Mesoporous Manganese Dioxide Coated Gold Nanorods as a Multiresponsive Nanoplatform for Drug Delivery

Nanomaterials can offer a chance to integrate many excellent physical and chemical performances into a single carrier for smart responsive drug delivery. Herein, gold nanorods/mesoporous manganese dioxide (Au/MnO2) hybrid nanoparticles were prepared to combine the photothermal effect of gold nanorods (AuNRs) with glutathione (GSH)-responsive and pH-responsive performances of MnO2. The near-infrared (NIR) responsive constituent of a Au/MnO2 nanoparticle was AuNRs. Doxorubicin hydrochloride (DOX), a widely used anticancer drug, was loaded into the Au/MnO2 hybrid nanoparticle via electrostatic force, hydrogen bonding, and physical absorption with a drug loading up to 99.1%. The results revealed that the mesoporous MnO2 was degraded in the media with high concentrations of GSH and acid microenvironment. The Au/MnO2 nanoparticles displayed satisfying drug release kinetics (ca. 47% of loaded drug released in 12 h) and showed excellent GSH-responsive, pH-responsive, and NIR-responsive performances. This multiresponsive nanoplatform is expected to have wide biomedical application for cancer therapy such as photothermal therapy, drug delivery, and tumor microenvironment improvement

Thermodynamic Mechanism of Physical Stability of Amorphous Pharmaceutical Formulations

Hygroscopicity is an important factor affecting the physical stability of amorphous solid dispersions (ASDs) during long-term storage. In this work, the effects of temperature, relative humidity (RH), and polymeric excipients on the phase behavior of amorphous irbesartan (IRB) and oxaprozin (OXA) were systematically investigated. The ASDs were prepared by the solvent evaporation method. The water sorption in formulations was measured under the conditions of 25 °C, 60% RH, 25 °C, 90% RH, and 40 °C, 75% RH. The results showed that the hygroscopicity of formulations containing polyvinylpyrrolidone (PVP) was stronger than that containing poly(vinylpyrrolidone-co-vinyl acetate) (PVPVA 46) and the hygroscopicity increased with increasing temperature and RH. With increasing content of active pharmaceutical ingredients (APIs), the recrystallization phenomenon is significant at the same RH. PVP can better inhibit IRB recrystallization than PVPVA 46. In contrast, PVPVA 46 can better maintain the amorphous form of OXA than PVP. The thermodynamic phase diagrams of amorphous API/polymer formulations at 60% RH and anhydrous conditions were predicted using perturbed chain statistical associated fluid theory (PC-SAFT) and the Gordon–Taylor equation. Furthermore, the electrostatic potential (ESP) and binding energies between APIs and polymers were calculated with density functional theory (DFT), which explained the molecular interaction mechanism of ASDs. This work is expected to guide the screening of excipients and API loadings for designing ASDs and the selection of storage conditions

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

With the environmental protection requirements brought about by the large-scale application of polymers in industrial fields, understanding the viscosities of polymers is becoming increasingly important. The different arrangements and crystallinity of the polymers make their viscosities difficult to calculate. To address this challenge, new strategies based on artificial intelligence algorithms are proposed. First, the strategy trains three artificial intelligence algorithms [extreme gradient boosting (XGBoost), convolutional neural network (CNN), and multilayer perceptron (MLP)] based on molecular descriptors of the polymer molecular properties. Next, the PC-SAFT parameters are input into the XGBoost and CNN algorithms as molecular descriptors representing the thermodynamic properties of the polymer to improve the accuracy of the algorithm prediction results. Subsequently, the Molecular ACCess Systems chemical fingerprinting was combined with the XGboost algorithm and CNN algorithm to further improve the accuracy of predicting viscosities. The XGboost algorithm was identified as the best predictive algorithm for predicting the viscosities of the polymer in different states. This discovery is expected to provide effective information for screening polymers for applications in medicine and the chemical industry

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

With the environmental protection requirements brought about by the large-scale application of polymers in industrial fields, understanding the viscosities of polymers is becoming increasingly important. The different arrangements and crystallinity of the polymers make their viscosities difficult to calculate. To address this challenge, new strategies based on artificial intelligence algorithms are proposed. First, the strategy trains three artificial intelligence algorithms [extreme gradient boosting (XGBoost), convolutional neural network (CNN), and multilayer perceptron (MLP)] based on molecular descriptors of the polymer molecular properties. Next, the PC-SAFT parameters are input into the XGBoost and CNN algorithms as molecular descriptors representing the thermodynamic properties of the polymer to improve the accuracy of the algorithm prediction results. Subsequently, the Molecular ACCess Systems chemical fingerprinting was combined with the XGboost algorithm and CNN algorithm to further improve the accuracy of predicting viscosities. The XGboost algorithm was identified as the best predictive algorithm for predicting the viscosities of the polymer in different states. This discovery is expected to provide effective information for screening polymers for applications in medicine and the chemical industry

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

With the environmental protection requirements brought about by the large-scale application of polymers in industrial fields, understanding the viscosities of polymers is becoming increasingly important. The different arrangements and crystallinity of the polymers make their viscosities difficult to calculate. To address this challenge, new strategies based on artificial intelligence algorithms are proposed. First, the strategy trains three artificial intelligence algorithms [extreme gradient boosting (XGBoost), convolutional neural network (CNN), and multilayer perceptron (MLP)] based on molecular descriptors of the polymer molecular properties. Next, the PC-SAFT parameters are input into the XGBoost and CNN algorithms as molecular descriptors representing the thermodynamic properties of the polymer to improve the accuracy of the algorithm prediction results. Subsequently, the Molecular ACCess Systems chemical fingerprinting was combined with the XGboost algorithm and CNN algorithm to further improve the accuracy of predicting viscosities. The XGboost algorithm was identified as the best predictive algorithm for predicting the viscosities of the polymer in different states. This discovery is expected to provide effective information for screening polymers for applications in medicine and the chemical industry

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

With the environmental protection requirements brought about by the large-scale application of polymers in industrial fields, understanding the viscosities of polymers is becoming increasingly important. The different arrangements and crystallinity of the polymers make their viscosities difficult to calculate. To address this challenge, new strategies based on artificial intelligence algorithms are proposed. First, the strategy trains three artificial intelligence algorithms [extreme gradient boosting (XGBoost), convolutional neural network (CNN), and multilayer perceptron (MLP)] based on molecular descriptors of the polymer molecular properties. Next, the PC-SAFT parameters are input into the XGBoost and CNN algorithms as molecular descriptors representing the thermodynamic properties of the polymer to improve the accuracy of the algorithm prediction results. Subsequently, the Molecular ACCess Systems chemical fingerprinting was combined with the XGboost algorithm and CNN algorithm to further improve the accuracy of predicting viscosities. The XGboost algorithm was identified as the best predictive algorithm for predicting the viscosities of the polymer in different states. This discovery is expected to provide effective information for screening polymers for applications in medicine and the chemical industry

Thermodynamic Phase Behavior of API/Polymer Solid Dispersions

To improve the bioavailability of poorly soluble active pharmaceutical ingredients (APIs), these materials are often integrated into a polymer matrix that acts as a carrier. The resulting mixture is called a solid dispersion. In this work, the phase behaviors of solid dispersions were investigated as a function of the API as well as of the type and molecular weight of the carrier polymer. Specifically, the solubility of artemisinin and indomethacin was measured in different poly­(ethylene glycol)­s (PEG 400, PEG 6000, and PEG 35000). The measured solubility data and the solubility of sulfonamides in poly­(vinylpyrrolidone) (PVP) K10 and PEG 35000 were modeled using the perturbed-chain statistical associating fluid theory (PC-SAFT). The results show that PC-SAFT predictions are in a good accordance with the experimental data, and PC-SAFT can be used to predict the whole phase diagram of an API/polymer solid dispersion as a function of the kind of API and polymer and of the polymer’s molecular weight. This remarkably simplifies the screening process for suitable API/polymer combinations

Solubility Prediction and Dissolution Mechanism Analysis of Etodolac in Complex Polymer Solutions Based on Thermodynamic and Interfacial Mass Transfer Models

In this work, the solubilities and dissolution profiles of etodolac were determined in water with four polymers (PEG 6000, PEG 20000, PVP K25, and HPMC E3) at 300.15 305.15, 310.15, 315.15, and 320.15 K. The results show that all four polymers used in this work have solubilization effects on etodolac, and the solubilities of etodolac in medium increase with the increase in the temperature and polymer mass fraction in medium. From the point of view of the solubilization effect on etodolac, HPMC E3, PEG 6000, PVP K25, and PEG 20000 are in order from the best to the worst. A two-step chemical potential gradient model combined with PC-SAFT (Perturbed-Chain Statistical Associating Fluid Theory) and interfacial mass transfer model was used to model the dissolution profiles of etodolac. The results show that the dissolution mechanism of etodolac is affected by the temperature, stirring speed, type of polymer, and mass fraction of polymer in medium so as to regulate the dissolution rate of etodolac, which is expected to provide theoretical guidance for the screening of pharmaceutical excipients. Based upon the determined rate constants, the predicted dissolution profiles of the tablets under different conditions were in good accordance with the experimental data

Measurement and Thermodynamic Modeling of Oxaprozin Solubility in Polymers and Mixed Solutions

Measuring and modeling the solubility of drugs in different solvent systems is helpful to guide the selection of appropriate solvents at various stages of drug formulation development. In this work, the gravimetrical method was used to determine the solubility data of oxaprozin (OXA) in different compositions of water/organic solvents (methanol and ethanol) binary mixtures within the range of 293.15 to 333.15 K. Subsequently, the differential scanning calorimetry method was used to measure the solubility of the drug in polymers (Polyethylene Glycol 6000 and Polyvinylpyrrolidone K30) at above 400 K. Then, the solubility of OXA in ultrapure water and polymer aqueous solution was acquired by UV spectrophotometry or HPLC within a temperature range of 303.15 to 323.15 K. Finally, the experimental values were compared with the calculated values from the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) to investigate the prediction accuracy of this model in different complex mixed solvent systems. The average relative deviations (ARD) were used to evaluate the model performance of PC-SAFT. Furthermore, PC-SAFT combined with solid–liquid equilibrium theory not only modeled the phase behavior between pure or mixed solvents and drugs independent of the molecular weight of the solvent but also did not require any experimental data or model parameters from the ternary system to predict the phase behavior of OXA in binary solvents. The results of this work illustrate that PC-SAFT is a beneficial model in drug development

Predicting the Solubility Advantage of Amorphous Pharmaceuticals: A Novel Thermodynamic Approach

For the solubility and bioavailability of poorly soluble active pharmaceutical ingredients (APIs) to be improved, the transformation of crystalline APIs to the amorphous state has often been shown to be advantageous. As it is often difficult to measure the solubility of amorphous APIs, the application of thermodynamic models is the method of choice for determining the solubility advantage. In this work, the temperature-dependent solubility advantage of an amorphous API versus its crystalline form was predicted for five poorly soluble APIs in water (glibenclamide, griseofulvin, hydrochlorothiazide, indomethacin, and itraconazole) based on modeling the API/solvent phase diagrams using the perturbed-chain statistical associating fluid theory (PC-SAFT). Evaluation of the performance of this approach was performed by comparing the predicted solubility advantage to experimental data and to the solubility advantage calculated by the commonly applied Gibbs-energy-difference method. For all of the systems considered, PC-SAFT predictions of the solubility advantage are significantly more accurate than the results obtained from the Gibbs-energy-difference method