The antimicrobial drug pyrimethamine inhibits STAT3 transcriptional activity by targeting the enzyme dihydrofolate reductase

Prognostic and therapeutic potential of STAT3: Opportunities and challenges in targeting HPV-mediated cervical carcinogenesis

Cervical cancer (CaCx) ranks as the fourth most prevalent cancer among women globally. Persistent infection of high-risk human papillomaviruses (HR-HPVs) is major etiological factor associated with CaCx. Signal Transducer and Activator of Transcription 3 (STAT3), a prominent member of the STAT family, has emerged as independent oncogenic driver. It is a target of many oncogenic viruses including HPV. How STAT3 influences HPV viral gene expression or gets affected by HPV is an area of active investigation. A better understanding of host-virus interaction will provide a prognostic and therapeutic window for CaCx control and management. In this comprehensive review, we delve into carcinogenic role of STAT3 in development of HPV-induced CaCx. With an emphasis on fascinating interplay between STAT3 and HPV genome, the review explores the diverse array of opportunities and challenges associated with this field to harness the prognostic and therapeutic potential of STAT3 in CaCx.

Stability-based approaches in chemoproteomics

Target deconvolution can help understand how compounds exert therapeutic effects and can accelerate drug discovery by helping optimise safety and efficacy, revealing mechanisms of action, anticipate off-target effects and identifying opportunities for therapeutic expansion. Chemoproteomics, a combination of chemical biology with mass spectrometry has transformed target deconvolution. This review discusses modification-free chemoproteomic approaches that leverage the change in protein thermodynamics induced by small molecule ligand binding. Unlike modification-based methods relying on enriching specific protein targets, these approaches offer proteome-wide evaluations, driven by advancements in mass spectrometry sensitivity, increasing proteome coverage and quantitation methods. Advances in methods based on denaturation/precipitation by thermal or chemical denaturation, or by protease degradation are evaluated, emphasising the evolving landscape of chemoproteomics and its potential impact on future drug-development strategies.

Dual molecule targeting HDAC6 leads to intratumoral CD4+ cytotoxic lymphocytes recruitment through MHC-II upregulation on lung cancer cells

BACKGROUND

Despite the current therapeutic treatments including surgery, chemotherapy, radiotherapy and more recently immunotherapy, the mortality rate of lung cancer stays high. Regarding lung cancer, epigenetic modifications altering cell cycle, angiogenesis and programmed cancer cell death are therapeutic targets to combine with immunotherapy to improve treatment success. In a recent study, we uncovered that a molecule called QAPHA ((E)-3-(5-((2-cyanoquinolin-4-yl)(methyl)amino)-2-methoxyphenyl)-N-hydroxyacrylamide) has a dual function as both a tubulin polymerization and HDAC inhibitors. Here, we investigate the impact of this novel dual inhibitor on the immune response to lung cancer.

METHODS

To elucidate the mechanism of action of QAPHA, we conducted a chemical proteomics analysis. Using an in vivo mouse model of lung cancer (TC-1 tumor cells), we assessed the effects of QAPHA on tumor regression. Tumor infiltrating immune cells were characterized by flow cytometry.

RESULTS

In this study, we first showed that QAPHA effectively inhibited histone deacetylase 6, leading to upregulation of HSP90, cytochrome C and caspases, as revealed by proteomic analysis. We confirmed that QAPHA induces immunogenic cell death (ICD) by expressing calreticulin at cell surface in vitro and demonstrated its efficacy as a vaccine in vivo. Remarkably, even at a low concentration (0.5 mg/kg), QAPHA achieved complete tumor regression in approximately 60% of mice treated intratumorally, establishing a long-lasting anticancer immune response. Additionally, QAPHA treatment promoted the infiltration of M1-polarized macrophages in treated mice, indicating the induction of a pro-inflammatory environment within the tumor. Very interestingly, our findings also revealed that QAPHA upregulated major histocompatibility complex class II (MHC-II) expression on TC-1 tumor cells both in vitro and in vivo, facilitating the recruitment of cytotoxic CD4+T cells (CD4+CTL) expressing CD4+, NKG2D+, CRTAM+, and Perforin+. Finally, we showed that tumor regression strongly correlates to MHC-II expression level on tumor cell and CD4+ CTL infiltrate.

CONCLUSION

Collectively, our findings shed light on the discovery of a new multitarget inhibitor able to induce ICD and MHC-II upregulation in TC-1 tumor cell. These two processes participate in enhancing a specific CD4+ cytotoxic T cell-mediated antitumor response in vivo in our model of lung cancer. This breakthrough suggests the potential of QAPHA as a promising agent for cancer treatment.

Oncogenic STAT Transcription Factors as Targets for Cancer Therapy: Innovative Strategies and Clinical Translation

Despite advances in our understanding of molecular aspects of oncogenesis, cancer remains a leading cause of death. The malignant behavior of a cancer cell is driven by the inappropriate activation of transcription factors. In particular, signal transducers and activators of transcription (STATs), which regulate many critical cellular processes such as proliferation, apoptosis, and differentiation, are frequently activated inappropriately in a wide spectrum of human cancers. Multiple signaling pathways converge on the STATs, highlighting their importance in the development and progression of oncogenic diseases. STAT3 and STAT5 are two members of the STAT protein family that are the most frequently activated in cancers and can drive cancer pathogenesis directly. The development of inhibitors targeting STAT3 and STAT5 has been the subject of intense investigations in the last decade, although effective treatment options remain limited. In this review, we investigate the specific roles of STAT3 and STAT5 in normal physiology and cancer biology, discuss the opportunities and challenges in pharmacologically targeting STAT proteins and their upstream activators, and offer insights into novel therapeutic strategies to identify STAT inhibitors as cancer therapeutics.

Investigating the anti-cancer potential of pyrimethamine analogues through a modern chemical biology lens

Pharmacological inhibition of dihydrofolate reductase (DHFR) is an established approach for treating a variety of human diseases, including foreign infections and cancer. However, treatment with classic DHFR inhibitors, such as methotrexate (MTX), are associated with negative side-effects and resistance mechanisms that have prompted the search for alternatives. The DHFR inhibitor pyrimethamine (Pyr) has compelling anti-cancer activity in in vivo models, but lacks potency compared to MTX, thereby requiring higher concentrations to induce therapeutic responses. The purpose of this work was to investigate structural analogues of Pyr to improve its in vitro and cellular activity. A series of 36 Pyr analogues were synthesized and tested in a sequence of in vitro and cell-based assays to monitor their DHFR inhibitory activity, cellular target engagement, and impact on breast cancer cell viability. Ten top compounds were identified, two of which stood out as potential lead candidates, 32 and 34. These functionalized Pyr analogues potently engaged DHFR in cells, at concentrations as low as 1 nM and represent promising DHFR inhibitors that could be further explored as potential anti-cancer agents.

Pyrimidine salvage in Toxoplasma gondii as a target for new treatment

Toxoplasmosis is a common protozoan infection that can have severe outcomes in the immunocompromised and during pregnancy, but treatment options are limited. Recently, nucleotide metabolism has received much attention as a target for new antiprotozoal agents and here we focus on pyrimidine salvage by Toxoplasma gondii as a drug target. Whereas uptake of [3H]-cytidine and particularly [3H]-thymidine was at most marginal, [3H]-uracil and [3H]-uridine were readily taken up. Kinetic analysis of uridine uptake was consistent with a single transporter with a Km of 3.3 ± 0.8 µM, which was inhibited by uracil with high affinity (Ki = 1.15 ± 0.07 µM) but not by thymidine or 5-methyluridine, showing that the 5-Me group is incompatible with uptake by T. gondii. Conversely, [3H]-uracil transport displayed a Km of 2.05 ± 0.40 µM, not significantly different from the uracil Ki on uridine transport, and was inhibited by uridine with a Ki of 2.44 ± 0.59 µM, also not significantly different from the experimental uridine Km. The reciprocal, complete inhibition, displaying Hill slopes of approximately -1, strongly suggest that uridine and uracil share a single transporter with similarly high affinity for both, and we designate it uridine/uracil transporter 1 (TgUUT1). While TgUUT1 excludes 5-methyl substitutions, the smaller 5F substitution was tolerated, as 5F-uracil inhibited uptake of [3H]-uracil with a Ki of 6.80 ± 2.12 µM (P > 0.05 compared to uracil Km). Indeed, we found that 5F-Uridine, 5F-uracil and 5F,2'-deoxyuridine were all potent antimetabolites against T. gondii with EC50 values well below that of the current first line treatment, sulfadiazine. In vivo evaluation also showed that 5F-uracil and 5F,2'-deoxyuridine were similarly effective as sulfadiazine against acute toxoplasmosis. Our preliminary conclusion is that TgUUT1 mediates potential new anti-toxoplasmosis drugs with activity superior to the current treatment.

Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

Small molecule screen identifies pyrimethamine as an inhibitor of NRF2-driven esophageal hyperplasia

OBJECTIVE

NRF2 is a master transcription factor that regulates the stress response. NRF2 is frequently mutated and activated in human esophageal squamous cell carcinoma (ESCC), which drives resistance to chemotherapy and radiation therapy. Therefore, a great need exists for NRF2 inhibitors for targeted therapy of NRF2high ESCC.

DESIGN

We performed high-throughput screening of two compound libraries from which hit compounds were further validated in human ESCC cells and a genetically modified mouse model. The mechanism of action of one compound was explored by biochemical assays.

RESULTS

Using high-throughput screening of two small molecule compound libraries, we identified 11 hit compounds as potential NRF2 inhibitors with minimal cytotoxicity at specified concentrations. We then validated two of these compounds, pyrimethamine and mitoxantrone, by demonstrating their dose- and time-dependent inhibitory effects on the expression of NRF2 and its target genes in two NRF2Mut human ESCC cells (KYSE70 and KYSE180). RNAseq and qPCR confirmed the suppression of global NRF2 signaling by these two compounds. Mechanistically, pyrimethamine reduced NRF2 half-life by promoting NRF2 ubiquitination and degradation in KYSE70 and KYSE180 cells. Expression of an Nrf2E79Q allele in mouse esophageal epithelium (Sox2CreER;LSL-Nrf2E79Q/+) resulted in an NRF2high phenotype, which included squamous hyperplasia, hyperkeratinization, and hyperactive glycolysis. Treatment with pyrimethamine (30 mg/kg/day, p.o.) suppressed the NRF2high esophageal phenotype with no observed toxicity.

CONCLUSION

We have identified and validated pyrimethamine as an NRF2 inhibitor that may be rapidly tested in the clinic for NRF2high ESCC.

Recent Advances in Transcription Factors Biomarkers and Targeted Therapies Focusing on Epithelial-Mesenchymal Transition

Transcription factors involve many proteins in the process of transactivating or transcribing (none-) encoded DNA to initiate and regulate downstream signals, such as RNA polymerase. Their unique characteristic is that they possess specific domains that bind to specific DNA element sequences called enhancer or promoter sequences. Epithelial-mesenchymal transition (EMT) is involved in cancer progression. Many dysregulated transcription factors-such as Myc, SNAIs, Twists, and ZEBs-are key drivers of tumor metastasis through EMT regulation. This review summarizes currently available evidence related to the oncogenic role of classified transcription factors in EMT editing and epigenetic regulation, clarifying the roles of the classified conserved transcription factor family involved in the EMT and how these factors could be used as therapeutic targets in future investigations.

The Knowns and Unknowns in Protein-Metabolite Interactions

Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.

Discovery of new STAT3 inhibitors as anticancer agents using ligand-receptor contact fingerprints and docking-augmented machine learning

STAT3 belongs to a family of seven vital transcription factors. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of STAT3 inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contact Fingerprints and scoring values were implemented as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to identify critical descriptors that determine anti-STAT3 bioactivity to be translated into pharmacophore model(s). Two successful pharmacophores were deduced and subsequently used for in silico screening against the National Cancer Institute (NCI) database. A total of 26 hits were evaluated in vitro for their anti-STAT3 bioactivities. Out of which, three hits of novel chemotypes, showed cytotoxic IC50 values in the nanomolar range (35 nM to 6.7 μM). However, two are potent dihydrofolate reductase (DHFR) inhibitors and therefore should have significant indirect STAT3 inhibitory effects. The third hit (cytotoxic IC50 = 0.44 μM) is purely direct STAT3 inhibitor (devoid of DHFR activity) and caused, at its cytotoxic IC50, more than two-fold reduction in the expression of STAT3 downstream genes (c-Myc and Bcl-xL). The presented work indicates that the concept of data augmentation using multiple docked poses is a promising strategy for generating valid machine learning models capable of discriminating active from inactive compounds.

Cycloguanil and Analogues Potently Target DHFR in Cancer Cells to Elicit Anti-Cancer Activity

Dihydrofolate reductase (DHFR) is an established anti-cancer drug target whose inhibition disrupts folate metabolism and STAT3-dependent gene expression. Cycloguanil was proposed as a DHFR inhibitor in the 1950s and is the active metabolite of clinically approved plasmodium DHFR inhibitor Proguanil. The Cycloguanil scaffold was explored to generate potential cancer therapies in the 1970s. Herein, current computational and chemical biology techniques were employed to re-investigate the anti-cancer activity of Cycloguanil and related compounds. In silico modeling was employed to identify promising Cycloguanil analogues from NCI databases, which were cross-referenced with NCI-60 Human Tumor Cell Line Screening data. Using target engagement assays, it was found that these compounds engage DHFR in cells at sub-nanomolar concentrations; however, growth impairments were not observed until higher concentrations. Folinic acid treatment rescues the viability impairments induced by some, but not all, Cycloguanil analogues, suggesting these compounds may have additional targets. Cycloguanil and its most promising analogue, NSC127159, induced similar metabolite profiles compared to established DHFR inhibitors Methotrexate and Pyrimethamine while also blocking downstream signaling, including STAT3 transcriptional activity. These data confirm that Cycloguanil and its analogues are potent inhibitors of human DHFR, and their anti-cancer activity may be worth further investigation.

Proteome Integral Solubility Alteration (PISA) Assay in Mammalian Cells for Deep, High-Confidence, and High-Throughput Target Deconvolution

Chemical proteomics focuses on the drug-target-phenotype relationship for target deconvolution and elucidation of the mechanism of action-key and bottleneck in drug development and repurposing. Majorly due to the limits of using chemically modified ligands in affinity-based methods, new, unbiased, proteome-wide, and MS-based chemical proteomics approaches have been developed to perform drug target deconvolution, using full proteome profiling and no chemical modification of the studied ligand. Of note among them, thermal proteome profiling (TPP) aims to identify the target(s) by measuring the difference in melting temperatures between each identified protein in drug-treated versus vehicle-treated samples, with the thermodynamic interpretation of "protein melting" and curve fitting of all quantified proteins, at all temperatures, in each biological replicate. Including TPP, all the other chemical proteomics approaches often fail to provide target deconvolution with sufficient proteome depth, statistical power, throughput, and sustainability, which could hardly fulfill the final purpose of drug development. The proteome integral solubility alteration (PISA) assay provides no thermodynamic interpretation, but a throughput 10-100-fold compared to the other proteomics methods, high sustainability, much lower time of analysis and sample amount requirements, high confidence in results, maximal proteome coverage (~10,000 protein IDs), and up to five drugs / test molecules in one assay, with at least biological triplicates of each treatment. Each drug-treated or vehicle-treated sample is split into many fractions and exposed to a gradient of heat as solubility perturbing agent before being recomposed into one sample; each soluble fraction is isolated, then deep and quantitative proteomics is applied across all samples. The proteins interacting with the tested molecules (targets and off-targets), the activated mechanistic factors, or proteins modified during the treatment show reproducible changes in their soluble amount compared to vehicle-treated controls. As of today, the maximal multiplexing capability is 18 biological samples per PISA assay, which enables statistical robustness and flexible experimental design accommodation for fuller target deconvolution, including integration of orthogonal chemical proteomics methods in one PISA assay. Living cells for studying target engagement in vivo or, alternatively, protein extracts to identify in vitro ligand-interacting proteins can be studied, and the minimal need in sample amount unlocks target deconvolution using primary cells and their derived cultures. This protocol was validated in: J Biol Chem (2021), DOI: 10.1016/j.jbc.2021.10153 Graphical abstract.

Use of Human Lung Tissue Models for Screening of Drugs against SARS-CoV-2 Infection

The repurposing of licenced drugs for use against COVID-19 is one of the most rapid ways to develop new and alternative therapeutic options to manage the ongoing pandemic. Given circa 7817 licenced compounds available from Compounds Australia that can be screened, this paper demonstrates the utility of commercially available ex vivo/3D airway and alveolar tissue models. These models are a closer representation of in vivo studies than in vitro models, but retain the benefits of rapid in vitro screening for drug efficacy. We demonstrate that several existing drugs appear to show anti-SARS-CoV-2 activity against both SARS-CoV-2 Delta and Omicron Variants of Concern in the airway model. In particular, fluvoxamine, as well as aprepitant, everolimus, and sirolimus, has virus reduction efficacy comparable to the current standard of care (remdesivir, molnupiravir, nirmatrelvir). Whilst these results are encouraging, further testing and efficacy studies are required before clinical use can be considered.

MDACT: A New Principle of Adjunctive Cancer Treatment Using Combinations of Multiple Repurposed Drugs, with an Example Regimen

In part one of this two-part paper, we present eight principles that we believe must be considered for more effective treatment of the currently incurable cancers. These are addressed by multidrug adjunctive cancer treatment (MDACT), which uses multiple repurposed non-oncology drugs, not primarily to kill malignant cells, but rather to reduce the malignant cells' growth drives. Previous multidrug regimens have used MDACT principles, e.g., the CUSP9v3 glioblastoma treatment. MDACT is an amalgam of (1) the principle that to be effective in stopping a chain of events leading to an undesired outcome, one must break more than one link; (2) the principle of Palmer et al. of achieving fractional cancer cell killing via multiple drugs with independent mechanisms of action; (3) the principle of shaping versus decisive operations, both being required for successful cancer treatment; (4) an idea adapted from Chow et al., of using multiple cytotoxic medicines at low doses; (5) the idea behind CUSP9v3, using many non-oncology CNS-penetrant drugs from general medical practice, repurposed to block tumor survival paths; (6) the concept from chess that every move creates weaknesses and strengths; (7) the principle of mass-by adding force to a given effort, the chances of achieving the goal increase; and (8) the principle of blocking parallel signaling pathways. Part two gives an example MDACT regimen, gMDACT, which uses six repurposed drugs-celecoxib, dapsone, disulfiram, itraconazole, pyrimethamine, and telmisartan-to interfere with growth-driving elements common to cholangiocarcinoma, colon adenocarcinoma, glioblastoma, and non-small-cell lung cancer. gMDACT is another example of-not a replacement for-previous multidrug regimens already in clinical use, such as CUSP9v3. MDACT regimens are designed as adjuvants to be used with cytotoxic drugs.