Novel agents for acute myeloid leukemia

Acute myeloid leukemia (AML) is a complex hematological disease characterized by genetic and clinical heterogeneity. Recent advances in the understanding of AML pathogenesis have paved the way for the development of new agents targeting specific molecules or mechanisms that contribute to finally move beyond the current standard of care, which is \u201c3 + 7\u201d regimen. In particular, new therapeutic options such as targeted therapies (midostaurin and enasidenib), monoclonal antibodies (gemtuzumab ozogamicin), and a novel liposomal formulation of cytarabine and daunorubicin (CPX-351) have been recently approved, and will be soon available for the treatment of adult patients with AML. In this review, we will present and describe these recently approved drugs as well as selected novel agents against AML that are currently under investigation, and show the most promising results as monotherapy or in combination with chemotherapy. The selection of these emerging treatments is based on the authors\u2019 opinion

scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data

With mass and flow cytometry, millions of single-cell profiles with dozens of parameters can be measured to comprehensively characterize complex tumor ecosystems. Here, we present scQUEST, an open-source Python library for cell type identification and quantification of tumor ecosystem heterogeneity in patient cohorts. We provide a step-by-step protocol on the application of scQUEST on our previously generated human breast cancer single-cell atlas using mass cytometry and discuss how it can be adapted and extended for other datasets and analyses. For complete details on the use and execution of this protocol, please refer to Wagner et al. (2019)

Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors

Carbonic anhydrase II (CAII) is a zinc-containing metalloenzyme whose aberrant activity is associated with various diseases such as glaucoma, osteoporosis, and different types of tumors; therefore, the development of CAII inhibitors, which can represent promising therapeutic agents for the treatment of these pathologies, is a current topic in medicinal chemistry. Molecular docking is a commonly used tool in structure-based drug design of enzyme inhibitors. However, there is still a need for improving docking reliability, especially in terms of scoring functions, since the complex pattern of energetic contributions driving ligand⁻protein binding cannot be properly described by mathematical functions only including approximated energetic terms. Here we report a novel CAII-specific fingerprint-based (IFP) scoring function developed according to the ligand⁻protein interactions detected in the CAII-inhibitor co-crystal structures of the most potent CAII ligands. Our IFP scoring function outperformed the ability of Autodock4 scoring function to identify native-like docking poses of CAII inhibitors and thus allowed a considerable improvement of docking reliability. Moreover, the ligand⁻protein interaction fingerprints showed a useful application in the binding mode analysis of structurally diverse CAII ligands

Receptor-based virtual screening evaluation for the identification of estrogen receptor β ligands.

In this paper, a receptor-based virtual screening study for the identification of estrogen receptor β (ERβ) ligands was developed. Starting from a commercial database of 400,000 molecules, only six compounds resulted to be potential active ligands of ERβ. Interestingly, all the six molecules possess scaffolds that had already been reported in known ERβ ligands. Therefore, the results obtained herein confirm the reliability of our virtual screening procedure, thus encouraging the application of this protocol to larger commercial databases in order to identify new ERβ ligands

Identification of Lactate Dehydrogenase 5 Inhibitors using Pharmacophore- Driven Consensus Docking

Background: Human lactate dehydrogenase 5 (hLDH5) represents a promising anticancer target, particularly for the treatment of hypoxic tumors, where it is often hyperexpressed. In fact, by catalyzing the reduction of pyruvate to lactate, hLDH5 allows the survival of tumor cells under hypoxic conditions by means of glycolysis. Despite the efforts dedicated to the identification and development of hLDH5 inhibitors, only few compounds showing promising activity in cancer cell lines have been reported. Objective: In the present study, we developed a virtual screening (VS) protocol aimed at identifying new small molecule inhibitors of hLDH5. Method: The VS strategy consisted in a pharmacophore-driven consensus docking (CD) approach, combining a structure-based pharmacophore screening and CD protocol employing three different docking methods. Results: The VS protocol was applied to filter the Enamine commercial database and allowed the selection of three candidate ligands to be subjected to hLDH5 inhibition assays. One of the selected compounds showed a promising activity, compared to its low molecular weight, with an IC50 of 180.7 ± 16.5 μM. Conclusion: We identified a new small-molecule inhibitor of hLDH5 that can be considered as a new lead for the development of potent hLDH5 inhibitors. Moreover, these results demonstrate the reliability of the VS protocol developed

Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching

Targeting Different Transthyretin Binding Sites with Unusual Natural Compounds

Misfolding and aggregation of the transthyretin (TTR) protein leads to certain forms of amyloidosis. Some nutraceuticals, such as flavonoids and natural polyphenols, have recently been investigated as modulators of the self-assembly process of TTR, but they generally suffer from limited bioavailability. To discover innovative and more bioavailable natural compounds able to inhibit TTR amyloid formation, a docking study was performed using the crystallographic structure of TTR. This computational strategy was projected as an adhoc inspection of the possible relationship between binding site location and modulation of the assembly process; interactions with the as-yet-unexplored epigallocatechin gallate (EGCG) sites and with the thyroxine (T4) pocket were simultaneously analyzed. All the compounds studied seem to prefer the traditional T4 binding site, but some interesting results emerged from the screening of an in-house database, used for validating the computational protocol, and of the Herbal Ingredients Targets (HIT) catalogue available on the ZINC database

Salicylaldoxime derivatives as new leads for the development of carbonic anhydrase inhibitors

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Development of terphenyl-2-methyloxazol-5(4H)-one derivatives as selective reversible MAGL inhibitors

Monoacylglycerol lipase is a serine hydrolase that plays a major role in the degradation of the endocannabinoid neurotransmitter 2-arachidonoylglycerol. A wide number of MAGL inhibitors are reported in literature; however, many of them are characterised by an irreversible mechanism of action and this behavior determines an unwanted chronic MAGL inactivation, which acquires a functional antagonism of the endocannabinoid system. The possible use of reversible MAGL inhibitors has only recently been explored, due to the lack of known compounds possessing efficient reversible inhibitory activities. In this work, we report a new series of terphenyl-2-methyloxazol-5(4H)-one derivatives characterised by a reversible MAGL-inhibition mechanism. Among them, compound 20b showed to be a potent MAGL reversible inhibitor (IC50= 348 nM) with a good MAGL/FAAH selectivity. Furthermore, this compound showed antiproliferative activities against two different cancer cell lines that overexpress MAGL

Organs on chip approach: A tool to evaluate cancer-immune cells interactions

In this paper we discuss the applicability of numerical descriptors and statistical physics concepts to characterize complex biological systems observed at microscopic level through organ on chip approach. To this end, we employ data collected on a micro uidic platform in which leukocytes can move through suitably built channels toward their target. Leukocyte behavior is recorded by standard time lapse imaging. In particular, we analyze three groups of human peripheral blood mononuclear cells (PBMC): heterozygous mutants (in which only one copy of the FPR1 gene is normal), homozygous mutants (in which both alleles encoding FPR1 are loss-of-function variants) and cells from ‘wild type’ donors (with normal expression of FPR1). We characterize the migration of these cells providing a quantitative con rmation of the essential role of FPR1 in cancer chemotherapy response. Indeed wild type PBMC perform biased random walks toward chemotherapy-treated cancer cells establishing persistent interactions with them. Conversely, heterozygous mutants present a weaker bias in their motion and homozygous mutants perform rather uncorrelated random walks, both failing to engage with their targets. We next focus on wild type cells and study the interactions of leukocytes with cancerous cells developing a novel heuristic procedure, inspired by Lyapunov stability in dynamical systems