS identified the products of reductive dechlorination, and the dominant pathways differed significantly between PdNPs and Pd-AuNPs. In particular, Pd-AuNPs favored reductive dechlorination, while PdNPs favored reduction of the –NO2 group, which led to partially dechlorinated products that were stable. Density functional theory (DFT) calculations showed how the Au atoms reduced the adsorption energy of reactants, which affected reaction selectivity. Toxicity prediction model confirmed that reducing dechlorination significantly decreased acute toxicity to fish, daphnia and green algae. Thus, the MCfR with Pd-AuNP catalysts offers promise for achieving complete dechlorination of CAP and inspires further studies for other halogenated antibiotics.

Phytochemical composition, antioxidant activity, and cytotoxicity of the aqueous extracts of Dracaena arborea and Bridelia ferruginea: In vitro and in silico studies

Dracaena arborea (DA) and Bridelia ferruginea (BF) are medicinal plants used in the treatment of various ailments such as diabetes, infertility, and sexual dysfunctions. The aim of this work was to characterize these plants chemically and investigate their antioxidant and cytotoxicity in vitro and in silico. The cytotoxicity was determined in PC3, NIH3T3 and BT474 cells, and through hemolysis using erythrocytes. Furthermore, selected compounds from DA and BF were docked against some oxidative stress (Keap1: Kelch-like ECH-associated protein 1 and GST: glutathione S-transferase) and cytotoxicity (PARP10: poly(ADP-ribose) polymerase family member 10 and p90 RSK: p90 ribosomal S6 kinases) proteins to predict the ligand-protein interaction. We found that the total phenolic and flavonoid content in BF was significantly (p < 0.001) higher than that of DA. 16 and 32 phytocompounds were identified in DA and BF, respectively. The 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS+) free radical scavenging activity, and cupric and ferric reducing power of BF were significantly (p < 0.05–0.001) higher than those of DA. Moreover, the ability of BF to inhibit lipid peroxidation was higher than that of DA. DA dose-dependently decreased the viability of PC3, NIH3T3, and BT474 cells, while BF tended to feed the cells. Drug-Likeness and toxicity prediction of selected compounds found in DA and BF were within the acceptable standards, according to Lipinski's rule. The majority of compounds found in DA and BF exhibited a strong binding affinity with Keap1, GST, PARP10 and p90 RSK, which supports the in vitro results. For instance, Cis-cyclohexane-1,3-diamine from DA showed a binding affinity of -6.73 kcal/mol and interacted with Keap1 through five conventional hydrogen bonds, while Nrf2 (a Keap1 inhibitor) exhibited a binding affinity of -11.82 kcal/mol and interacted with Keap1 via two conventional hydrogen bonds. 1,3-propanediamine, n-methyl identified in BF exhibited a high stability with Keap1 (-5.95 kcal/mol) due to the presence of 6 conventional hydrogen bonds. Among the compounds identified in DA, the best stable ligand interacting with PARP10 was 2-deoxy-alpha-d-ribopyranose (-7.62 kcal/mol), as it was well-matched in the PARP10 pocket through five conventional hydrogen bonds. In parallel, among the compounds present in BF, 2-[(methylamino)methyl]cyclohexanol showed the best binding affinity with PARP10 (-9.80 kcal/mol), which was higher than that of veliparib (-8.26 kcal/mol), used as a reference inhibitor. In conclusion, BF has the possibility to be exploited in the prevention/treatment of oxidative stress-related diseases, while DA could be a potential anticancer agent. This could justify their ethnomedicinal uses.

MOLECULAR DOCKING STUDIES OF FLAVONOIDS FROM SECANG WOOD (Caesalpinia sappan L.) AGAINST GLUCOKINASE ENZYME AS ANTIDIABETIC CANDIDATES

Diabetes mellitus (DM) is a metabolic disease caused by a deficiency of insulin secretion, insulin resistance, and increased hepatic glucose production. Secang wood (Caesalpinia sappan L.) is known to have antihyperglycemic activity. However, these compounds are not yet known. In silico studies are needed to determine the compounds that act as antidiabetics. This study performed molecular docking of flavonoid compounds in sappan wood against the 1V4S glucokinase receptor. The results showed that all flavonoid compounds of sappan wood were predicted to have antidiabetic activity because they had a lower docking score than metformin, the first-line therapy of type 2 diabetes mellitus. Butein is expected to have the best activity. It has the lowest docking score (-94.4836). Visualization of the docking results shows that butein interacts with the identical amino acid residues as metformin, namely ARG 63 and THR 65, through the formation of hydrogen bonds and Van der Waals interactions. SWISS-ADME web tool predicted that butein has good oral absorption and excretion. The toxicity prediction tool showed a slight contradiction in the mutagenic effect. Based on this research, molecular docking may be able to design new drugs, especially from butein in sappan wood (Caesalpinia sappan L.), as antidiabetic candidates.

Polychloride and benzene ring synergistically boosting photocatalytic degradation and nitrogen fixation performances of carbon nitride

The limited active site exposure, insufficient light absorption and poor charge behavior of carbon nitride (CN) limit its application in environmental remediation and clean energy production. In this study, ultrathin porous CN with enhanced photocatalytic activity was constructed by doping 2,2′,5,5′-Tetrachlorobenzidine (TB) into CN framework. The synergistic effect of polychloride and benzene rings on CN leads to the significant enhancement of range/intensity of visible-light harvest and charge behavior, as well as specific surface area. The optimal sample achieves 100% tetracycline photodegradation efficiency with a rate constant approximately 9 times of pristine CN, and total organic carbon removal efficiency reaches 78.2%. Moreover, photocatalytic ammonia production rate is 7 times of pristine CN. Through active species trapping experiments, ESR, LC-MS, toxicity prediction and DFT calculations, the main active species, degradation pathway, intermediates toxicity and enhanced mechanism of photocatalytic activity are investigated. This work provides a reference for simultaneously regulating the morphology, light absorption and charge behavior of CN-based photocatalyst for addressing environmental and energy concerns.

Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system

Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution system (DWDS) remains challenging. Predicting DBPs using readily available water quality parameters can help to understand DBPs associated risks and capture the complex interrelationships between water quality and DBP occurrence. In this study, we collected drinking water samples from a distribution network throughout a year and measured the related water quality parameters (WQPs) and haloacetic acids (HAAs). 12 machine learning (ML) algorithms were evaluated. Random Forest (RF) achieved the best performance (i.e., R2 of 0.78 and RMSE of 7.74) for predicting HAAs concentration. Instead of using cytotoxicity or genotoxicity separately as the surrogate for evaluating toxicity associated with HAAs, we created a health risk index (HRI) that was calculated as the sum of cytotoxicity and genotoxicity of HAAs following the widely used Tic-Tox approach. Similarly, ML models were developed to predict the HRI, and RF model was found to perform the best, obtaining R2 of 0.69 and RMSE of 0.38. To further explore advanced ML approaches, we developed 3 models using uncertainty-based active learning. Our findings revealed that Categorical Boosting Regression (CAT) model developed through active learning substantially outperformed other models, achieving R2 of 0.87 and 0.82 for predicting concentration and the HRI, respectively. Feature importance analysis with the CAT model revealed that temperature, ions (e.g., chloride and nitrate), and DOC concentration in the distribution network had a significant impact on the occurrence of HAAs. Meanwhile, chloride ion, pH, ORP, and free chlorine were found as the most important features for HRI prediction. This study demonstrates that ML has the potential in the prediction of HAA occurrence and toxicity. By identifying key WQPs impacting HAA occurrence and toxicity, this research offers valuable insights for targeted DBP mitigation strategies.

An Assessment of Polycyclic Aromatic Hydrocarbons Using Estimation Programs.

In the environment, the class of chemicals known as polycyclic aromatic hydrocarbons (PAHs) behave somewhat differently. This review covers situations where PAHs can be 'labile' and where they can be persistent. The in-silico prediction of toxicity and the properties of selected 29 PAHs were estimated using programs developed by the U.S. Environmental Protection Agency (EPA), such as the Estimation Programs Interface (E.P.I.) and the Toxicity Estimation Software Tool (version 5.1.2) (TEST), with online software such as SwissADME and SwissDock. TEST was used to estimate the LC50 of the fathead minnow (with a range of 14.53 mg/L for 1-indanone and 2.14 × 10-2 mg/L for cyclopenta[c,d]pyrene), the LC50 of Daphnia magna (with a range of 14.95 mg/L for 1-indanone and 7.53 × 10-2 mg/L for coronene), the IGC50 of Tetrahymena pyriformis (with a range of 66.14 mg/L for 1-indanone and 0.36 mg/L for coronene), the bioconcentration factor (8.36 for 1,2-acenaphthylenedione and 910.1 for coronene), the developmental toxicity (0.30 (-) for 1,2-acenaphthylenedione and 0.82 (+) for 4-hydroxy-9-fluorenone), and the mutagenicity (0.25 (-) for 2-methyl-9-fluorenone and 1.09 (+) for coronene). The carbon chain and molecular weight have a significant effect on the properties of PAHs. Overall, it was found that PAHs with a lower molecular weight (LMW) have a higher water solubility and LC50 value and a smaller LogKow value, whereas the opposite is true for heavier PAHs, with TEST predicting that PAHs with an MW of over 168.2 g/mol, with a few exceptions, are mutagenic. Hence, LMW PAHs have a higher potential to be in the environment but are less toxic.

Identification and simultaneous quantification of potential genotoxic impurities in first-line HIV drug dolutegravir sodium using fast ultrasonication-assisted extraction method coupled with GC–MS and in-silico toxicity assessment

Dolutegravir (DLG) has become a distinctive first-line antiretroviral therapy for the treatment of HIV in most countries due to its affordability, high efficacy, and low drug-drug interactions. However, the evaluation of genotoxic impurities (GTIs) in DLG and their toxicity assessment has not been explored thoroughly. Thus, in this study, a simple, fast, and selective analytical methodology was developed for the identification and determination of 7 GTIs in the comprehensive, explicit route of synthesis for the dolutegravir sodium (DLG-Na) drug. A facile, fast ultrasonication-assisted liquid–liquid extraction procedure was adapted to isolate the GTIs in DLG-Na and then analyzed using the gas chromatography (GC)-electron impact (EI)/mass spectrometer (MS) quantification (using selective ion monitoring mode) technique. This EI-GC/MS method was validated as per the current requirements of ICH Q2 (R1) guidelines. Under optimal method conditions, excellent linearities were achieved with R between 0.9959 and 0.9995, and high sensitivity was obtained in terms of detection limits (LOD) between 0.15 to 0.63 µg/g, and quantification limits (LOQ) between 0.45 to 1.66 µg/g for the seven GTIs in DLG. The obtained recoveries ranged from 98.2 to 104.3 % at LOQ, 15 µg/g, and 18 µg/g concentration levels (maximum daily dose of 100 mg). This developed and validated method is rapid, easy to adopt, specific, sensitive, and accurate in estimating the seven GTIs in a relatively complex sodium matrix of the DLG-Na drug moiety. As a method application, two different manufactured samples of DLG-Na drug substances were analyzed for the fate of the GTIs and drug safety for the intended dosage applications. Moreover, an in-silico QSAR toxicity prediction assessment was carried out to prove scientifically the potential GTI nature of each impurity from the alerting functional groups.