Amniotic fluid stem cell-derived extracellular vesicles educate type 2 conventional dendritic cells to rescue autoimmune disorders in a multiple sclerosis mouse model.

Dendritic cells (DCs) are essential orchestrators of immune responses and represent potential targets for immunomodulation in autoimmune diseases. Human amniotic fluid secretome is abundant in immunoregulatory factors, with extracellular vesicles (EVs) being a significant component. However, the impact of these EVs on dendritic cells subsets remain unexplored. In this study, we investigated the interaction between highly purified dendritic cell subsets and EVs derived from amniotic fluid stem cell lines (HAFSC-EVs). Our results suggest that HAFSC-EVs are preferentially taken up by conventional dendritic cell type 2 (cDC2) through CD29 receptor-mediated internalization, resulting in a tolerogenic DC phenotype characterized by reduced expression and production of pro-inflammatory mediators. Furthermore, treatment of cDC2 cells with HAFSC-EVs in coculture systems resulted in a higher proportion of T cells expressing the regulatory T cell marker Foxp3 compared to vehicle-treated control cells. Moreover, transfer of HAFSC-EV-treated cDC2s into an EAE mouse model resulted in the suppression of autoimmune responses and clinical improvement. These results suggest that HAFSC-EVs may serve as a promising tool for reprogramming inflammatory cDC2s towards a tolerogenic phenotype and for controlling autoimmune responses in the central nervous system, representing a potential platform for the study of the effects of EVs in DC subsets.

Identification of small molecules affecting the interaction between human hemoglobin and Staphylococcus aureus IsdB hemophore.

Human hemoglobin (Hb) is the preferred iron source of Staphylococcus aureus. This pathogenic bacterium exploits a sophisticated protein machinery called Iron-regulated surface determinant (Isd) system to bind Hb, extract and internalize heme, and finally degrade it to complete iron acquisition. IsdB, the surface exposed Hb receptor, is a proven virulence factor of S. aureus and the inhibition of its interaction with Hb can be pursued as a strategy to develop new classes of antimicrobials. To identify small molecules able to disrupt IsdB:Hb protein-protein interactions (PPIs), we carried out a structure-based virtual screening campaign and developed an ad hoc immunoassay to screen the retrieved set of commercially available compounds. Saturation-transfer difference (STD) NMR was applied to verify specific interactions of a sub-set of molecules, chosen based on their efficacy in reducing the amount of Hb bound to IsdB. Among molecules for which direct binding was verified, the best hit was submitted to ITC analysis to measure the binding affinity to Hb, which was found to be in the low micromolar range. The results demonstrate the viability of the proposed in silico/in vitro experimental pipeline to discover and test IsdB:Hb PPI inhibitors. The identified lead compound will be the starting point for future SAR and molecule optimization campaigns.

Use of ionic liquids in amidation reactions for proteolysis targeting chimera synthesis.

Selective degradation of disease-causing proteins using proteolysis targeting chimeras (PROTACs) has gained great attention, thanks to its several advantages over traditional therapeutic modalities. Despite the advances made so far, the structural chemical complexity of PROTACs poses challenges in their synthetic approaches. PROTACs are typically prepared through a convergent approach, first synthesizing two fragments separately (target protein and E3 ligase ligands) and then coupling them to produce a fully assembled PROTAC. The amidation reaction represents the most common coupling exploited in PROTACs synthesis. Unfortunately, the overall isolated yields of such synthetic procedures are usually low due to one or more purification steps to obtain the final PROTAC with acceptable purity. In this work, we focused our attention on the optimization of the final amidation step for the synthesis of an anti-SARS-CoV-2 PROTAC by investigating different amidation coupling reagents and a range of alternative solvents, including ionic liquids (ILs). Among the ILs screened, [OMIM][ClO4] emerged as a successful replacement for the commonly used DMF within the HATU-mediated amidation reaction, thus allowing the synthesis of the target PROTAC under mild and sustainable conditions in very high isolated yields. With the optimised conditions in hand, we explored the scalability of the synthetic approach and the substrate scope of the reaction by employing different E3 ligase ligand (VHL and CRBN)-based intermediates containing linkers of different lengths and compositions or by using different target protein ligands. Interestingly, in all cases, we obtained high isolated yields and complete conversion in short reaction times.

Discovery and Structure-Activity Relationships of Novel ssDAF-12 Receptor Modulators.

The nuclear receptor ssDAF-12 has been recognized as the key molecular player regulating the life cycle of the nematode parasite Strongyloides stercoralis. ssDAF-12 ligands permit the receptor to function as an on/off switch modulating infection, making it vulnerable to therapeutic intervention. In this study, we report the design and synthesis of a set of novel dafachronic acid derivatives, which were used to outline the first structure-activity relationship targeting the ssDAF-12 receptor and to unveil hidden properties shared by the molecular shape of steroidal ligands that are relevant to the receptor binding and modulation. Moreover, biological results led to the discovery of sulfonamide 3 as a submicromolar ssDAF-12 agonist endowed with a high receptor selectivity, no toxicity, and improved properties, as well as to the identification of unprecedented ssDAF-12 antagonists that can be exploited in the search for novel chemical tools and alternative therapeutic approaches for treating parasitism such as Strongyloidiasis.

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics.

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

Structure-Based Optimization of 1,2,4-Triazole-3-Thione Derivatives: Improving Inhibition of NDM-/VIM-Type Metallo-ß-Lactamases and Synergistic Activity on Resistant Bacteria.

The worldwide emergence and dissemination of Gram-negative bacteria expressing metallo-ß-lactamases (MBLs) menace the efficacy of all ß-lactam antibiotics, including carbapenems, a last-line treatment usually restricted to severe pneumonia and urinary tract infections. Nonetheless, no MBL inhibitor is yet available in therapy. We previously identified a series of 1,2,4-triazole-3-thione derivatives acting as micromolar inhibitors of MBLs in vitro, but devoid of synergistic activity in microbiological assays. Here, via a multidisciplinary approach, including molecular modelling, synthesis, enzymology, microbiology, and X-ray crystallography, we optimized this series of compounds and identified low micromolar inhibitors active against clinically relevant MBLs (NDM-1- and VIM-type). The best inhibitors increased, to a certain extent, the susceptibility of NDM-1- and VIM-4-producing clinical isolates to meropenem. X-ray structures of three selected inhibitors in complex with NDM-1 elucidated molecular recognition at the base of potency improvement, confirmed in silico predicted orientation, and will guide further development steps.

VHL-Modified PROteolysis TArgeting Chimeras (PROTACs) as a Strategy to Evade Metabolic Degradation in In Vitro Applications.

PROteolysis TArgeting Chimeras (PROTACs) are tripartite molecules consisting of a linker connecting a ligand for a protein of interest to an E3 ligase recruiter, whose rationale relies on proteasome-based protein degradation. PROTACs have expanded as a therapeutic strategy to open new avenues for unmet medical needs. Leveraging our expertise, we undertook a series of in vitro experiments aimed at elucidating PROTAC metabolism. In particular, we focused on PROTACs recruiting the von Hippel-Lindau (VHL) E3 ligase. After high-resolution mass spectrometry measurements, a characteristic metabolite with mass reduction of 200 units was detected and successively confirmed as a product deriving from the cleavage of the VHL ligand moiety. Subsequently, we identified hepatic and extrahepatic prolyl endopeptidases as the main putative metabolic enzymes involved. Finally, we designed and synthesized analogs of the VHL ligands that we further exploited for the synthesis of novel VHL-directed PROTACs with an improved metabolic stability in in vitro applications.

DeepGRID: Deep Learning Using GRID Descriptors for BBB Prediction.

Deep Learning approaches are able to automatically extract relevant features from the input data and capture nonlinear relationships between the input and output. In this work, we present the GRID-derived AI (GrAId) descriptors, a simple modification to GRID MIFs that facilitate their use in combination with Convolutional Neural Networks (CNNs) to build Deep Learning models in a rotationally, conformationally, and alignment-independent approach we are calling DeepGRID. To our knowledge, this is the first time that GRID MIFs have been combined with CNNs in a Deep Learning approach. We applied the approach to build regression and classification models for blood-brain barrier permeation, an important factor when designing CNS drugs and conversely when designing to avoid off-target effects for CNS-inactive drugs. The VolSurf approach was one of the first to successfully model this property from three-dimensional structures, using descriptors derived from their GRID Molecular Interaction Fields (MIFs) in combination with PLS. We compared the DeepGRID models with others built using the hand-crafted VolSurf descriptors in combination with both PLS and Random Forest (RF). Both the DeepGRID and RF regression models performed best according to the % of compounds with a Geometric Mean Fold Error (GMFE) within 2-fold of the experimental data. Applying these regression models as classifiers, for the smaller 332 and 416 compound data sets all models performed well with ROC AUC values of ∼0.9 on the external test set. For the larger 2105 compound data set, the DeepGRID classifier performed the best with an AUC of 0.87 on the external test set with the RF model having an AUC of 0.84 and the original VolSurf lgBB model having an AUC of 0.83.

Growth hormone secretagogues modulate inflammation and fibrosis in mdx mouse model of Duchenne muscular dystrophy.

Introduction: Growth hormone secretagogues (GHSs) exert multiple actions, being able to activate GHS-receptor 1a, control inflammation and metabolism, to enhance GH/insulin-like growth factor-1 (IGF-1)-mediated myogenesis, and to inhibit angiotensin-converting enzyme. These mechanisms are of interest for potentially targeting multiple steps of pathogenic cascade in Duchenne muscular dystrophy (DMD). Methods: Here, we aimed to provide preclinical evidence for potential benefits of GHSs in DMD, via a multidisciplinary in vivo and ex vivo comparison in mdx mice, of two ad hoc synthesized compounds (EP80317 and JMV2894), with a wide but different profile. 4-week-old mdx mice were treated for 8 weeks with EP80317 or JMV2894 (320 µg/kg/d, s.c.). Results: In vivo, both GHSs increased mice forelimb force (recovery score, RS towards WT: 20% for EP80317 and 32% for JMV2894 at week 8). In parallel, GHSs also reduced diaphragm (DIA) and gastrocnemius (GC) ultrasound echodensity, a fibrosis-related parameter (RS: ranging between 26% and 75%). Ex vivo, both drugs ameliorated DIA isometric force and calcium-related indices (e.g., RS: 40% for tetanic force). Histological analysis highlighted a relevant reduction of fibrosis in GC and DIA muscles of treated mice, paralleled by a decrease in gene expression of TGF-ß1 and Col1a1. Also, decreased levels of pro-inflammatory genes (IL-6, CD68), accompanied by an increment in Sirt-1, PGC-1α and MEF2c expression, were observed in response to treatments, suggesting an overall improvement of myofiber metabolism. No detectable transcript levels of GHS receptor-1a, nor an increase of circulating IGF-1 were found, suggesting the presence of a novel receptor-independent mechanism in skeletal muscle. Preliminary docking studies revealed a potential binding capability of JMV2894 on metalloproteases involved in extracellular matrix remodeling and cytokine production, such as ADAMTS-5 and MMP-9, overactivated in DMD. Discussion: Our results support the interest of GHSs as modulators of pathology progression in mdx mice, disclosing a direct anti-fibrotic action that may prove beneficial to contrast pathological remodeling.

Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery.

INTRODUCTION: Protein-protein interactions (PPIs) have been often considered undruggable targets although they are attractive for the discovery of new therapeutics. The spread of artificial intelligence and machine learning complemented with experimental methods is likely to change the perspectives of protein-protein modulator research. Noteworthy, some novel low molecular weight (LMW) and short peptide modulators of PPIs are already in clinical trials for the treatment of relevant diseases. AREAS COVERED: This review focuses on the main molecular properties of protein-protein interfaces and on key concepts pertaining to the modulation of PPIs. The authors survey recently reported state-of-the-art methods dealing with the rational design of PPI modulators and highlight the role of several computer-based approaches. EXPERT OPINION: Interfering specifically with large protein interfaces is still an open challenge. The initial concerns about the unfavorable physicochemical properties of many of these modulators are nowadays less acute with several molecules lying beyond the rule of 5, orally available and successful in clinical trials. As the cost of biologics interfering with PPIs is very high, it would seem reasonable to put more effort, both in academia and the private sectors, on actively developing novel low molecular weight compounds and short peptides to perform this task.

ELIOT: A platform to navigate the E3 pocketome and aid the design of new PROTACs.

Proteolysis-targeting chimeras (PROTACs) are novel therapeutics for the treatment of human disease. They exploit the enormous potential of the E3 ligases, a class of proteins that mark a target protein for degradation via the ubiquitin-proteasome system. Despite the existence of several E3 ligase-related databases, the choice of the functioning ligase is limited to only 1.6% of those available, probably due to the fragmentary understanding of their structures and their known ligands; in fact, none of the existing databases report detailed studies covering their 3D structure or their pockets. Here, we report ELIOT (E3 LIgase pocketOme navigaTor), an accurate and complete platform containing the E3 ligase pocketome to enable navigation and selection of new E3 ligases and new ligands for the design of new PROTACs. All E3 ligase pockets were characterized with innovative 3D descriptors including their PROTAC-ability score, and similarity analyses between E3 pockets are presented. Tissue specificity and their degree of involvement in patients with specific cancer types are also annotated for each E3 ligase, enabling appropriate selection for the design of a PROTAC with improved specificity. All data are available at https://eliot.moldiscovery.com.

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.

Analytical and computational workflow for in-depth analysis of oxidized complex lipids in blood plasma.

Lipids are a structurally diverse class of biomolecules which can undergo a variety of chemical modifications. Among them, lipid (per)oxidation attracts most of the attention due to its significance in the regulation of inflammation, cell proliferation and death programs. Despite their apparent regulatory significance, the molecular repertoire of oxidized lipids remains largely elusive as accurate annotation of lipid modifications is complicated by their low abundance and often unknown, biological context-dependent structural diversity. Here, we provide a workflow based on the combination of bioinformatics and LC-MS/MS technologies to support identification and relative quantification of oxidized complex lipids in a modification type- and position-specific manner. The developed methodology is used to identify epilipidomics signatures of lean and obese individuals with and without type 2 diabetes. The characteristic signature of lipid modifications in lean individuals, dominated by the presence of modified octadecanoid acyl chains in phospho- and neutral lipids, is drastically shifted towards lipid peroxidation-driven accumulation of oxidized eicosanoids, suggesting significant alteration of endocrine signalling by oxidized lipids in metabolic disorders.

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening.

The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.

Exploring Ligand Binding Domain Dynamics in the NRs Superfamily.

Nuclear receptors (NRs) are transcription factors that play an important role in multiple diseases, such as cancer, inflammation, and metabolic disorders. They share a common structural organization composed of five domains, of which the ligand-binding domain (LBD) can adopt different conformations in response to substrate, agonist, and antagonist binding, leading to distinct transcription effects. A key feature of NRs is, indeed, their intrinsic dynamics that make them a challenging target in drug discovery. This work aims to provide a meaningful investigation of NR structural variability to outline a dynamic profile for each of them. To do that, we propose a methodology based on the computation and comparison of protein cavities among the crystallographic structures of NR LBDs. First, pockets were detected with the FLAPsite algorithm and then an "all against all" approach was applied by comparing each pair of pockets within the same sub-family on the basis of their similarity score. The analysis concerned all the detectable cavities in NRs, with particular attention paid to the active site pockets. This approach can guide the investigation of NR intrinsic dynamics, the selection of reference structures to be used in drug design and the easy identification of alternative binding sites.

Discovery of a new class of triazole based inhibitors of acetyl transferase KAT2A.

We have recently developed a new synthetic methodology that provided both N-aryl-5-hydroxytriazoles and N-pyridine-4-alkyl triazoles. A selection of these products was carried through virtual screening towards targets that are contemporary and validated for drug discovery and development. This study determined a number of potential structure target dyads of which N-pyridinium-4-carboxylic-5-alkyl triazole displayed the highest score specificity towards KAT2A. Binding affinity tests of abovementioned triazole and related analogs towards KAT2A confirmed the predictions of the in-silico assay. Finally, we have run in vitro inhibition assays of selected triazoles towards KAT2A; the ensemble of binding and inhibition assays delivered pyridyl-triazoles carboxylates as the prototype of a new class of inhibitors of KAT2A.

CROMATIC: Cross-Relationship Map of Cavities from Coronaviruses.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of COVID-19 disease, has rapidly imposed an urgent need to identify effective drug candidates. In this context, the high resolution and non-redundant beta-Coronavirus protein cavities database is pivotal to help virtual screening protocols. Furthermore, the cross-relationship among cavities can lead to highlighting multitarget therapy chances. Here, we first collect all protein cavities on SARS-CoV-2, SARS-CoV, and MERS-CoV X-ray structures, and then, we compute a similarity map by using molecular interaction fields (MIFs). All the results come together in CROMATIC (CROss-relationship MAp of CaviTIes from Coronaviruses). CROMATIC encloses both a comprehensive and a non-redundant version of the cavities collection and a similarity map revealing, on the one hand, cavities that are conserved among the three Coronaviruses and, on the other hand, unexpected similarities among cavities that can represent a key starting point for multitarget therapy strategies. Similarity analysis was also performed for the available structures of SARS-CoV-2 spike variants, linking sequence mutations to three-dimensional interaction alterations. The CROMATIC repository is freely available to the scientific community at https://github.com/moldiscovery/sars-cromatic.

Discovery of novel SARS-CoV-2 inhibitors targeting the main protease Mpro by virtual screenings and hit optimization.

Two years after its emergence, SARS-CoV-2 still represents a serious and global threat to human health. Antiviral drug development usually takes a long time and, to increase the chances of success, chemical variability of hit compounds represents a valuable source for the discovery of new antivirals. In this work, we applied a platform of variably oriented virtual screening campaigns to seek for novel chemical scaffolds for SARS-CoV-2 main protease (Mpro) inhibitors. The study on the resulting 30 best hits led to the identification of a series of structurally unrelated Mpro inhibitors. Some of them exhibited antiviral activity in the low micromolar range against SARS-CoV-2 and other human coronaviruses (HCoVs) in different cell lines. Time-of-addition experiments demonstrated an antiviral effect during the viral replication cycle at a time frame consistent with the inhibition of SARS-CoV-2 Mpro activity. As a proof-of-concept, to validate the pharmaceutical potential of the selected hits against SARS-CoV-2, we rationally optimized one of the hit compounds and obtained two potent SARS-CoV-2 inhibitors with increased activity against Mpro both in vitro and in a cellular context, as well as against SARS-CoV-2 replication in infected cells. This study significantly contributes to the expansion of the chemical variability of SARS-CoV-2 Mpro inhibitors and provides new scaffolds to be exploited for pan-coronavirus antiviral drug development.

Melatonin modulates Nrf2 activity to protect porcine pre-pubertal Sertoli cells from the abnormal H2 O2 generation and reductive stress effects of cadmium.

Melatonin (MLT) is a cytoprotective agent holding potential to prevent cadmium (Cd) toxicity and its impact in testicular function and fertility. In this study, we explored such potential in porcine pre-pubertal Sertoli cells (SCs). Cd toxicity resulted in impaired SC viability and function, abnormal cellular H2 O2 generation and efflux, and induction of reductive stress by the upregulation of Nrf2 expression and activity, cystine uptake and glutathione biosynthesis, glutathione-S-transferase P (GSTP) expression, and protein glutathionylation inhibition. Cd toxicity also stimulated the activity of cellular kinases (MAPK-ERK1/2 and Akt) and NFkB transcription factor, and cJun expression was increased. MLT produced a potent cytoprotective effect when co-administered with Cd to SCs; its efficacy and the molecular mechanism behind its cytoprotective function varied according to Cd concentrations. However, a significant restoration of cell viability and function, and of H2 O2 levels, was observed both at 5 and 10 µM Cd. Mechanistically, these effects of MLT were associated with a significant reduction of the Cd-induced activation of Nrf2 and GSTP expression at all Cd concentrations. CAT and MAPK-ERK1/2 activity upregulation was associated with these effects at 5 µM Cd, whereas glutathione biosynthesis and efflux were involved at 10 µM Cd together with an increased expression of the cystine transporter xCT, of cJun and Akt and NFkB activity. MLT protects SCs from Cd toxicity reducing its H2 O2 generation and reductive stress effects. A reduced activity of Nrf2 and the modulation of other molecular players of MLT signaling, provide a mechanistic rational for the cytoprotective effect of this molecule in SCs.

Resistance to PI3Kδ inhibitors in marginal zone lymphoma can be reverted by targeting the IL-6/PDGFRA axis.

PI3Kδ inhibitors are active in patients with lymphoid neoplasms and a first series of them have been approved for the treatment of multiple types of B-cell lymphoid tumors, including marginal zone lymphoma (MZL). The identification of the mechanisms underlying either primary or secondary resistance is fundamental to optimize the use of novel drugs. Here we present a model of secondary resistance to PI3Kδ inhibitors obtained by prolonged exposure of a splenic MZL cell line to idelalisib. The VL51 cell line was kept under continuous exposure to idelalisib. The study included detailed characterization of the model, pharmacological screens, silencing experiments, and validation experiments on multiple cell lines and on clinical specimens. VL51 developed resistance to idelalisib, copanlisib, duvelisib, and umbralisib. An integrative analysis of transcriptome and methylation data highlighted an enrichment of upregulated transcripts and low-methylated promoters in resistant cells, including IL-6/STAT3- and PDGFRA-related genes and surface CD19 expression, alongside the repression of the let-7 family of miRNA, and miR-125, miR-130, miR-193 and miR-20. The IL-6R blocking antibody tocilizumab, the STAT3 inhibitor stattic, the LIN28 inhibitor LIN1632, the PDGFR inhibitor masitinib and the anti-CD19 antibody drug conjugate loncastuximab tesirine were active compounds in the resistant cells as single agents and/or in combination with PI3Kδ inhibition. Findings were validated on additional in vitro lymphoma models and on clinical specimens. A novel model of resistance obtained from splenic MZL allowed the identification of therapeutic approaches able to improve the antitumor activity of PI3Kδ inhibitors in B-cell lymphoid tumors.