Conversion of RNA Aptamer into Modified DNA Aptamers Provides for Prolonged Stability and Enhanced Antitumor Activity.

Aptamers, synthetic single-strand oligonucleotides that are similar in function to antibodies, are promising as therapeutics because of their minimal side effects. However, the stability and bioavailability of the aptamers pose a challenge. We developed aptamers converted from RNA aptamer to modified DNA aptamers that target phospho-AXL with improved stability and bioavailability. On the basis of the comparative analysis of a library of 17 converted modified DNA aptamers, we selected aptamer candidates, GLB-G25 and GLB-A04, that exhibited the highest bioavailability, stability, and robust antitumor effect in in vitro experiments. Backbone modifications such as thiophosphate or dithiophosphate and a covalent modification of the 5'-end of the aptamer with polyethylene glycol optimized the pharmacokinetic properties, improved the stability of the aptamers in vivo by reducing nuclease hydrolysis and renal clearance, and achieved high and sustained inhibition of AXL at a very low dose. Treatment with these modified aptamers in ovarian cancer orthotopic mouse models significantly reduced tumor growth and the number of metastases. This effective silencing of the phospho-AXL target thus demonstrated that aptamer specificity and bioavailability can be improved by the chemical modification of existing aptamers for phospho-AXL. These results lay the foundation for the translation of these aptamer candidates and companion biomarkers to the clinic.

Caspase-3 Substrates for Noninvasive Pharmacodynamic Imaging of Apoptosis by PET/CT.

Quantitative imaging of apoptosis in vivo could enable real-time monitoring of acute cell death pathologies such as traumatic brain injury, as well as the efficacy and safety of cancer therapy. Here, we describe the development and validation of F-18-labeled caspase-3 substrates for PET/CT imaging of apoptosis. Preliminary studies identified the O-benzylthreonine-containing substrate 2MP-TbD-AFC as a highly caspase 3-selective and cell-permeable fluorescent reporter. This lead compound was converted into the radiotracer [18F]-TBD, which was obtained at 10% decay-corrected yields with molar activities up to 149 GBq/µmol on an automated radiosynthesis platform. [18F]-TBD accumulated in ovarian cancer cells in a caspase- and cisplatin-dependent fashion. PET imaging of a Jo2-induced hepatotoxicity model showed a significant increase in [18F]-TBD signal in the livers of Jo2-treated mice compared to controls, driven through a reduction in hepatobiliary clearance. A chemical control tracer that could not be cleaved by caspase 3 showed no change in liver accumulation after induction of hepatocyte apoptosis. Our data demonstrate that [18F]-TBD provides an immediate pharmacodynamic readout of liver apoptosis in mice by dynamic PET/CT and suggest that [18F]-TBD could be used to interrogate apoptosis in other disease states.

Improving homology modeling of G-protein coupled receptors through multiple-template derived conserved inter-residue interactions.

Evidenced by the three-rounds of G-protein coupled receptors (GPCR) Dock competitions, improving homology modeling methods of helical transmembrane proteins including the GPCRs, based on templates of low sequence identity, remains an eminent challenge. Current approaches addressing this challenge adopt the philosophy of "modeling first, refinement next". In the present work, we developed an alternative modeling approach through the novel application of available multiple templates. First, conserved inter-residue interactions are derived from each additional template through conservation analysis of each template-target pairwise alignment. Then, these interactions are converted into distance restraints and incorporated in the homology modeling process. This approach was applied to modeling of the human ß2 adrenergic receptor using the bovin rhodopsin and the human protease-activated receptor 1 as templates and improved model quality was demonstrated compared to the homology model generated by standard single-template and multiple-template methods. This method of "refined restraints first, modeling next", provides a fast and complementary way to the current modeling approaches. It allows rational identification and implementation of additional conserved distance restraints extracted from multiple templates and/or experimental data, and has the potential to be applicable to modeling of all helical transmembrane proteins.

Development and evaluation of a java-based deep neural network method for drug response predictions.

Accurate prediction of drug response is a crucial step in personalized medicine. Recently, deep learning techniques have been witnessed with significant breakthroughs in a variety of areas including biomedical research and chemogenomic applications. This motivated us to develop a novel deep learning platform to accurately and reliably predict the response of cancer cells to different drug treatments. In the present work, we describe a Java-based implementation of deep neural network method, termed JavaDL, to predict cancer responses to drugs solely based on their chemical features. To this end, we devised a novel cost function and added a regularization term which suppresses overfitting. We also adopted an early stopping strategy to further reduce overfit and improve the accuracy and robustness of our models. To evaluate our method, we compared with several popular machine learning and deep neural network programs and observed that JavaDL either outperformed those methods in model building or obtained comparable predictions. Finally, JavaDL was employed to predict drug responses of several aggressive breast cancer cell lines, and the results showed robust and accurate predictions with r 2 as high as 0.81.

Directed evolution of cyclic peptides for inhibition of autophagy.

In recent decades it has become increasingly clear that induction of autophagy plays an important role in the development of treatment resistance and dormancy in many cancer types. Unfortunately, chloroquine (CQ) and hydroxychloroquine (HCQ), two autophagy inhibitors in clinical trials, suffer from poor pharmacokinetics and high toxicity at therapeutic dosages. This has prompted intense interest in the development of targeted autophagy inhibitors to re-sensitize disease to treatment with minimal impact on normal tissue. We utilized Scanning Unnatural Protease Resistant (SUPR) mRNA display to develop macrocyclic peptides targeting the autophagy protein LC3. The resulting peptides bound LC3A and LC3B-two essential components of the autophagosome maturation machinery-with mid-nanomolar affinities and disrupted protein-protein interactions (PPIs) between LC3 and its binding partners in vitro. The most promising LC3-binding SUPR peptide accessed the cytosol at low micromolar concentrations as measured by chloroalkane penetration assay (CAPA) and inhibited starvation-mediated GFP-LC3 puncta formation in a concentration-dependent manner. LC3-binding SUPR peptides re-sensitized platinum-resistant ovarian cancer cells to cisplatin treatment and triggered accumulation of the adapter protein p62 suggesting decreased autophagic flux through successful disruption of LC3 PPIs in cell culture. In mouse models of metastatic ovarian cancer, treatment with LC3-binding SUPR peptides and carboplatin resulted in almost complete inhibition of tumor growth after four weeks of treatment. These results indicate that SUPR peptide mRNA display can be used to develop cell-penetrating macrocyclic peptides that target and disrupt the autophagic machinery in vitro and in vivo.

Experimental validation of influenza A virus matrix protein (M1) interaction with host cellular alpha enolase and pyruvate kinase.

Influenza A virus, a respiratory pathogen manipulates various host cellular processes to establish a successful infection in a host. We had reported earlier the interaction of influenza A virus nucleoprotein with host glycolytic enzymes; alpha enolase and pyruvate kinase in A549 cells. Matrix protein (M1), another multifunctional protein encoded by genome segment 7 forms the inner layer of the virion and interacts with the ribonucleoprotein complex. Nucleoprotein and matrix protein, major structural components of the virion together contribute to the stability of the capsid. Thus, we have investigated the interaction of viral matrix protein with host glycolytic enzymes; alpha enolase and pyruvate kinase. Results had demonstrated differential expression of these two glycolytic enzymes in response to matrix protein and their interaction with matrix protein by in vitro binding, co-immunoprecipitation and co-localization studies. Our results confirmed that viral matrix protein interacts with host glycolytic enzymes in association with viral nucleoprotein.

An up-to-date overview of computational polypharmacology in modern drug discovery.

INTRODUCTION: In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success. AREAS COVERED: In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies. EXPERT OPINION: Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Targeting Forward and Reverse EphB4/EFNB2 Signaling by a Peptide with Dual Functions.

The tyrosine kinase receptor EphB4 is frequently overexpressed in ovarian and other solid tumors and is involved in interactions between tumor cells and the tumor microenvironment, contributing to metastasis. Trans-interaction between EphB4 and its membrane-bound ligand ephrin B2 (EFNB2) mediates bi-directional signaling: forward EFNB2-to-EphB4 signaling suppresses tumor cell proliferation, while reverse EphB4-to-EFNB2 signaling stimulates the invasive and angiogenic properties of endothelial cells. Currently, no small molecule-based, dual-function, EphB4-binding peptides are available. Here, we report our discovery of a bi-directional ephrin agonist peptide, BIDEN-AP which, when selectively internalized via receptor-mediated endocytosis, suppressed invasion and epithelial-mesenchymal transition of ovarian cancer cells. BIDEN-AP also inhibited endothelial migration and tube formation. In vivo, BIDEN-AP and its nanoconjugate CCPM-BIDEN-AP significantly reduced growth of orthotopic ovarian tumors, with CCPM-BIDEN-AP displaying greater antitumor potency than BIDEN-AP. Both BIDEN-AP and CCPM-BIDEN-AP compromised angiogenesis by downregulating epithelial-mesenchymal transition and angiogenic pathways. Thus, we report a novel EphB4-based therapeutic approach against ovarian cancer.

Structural Modeling and in Silico Screening of Potential Small-Molecule Allosteric Agonists of a Glucagon-like Peptide 1 Receptor.

The glucagon-like peptide 1 receptor (GLP-1R) belongs to the pharmaceutically important class B family of G-protein-coupled receptors (GPCRs), and its incretin peptide ligand GLP-1 analogs are adopted drugs for the treatment of type 2 diabetes. Despite remarkable antidiabetic effects, GLP-1 peptide-based drugs are limited by the need of injection. On the other hand, developing nonpeptidic small-molecule drugs targeting GLP-1R remains elusive. Here, we first constructed a three-dimensional structure model of the transmembrane (TM) domain of human GLP-1R using homology modeling and conformational sampling techniques. Next, a potential allosteric binding site on the TM domain was predicted computationally. In silico screening of druglike compounds against this predicted allosteric site has identified nine compounds as potential GLP-1R agonists. The independent agonistic activity of two compounds was subsequently confirmed using a cAMP response element-based luciferase reporting system. One compound was also shown to stimulate insulin secretion through in vitro assay. In addition, this compound synergized with GLP-1 to activate human GLP-1R. These results demonstrated that allosteric regulation potentially exists in GLP-1R and can be exploited for developing small-molecule agonists. The success of this work will help pave the way for small-molecule drug discovery targeting other class B GPCRs through allosteric regulations.

Creating novel translation inhibitors to target pro-survival proteins in chronic lymphocytic leukemia.

The viability of chronic lymphocytic leukemia (CLL) is critically dependent upon staving off death by apoptosis, a hallmark of CLL pathophysiology. The recognition that Mcl-1, a major component of the anti-apoptotic response, is intrinsically short-lived and must be continually resynthesized suggested a novel therapeutic approach. Pateamine A (PatA), a macrolide marine natural product, inhibits cap-dependent translation by binding to the initiation factor eIF4A. In this study, we demonstrated that a synthetic derivative of PatA, des-methyl des-amino PatA (DMDAPatA), blocked mRNA translation, reduced Mcl-1 protein and initiated apoptosis in CLL cells. This action was synergistic with the Bcl-2 antagonist ABT-199. However, avid binding to human plasma proteins limited DMDAPatA potency, precluding further development. To address this, we synthesized a new series of PatA analogs and identified three new leads with potent inhibition of translation. They exhibited less plasma protein binding and increased cytotoxic potency toward CLL cells than DMDAPatA, with greater selectivity towards CLL cells over normal lymphocytes. Computer modeling analysis correlated their structure-activity relationships and suggested that these compounds may act by stabilizing the closed conformation of eIF4A. Thus, these novel PatA analogs hold promise for application to cancers within the appropriate biological context, such as CLL.

Computational polypharmacology: a new paradigm for drug discovery.

INTRODUCTION: Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.

PyMine: a PyMOL plugin to integrate and visualize data for drug discovery.

BACKGROUND: Tremendous amount of chemical and biological data are being generated by various high-throughput biotechnologies that could facilitate modern drug discovery. However, lack of integration makes it very challenging for individual scientists to access and understand all the data related to a specific protein of interest. FINDINGS: To overcome this challenge, we developed PyMine, a PyMOL plugin that retrieves chemical, structural, pathway and other related biological data of a receptor and small molecules from a variety of high-quality databases and presents them in a graphic and uniformed way. CONCLUSIONS: Developed as an interactive and user-friendly tool, PyMine can be used as a central data-hub for users to access and visualize multiple types of data and to generate new ideas intuitively for structure-based molecule design.