Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology

Developing kinase inhibitors for malaria: an opportunity or liability?

Highly druggable and essential to almost all aspects of cellular life, the protein and phosphoinositide kinase gene families offer a wealth of potential targets for pharmacological modulation for both noncommunicable and infectious diseases. Despite the success of kinase inhibitors in oncology and other disease indications, targeting kinases comes with significant challenges. Key hurdles for kinase drug discovery include selectivity and acquired resistance. The phosphatidylinositol 4-kinase beta inhibitor MMV390048 showed good efficacy in Phase 2a clinical trials, demonstrating the potential of kinase inhibitors for malaria treatment. Here we argue that the potential benefits of Plasmodium kinase inhibitors outweigh the risks, and we highlight the opportunity for designed polypharmacology to reduce the risk of resistance.

Machine Learning-driven Fragment-based Discovery of CIB1-directed Anti-Tumor Agents by FRASE-bot

Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify. Novel therapeutic targets are often hard-to-drug proteins, such as messengers or transcription factors. Computational strategies arise as a promising solution to expedite drug discovery for unconventional therapeutic targets. FRASE-bot exploits big data and machine learning (ML) to distill 3D information relevant to the target protein from thousands of protein-ligand complexes to seed it with ligand fragments. The seeded fragments can then inform either (i) de novo design of 3D ligand structures or (ii) ultra-large-scale virtual screening of commercially available compounds. Here, FRASE-bot was applied to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising but ligand-orphan drug target implicated in triple negative breast cancer. The signaling function of CIB1 relies on protein-protein interactions and its structure does not feature any natural ligand-binding pocket. FRASE-based virtual screening identified the first small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depleted cells.

Targeting NEDD8-activating enzyme for cancer therapy: developments, clinical trials, challenges and future research directions

NEDDylation, a post-translational modification through three-step enzymatic cascades, plays crucial roles in the regulation of diverse biological processes. NEDD8-activating enzyme (NAE) as the only activation enzyme in the NEDDylation modification has become an attractive target to develop anticancer drugs. To date, numerous inhibitors or agonists targeting NAE have been developed. Among them, covalent NAE inhibitors such as MLN4924 and TAS4464 currently entered into clinical trials for cancer therapy, particularly for hematological tumors. This review explains the relationships between NEDDylation and cancers, structural characteristics of NAE and multistep mechanisms of NEDD8 activation by NAE. In addition, the potential approaches to discover NAE inhibitors and detailed pharmacological mechanisms of NAE inhibitors in the clinical stage are explored in depth. Importantly, we reasonably investigate the challenges of NAE inhibitors for cancer therapy and possible development directions of NAE-targeting drugs in the future.

Multidisciplinary Advances Address the Challenges in Developing Drugs against Transient Receptor Potential Channels to Treat Metabolic Disorders

Transient receptor potential (TRP) channels are cation channels that regulate key physiological and pathological processes in response to a broad range of stimuli. Moreover, they systemically regulate the release of hormones, metabolic homeostasis, and complications of diabetes, which positions them as promising therapeutic targets to combat metabolic disorders. Nevertheless, there are significant challenges in the design of TRP ligands with high potency and durability. Herein we summarize the four challenges as hydrophobicity, selectivity, mono-target therapy, and interspecies discrepancy. We present 1134 TRP ligands with diversified modes of TRP-ligand interaction and provide a detailed discussion of the latest strategies, especially cryogenic electron microscopy (cryo-EM) and computational methods. We propose solutions to address the challenges with a critical analysis of advances in membrane partitioning, polypharmacology, biased agonism, and biochemical screening of transcriptional modulators. They are fueled by the breakthrough from cryo-EM, chemoinformatics and bioinformatics. The discussion is aimed to shed new light on designing next-generation drugs to treat obesity, diabetes and its complications, with optimal hydrophobicity, higher mode selectivity, multi-targeting and consistent activities between human and rodents.

Advances in Computational Polypharmacology

In drug discovery, polypharmacology encompasses the use of small molecules with defined multi-target activity and in vivo effects resulting from multi-target engagement. Multi-target compounds are often efficacious in the treatment of complex diseases involving target and pathway networks, but might also elicit unwanted side effects. Computational approaches such as target prediction or multi-target ligand design have been used to support polypharmacological drug discovery. In addition to efforts directed at the identification or design of new multi-target compounds, other computational investigations have aimed to differentiate such compounds from potential false-positives or explore the molecular basis of multi-target activities. Herein, a concise overview of the field is provided and recent advances in computational polypharmacology through machine learning are discussed.

Network Pharmacology of Adaptogens in the Assessment of Their Pleiotropic Therapeutic Activity

The reductionist concept, based on the ligand–receptor interaction, is not a suitable model for adaptogens, and herbal preparations affect multiple physiological functions, revealing polyvalent pharmacological activities, and are traditionally used in many conditions. This review, for the first time, provides a rationale for the pleiotropic therapeutic efficacy of adaptogens based on evidence from recent gene expression studies in target cells and where the network pharmacology and systems biology approaches were applied. The specific molecular targets and adaptive stress response signaling mechanisms involved in nonspecific modes of action of adaptogens are identified.

Organocatalytic asymmetric azidation of sulfoxonium ylides: mild synthesis of enantioenriched α-azido ketones bearing a labile tertiary stereocenter

Disclosed here is a catalytic asymmetric azidation reaction for the efficient synthesis of α-azido ketones bearing a labile tertiary stereocenter. With a superb chiral squaramide catalyst, a mild asymmetric formal H–N₃ insertion of α-carbonyl sulfoxonium ylides proceeded with excellent efficiency and enantioselectivity. This organocatalytic process not only complements the previous α-azidation approaches for the formation of quaternary stereocenters and mostly for 1,3-dicarbonyl compounds, but also has advantages over the well-known metal-catalyzed asymmetric carbene insertion chemistry using α-diazocarbonyl compounds. Detailed mechanistic studies via control reactions and NMR studies provided important insights into the reaction pathway, which features reversible protonation and dynamic kinetic resolution. The curiosity in mechanism also led to the development of a simplified alternative protocol with a cheaper HN₃ source.

An organocatalytic asymmetric H–N₃ insertion of α-carbonyl sulfoxonium ylides has been developed, providing efficient access to α-azido ketones bearing labile tertiary stereocenters and complementing the metal carbene insertion chemistry.

MerTK activity is not necessary for the proliferation of glioblastoma stem cells

MerTK has been identified as a promising target for therapeutic intervention in glioblastoma. Genetic studies documented a range of oncogenic processes that MerTK targeting could influence, however robust pharmacological validation has been missing. The aim of this study was to assess therapeutic potential of MerTK inhibitors in glioblastoma therapy. Unlike previous studies, our work provides several lines of evidence that MerTK activity is dispensable for glioblastoma growth. We observed heterogeneous responses to MerTK inhibitors that could not be correlated to MerTK inhibition or MerTK expression in cells. The more selective MerTK inhibitors UNC2250 and UNC2580A lack the anti-proliferative potency of less-selective inhibitors exemplified by UNC2025. Functional assays in MerTK-high and MerTK-deficient cells further demonstrate that the anti-cancer efficacy of UNC2025 is MerTK-independent. However, despite its efficacy in vitro, UNC2025 failed to attenuate glioblastoma growth in vivo. Gene expression analysis from cohorts of glioblastoma patients identified that MerTK expression correlates negatively with proliferation and positively with quiescence genes, suggesting that MerTK regulates dormancy rather than proliferation in glioblastoma. In summary, this study demonstrates the importance of orthogonal inhibitors and disease-relevant models in target validation studies and raises a possibility that MerTK inhibitors could be used to target dormant glioblastoma cells.

Enhancing Chemogenomics with Predictive Pharmacology

One of the grand challenges in contemporary chemical biology is the generation of a probe for every member of the human proteome. Probe selection and optimization strategies typically rely on experimental bioactivity data to determine the potency and selectivity of candidate molecules. However, this approach is profoundly limited by the sparsity of the known data, the annotation bias often found in the literature, and the cost of physical screening. Recent advancements in predictive pharmacology, such as the application of multitask and transfer learning, as well as the use of biologically motivated, structure-agnostic features to characterize molecules, should serve to mitigate these issues. Computational modeling likely offers the only cost-effective approach to substantially increasing the bioactivity annotation density both on the local and global scale and thus, we argue, will need to make a substantial contribution if the ambitious goals of probing the human proteome are to be realized in the foreseeable future.

MERTK in cancer therapy: Targeting the receptor tyrosine kinase in tumor cells and the immune system

The receptor tyrosine kinase MERTK is aberrantly expressed in numerous human malignancies, and is a novel target in cancer therapeutics. Physiologic roles of MERTK include regulation of tissue homeostasis and repair, innate immune control, and platelet aggregation. However, aberrant expression in a wide range of liquid and solid malignancies promotes neoplasia via growth factor independence, cell cycle progression, proliferation and tumor growth, resistance to apoptosis, and promotion of tumor metastases. Additionally, MERTK signaling contributes to an immunosuppressive tumor microenvironment via induction of an anti-inflammatory cytokine profile and regulation of the PD-1 axis, as well as regulation of macrophage, myeloid-derived suppressor cell, natural killer cell and T cell functions. Various MERTK-directed therapies are in preclinical development, and clinical trials are underway. In this review we discuss MERTK inhibition as an emerging strategy for cancer therapy, focusing on MERTK expression and function in neoplasia and its role in mediating resistance to cytotoxic and targeted therapies as well as in suppressing anti-tumor immunity. Additionally, we review preclinical and clinical pharmacological strategies to target MERTK.

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.

MERTK in cancer therapy: Targeting the receptor tyrosine kinase in tumor cells and the immune system

The receptor tyrosine kinase MERTK is aberrantly expressed in numerous human malignancies, and is a novel target in cancer therapeutics. Physiologic roles of MERTK include regulation of tissue homeostasis and repair, innate immune control, and platelet aggregation. However, aberrant expression in a wide range of liquid and solid malignancies promotes neoplasia via growth factor independence, cell cycle progression, proliferation and tumor growth, resistance to apoptosis, and promotion of tumor metastases. Additionally, MERTK signaling contributes to an immunosuppressive tumor microenvironment via induction of an anti-inflammatory cytokine profile and regulation of the PD-1 axis, as well as regulation of macrophage, myeloid-derived suppressor cell, natural killer cell and T cell functions. Various MERTK-directed therapies are in preclinical development, and clinical trials are underway. In this review we discuss MERTK inhibition as an emerging strategy for cancer therapy, focusing on MERTK expression and function in neoplasia and its role in mediating resistance to cytotoxic and targeted therapies as well as in suppressing anti-tumor immunity. Additionally, we review preclinical and clinical pharmacological strategies to target MERTK.