Synthesis of a selective HDAC6 inhibitor active in neuroblasts.

In recent years, the role of HDAC6 in neurodegeneration has been partially elucidated, which led some authors to propose HDAC6 inhibitors as a therapeutic strategy to treat neurodegenerative diseases. In an effort to develop a selective HDAC6 inhibitor which can cross the blood brain barrier (BBB), a modified hydroxamate derivative (compound 3) was designed and synthetized. This compound was predicted to have potential for BBB penetration based on in silico and in vitro evaluation of passive permeability. When tested for its HDAC inhibitory activity, the IC50 value of compound 3 towards HDAC6 was in the nM range in both enzymatic and cell-based assays. Compound 3 showed a cell-based selectivity profile close to that of tubastatin A in SH-SY5Y human neuroblastoma cells, and a good BBB permeability profile.

Steroid identification via deep learning retention time predictions and two-dimensional gas chromatography-high resolution mass spectrometry.

Untargeted steroid identification represents a great analytical challenge even when using sophisticated technology such as two-dimensional gas chromatography coupled to high resolution mass spectrometry (GC × GCHRMS) due to the chemical similarity of the analytes. Moreover, when analytical standards, mass spectral and retention index databases are not available, compound annotation is cumbersome. Hence, there is a need for the development of retention time prediction models in order to explore new annotation approaches. In this work, we evaluated the use of several in silico methods for retention time prediction in multidimensional gas chromatography. We use three classical machine learning (CML) algorithms (Partial Least Squares (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR)) and two deep learning approaches (dense neural network (DNN) and three-dimensional convolutional neural network (CNN)). Whereas molecular descriptors were utilized for the CLM and DNN algorithms, three-dimensional molecular representation based on the electrostatic potential (ESP) was studied as input data as is for the CNN. All the developed models showed similar performances with Q2 values over 0.9. However, among all CNN showed the best performance, resulting in average retention time prediction errors of 2% and 6% for the first and second separation dimension, respectively. Additionally, only the three-dimensional ESP representation coupled with CNN was able to extract the stereochemical information crucial for the separation of diastereomers. The combination of retention time prediction and high-resolution mass spectral data applied to clinical samples enabled the untargeted annotation of 12 steroid metabolites in the urine of new-borns.

DynaStI: A Dynamic Retention Time Database for Steroidomics.

: Steroidomics studies face the challenge of separating analytical compounds with very similar structures (i.e., isomers). Liquid chromatography (LC) is commonly used to this end, but the shared core structure of this family of compounds compromises effective separations among the numerous chemical analytes with comparable physico-chemical properties. Careful tuning of the mobile phase gradient and an appropriate choice of the stationary phase can be used to overcome this problem, in turn modifying the retention times in different ways for each compound. In the usual workflow, this approach is suboptimal for the annotation of features based on retention times since it requires characterizing a library of known compounds for every fine-tuned configuration. We introduce a software solution, DynaStI, that is capable of annotating liquid chromatography-mass spectrometry (LC-MS) features by dynamically generating the retention times from a database containing intrinsic properties of a library of metabolites. DynaStI uses the well-established linear solvent strength (LSS) model for reversed-phase LC. Given a list of LC-MS features and some characteristics of the LC setup, this software computes the corresponding retention times for the internal database and then annotates the features using the exact masses with predicted retention times at the working conditions. DynaStI (https://dynasti.vital-it.ch) is able to automatically calibrate its predictions to compensate for deviations in the input parameters. The database also includes identification and structural information for each annotation, such as IUPAC name, CAS number, SMILES string, metabolic pathways, and links to external metabolomic or lipidomic databases.

Optimization of non-linear gradient in hydrophobic interaction chromatography for the analytical characterization of antibody-drug conjugates.

The goal of this work was to evaluate the potential of non-linear gradients in hydrophobic interaction chromatography (HIC), to improve the separation between the different homologous species (drug-to-antibody, DAR) of commercial antibody-drug conjugates (ADC). The selectivities between Brentuximab Vedotin species were measured using three different gradient profiles, namely linear, power function based and logarithmic ones. The logarithmic gradient provides the most equidistant retention distribution for the DAR species and offers the best overall separation of cysteine linked ADC in HIC. Another important advantage of the logarithmic gradient, is its peak focusing effect for the DAR0 species, which is particularly useful to improve the quantitation limit of DAR0. Finally, the logarithmic behavior of DAR species of ADC in HIC was modelled using two different approaches, based on i) the linear solvent strength theory (LSS) and two scouting linear gradients and ii) a new derived equation and two logarithmic scouting gradients. In both cases, the retention predictions were excellent and systematically below 3% compared to the experimental values.

Enhanced metabolite annotation via dynamic retention time prediction: Steroidogenesis alterations as a case study.

The development of metabolomics based on ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) now allows hundreds to thousands of metabolites to be simultaneously monitored in biological matrices. In that context, bioinformatics and multivariate data analysis (MVA) play a crucial role in the detection of relevant alteration patterns. However, sound biological interpretations must necessarily be supported by metabolite identifications to be definitive or at least have a high degree of confidence. Each compound, should be characterised by unique molecular properties. Among them, the exact mass and the chromatographic retention time are recognised as major and complementary criteria for compound identification. While the former is easily derived from the molecular structure, building generic and accurate retention time open databases still constitutes a critical issue because of the vast diversity of instruments, stationary phases and operating conditions in UHPLC-HRMS. Because several hits matching a molecular formula obtained from an exact mass and an isotopic pattern are often generated for each analyte, this methodology rarely allows a unique and unambiguous molecular identity to be gained. This work aims to provide a flexible solution to facilitate reliable compound annotation based on retention time in reversed-phase liquid chromatography (RPLC). It proposes an innovative approach based on the chromatographic linear solvent strength (LSS) theory, allowing retention times under any gradient conditions at fixed temperature, stationary phase and mobile phase type to be predicted. Starting from a subset of the Human Metabolite Database (HMDB), a new dynamic database involving LSS parameters was developed. A real case study involving steroidogenesis alterations due to forskolin exposure was conducted using the adrenal H295R OECD reference cell model for endocrine disruptor screening. The prediction of retention times was successfully achieved, facilitating steroid identification. An automated procedure which implements the compound annotation levels encouraged by the Metabolite Standard Initiative (MSI) and the Coordination of Standards in Metabolomics (COSMOS) was also developed to speed up the process and enhance the data reusability.

A Rational Approach for the Identification of Non-Hydroxamate HDAC6-Selective Inhibitors.

The human histone deacetylase isoform 6 (HDAC6) has been demonstrated to play a major role in cell motility and aggresome formation, being interesting for the treatment of multiple tumour types and neurodegenerative conditions. Currently, most HDAC inhibitors in preclinical or clinical evaluations are non-selective inhibitors, characterised by a hydroxamate zinc-binding group (ZBG) showing off-target effects and mutagenicity. The identification of selective HDAC6 inhibitors with novel chemical properties has not been successful yet, also because of the absence of crystallographic information that makes the rational design of HDAC6 selective inhibitors difficult. Using HDAC inhibitory data retrieved from the ChEMBL database and ligand-based computational strategies, we identified 8 original new non-hydroxamate HDAC6 inhibitors from the SPECS database, with activity in the low µM range. The most potent and selective compound, bearing a hydrazide ZBG, was shown to increase tubulin acetylation in human cells. No effects on histone H4 acetylation were observed. To the best of our knowledge, this is the first report of an HDAC6 selective inhibitor bearing a hydrazide ZBG. Its capability to passively cross the blood-brain barrier (BBB), as observed through PAMPA assays, and its low cytotoxicity in vitro, suggested its potential for drug development.

Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification.

The untargeted profiling of steroids constitutes a growing research field because of their importance as biomarkers of endocrine disruption. New technologies in analytical chemistry, such as ultra high-pressure liquid chromatography coupled with mass spectrometry (MS), offer the possibility of a fast and sensitive analysis. Nevertheless, difficulties regarding steroid identification are encountered when considering isotopomeric steroids. Thus, the use of retention times is of great help for the unambiguous identification of steroids. In this context, starting from the linear solvent strength (LSS) theory, quantitative structure retention relationship (QSRR) models, based on a dataset composed of 91 endogenous steroids and VolSurf + descriptors combined with a new dedicated molecular fingerprint, were developed to predict retention times of steroid structures in any gradient mode conditions. Satisfactory performance was obtained during nested cross-validation with a predictive ability (Q(2)) of 0.92. The generalisation ability of the model was further confirmed by an average error of 4.4% in external prediction. This allowed the list of candidates associated with identical monoisotopic masses to be strongly reduced, facilitating definitive steroid identification.

5-Benzylidene-hydantoin is a new scaffold for SIRT inhibition: From virtual screening to activity assays.

Sirtuins (SIRTs) are a family of enzymes able to catalyze the deacetylation of the N-acetyl lysines of both histone and non-histone substrates. Inhibition of SIRTs catalytic activity was recently reported in the literature as being beneficial in human diseases, with very promising applications in cancer therapy and enzymatic neurodegeneration. By combining a structure-based virtual screening of the Specs database with cell-based assays, we identified the 5-benzylidene-hydantoin as new scaffold for the inhibition of SIRT2 catalytic activity. Compound 97 (Specs ID AH-487/41657829), active in the low µM range against SIRT2, showed the optimal physicochemical properties for passive absorption as well as relatively low cytotoxicity in vitro. Further studies revealed non-competitive and mixed-type kinetics toward acetyl-lysine substrates and NAD(+), respectively, and a non-selective profile for SIRT inhibition. A binding mode consistent with the experimental evidence was proposed by molecular modeling. Additionally, the levels of acetyl-p53 were shown to be increased in HeLa cells treated with 97. Taken together, these results encourage further investigation of 5-benzylidene-hydantoin derivatives for their SIRT-related therapeutic effects.

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.

Computational studies on sirtuins from Trypanosoma cruzi: structures, conformations and interactions with phytochemicals.

BACKGROUND: The silent-information regulator 2 proteins, otherwise called sirtuins, are currently considered as emerging anti-parasitic targets. Nicotinamide, a pan-sirtuin inhibitor, is known to cause kinetoplast alterations and the arrested growth of T. cruzi, the protozoan responsible for Chagas disease. These observations suggested that sirtuins from this parasite (TcSir2rp1 and TcSir2rp3) could play an important role in the regulation of the parasitic cell cycle. Thus, their inhibition could be exploited for the development of novel anti-trypanosomal compounds. METHODS: Homology modeling was used to determine the three-dimensional features of the sirtuin TcSir2rp1 from T. cruzi. The apo-form of human SIRT2 and the same structure solved in complex with its co-substrate NAD(+) allowed the modeling of TcSir2rp1 in the open and closed conformational states. Molecular docking studies were then carried out. A library composed of fifty natural and diverse compounds that are known to be active against this parasite, was established based on the literature and virtually screened against TcSir2rp1 and TcSir2rp3, which was previously modeled by our group. RESULTS: In this study, two conformational states of TcSir2rp1 were described for the first time. The molecular docking results of compounds capable of binding sirtuins proved to be meaningful when the closed conformation of the protein was taken into account for calculations. This specific conformation was then used for the virtual screening of antritrypanosomal phytochemicals against TcSir2rp1 and TcSir2rp3. The calculations identified a limited number of scaffolds extracted from Vismia orientalis, Cussonia zimmermannii, Amomum aculeatum and Anacardium occidentale that potentially interact with both proteins. CONCLUSIONS: The study provided reliable models for future structure-based drug design projects concerning sirtuins from T. cruzi. Molecular docking studies highlighted not only the advantages of performing in silico interaction studies on their closed conformations but they also suggested the potential mechanism of action of four phytochemicals known for their anti-trypanosomal activity in vitro.