Quantitative Structure-activity Relationship Strategy Research Articles

Quantitative Structure-Activity Relationship Enables the Rational Design of Lipid Droplet-Targeting Carbon Dots for Visualizing Bisphenol A-Induced Nonalcoholic Fatty Liver Disease-like Changes.

Lipid droplets (LDs) play indispensable roles in numerous physiological processes; hence, the visualization of the dynamic behavior of LDs in living cells is of great importance in physiological and pathological research. In this article, the quantitative structure-activity relationship (QSAR) theory was employed as an effective design strategy for the development of organelle-targeting carbon dots (CDs). The lipid-water partition coefficient (Log P) of the QSAR was adopted as a key parameter to predict the cellular uptake and subcellular localization of CDs in live cells. By carefully adjusting the molecular structure and lipophilicity of the precursors, p-phenylenediamine-derivatized nucleolus-targeting hydrophilic CDs were converted to lipophilic CDs [4-piperidinoaniline (PA) CDs] with inherent LD-targeting performance. The PA CDs were able to indicate the dynamic behavior of LDs and visualize the changes of bisphenol A-induced nonalcoholic fatty liver disease-like changes in a cellular model. The QSAR strategy of CDs demonstrated here is expected to be increasingly exploited as a powerful design tool for developing various organelle-targeting CDs.

Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies.

The design of new drugs that target vasopressin 2 receptor (V2R) is of vital importance to develop new therapeutic alternatives to treat diseases such as heart failure, polycystic kidney disease. To get structural insights related to V2R-ligand recognition, we have used a combined approach of docking, molecular dynamics simulations (MD) and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the V2R with 119 of its antagonists. The three-dimensional model of V2R was built by threading methods refining its structure through MD simulations upon which the 119 ligands were subjected to docking studies. The theoretical results show that binding recognition of these ligands on V2R is diverse, but the main pharmacophore (electronic and π-π interactions) is maintained; thus, this information was validated under QSAR results. QSAR studies were performed using MLR analysis followed by ANN analysis to increase the model quality. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals, and they can be useful tools for screening new chemicals for safety assessment.

QSRR and QSAR Studies of Antitumor Drugs in View of their Biological Activity Prediction.

The QSRRs and QSARs are relatively new approaches to relate internal chemical structure and particular biological activity. This methodology is based on theory that mechanisms which took place into chromatography column are similar to those that occur in a living organism at the molecular level, for example when compounds penetrate into cells. In this paper, we aim to describe different cytostatic activities of selected anticancer drugs as QSRR and QSAR models, and prove usefulness of connected QSRR and QSAR methodology in different types of studies. Chromatographic experiments using gradient RP-HPLC method and different C18 stationary phases were performed. As a result we obtained retention parameter log kw. Moreover, to calculate descriptors, which characterize lipophilicity of analyzed antitumor drugs, DryLab program was utilized. Molecular modeling studies were performed by using HyperChem program. Dragon software was used to calculate structural descriptors, and then selected descriptors were used to build QSRR and QSAR models. Obtained data were analyzed by multiple regression analysis (MLR). Experimental log kw and predicted log kw from QSRR models developed, were further used in QSAR analysis. The goodness of fit was in the range of R2= 0.75 - 0.95, and the predictive performance of the models was Q2 = 0.6 - 0.81. Both QSRR and QSAR strategies presented in this paper, allowed predicting HPLC retention parameters and cytotoxic activities of anticancer medicines without the necessity to carry out time-consuming and expensive experimental tests.

The application of connected QSRR and QSAR strategies to predict the physicochemical interaction of acridinone derivatives with DNA.

Acridinone derivatives as imidazoacridinones and triazoloacridinones are the new potent antitumor agents characterized by different mechanisms of action related to their ability to interact with DNA. The analysis undertaken in this study involves searching of QSAR (Quantitative Structure-Activity Relationship) and QSRR (Quantitative Structure- Retention Relationship) models, which would allow to predict the biological activity of acridinones expressed as the ability to stabilize the secondary structure of DNA (ΔT), based on their structural parameters and chromatographic retention data. For this purpose, 20 acridinone derivatives were subjected to chromatographic analyses and molecular modeling, followed by statistical analyses using multiple linear regression method (MLR). As a novelty aspect, except for RP-HPLC approach, hydrophilic interaction chromatography (HILIC) columns were tested. As a result of performed analysis, appropriate QSAR and QSRR models were obtained, and each model was analyzed in terms of prediction of acridinones' ability to interact with DNA. Derived QSAR and QSRR models were characterized as one, with good prediction performance. Conclusively, the proposed connected QSAR and QSRR strategies allow to predict in silico the ability of acridinones to interact with DNA without the necessity of performing any biological experiments under in vitro and in vivo conditions.

Rationalization of the entrapping of bioactive molecules into a series of functionalized porous zirconium terephthalate MOFs.

The encapsulation of two different bioactive molecules, the cosmetic caffeine and the analgesic and anti-inflammatory ibuprofen, has been evaluated by combining impregnation and advanced characterization experimental tools in a series of microporous rigid zirconium(iv) terephthalates UiO-66 bearing different polar or apolar functional groups (-H, -Br, -NH2, -2OH, -NO2, -Cl, -2CF3, -CH3, -2CH3). It has been first evidenced that these hybrid solids exhibit drug payloads that significantly outperform those obtained using current drug formulations or other conventional porous solids. A quantitative structure-activity relationship strategy has been further conducted with the aim of rationalizing the experimental drug uptakes and further emphasizing the most relevant chemical and structural features that significantly impact their encapsulation performances. Indeed, it appears that the caffeine loading is optimized when the functionalized organic linker both shows a large octanol-water partition coefficient and contains grafted functions with low hydrogen bond acceptor abilities, whereas the ibuprofen entrapping is enhanced when the organic linker contains functional groups with a large solvent surface area and free volume, and to a lesser extent low hydrogen bond acceptor abilities. Moreover, it has been shown that the solvent used as media for the biomolecule impregnation plays a crucial role in the encapsulation performance due to the formation of a competitive adsorption process between the solvent and the active molecule.

Quantitative Sequence-Activity Model (QSAM): Applying QSAR Strategy to Model and Predict Bioactivity and Function of Peptides, Proteins and Nucleic Acids

Traditional quantitative structure-activity relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. Quantitative sequence-activity model (QSAM), applying QSAR strategy to explore sequence-activity/function relationship for biosystems, is greatly meaningful but meanwhile extremely difficult. For biomolecules, high molecular weight, diverse structural morphology and intricate interaction network all bring in traditional QSAR methodologies unprecedented challenges. This article comprehensively reviewed developing process, current state and future perspective of QSAM, concerning its applications into fields of pharmacy, food science, immunology and molecular biology. Besides, discipline-crossing and amalgamation of QSAM with QSAR, bioinformatics and computational biology were also discussed.

Multivariate design and modeling in QSAR

Quantitative structure-activity relationship (QSAR) modeling provides a rational basis for understanding mechanisms of biological performance and how to alter chemical structures to achieve improved performance. However, in the quest for a valid QSAR model, several critical problems must be dealt with in an appropriate manner. Chemometric techniques are relevant in QSAR development and will help the inexperienced QSAR analyst in avoiding trivial mistakes. The chemometric QSAR strategy, applicable both in drug design and environmentally related sciences, highlights some crucial steps that otherwise often are neglected. These steps include how to select the proper data analytical method, how to design the training set, how to describe chemical and biological properties of compounds and analyze these data, and finally how to validate the relevance of an established QSAR model. A discussion of these steps is given, using four illustrative examples.