BEAR, a Molecular Docking Refinement and Rescoring Method

BEAR (Binding Estimation After Refinement) is a computational method for structure-based virtual screening. It was set up as a post-docking processing tool for the refinement of ligand binding modes predicted by molecular docking programs and the accurate evaluation of free energies of binding. BEAR has been validated in a number of computational drug discovery applications. It performed well in discriminating active ligands with respect to molecular decoys of biological targets belonging to different protein families as well as in discovering biologically active hits. Recently, it has been validated also in the emerging field of G-protein coupled receptors structure based virtual screening

Structure-based discovery of the first allosteric inhibitors of cyclin-dependent kinase 2

Allosteric targeting of protein kinases via displacement of the structural αC helix with type III allosteric inhibitors is currently gaining a foothold in drug discovery. Recently, the first crystal structure of CDK2 with an open allosteric pocket adjacent to the αC helix has been described, prospecting new opportunities to design more selective inhibitors, but the structure has not yet been exploited for the structure-based design of type III allosteric inhibitors. In this work we report the results of a virtual screening campaign that resulted in the discovery of the first-in-class type III allosteric ligands of CDK2. Using a combination of docking and post-docking analyses made with our tool BEAR, 7 allosteric ligands (hit rate of 20%) with micromolar affinity for CDK2 were identified, some of them inhibiting the growth of breast cancer cell lines in the micromolar range. Competition experiments performed in the presence of the ATP-competitive inhibitor staurosporine confirmed that the 7 ligands are truly allosteric, in agreement with their design. Of these, compound 2 bound CDK2 with an EC50 value of 3 μM and inhibited the proliferation of MDA-MB231 and ZR-75-1 breast cancer cells with IC50 values of approximately 20 μM, while compound 4 had an EC50 value of 71 μM and IC50 values around 4 μM. Remarkably, the most potent compound 4 was able to selectively inhibit CDK2-mediated Retinoblastoma phosphorylation, confirming that its mechanism of action is fully compatible with a selective inhibition of CDK2 phosphorylation in cells. Finally, hit expansion through analog search of the most potent inhibitor 4 revealed an additional ligand 4g with similar in vitro potency on breast cancer cells

Targeting the Hsp90 interactome using in silico polypharmacology approaches

In recent years, polypharmacology has gained popularity in drug discovery. [1] Especially for complex diseases such as cancer, the ability of a drug to bind to and interfere with multiple targets provides new opportunities for therapeutic intervention In this article, we focus on Hsp90 and its interactome, whose pivotal role in survival and proliferation of cancer cells renders this array of targets particularly attractive polypharmacological drug design strategies. The primary goal of our work is the identification and selection of suitable target proteins from the interactome that might be combined with Hsp90 to explore and exploit a multi-target inhibition approach. This task is accomplished by applying computational methods to mine the structural and biological information associated with potential ligands in public databases and assess the degree of structural similarity between known inhibitors of different targets. Therefore, we propose an integrated ligand- and structure-based approach to select small molecules from databases suitable for consideration as multi-target inhibitors

The Effectiveness of Integrating Religious/Spirituality Beliefs into Psychotherapy: An Integrative Review

This comprehensive review explores how integrating religious beliefs into psychotherapy impacts mental health outcomes. Following a structured approach guided by Whittemore and Knafl\u27s (Nov 2005) methodology, the review involves a systematic literature search across academic databases, identifying 25 studies with diverse methods. Results indicate significant enhancements in both psychological and spiritual outcomes when religious beliefs are integrated into psychotherapy, compared to no-treatment control conditions. Compared to alternate treatments, adapted psychotherapy demonstrates a small to medium effect. Particularly in an additive design, while there\u27s no significant superiority in psychological outcomes, there is in spiritual outcomes, emphasizing the potential benefits of incorporating R/S elements. The study highlights empirical support for integrating R/S elements into psychotherapy, underscoring its potential to enhance psychological and spiritual well-being across diverse populations. Future research should diversify cultural and religious contexts, expand follow-up studies, and delineate the extent of R/S integration in treatments. Dissemination of these findings to mental health professionals is vital to encourage the integration of religious beliefs into psychotherapy for improved mental health outcomes

Sustainable urban transect. Rigenerazione urbana sostenibile del territorio di Nembro (BG)

LAUREA MAGISTRAL

Epigenetic polypharmacology: from combination therapy to multitargeted drugs

The modern drug discovery process has largely focused its attention in the so-called magic bullets, single chemical entities that exhibit high selectivity and potency for a particular target. This approach was based on the assumption that the deregulation of a protein was causally linked to a disease state, and the pharmacological intervention through inhibition of the deregulated target was able to restore normal cell function. However, the use of cocktails or multicomponent drugs to address several targets simultaneously is also popular to treat multifactorial diseases such as cancer and neurological disorders. We review the state of the art with such combinations that have an epigenetic target as one of their mechanisms of action. Epigenetic drug discovery is a rapidly advancing field, and drugs targeting epigenetic enzymes are in the clinic for the treatment of hematological cancers. Approved and experimental epigenetic drugs are undergoing clinical trials in combination with other therapeutic agents via fused or linked pharmacophores in order to benefit from synergistic effects of polypharmacology. In addition, ligands are being discovered which, as single chemical entities, are able to modulate multiple epigenetic targets simultaneously (multitarget epigenetic drugs). These multiple ligands should in principle have a lower risk of drug-drug interactions and drug resistance compared to cocktails or multicomponent drugs. This new generation may rival the so-called magic bullets in the treatment of diseases that arise as a consequence of the deregulation of multiple signaling pathways provided the challenge of optimization of the activities shown by the pharmacophores with the different targets is addressed

Systematic mapping of cancer cell target dependencies using high-throughput drug screening in triple-negative breast cancer

While high-throughput drug screening offers possibilities to profile phenotypic responses of hundreds of compounds, elucidation of the cell context-specific mechanisms of drug action requires additional analyses. To that end, we developed a computational target deconvolution pipeline that identifies the key target dependencies based on collective drug response patterns in each cell line separately. The pipeline combines quantitative drug-cell line responses with drug-target interaction networks among both intended on- and potent off-targets to identify pharmaceutically actionable and selective therapeutic targets. To demonstrate its performance, the target deconvolution pipeline was applied to 310 small molecules tested on 20 genetically and phenotypically heterogeneous triple-negative breast cancer (TNBC) cell lines to identify cell line-specific target mechanisms in terms of cytotoxic and cytostatic drug target vulnerabilities. The functional essentiality of each protein target was quantified with a target addiction score (TAS), as a measure of dependency of the cell line on the therapeutic target. The target dependency profiling was shown to capture inhibitory information that is complementary to that obtained from the structure or sensitivity of the drugs. Comparison of the TAS profiles and gene essentiality scores from CRISPR-Cas9 knockout screens revealed that certain proteins with low gene essentiality showed high target addictions, suggesting that they might be functioning as protein groups, and therefore be resistant to single gene knock-out. The comparative analysis discovered protein groups of potential multi-target synthetic lethal interactions, for instance, among histone deacetylases (HDACs). Our integrated approach also recovered a number of well-established TNBC cell line-specific drivers and known TNBC therapeutic targets, such as HDACs and cyclin-dependent kinases (CDKs). The present work provides novel insights into druggable vulnerabilities for TNBC, and opportunities to identify multi-target synthetic lethal interactions for further studies. (C) 2020 The Author(s). Published by Elsevier B.V.Peer reviewe

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine-learning methods, and their applications in polypharmacology prediction and target de-convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi-party or multi-party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so-called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area

Effect of missing data on multitask prediction methods

There has been a growing interest in multitask prediction in chemoinformatics, helped by the increasing use of deep neural networks in this field. This technique is applied to multitarget data sets, where compounds have been tested against different targets, with the aim of developing models to predict a profile of biological activities for a given compound. However, multitarget data sets tend to be sparse; i.e., not all compound-target combinations have experimental values. There has been little research on the effect of missing data on the performance of multitask methods. We have used two complete data sets to simulate sparseness by removing data from the training set. Different models to remove the data were compared. These sparse sets were used to train two different multitask methods, deep neural networks and Macau, which is a Bayesian probabilistic matrix factorization technique. Results from both methods were remarkably similar and showed that the performance decrease because of missing data is at first small before accelerating after large amounts of data are removed. This work provides a first approximation to assess how much data is required to produce good performance in multitask prediction exercises