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This research presents the development and validation of chromatographic method for analyzing sorafenib and its pharmacopeial impurities, with a focus on stability studies and degradation product (DP) characterization. Initial method optimization involved exploring various column and buffer combinations, ultimately achieving optimal separation and peak symmetry using an ODS‐AQ YMC (150 mm) column with 0.6 mL/min gradient flow of 10 mM ammonium formate buffer adjusted to pH 3.4 with formic acid as solvent A, and ethanol as solvent B as mobile phase and 246 nm wavelength. Method exhibits calibration curve linear in 50–300 µg/mL for sorafenib and 0.050–0.30 µg/mL for impurities with a detection limit of 0.015 µg/mL for impurities. A structural elucidation of DPs was performed using LC–MS/MS, providing valuable insights into their molecular compositions, and was characterized as 4‐[4‐(carboxyamino)phenoxy]pyridine‐2‐carboxylic acid (DP 1) and 4‐(4‐aminophenoxy)pyridine‐2‐carboxamide (DP 2). Using AGREE and GAPI metrics, evaluation highlighted method sustainability through ethanol–water solvents and shorter column to reduce energy consumption. Toxicity assessments revealed differences in environmental impact and toxicological profiles of DPs, emphasizing importance of managing safety considerations for sorafenib and its DPs. This research offers novel insights into sorafenib analysis by addressing pharmacopeial impurities, characterizing DPs, and evaluating method sustainability and safety. [ABSTRACT FROM AUTHOR]

This research delves into the exploration of the potential of tocopherol‐based nanoemulsion as a therapeutic agent for cardiovascular diseases (CVD) through an in‐depth molecular docking analysis. The study focuses on elucidating the molecular interactions between tocopherol and seven key proteins (1O8a, 4YAY, 4DLI, 1HW9, 2YCW, 1BO9 and 1CX2) that play pivotal roles in CVD development. Through rigorous in silico docking investigations, assessment was conducted on the binding affinities, inhibitory potentials and interaction patterns of tocopherol with these target proteins. The findings revealed significant interactions, particularly with 4YAY, displaying a robust binding energy of −6.39 kcal/mol and a promising Ki value of 20.84 μM. Notable interactions were also observed with 1HW9, 4DLI, 2YCW and 1CX2, further indicating tocopherol's potential therapeutic relevance. In contrast, no interaction was observed with 1BO9. Furthermore, an examination of the common residues of 4YAY bound to tocopherol was carried out, highlighting key intermolecular hydrophobic bonds that contribute to the interaction's stability. Tocopherol complies with pharmacokinetics (Lipinski's and Veber's) rules for oral bioavailability and proves safety non‐toxic and non‐carcinogenic. Thus, deep learning‐based protein language models ESM1‐b and ProtT5 were leveraged for input encodings to predict interaction sites between the 4YAY protein and tocopherol. Hence, highly accurate predictions of these critical protein–ligand interactions were achieved. This study not only advances the understanding of these interactions but also highlights deep learning's immense potential in molecular biology and drug discovery. It underscores tocopherol's promise as a cardiovascular disease management candidate, shedding light on its molecular interactions and compatibility with biomolecule‐like characteristics. [ABSTRACT FROM AUTHOR]

Tetramethrin (TMT) is a commonly used insecticide and has a carcinogenic and neurodegenerative effect on humans. The binding mechanism and toxicological implications of TMT to human serum albumin (HSA) were examined in this study employing a combination of biophysical and computational methods indicating moderate binding affinity and potential hepato and renal toxicity. Fluorescence quenching experiments showed that TMT binds to HSA with a moderate affinity, and the binding process was spontaneous and predominantly enthalpy‐driven. Circular dichroism spectroscopy revealed that TMT binding did not induce any significant conformational changes in HSA, resulting in no changes in its alpha‐helix content. The binding site and modalities of TMT interactions with HSA as computed by molecular docking and molecular dynamics simulations revealed that it binds to Sudlow site II of HSA via hydrophobic interactions through its dimethylcyclopropane carboxylate methyl propanyl group. The structural dynamics of TMT induce proper fit into the binding site creating increased and stabilizing interactions. Additionally, molecular mechanics–Poisson Boltzmann surface area calculations also indicated that non‐polar and van der Waals were found to be the major contributors to the high binding free energy of the complex. Quantum mechanics (QM) revealed the conformational energies of the binding confirmation and the degree of deviation from the global minimum energy conformation of TMT. The results of this study provide a comprehensive understanding of the binding mechanism of TMT with HSA, which is important for evaluating the toxicity of this insecticide in humans. [ABSTRACT FROM AUTHOR]

The toxicity of compounds is closely related to the effectiveness and safety of drug development, and accurately predicting the toxicity of compounds is one of the most challenging tasks in medicinal chemistry and pharmacology. In this paper, we construct three types of models for single and multi‐tasking based on 2D and 3D descriptors, fingerprints and molecular graphs, and then validate the models with benchmark tests on the Tox21 data challenge. We found that due to the information sharing mechanism of multi‐task learning, it could address the imbalance problem of the Tox21 data sets to some extent, and the prediction performance of the multi‐task was significantly improved compared with the single task in general. Given the complement of the different molecular representations and modeling algorithms, we attempted to integrate them into a robust Co‐Model. Our Co‐Model performs well in various evaluation metrics on the test set and also achieves significant performance improvement compared to other models in the literature, which clearly demonstrates its superior predictive power and robustness. [ABSTRACT FROM AUTHOR]

Background: Accurate prediction of radiation toxicity of healthy organs‐at‐risks (OARs) critically determines the radiation therapy (RT) success. The existing dose–volume histogram‐based metric may grossly under/overestimate the therapeutic toxicity after 27% in liver RT and 50% in head‐and‐neck RT. We propose the novel paradigm for toxicity prediction by leveraging the enormous potential of deep learning and go beyond the existing dose/volume histograms. Experimental Design: We employed a database of 125 liver stereotactic body RT (SBRT) cases with follow‐up data to train deep learning‐based toxicity predictor. Convolutional neural networks (CNNs) were applied to discover the consistent patterns in 3D dose plans associated with toxicities. To enhance the predicting power, we first pretrain the CNNs with transfer learning from 3D CT images of 2644 human organs. CNNs were then trained on liver SBRT cases. Furthermore, nondosimetric pretreatment features, such as patients' demographics, underlying liver diseases, liver‐directed therapies, were inputted into the fully connected neural network for more comprehensive prediction. The saliency maps of CNNs were used to estimate the toxicity risks associated with irradiation of anatomical regions of specific OARs. In addition, we applied machine learning solutions to map numerical pretreatment features with hepatobiliary toxicity manifestation. Results: Among 125 liver SBRT patients, 58 were treated for liver metastases, 36 for hepatocellular carcinoma, 27 for cholangiocarcinoma, and 4 for other histologies. We observed that CNN we able to achieve accurate hepatobiliary toxicity prediction with the AUC of 0.79, whereas combining CNN for 3D dose plan analysis and fully connected neural networks for numerical feature analysis resulted in AUC of 0.85. Deep learning produces almost two times fewer false‐positive toxicity predictions in comparison to DVH‐based predictions, when the number of false negatives, i.e., missed toxicities, was minimized. The CNN saliency maps automatically estimated the toxicity risks for portal vein (PV) regions. We discovered that irradiation of the proximal portal vein is associated with two times higher toxicity risks (risk score: 0.66) that irradiation of the left portal vein (risk score: 0.31). Conclusions: The framework offers clinically accurate tools for hepatobiliary toxicity prediction and automatic identification of anatomical regions that are critical to spare during SBRT. [ABSTRACT FROM AUTHOR]

Photodegradation of 3 commonly used nonsteroidal anti-inflammatory drugs, ketoprofen, carprofen, and diclofenac acid, was conducted under ultraviolet (UV) irradiation. The kinetic results showed that the 3 pharmaceuticals obeyed the first-order reaction with decreasing rate constants of 1.54 × 10−4, 5.91 × 10−5, and 7.78 × 10−6 s−1 for carprofen, ketoprofen, and diclofenac acid, respectively. Moreover, the main transformation products were identified by ion-pair liquid-liquid extraction combined with injection port derivatization-gas chromatography-mass spectrometry and high-performance liquid chromatography-quadrupole-time of flight mass spectrometric analysis. There were 8, 3, and 6 transformation products identified for ketoprofen, carprofen, and diclofenac acid, respectively. Decarboxylation, dechlorination, oxidation, demethylation, esterification, and cyclization were proposed to be associated with the transformation of the 3 pharmaceuticals. Toxicity prediction of the transformation products was conducted on the EPI Suite software based on ECOSAR model, and the results indicate that some of the transformation products were more toxic than the parent compounds. The present study provides the foundation to understand the transformation behavior of the studied pharmaceuticals under UV irradiation. Environ Toxicol Chem 2017;36:3232-3239. © 2017 SETAC [ABSTRACT FROM AUTHOR]

Rapid safety assessment is more and more needed for the increasing chemicals both in chemical industries and regulators around the world. The traditional experimental methods couldn't meet the current demand any more. With the development of the information technology and the growth of experimental data, in silico modeling has become a practical and rapid alternative for the assessment of chemical properties, especially for the toxicity prediction of organic chemicals. In this study, a quantitative regression workflow was built by KNIME to predict chemical properties. With this regression workflow, quantitative values of chemical properties can be obtained, which is different from the binary-classification model or multi-classification models that can only give qualitative results. To illustrate the usage of the workflow, two predictive models were constructed based on datasets of Tetrahymena pyriformis toxicity and Aqueous solubility. The qcv2 and qtest2 of 5-fold cross validation and external validation for both types of models were greater than 0.7, which implies that our models are robust and reliable, and the workflow is very convenient and efficient in prediction of various chemical properties. [ABSTRACT FROM AUTHOR]

Early prediction of safety issues in drug development is at the same time highly desirable and highly challenging. Recent advances emphasize the importance of understanding the whole chain of causal events leading to observable toxic outcomes. Here we describe an integrative modeling strategy based on these ideas that guided the design of eTOXsys, the prediction system used by the eTOX project. Essentially, eTOXsys consists of a central server that marshals requests to a collection of independent prediction models and offers a single user interface to the whole system. Every of such model lives in a self-contained virtual machine easy to maintain and install. All models produce toxicity-relevant predictions on their own but the results of some can be further integrated and upgrade its scale, yielding in vivo toxicity predictions. Technical aspects related with model implementation, maintenance and documentation are also discussed here. Finally, the kind of models currently implemented in eTOXsys is illustrated presenting three example models making use of diverse methodology (3D-QSAR and decision trees, Molecular Dynamics simulations and Linear Interaction Energy theory, and fingerprint-based QSAR). [ABSTRACT FROM AUTHOR]

Multitiered frameworks that are designed for risk assessment of contaminated sediment rely on sediment quality guidelines (SQGs) at the first tier or screening level. In the case of contamination by multiple pollutants, results can be aggregated under indices such as the mean quotient. A decision is then reached (e.g., to dispose of dredged materials in open water) without further investigation, provided that the SQGs or the specific values of indices or quotients derived from the SQGs are not exceeded. In this way, SQGs and quotients play a critical role in environmental protection. As part of the development of a tiered framework to assess the environmental risk of materials dredged from the St. Lawrence River, we evaluated various quotients based on SQGs available for this river with a data set that matches chemistry and toxicity test endpoints. The overall efficiency of all tested quotients was rather low, and we then examined factors such as sediment grain size, nutrients, metal-binding phases (e.g., Al, Fe), and dissolved organic carbon to explain misclassified samples. This examination led to the design of a modified tier 1 framework in which SQGs are used in combination with decision rules based on certain explanatory factors. Integr Environ Assess Manag 2010;6:225-239. © 2009 SETAC [ABSTRACT FROM AUTHOR]

Aim: Two 21-day gemcitabine–carboplatin schedules were evaluated in patients with advanced non-small cell lung cancer in order to assess the effect of timing of the carboplatin dose on toxicity and efficacy. Methods: Patients were randomized to gemcitabine (1000 mg/m2 on days 1 and 8 of a 21-day cycle) and carboplatin (AUC 5, on day 1) (Carbo d1 arm) or the same gemcitabine schedule with carboplatin given on day 8 (Carbo d8 arm). Twenty patients with Stage IIIB or IV non-small-cell lung cancer were enrolled in each arm. Results: The achieved dose intensities of both gemcitabine and carboplatin were significantly higher in the Carbo d1 arm. The total rates of grade 3 or 4 hematological and non-hematological toxicities (any toxicity, any cycle) were 80% and 65%, respectively, with no significant differences between the two arms. Nine patients in the Carbo d1 arm, but only one patient in the Carbo d8 arm, required a platelet transfusion. There were 10 partial responses (four Carbo d1 arm, six Carbo d8 arm), giving an overall response rate of 25% (95% CI 13–41%). Conclusion: Administration of carboplatin on day 8 of this regimen confers no clear advantage compared with day 1 carboplatin, with similar toxicity but lower dose intensity. A formula for the prediction of thrombocytopenia is proposed. [ABSTRACT FROM AUTHOR]

Derek for Windows (DfW) is a knowledge-based expert system that predicts the toxicity of a chemical from its structure. Its predictions are based in part on alerts that describe structural features or toxicophores associated with toxicity. Recently, improvements have been made to skin sensitization alerts within the DfW knowledge base in collaboration with Unilever. These include modifications to the alerts describing the skin sensitization potential of aldehydes, 1,2-diketones, and isothiazolinones and consist of enhancements to the toxicophore definition, the mechanistic classification, and the extent of supporting evidence provided. The outcomes from this collaboration demonstrate the importance of updating and refining computer models for the prediction of skin sensitization as new information from experimental and theoretical studies becomes available. [ABSTRACT FROM AUTHOR]