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Histone deacetylase 8 (HDAC8) is a member of class I histone deacetylases (HDACs) that catalyzes the deacetylation of both histone and non-histone proteins. Dysregulation and overexpression of HDAC8 are implicated in the development of various complex diseases, including cancer and neurodegenerative disorders. HDAC8 plays a significant role in cancer progression, contributing to cancer cell proliferation, metastasis, immune evasion, and drug resistance. The available HDAC8-targeting inhibitors suffer from poor target engagement and low tolerability, and demonstrate off-target toxicity due to limited selectivity, leading to adverse effects in patients, and thus urging for the identification and development of new molecules. Drug repurposing is a useful strategy for identifying useful drugs for predefined targets which can be exploited here for identifying promising drug molecules against HDAC8. This study involved an integrated virtual screening against HDAC8 using the DrugBank database to identify repurposed drugs capable of inhibiting HDAC8 activity. The process started by selecting the top 10 drug molecules based on their binding affinity. The drug profiling and biological function of selected molecules were then evaluated, showing anti-cancer and anti-neurological properties with a high probability of being active. Interaction analysis revealed crucial binding of radotinib and sertindole molecules with the HDAC8 protein. Both molecules showed higher binding affinity than reference inhibitor droxinostat. The elucidated molecules were further evaluated for 500 ns long-run molecular dynamics (MD) simulation with HDAC8. Structural deviation, compactness, folding behavior, hydrogen bonds analysis, and secondary structure content profiling revealed complex stability formed by HDAC8 and the selected compounds. Principal component analysis and Gibbs free energy calculations strongly recommend that both complexes were highly stable during the simulation. Overall, the results indicate that radotinib and sertindole can be promising candidates as HDAC8-targeting repurposed drugs against cancer and neuropathological conditions.

Human transferrin (htf) plays a crucial role in regulating the balance of iron within brain cells; any disruption directly contributes to the development of Neurodegenerative Diseases (NDs) and other related pathologies, especially Alzheimer’s Disease (AD). In recent times, a transition towards natural compounds is evident to treat diseases and this shift is mainly attributed to their broad therapeutic potential along with minimal side effects. Capsaicin, a natural compound abundantly found in red and chili peppers, possess neuroprotective potential. The current work targets to decipher the interaction mechanism of capsaicin with htf using experimental and computational approaches. Molecular docking analysis revealed that capsaicin occupies the iron binding pocket of htf, with good binding affinity. Further, the binding mechanism was investigated atomistically using Molecular dynamic (MD) simulation approach. The results revealed no significant alterations in the structure of htf implying the stability of the complex. In silico observations were validated by fluorescence binding assay. Capsaicin binds to htf with a binding constant (K) of 3.99 × 106 M−1, implying the stability of the htf-capsaicin complex. This study lays a platform for potential applications of capsaicin in treatment of NDs in terms of iron homeostasis.

Industrial application of novel, green and effective liquid absorbents for improving the separation yield of CO2 greenhouse pollutant is an important milestone to reduce its emission to the atmosphere. In this research, the separation performance of three novel and green ionic liquids (ILs) including [bmim][PF6], [bmim][BF4] and [emim][etSO4] are compared with pure water to evaluate the feasibility of their employment to enhance the removal potency of CO2 through the membrane contactor. With the aim of reaching this goal, a finite element-based model and a numerical simulation are developed to solve the momentum/mass transfer equations and predict the results. Based on the modeling/simulation outcomes, the addition of the abovementioned ILs could substantially increase the separation of CO2 molecules compared to pure water. Based on the results, [bmim][BF4] is introduced as the most efficient IL with the separation efficiency of 100 % (100 % [bmim][BF4]> 99 % [bmim][PF6]> 97.5 % [emim][etSO4]> 55 % pure water). Furthermore, the effects of different operational/functional parameters (i.e., gas flow rate, porosity, fibers’ number and packing density) on the CO2 separation efficacy is comprehensively discussed.

Abstract Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120–400 bar, 308–338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E−06, 1.95E−06, and 3.45E−06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E−06, 2.34E−06, and 5.26E−06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e−05).

Abstract Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308–338 K, and pressure 120–400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E−04, 7.40E−05, and 9.73E−06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E−01, DTR shows an error rate equal to 1.63E−01, and GPR as the best mode illustrates 5.06E−02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E−03).

Abstract These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO2). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO2 through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E−09, 9.71E−09, and 3.78E−11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.

Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

Metformin, as an anti-hyperglycemic, is the main medicine for treating diabetes of type II. However, its daily dose is high due to its incomplete absorption and short half-life, which leads to digestive problems and patient complaints. Controlled release and increased bioavailability of this drug can be achieved using novel drug delivery systems produced with an appropriate supercritical carbon dioxide (scCO2) method. However, the solubility of Metformin in scCO2 is an essential parameter for this purpose which needs to be evaluated prior to the processing. In the current research, solubility of Metformin in scCO2 was determined at temperatures of 308, 318, 328, and 338 K, and pressures of 140, 170, 200, 230, 260, and 290 bar. It was obtained between 0.39 × 10−6 up to 1.23 × 10−6 (mole fraction). Also, the resulting values were correlated through an equation of state (Peng-Robinson (PR-EoS)) and eight well-known empirical correlations (Chrastil, Sung and Shim, Adachi and Lu, Si-Moussa, Bartle, Jafari Nejad, Keshmiri, and Jouyban). The average AARD% value of 8.24 for the PR-EoS model confirmed its satisfactory precision to predict the Metformin solubility in scCO2. Among the mentioned empirical models, the models defined by Jafari Nejad (AARD% of 4.36 and AICc of −997.07) and Keshmiri (AARD% of 4.73 and AICc of −1048.81) show the highest correlation accuracy. Moreover, the total mixing heat, the vaporization enthalpy, and the solvation enthalpies of the Metformin-scCO2 mixture were computed.

Several medicinal plants have the potential to be a promising alternative pharmacological therapy for a variety of human illnesses. Many insects, including mosquitoes, are important vectors of deadly pathogens and parasites, which in the world’s growing human and animal populations can cause serious epidemics and pandemics. Medicinal plants continue to provide a large library of phytochemicals, which can be used to replace chemically synthesized insecticides, and utilization of herbal product-based insecticides is one of the best and safest alternatives for mosquito control. Identifying new effective phyto-derived insecticides is important to counter increasing insect resistance to synthetic compounds and provide a safer environment. Solanum genus (Solanaceae family or nightshades) comprises more than 2500 species, which are widely used as food and traditional medicine. All research publications on insecticidal properties of Solanaceae plants and their phytoconstituents against mosquitoes and other insects published up to July 2020 were systematically analyzed through PubMed/MEDLINE, Scopus, EBSCO, Europe PMC, and Google Scholar databases, with focus on species containing active phytoconstituents that are biodegradable and environmentally safe. The current state of knowledge on larvicidal plants of Solanum species, type of extracts, target insect species, type of effects, name of inhibiting bioactive compounds, and their lethal doses (LC50 and LC90) were reviewed in this study. These studies provide valuable information about the activity of various species of Solanum and their phytochemical diversity, as well as a roadmap for optimizing select compounds for botanical repellents against a variety of vectors that cause debilitating and life-threatening human diseases.

The efficient production of solid-dosage oral formulations using eco-friendly supercritical solvents is known as a breakthrough technology towards developing cost-effective therapeutic drugs. Drug solubility is a significant parameter which must be measured before designing the process. Decitabine belongs to the antimetabolite class of chemotherapy agents applied for the treatment of patients with myelodysplastic syndrome (MDS). In recent years, the prediction of drug solubility by applying mathematical models through artificial intelligence (AI) has become known as an interesting topic due to the high cost of experimental investigations. The purpose of this study is to develop various machine-learning-based models to estimate the optimum solubility of the anti-cancer drug decitabine, to evaluate the effects of pressure and temperature on it. To make models on a small dataset in this research, we used three ensemble methods, Random Forest (RFR), Extra Tree (ETR), and Gradient Boosted Regression Trees (GBRT). Different configurations were tested, and optimal hyper-parameters were found. Then, the final models were assessed using standard metrics. RFR, ETR, and GBRT had R2 scores of 0.925, 0.999, and 0.999, respectively. Furthermore, the MAPE metric error rates were 1.423 × 10−1 7.573 × 10−2, and 7.119 × 10−2, respectively. According to these facts, GBRT was considered as the primary model in this paper. Using this method, the optimal amounts are calculated as: P = 380.88 bar, T = 333.01 K, Y = 0.001073.

Industrial-based application of supercritical CO2 (SCCO2) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C15H18O3) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO2, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10−4, 7.814 × 10−5, and 1.664 × 10−4 for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10−4, 6.197 × 10−5, and 8.777 × 10−5errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x1 = 40.0, x2 = 338.0, Y = 1.27 × 10−3) vector.

Kleinia pendula (Forssk.) DC. is a prostrate or pendent dark green succulent herb found in the southwestern mountain regions of Saudi Arabia. The literature survey of the plant reveals a lack of phytochemical and pharmacological studies, although traditional uses have been noted. The objective of the present work was to assess the in vivo analgesic and anti-inflammatory activities, as well as, the in vitro cytotoxic potential of the fractions of Kleinia pendula, and correlate these activities to the plant metabolites. The methanolic extract of Kleinia pendula was subjected to fractionation with n-hexane, ethyl acetate, chloroform, n-butanol, and water. The fractions were screened for their analgesic and anti-inflammatory activities, as well as cytotoxic activity against breast, liver, and colon cancer cell lines. The n-hexane and chloroform fractions of Kleinia pendula showed significant cytotoxic activity against all three cancer cell lines tested. The ethyl acetate and chloroform fractions showed significant analgesic and anti-inflammatory activities. The metabolites in these three active fractions were determined using UPLC-PDA-ESI-MS. Thus, the analgesic and anti-inflammatory activities of the plant were attributed to its phenolic acids (caffeoylquinic acid derivatives, protocatechuic, and chlorogenic acids). While fatty acids and triterpenoids such as (tormentic acid) in the hexane fraction are responsible for the cytotoxic activity; thus, these fractions of Kleinia pendula may be a novel source for the development of new plant-based analgesic, anti-inflammatory, and anticancer drugs.

A boy in middle childhood presented with complaints of swelling and pain in his right forearm for 4months. He had previously undergone left above-elbow amputation due to a suspected malignant tumour in that arm. Radiological imaging revealed a locally aggressive bone tumour of the right distal radius. Two biopsies failed to provide a diagnosis, and in the meantime the lesion became fungating. Consequently, it was decided to perform wide resection together with wrist arthrodesis. Postoperatively, on day 4, the patient developed wound dehiscence and impending compartment syndrome. During revision surgery, a significant quantity of blood clots was removed, without any active bleeders. Due to the uncertain diagnosis, thromboelastography (TEG) analysis was done, which showed a fibrinolytic pattern, and the patient’s factor IX level was found to be 4% of the normal value. As a result, a diagnosis of haemophilia B was established. The patient was started on tranexamic acid and factor IX replacement therapy. [ABSTRACT FROM AUTHOR]

This study investigates the prediction of the thermophysical properties of butyl stearate in solutions with citric acid, urea, and nicotinamide using Artificial Neural Networks (ANNs). The ANN model uses hydrotropic concentration and temperature to predict these properties. The study focuses on binary mixtures at various temperatures (303, 313, 323, and 333 K). To achieve accurate predictions, researchers trained a committee of ANNs using experimental data. This iterative process optimizes the network architecture and avoids overfitting, a common problem in machine learning. The trained ANN can then predict thermophysical properties for intermediate hydrotropic concentrations without additional experiments. Besides, the study demonstrates the versatility of ANNs by implementing a successful model for multi-pass turning operations in MATLAB, showing superior accuracy compared to other methods. Visualizations like magnitude response curves, FFT spectrums, contour plots, and 3D surface plots helped to identify the optimal hydrotropic concentration with a remarkable 2% margin of error. [ABSTRACT FROM AUTHOR]

Byline: Manish. Khattar, Prabhjot. Singh, Brusabhanu. Nayak, Shah. Khan, Venkatesan. Kumar Inflammatory well-differentiated liposarcoma is a rare soft-tissue tumor which is predominantly retroperitoneal in origin. We report a 72-year-old male [...]