Enzymatic Synthesis and Structural Modeling of Bio-Based Oligoesters as an Approach for the Fast Screening of Marine Biodegradation and Ecotoxicity.

Given the widespread use of esters and polyesters in products like cosmetics, fishing nets, lubricants and adhesives, whose specific application(s) may cause their dispersion in open environments, there is a critical need for stringent eco-design criteria based on biodegradability and ecotoxicity evidence. Our approach integrates experimental and computational methods based on short oligomers, offering a screening tool for the rapid identification of sustainable monomers and oligomers, with a special focus on bio-based alternates. We provide insights into the relationships between the chemical structure and properties of bio-based oligomers in terms of biodegradability in marine environments and toxicity in benchmark organisms. The experimental results reveal that the considered aromatic monomers (terephthalic acid and 2,5-furandicarboxylic acid) accumulate under the tested conditions (OECD 306), although some slight biodegradation is observable when the inoculum derives from sites affected by industrial and urban pollution, which suggests that ecosystems adapt to non-natural chemical pollutants. While clean seas are more susceptible to toxic chemical buildup, biotic catalytic activities offer promise for plastic pollution mitigation. Without prejudice to the fact that biodegradability inherently signifies a desirable trait in plastic products, nor that it automatically grants them a sustainable "license", this study is intended to facilitate the rational design of new polymers and materials on the basis of specific uses and applications.

Gaucher Disease: A Glance from a Medicinal Chemistry Perspective.

Rare diseases are particular pathological conditions affecting a limited number of people and few drugs are known to be effective as therapeutic treatment. Gaucher disease, caused by a deficiency of the lysosomal enzyme glucocerebrosidase, belongs to this class of disorders, and it is considered the most common among the Lysosomal Storage Diseases. The two main therapeutic approaches are the Enzyme Replacement Therapy (ERT) and the Substrate Reduction Therapy (SRT). ERT, consisting in replacing the defective enzyme by administering a recombinant enzyme, is effective in alleviating the visceral symptoms, hallmarks of the most common subtype of the disease whereas it has no effects when symptoms involve CNS, since the recombinant protein is unable to significantly cross the Blood Brain Barrier. The SRT strategy involves inhibiting glucosylceramide synthase (GCS), the enzyme responsible for the production of the associated storage molecule. The rational design of new inhibitors of GCS has been hampered by the lack of either the crystal structure of the enzyme or an in-silico model of the active site which could provide important information regarding the interactions of potential inhibitors with the target, but, despite this, interesting results have been obtained and are herein reviewed.

MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.

MOTIVATION: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher. RESULTS: We have engineered an artificial neural network model designed specifically for predicting drug sensitivity. This model utilizes a biologically informed visible neural network, thereby enhancing its interpretability. The trained model allows for an in-depth exploration of the biological pathways integral to prediction and the chemical attributes of drugs that impact sensitivity. Our model harnesses multiomics data derived from a different tumor tissue sources, as well as molecular descriptors that encapsulate the properties of drugs. We extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the imbalanced nature of publicly available drug screening datasets, our model demonstrated superior performance to state-of-the-art visible machine learning algorithms. AVAILABILITY AND IMPLEMENTATION: MOViDA is implemented in Python using PyTorch library and freely available for download at https://github.com/Luigi-Ferraro/MOViDA. Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380.

Destabilizers of the thymidylate synthase homodimer accelerate its proteasomal degradation and inhibit cancer growth.

Drugs that target human thymidylate synthase (hTS), a dimeric enzyme, are widely used in anticancer therapy. However, treatment with classical substrate-site-directed TS inhibitors induces over-expression of this protein and development of drug resistance. We thus pursued an alternative strategy that led us to the discovery of TS-dimer destabilizers. These compounds bind at the monomer-monomer interface and shift the dimerization equilibrium of both the recombinant and the intracellular protein toward the inactive monomers. A structural, spectroscopic, and kinetic investigation has provided evidence and quantitative information on the effects of the interaction of these small molecules with hTS. Focusing on the best among them, E7, we have shown that it inhibits hTS in cancer cells and accelerates its proteasomal degradation, thus causing a decrease in the enzyme intracellular level. E7 also showed a superior anticancer profile to fluorouracil in a mouse model of human pancreatic and ovarian cancer. Thus, over sixty years after the discovery of the first TS prodrug inhibitor, fluorouracil, E7 breaks the link between TS inhibition and enhanced expression in response, providing a strategy to fight drug-resistant cancers.

Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications.

Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact with any chemical entity, are one of the most established and versatile concepts in drug discovery. Improvement of this molecular description is highly desirable for in silico drug discovery and medicinal chemistry applications. In this work, we revised a well-established molecular mechanics' force field and applied a hybrid quantum mechanics and machine learning approach to parametrize the hydrogen-bonding (HB) potentials of small molecules, improving this aspect of the molecular description. Approximately 66,000 molecules were chosen from available drug databases and subjected to density functional theory calculations (DFT). For each atom, the molecular electrostatic potential (EP) was extracted and used to derive new HB energy contributions; this was subsequently combined with a fingerprint-based description of the structural environment via partial least squares modeling, enabling the new potentials to be used for molecules outside of the training set. We demonstrate that parameter prediction for molecules outside of the training set correlates with their DFT-derived EP, and that there is correlation of the new potentials with hydrogen-bond acidity and basicity scales. We show the newly derived MIFs vary in strength for various ring substitution in accordance with chemical intuition. Finally, we report that this derived parameter, when extended to non-HB atoms, can also be used to estimate sites of reaction.

ADME-Space: a new tool for medicinal chemists to explore ADME properties.

We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.

Side-Chain Modified Ergosterol and Stigmasterol Derivatives as Liver X Receptor Agonists.

A series of stigmasterol and ergosterol derivatives, characterized by the presence of oxygenated functions at C-22 and/or C-23 positions, were designed as potential liver X receptor (LXR) agonists. The absolute configuration of the newly created chiral centers was definitively assigned for all the corresponding compounds. Among the 16 synthesized compounds, 21, 27, and 28 were found to be selective LXRα agonists, whereas 20, 22, and 25 showed good selectivity for the LXRß isoform. In particular, 25 showed the same degree of potency as 22R-HC (3) at LXRß, while it was virtually inactive at LXRα (EC50 = 14.51 µM). Interestingly, 13, 19, 20, and 25 showed to be LXR target gene-selective modulators, by strongly inducing the expression of ABCA1, while poorly or not activating the lipogenic genes SREBP1 and SCD1 or FASN, respectively.

Understanding Oxadiazolothiazinone Biological Properties: Negative Inotropic Activity versus Cytochrome P450-Mediated Metabolism.

We present a series of oxadiazolothiazinones, selective inotropic agents on isolated cardiac tissues, devoid of chronotropy and vasorelaxant activity. Functional and binding data for the precursor of the series (compound 1) let us hypothesize LTCC blocking activity and the existence of a recognition site specific for this scaffold. We synthesized and tested 22 new derivatives: introducing a para-methoxyphenyl at C-8 led to compound 12 (EC50 = 0.022 µM), twice as potent as its para-bromo analogue (1). For 10 analogues, we extended the characterization of the biological properties by including the assessment of metabolic stability in human liver microsomes and cytochrome P450 inhibition potential. We observed that the methoxy group led to active compounds with low metabolic stability and high CYP inhibition, whereas the protective effect of bromine resulted in enhanced metabolic stability and reduced CYP inhibition. Thus, we identified two para-bromo benzothiazino-analogues as candidates for further studies.

Discovery of Novel, Potent, and Specific Cell-Death Inducers in the Jurkat Acute Lymphoblastic Leukemia Cell Line.

The limited clinical efficacy of many cancer therapeutics has initiated intense research efforts toward the discovery of novel chemical entities in this field. In this study, 31 hit candidates were selected from nearly 800,000 database compounds in a ligand-based virtual screening campaign. In turn, three of these hits were found to have (sub)micromolar potencies in proliferation assays with the Jurkat acute lymphatic leukemic cell line. In this assay, the three hits were found to exhibit higher potency than clinically tested cell-death inducers (GDC-0152, AT-406, and birinapant). Importantly, antiproliferative activity toward non-cancer peripheral blood mononuclear cells (PBMCs) was found to be marginal. Further biological characterization demonstrated the cell-death-inducing properties of these compounds. Biological testing of hit congeners excluded a nonspecific, toxic effect of the novel structures. Altogether, these findings may have profound relevance for the development of clinical candidates in tumor therapy.

Synthesis and L-type calcium channel blocking activity of new chiral oxadiazolothiazinones.

Oxadiazolo[3,4-c][1,4]thiazin-3-ones are cardiovascular agents that block L-type calcium channels. Previous data of cardiac and vasorelaxant activity on guinea-pig for several derivatives indicated the two positions ortho to the thiazine's sulphur as crucial for modulating the activity; but these positions are likely susceptible to metabolic biotransformations, as indicated by in silico predictions. We designed new derivatives, and obtained three negative inotropic agents with EC50 in the low nanomolar range, more potent than all the precursors published so far. In particular, benzocondensation at the thiazine ring led to 3a (EC50 = 0.013 µM) and 3b (EC50 = 0.006 µM). Besides negative inotropy, we also observed relaxant activity on nonvascular muscle in the micromolar range. We resolved the new derivatives by chiral chromatography, and determined their absolute configuration by comparing experimental and calculated chiroptical properties (VCD, ECD and ORD): they hold the same absolute configuration-optical rotation relationship, (S)-(+)/(R)-(-). Both cardiac and nonvascular activity are majorly or mostly retained in the R-form for all the compounds, but for the nonvascular activity we observed a strong stereoselectivity for 3a, with the R-form in the nanomolar range (IC50 = 0.020 µM) and 259-fold more potent than the S-one.

Playing with opening and closing of heterocycles: using the cusmano-ruccia reaction to develop a novel class of oxadiazolothiazinones, active as calcium channel modulators and P-glycoprotein inhibitors.

As a result of the ring-into-ring conversion of nitrosoimidazole derivatives, we obtained a molecular scaffold that, when properly decorated, is able to decrease inotropy by blocking L-type calcium channels. Previously, we used this scaffold to develop a quantitative structure-activity relationship (QSAR) model, and we used the most potent oxadiazolothiazinone as a template for ligand-based virtual screening. Here, we enlarge the diversity of chemical decorations, present the synthesis and in vitro data for 11 new derivatives, and develop a new 3D-QSAR model with recent in silico techniques. We observed a key role played by the oxadiazolone moiety: given the presence of positively charged calcium ions in the transmembrane channel protein, we hypothesize the formation of a ternary complex between the oxadiazolothiazinone, the Ca2+ ion and the protein. We have supported this hypothesis by means of pharmacophore generation and through the docking of the pharmacophore into a homology model of the protein. We also studied with docking experiments the interaction with a homology model of P-glycoprotein, which is inhibited by this series of molecules, and provided further evidence toward the relevance of this scaffold in biological interactions.

Targeting cystalysin, a virulence factor of treponema denticola-supported periodontitis.

Cystalysin from Treponema denticola is a pyridoxal 5'-phosphate dependent lyase that catalyzes the formation of pyruvate, ammonia, and sulfide from cysteine. It is a virulence factor in adult periodontitis because its reaction contributes to hemolysis, which sustains the pathogen. Therefore, it was proposed as a potential antimicrobial target. To identify specific inhibitors by structure-based in silico methods, we first validated the crystal structure of cystalysin as a reliable starting point for the design of ligands. By using single-crystal absorption microspectrophotometry, we found that the enzyme in the crystalline state, with respect to that in solution, exhibits: 1) the same absorption spectra for the catalytic intermediates, 2) a close pKa value for the residue controlling the keto enamine ionization, and 3) similar reactivity with glycine, L-serine, L-methionine, and the nonspecific irreversible inhibitor aminoethoxyvinylglycine. Next, we screened in silico a library of 9357 compounds with the Fingerprints for Ligands and Proteins (FLAP) software, by using the three-dimensional structure of cystalysin as a template. From the library, 17 compounds were selected and experimentally evaluated by enzyme assays and spectroscopic methods. Two compounds were found to competitively inhibit recombinant T. denticola cystalysin, with inhibition constant (Ki ) values of 25 and 37 µM. One of them exhibited a minimum inhibitory concentration (MIC) value of 64 µg mL(-1) on Moraxella catarrhalis ATCC 23246, which proves its ability to cross bacterial membranes.

Modelling cytochromes P450 binding modes to predict P450 inhibition, metabolic stability and isoform selectivity.

The cytochromes P450 (P450) superfamily is a diverse group of enzymes involved in the metabolism of xenobiotics, whose orientations within the catalytic site can lead to different binding modes, namely productive, nonproductive, and inhibitory. This article collects the most recent approaches that individually study P450- ligand interactions, including a novel in silico technology, developed in the framework of the Human Cytochrome P450 Consortium initiative, that provides reliable in silico predictions of P450 inhibition, metabolic stability and isoform selectivity.

Large, chemically diverse dataset of logP measurements for benchmarking studies.

Lipophilicity is a crucial parameter in drug development since it impacts both ADME properties and target affinity of drug candidates. In early drug discovery stage, accurate tools for logP prediction are highly desired. Many calculation methods were developed to aid pharmaceutical scientists in drug research; however almost all suffer from insufficient accuracy and variation of performance in several regions of the chemical space associated with new chemical entities. The low predictive power of existing software packages can be explained by limited availability and/or variable quality of experimental logP values associated with training set used, which stem from various protocols and poorly cover chemical space. In this study, a dataset of 1000 diverse test compounds out of 4.5 million was generated; logP values of 759 purchasable compounds (46% non-ionizable, 30% basic, 17% acidic, 0.5% zwitterionic and 6.5% ampholytes) from this selected set were experimentally determined by UHPLC followed by UV detection or MS detection when necessary. Finally, a data collection of 707 validated logP values ranging from 0.30 to 7.50 is now available for benchmarking of existing and development of new approaches to predict octanol/water partition coefficients of chemical compounds.

Absolute configuration and biological profile of two thiazinooxadiazol-3-ones with L-type calcium channel activity: a study of the structural effects.

In the framework of our interest in racemic thiazinooxadiazol-3-ones we determined the absolute configuration and the biological activity as L-type calcium channel blockers of two compounds that differ in the length of the acetal chain, which could affect the pharmacological profile. We observed an interesting inversion of the stereoselectivity, with the activity residing on the R-form for a short chain compound (n = 1) and on the S-form for a long chain one (n = 12). The length of the linear acetal chain appears to be able to invert the stereoselectivity of such a class of compounds, and in silico simulations suggested that this different behaviour might be explained by different hydrophilic and hydrophobic interactions with the binding site.

Inhibitor of ovarian cancer cells growth by virtual screening: a new thiazole derivative targeting human thymidylate synthase.

Human thymidylate synthase (hTS) was targeted through a virtual screening approach. The most optimal inhibitor identified, 2-{4-hydroxy-2-[(2-hydroxybenzylidene)hydrazono]-2,5-dihydrothiazol-5-yl}-N-(3-trifluoromethylphenyl)acetamide (5), showed a mixed-type inhibition pattern, with a K(i) of 1.3 µM and activity against ovarian cancer cell lines with the same potency as cisplatin. X-ray studies revealed that it binds the inactive enzyme conformation. This study is the first example of a nonpeptidic inhibitor that binds the inactive hTS and exhibits anticancer activity against ovarian cancer cells.

QSAR modeling and data mining link Torsades de Pointes risk to the interplay of extent of metabolism, active transport, and HERG liability.

We collected 1173 hERG patch clamp (PC) data (IC50) from the literature to derive twelve classification models for hERG inhibition, covering a large variety of chemical descriptors and classification algorithms. Models were generated using 545 molecules and validated through 258 external molecules tested in PC experiments. We also evaluated the suitability of the best models to predict the activity of 26 proprietary compounds tested in radioligand binding displacement (RBD). Results proved the necessity to use multiple validation sets for a true estimation of model accuracy and demonstrated that using various descriptors and algorithms improves the performance of ligand-based models. Intriguingly, one of the most accurate models uncovered an unexpected link between extent of metabolism and hERG liability. This hypothesis was fairly reinforced by using the Biopharmaceutics Drug Disposition Classification System (BDDCS) that recognized 94% of the hERG inhibitors as extensively metabolized in vivo. Data mining suggested that high Torsades de Pointes (TdP) risk results from an interplay of hERG inhibition, extent of metabolism, active transport, and possibly solubility. Overall, these new findings might improve both the decision making skills of pharmaceutical scientists to mitigate hERG liability during the drug discovery process and the TdP risk assessment during drug development.

Pyrazolo[4,3-c][1,2]benzothiazines 5,5-dioxide: a promising new class of Staphylococcus aureus NorA efflux pump inhibitors.

The increasing resistance to antibacterials commonly employed in the clinic and the growth of multidrug resistant strains suggest that the development of new therapeutic approaches should be of primary concern. In this context, EPIs may restore life to old drugs. In the present work, the EPI activity of the COX-2 inhibitor celecoxib was confirmed and a new class of pyrazolo[4,3-c][1,2]benzothiazine 5,5-dioxide analogues acting as inhibitors of the Staphylococcus aureus NorA multidrug efflux pump was identified.

Ligand Promiscuity between the Efflux Pumps Human P-Glycoprotein and S. aureus NorA.

Thirty-two diverse compounds were evaluated for their ability to inhibit both Pgp-mediated efflux in mouse T-lymphoma L5178 MDR1 and NorA-mediated efflux in S. aureus SA-1199B. Only four compounds were strong inhibitors of both efflux pumps. Three compounds were found to inhibit Pgp exclusively and strongly, while seven compounds inhibited only NorA. These results demonstrate that Pgp and NorA inhibitors do not necessarily overlap, opening the way to safer therapeutic use of effective NorA inhibitors.

A novel approach for predicting P-glycoprotein (ABCB1) inhibition using molecular interaction fields.

P-glycoprotein (Pgp or ABCB1) is an ABC transporter protein involved in intestinal absorption, drug metabolism, and brain penetration, and its inhibition can seriously alter a drug's bioavailability and safety. In addition, inhibitors of Pgp can be used to overcome multidrug resistance. Given this dual purpose, reliable in silico procedures to predict Pgp inhibition are of great interest. A large and accurate literature collection yielded more than 1200 structures; a model was then constructed using various molecular interaction field-based technologies, considering pharmacophoric features and those physicochemical properties related to membrane partitioning. High accuracy was demonstrated internally with two different validation sets and, moreover, using a number of molecules, for which Pgp inhibition was not experimentally available but was evaluated in-house. All of the validations confirmed the robustness of the model and its suitability to help medicinal chemists in drug discovery. The information derived from the model was rationalized as a pharmacophore for competitive Pgp inhibition.