Recent developments in the medicinal chemistry of single boron atom-containing compounds

Various boron-containing drugs have been approved for clinical use over the past two decades, and more are currently in clinical trials. The increasing interest in boron-containing compounds is due to their unique binding properties to biological targets; for example, boron substitution can be used to modulate biological activity, pharmacokinetic properties, and drug resistance. In this perspective, we aim to comprehensively review the current status of boron compounds in drug discovery, focusing especially on progress from 2015 to December 2020. We classify these compounds into groups showing anticancer, antibacterial, antiviral, antiparasitic and other activities, and discuss the biological targets associated with each activity, as well as potential future developments.

Ligand and structure-based classification models for prediction of P-glycoprotein inhibitors.

The ABC transporter P-glycoprotein (P-gp) actively transports a wide range of drugs and toxins out of cells, and is therefore related to multidrug resistance and the ADME profile of therapeutics. Thus, development of predictive in silico models for the identification of P-gp inhibitors is of great interest in the field of drug discovery and development. So far in silico P-gp inhibitor prediction was dominated by ligand-based approaches because of the lack of high-quality structural information about P-gp. The present study aims at comparing the P-gp inhibitor/noninhibitor classification performance obtained by docking into a homology model of P-gp, to supervised machine learning methods, such as Kappa nearest neighbor, support vector machine (SVM), random fores,t and binary QSAR, by using a large, structurally diverse data set. In addition, the applicability domain of the models was assessed using an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and noninhibitors, correctly predicting 73/75% of the external test set compounds. Classification based on the docking experiments using the scoring function ChemScore resulted in the correct prediction of 61% of the external test set. This demonstrates that ligand-based models currently remain the methods of choice for accurately predicting P-gp inhibitors. However, structure-based classification offers information about possible drug/protein interactions, which helps in understanding the molecular basis of ligand-transporter interaction and could therefore also support lead optimization.

Annotating Human P-Glycoprotein Bioassay Data.

Huge amounts of small compound bioactivity data have been entering the public domain as a consequence of open innovation initiatives. It is now the time to carefully analyse existing bioassay data and give it a systematic structure. Our study aims to annotate prominent in vitro assays used for the determination of bioactivities of human P-glycoprotein inhibitors and substrates as they are represented in the ChEMBL and TP-search open source databases. Furthermore, the ability of data, determined in different assays, to be combined with each other is explored. As a result of this study, it is suggested that for inhibitors of human P-glycoprotein it is possible to combine data coming from the same assay type, if the cell lines used are also identical and the fluorescent or radiolabeled substrate have overlapping binding sites. In addition, it demonstrates that there is a need for larger chemical diverse datasets that have been measured in a panel of different assays. This would certainly alleviate the search for other inter-correlations between bioactivity data yielded by different assay setups.

Computational prediction of binding affinity for CYP1A2-ligand complexes using empirical free energy calculations.

Predicting binding affinities for receptor-ligand complexes is still one of the challenging processes in computational structure-based ligand design. Many computational methods have been developed to achieve this goal, such as docking and scoring methods, the linear interaction energy (LIE) method, and methods based on statistical mechanics. In the present investigation, we started from an LIE model to predict the binding free energy of structurally diverse compounds of cytochrome P450 1A2 ligands, one of the important human metabolizing isoforms of the cytochrome P450 family. The data set includes both substrates and inhibitors. It appears that the electrostatic contribution to the binding free energy becomes negligible in this particular protein and a simple empirical model was derived, based on a training set of eight compounds. The root mean square error for the training set was 3.7 kJ/mol. Subsequent application of the model to an external test set gives an error of 2.1 kJ/mol, which is remarkably good, considering the simplicity of the model. The structures of the protein-ligand interactions are further analyzed, again demonstrating the large versatility and plasticity of the cytochrome P450 active site.

Fast prediction of cytochrome P450 mediated drug metabolism.

Cytochrome P450 mediated metabolism of drugs is one of the major determinants of their kinetic profile, and prediction of this metabolism is therefore highly relevant during the drug discovery and development process. A new rule-based method, based on results from density functional theory calculations, for predicting activation energies for aliphatic and aromatic oxidations by cytochromes P450 is developed and compared with several other methods. Although the applicability of the method is currently limited to a subset of P450 reactions, these reactions describe more than 90 % of the metabolites. The rules employed are relatively few and general, and when combined with solvent-accessible surface area calculations to account for steric accessibility, the method gives a major P450 metabolite as first-ranked position for 75 % of the substrates, and ranked in the top three for 90 % of substrates for a set of 20 substrates. In combination with docking, it can predict isoform-specific metabolism, and we apply this on CYP1A2 with very good results on 81 substrates, for which we find a major metabolite ranked in the top three for 90 % of the substrates (100 % in the training set and 87 % in the larger test set).

Virtual screening and prediction of site of metabolism for cytochrome P450 1A2 ligands.

With the availability of an increasing number of high resolution 3D structures of human cytochrome P450 enzymes, structure-based modeling tools are more readily used. In this study we explore the possibilities of using docking and scoring experiments on cytochrome P450 1A2. Three different questions have been addressed: 1. Binding orientations and conformations were successfully predicted for various substrates. 2. A virtual screen was performed with satisfying enrichment rates. 3. A classification of individual compounds into active and inactive was performed. It was found that while docking can be used successfully to address the first two questions, it seems to be more difficult to perform the classification. Different scoring functions were included, and the well-characterized water molecule in the active site was included in various ways. Results are compared to experimental data and earlier classification data using machine learning methods. The possibilities and limitations of using structure-based drug design tools for cytochrome P450 1A2 come to light and are discussed.

Classification of cytochrome P450 1A2 inhibitors and noninhibitors by machine learning techniques.

The cytochrome P450 (P450) superfamily plays an important role in the metabolism of drug compounds, and it is therefore highly desirable to have models that can predict whether a compound interacts with a specific isoform of the P450s. In this work, we provide in silico models for classification of CYP1A2 inhibitors and noninhibitors. Training and test sets consisted of approximately 400 and 7000 compounds, respectively. Various machine learning techniques, such as binary quantitative structure activity relationship, support vector machine (SVM), random forest, kappa nearest neighbor (kNN), and decision tree methods were used to develop in silico models, based on Volsurf and Molecular Operating Environment descriptors. The best models were obtained using the SVM, random forest, and kNN methods in combination with the BestFirst variable selection method, resulting in models with 73 to 76% of accuracy on the test set prediction (Matthews correlation coefficients of 0.51 and 0.52). Finally, a decision tree model based on Lipinski's Rule-of-Five descriptors was also developed. This model predicts 67% of the compounds correctly and gives a simple and interesting insight into the issue of classification. All of the models developed in this work are fast and precise enough to be applicable for virtual screening of CYP1A2 inhibitors or noninhibitors or can be used as simple filters in the drug discovery process.

Influence of thermodynamic parameter in Lanosterol 14alpha-demethylase inhibitory activity as antifungal agents: a QSAR approach.

A quantitative structure activity relationship, Hansch approach was applied on twenty compounds of chromene derivatives as Lanosterol 14alpha-demethylase inhibitory activity against eight fungal organisms. Various physicochemical descriptors and reported minimum inhibitory concentration values of different fungal organisms were used as independent variables and dependent variable respectively. The best models for eight different fungal organisms were first validated by leave-one-out cross validation procedure. It was revealed that thermodynamic parameters were found to have overall significant correlationship with anti fungal activity and these studies provide an insight to design new molecules.

QSAR study of 3-phenyl-5-acyloxymethyl-2H,5H-furan-2-ones as antifungal agents: the dominant role of electronic parameter.

To explore physicochemical properties of 3-phenyl-5-acyloxymethyl-2H,5H-furan-2-ones derivatives responsible for their antifungal activity, a quantitative structure activity relationship, Hansch approach was applied on sixteen compounds of above mentioned derivatives. Various physicochemical descriptors and reported minimum inhibitory concentration values of different fungal organisms were used as independent variables and dependent variable respectively. The best models for twelve different fungal organisms were first validated by leave-one-out cross validation procedure. Further, bootstrapping method was adopted to assess the robustness of the models. It was revealed that electronic parameters were found to have overall significant correlation with antifungal activity and these studies provide an insight to design new molecules.

Synthesis, analgesic and anti-inflammatory activities of some novel 2,3-disubstituted quinazolin-4(3H)-ones.

A series of novel 2-benzylamino-3-substituted quinazolin-4(3H)-ones have been synthesized by treating 3-amino-2-benzylamino quinazolin-4(3H)-one, with different aldehydes and ketones. The starting material 3-amino-2-benzylamino quinazolin-4(3H)-one was synthesized by nucleophilic substitution of thiomethyl group of 3-amino-2-methylthio quinazolin-4(3H)-one by benzylamine. The title compounds were investigated for analgesic and anti-inflammatory activities. All the test compounds exhibited significant analgesic activity, whereas the compound III is equipotent with diclofenac sodium. The compounds I, II and III showed more potent anti-inflammatory activity than diclofenac sodium.