Interpreting the Physicochemical Meaning of a Molecular Descriptor Which Is Predictive of Amorphous Solid Dispersion Formation in Polyvinylpyrrolidone Vinyl Acetate

Understanding the Influence of API Conformations on Amorphous Dispersion Formation Potential Predictions using the R3m Molecular Descriptor

The R3m molecular descriptor (R-GETAWAY third-order autocorrelation index weighted by the atomic mass) has previously been shown to encode molecular attributes that appear to be physically and chemically relevant to grouping diverse active pharmaceutical ingredients (API) according to their potential to form persistent amorphous solid dispersions (ASDs) with polyvinylpyrrolidone-vinyl acetate copolymer (PVPVA). The initial R3m dispersibility model was built by using a single three-dimensional (3D) conformation for each drug molecule. Since molecules in the amorphous state will adopt a distribution of conformations, molecular dynamics simulations were performed to sample conformations that are probable in the amorphous form, which resulted in a distribution of R3m values for each API. Although different conformations displayed R3m values that differed by as much as 0.4, the median of each R3m distribution and the value predicted from the single 3D conformation were very similar for most structures studied. The variability in R3m resulting from the distribution of conformations was incorporated into a logistic regression model for the prediction of ASD formation in PVPVA, which resulted in a refinement of the classification boundary relative to the model that only incorporated a single conformation of each API.

Predicting rejection of emerging contaminants through RO membrane filtration based on ANN-QSAR modeling approach: trends in molecular descriptors and structures towards rejections

In this work, a quantitative structure-activity relationship (QSAR) study was performed on a set of emerging contaminants (ECs) to predict their rejections by reverse osmosis membrane (RO). A wide range of molecular descriptors was calculated by Dragon software for 72 ECs. The QSAR data was analyzed by an artificial neural network method (ANN), in which four out of 3000 theoretical molecular descriptors were chosen and their significance was computed based on the Garson method. The significance trends of descriptors were as follows in descending order: ESpm14u > R2e > SIC1 > EEig03d. The selected descriptors were ranked based on their importance and then an explorative study was conducted on the QSAR data to show the trends in molecular descriptors and structures toward the rejections values of ECs. The MLR algorithm was used to make a linear model and the results were compared with those of the nonlinear ANN algorithm. The comparison results revealed it is necessary to apply the ANN model to this data with non-linear properties. For the whole dataset, the correlation coefficient (R2) and residual mean squared error (RMSE) of the ANN and MLR methods were 0.9528, 6.4224; and 0.8753, 11.3400, respectively. The comparison results showed the superiority of ANN modeling in the analysis of ECs' QSAR data.

Investigating and Comparing the Applicability of the R3m Molecular Descriptor and Solubility Parameter Estimation Approaches in Predicting Dispersion Formation Potential of APIs in a Random Co-Polymer Polyvinylpyrrolidone Vinyl Acetate and its Homopolymer

Evaluation of different amorphous solid dispersion carrier matrices is enabled by active pharmaceutical ingredient (API) structure-based predictions. This study compares the utility of Hansen Solubility Parameters with the R3m molecular descriptor for identifying dispersion polymers based on the structure of the drug molecule. Twelve API-polymer combinations (4 APIs and 3 interrelated polymers) were used to test each approach. Co-solidified mixtures containing 75% API were prepared by melt-quenching. Phase behavior was evaluated and classified using differential scanning calorimetry, powder X-ray diffraction, polarized light microscopy, and hot stage microscopy. Observations of dispersion behavior were compared to predictions made using the Hansen Solubility Parameter and R3m. The solubility parameter approach misclassified the dispersion behavior of 1 API-polymer combination and also did not produce definite predictions in 3 out of 12 of the API-polymer combinations. In contrast, R3m classifications of dispersion behavior were correct in all but two cases, with one misclassification and one ambiguous prediction. The solubility parameters best classify dispersion behavior when specific drug-polymer intermolecular interactions are present, but may be less useful otherwise. Ultimately, these two methods are most effectively used together, as they are based on distinct features of the same molecular structure.

Implications of Coexistent Halogen and Hydrogen Bonds in Amorphous Solid Dispersions on Drug Solubility, Miscibility, and Mobility

Specific noncovalent drug-polymer interactions were analytically identified using Raman and Fourier transform infrared spectroscopy for amorphous solid dispersions (ASD) formed between either chlorpropamide or tolbutamide and polyvinylpyrrolidone vinyl acetate random copolymer (PVPVA). Spectral changes in the C-Cl stretching vibrations due to changes in the electronic environment of the Cl atom confirmed halogen bond formation in chlorpropamide-PVPVA ASDs, the extent of which was established to be inversely related to the concentration of the drug using 2D correlation spectroscopy analysis. Hydrogen bonding between the secondary amide of each drug and the pyrrolidone carbonyl of the copolymer was also confirmed in all dispersions. Implications of coexistent interactions were investigated for drug-polymer solubility, mixing free energy, and molecular mobility relative to tolbutamide, which only formed hydrogen bonds with PVPVA. Chlorpropamide had a higher solubility, a larger negative mixing free energy, and lower mobility in PVPVA relative to tolbutamide. These thermodynamic and kinetic differences demonstrate the significance of halogen bond formation even when hydrogen bonding is present.

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

HATS5m as an Example of GETAWAY Molecular Descriptor in Assessing the Similarity/Diversity of the Structural Features of 4-Thiazolidinone

Among the various methods for drug design, the approach using molecular descriptors for quantitative structure–activity relationships (QSAR) bears promise for the prediction of innovative molecular structures with bespoke pharmacological activity. Despite the growing number of successful potential applications, the QSAR models often remain hard to interpret. The difficulty arises from the use of advanced chemometric or machine learning methods on the one hand, and the complexity of molecular descriptors on the other hand. Thus, there is a need to interpret molecular descriptors for identifying the features of molecules crucial for desirable activity. For example, the development of structure–activity modeling of different molecule endpoints confirmed the usefulness of H-GETAWAY (H-GEometry, Topology, and Atom-Weights AssemblY) descriptors in molecular sciences. However, compared with other 3D molecular descriptors, H-GETAWAY interpretation is much more complicated. The present study provides insights into the interpretation of the HATS5m descriptor (H-GETAWAY) concerning the molecular structures of the 4-thiazolidinone derivatives with antitrypanosomal activity. According to the published study, an increase in antitrypanosomal activity is associated with both a decrease and an increase in HATS5m (leverage-weighted autocorrelation with lag 5, weighted by atomic masses) values. The substructure-based method explored how the changes in molecular features affect the HATS5m value. Based on this approach, we proposed substituents that translate into low and high HATS5m. The detailed interpretation of H-GETAWAY descriptors requires the consideration of three elements: weighting scheme, leverages, and the Dirac delta function. Particular attention should be paid to the impact of chemical compounds’ size and shape and the leverage values of individual atoms.