Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes

Quantitative Structure-Activity Relationship Modeling of Pea Protein-Derived Acetylcholinesterase and Butyrylcholinesterase Inhibitory Peptides

The aim of this work was to determine the structural requirements for peptides that inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) activities. The data set used consisted of 19 oligopeptides that had been identified through mass spectrometry analysis of enzymatic digests of yellow field pea protein. The structure-function relationship was analyzed by partial least squares regression using the 5z scores. A nine-component model was created from 16 peptides for AChE inhibitory peptides (Q2 = 67.2% and R2 = 0.9974), while three data sets were prepared for BuChE inhibitory peptides to improve the quality of the models (Q2 = 26.7-46.4% and R2 = 0.9577-0.9958). The most active peptides from the PLS models have threonine, leucine, alanine, and valine at the N terminal, asparagine, histidine, proline, and arginine at the second position, with aspartic acid and serine at the third, and arginine at the C terminal.

Application of Rapid Identification and Determination of Moisture Content of Coptidis Rhizoma From Different Species Based on Data Fusion

BACKGROUND

For thousands of years, traditional Chinese medicine (TCM) has been clinically proven, and doctors have highly valued the differences in utility between different species.

OBJECTIVE

This study aims to replace the complex methods traditionally used for empirical identification by compensating for the information loss of a single sensor through data fusion. The research object of the study is Coptidis rhizoma (CR).

METHOD

Using spectral optimization and data fusion technology, near infrared (NIR) and mid-infrared (MIR) spectra were collected for CR. PLS-DA (n = 134) and PLSR (n = 63) models were established to identify the medicinal materials and to determine the moisture content in the medicinal materials.

RESULTS

For the identification of the three species of CR, the mid-level fusion model performed better than the single-spectrum model. The sensitivity and specificity of the prediction set coefficients for NIR, MIR, and data fusion qualitative models were all higher than 0.95, with an AUC value of 1. The NIR data model was superior to the MIR data model. The results of low-level fusion were similar to those of the NIR optimization model. The RPD of the test set of NIR and low-level fusion model was 3.6420 and 3.4216, respectively, indicating good prediction ability of the model.

CONCLUSIONS

Data fusion technology using NIR and MIR can be applied to identify CR species and to determine the moisture content of CR. It provides technical support for the rapid determination of moisture content, with a fast analysis speed and without the need for complex pretreatment methods.

HIGHLIGHTS

This study is the first to introduce spectral data fusion technology to identify CR species. Data fusion technology is feasible for multivariable calibration model performance and reduces the cost of manual identification. The moisture content of CR can be quickly evaluated, reducing the difficulty of traditional methods.

Multivariate Data Analysis and Central Composite Design-Oriented Optimization of Solid Carriers for Formulation of Curcumin-Loaded Solid SNEDDS: Dissolution and Bioavailability Assessment

The study was initiated with two major purposes: investigating the role of isomalt (GIQ9) as a pharmaceutical carrier for solid self-nanoemulsifying drug delivery systems (S-SNEDDSs) and improving the oral bioavailability of lipophilic curcumin (CUN). GIQ9 has never been explored for solidification of liquid lipid-based nanoparticles such as a liquid isotropic mixture of a SNEDDS containing oil, surfactant and co-surfactant. The suitability of GIQ9 as a carrier was assessed by calculating the loading factor, flow and micromeritic properties. The S-SNEDDSs were prepared by surface adsorption technique. The formulation variables were optimized using central composite design (CCD). The optimized S-SNEDDS was evaluated for differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), microscopy, dissolution and pharmacokinetic studies. The S-SNEDDS showed a particle size, zeta potential and PDI of 97 nm, -26.8 mV and 0.354, respectively. The results of DSC, XRD, FTIR and microscopic studies revealed that the isotropic mixture was adsorbed onto the solid carrier. The L-SNEDDS and S-SNEDDS showed no significant difference in drug release, indicating no change upon solidification. The optimized S-SNEDDS showed 5.1-fold and 61.7-fold enhancement in dissolution rate and oral bioavailability as compared to the naïve curcumin. The overall outcomes of the study indicated the suitability of GIQ9 as a solid carrier for SNEDDSs.