Recent analyses have highlighted the promotion of cancer migration and invasion, mediated through HDAC via MMP-2 and MMP-9. Since both class 1 HDACs and MMP-2/9 are involved in the migration and invasion of cancer, an attempt has been taken to design dual MMP-2/HDAC-8 inhibitors by pharmacophore mapping and molecular docking approaches. The designed molecules were synthesized and showed a range of inhibitory activity against different MMP subtypes. Most of these designed compounds were selective towards MMP-2 but less potent against anti-targets like MMP-8, -12, etc. The highly active MMP-2 inhibitors were also found to be active towards HDAC-8 but less potent against other class 1 HDACs (HDAC-1 and -2). Molecular dynamics simulations revealed that the designed compounds may be acting through a distinct mechanism of action in the ‘acetate ion channel’ of HDAC-8. Some potent dual MMP-2/HDAC-8 inhibitors were further explored for in vitro cellular assays against human lung carcinoma cell line A549. These analyses revealed that some of these dual inhibitors have considerable anti-migratory and anti-invasive properties. The work may help to obtain some useful dual inhibitors.

Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.