Targeting Gatekeeper Mutations for Kinase Drug Discovery

Synthesis and evaluation of chemical linchpins for highly selective CK2α targeting

Casein kinase-2 (CK2) are serine/threonine kinases with dual co-factor (ATP and GTP) specificity, that are involved in the regulation of a wide variety of cellular functions. Small molecules targeting CK2 have been described in the literature targeting different binding pockets of the kinase with a focus on type I inhibitors such as the recently published chemical probe SGC-CK2-1. In this study, we investigated whether known allosteric inhibitors binding to a pocket adjacent to helix αD could be combined with ATP mimetic moieties defining a novel class of ATP competitive compounds with a unique binding mode. Linking both binding sites requires a chemical linking moiety that would introduce a 90-degree angle between the ATP mimetic ring system and the αD targeting moiety, which was realized using a sulfonamide. The synthesized inhibitors were highly selective for CK2 with binding constants in the nM range and low micromolar activity. While these inhibitors need to be further improved, the present work provides a structure-based design strategy for highly selective CK2 inhibitors.

Side Chain Investigation of Imidazopyridazine as a Hinge Binder for Targeting Actionable Mutations of RET Kinase

Actionable mutations of RET kinase have been identified as oncogenic drivers of solid tumors, including thyroid cancer, metastatic colorectal cancer, and nonsmall cell lung cancer. Although multikinase inhibitors and RET selective inhibitors are used to treat patients with RET alterations, there is insufficient research addressing certain issues: which actionable mutations arise from these therapies, how to improve the clinical response rate to RET inhibitors, and how to design new inhibitors to overcome drug resistance. Therefore, the development of sophisticated tool compounds is required to investigate the molecular mechanisms of actionable mutations and to develop breakthrough therapeutics for different RET alterations. Herein, we present our investigation into the side chains of imidazopyridazine hinge binders that are capable of inducing protein-ligand interaction patterns from the gatekeeper to the waterfront regions. Extending the substituents at the second and sixth positions enhanced the IC50 up to < 0.5 nM for diverse RET alterations.

Targeting Solvent-Front Mutations for Kinase Drug Discovery: From Structural Basis to Design Strategies

Solvent-front mutations have emerged as a common mechanism leading to acquired resistance to kinase inhibitors, representing a major challenge in the clinic. Several new-generation kinase inhibitors targeting solvent-front mutations have either been approved or advanced to clinical trials. However, there remains a need to discover effective, new-generation inhibitors. In this Perspective, we systematically summarize the general types of solvent-front mutations across the kinome and describe the development of inhibitors targeting some key solvent-front mutations. Additionally, we highlight the challenges and opportunities for the next generation of kinase inhibitors directed toward overcoming solvent-front mutations.

Design, synthesis and antitumor activity of a novel FGFR2-selective degrader to overcome resistance of the FGFR2V564F gatekeeper mutation based on a pan-FGFR inhibitor

Aberrant activation of fibroblast growth factor receptors (FGFRs) contributes to the development and progression of multiple types of cancer. Although many FGFR inhibitors have been approved by the FDA, their long-term therapeutic efficacy is hampered by acquired resistance to gatekeeper mutations and low subtype selectivity. FGFR2 has been found to be frequently amplified or mutated in many tumors. In this study, we designed several PROTACs with different E3 ligands based on LY2874455. By screening the length of the linker and the binding site in various degraders, we obtained a novel and highly efficient FGFR2-selective degrader 28e (DC50 = 0.645 nM, DCmax = 86 %). Compound 28e selectively degraded FGFR2 and essentially avoided degradation of FGFR1,3,4 isoforms (DC50 > 300 nM). Compound 28e significantly inhibited the proliferation of FGFR2-overexpressing cell lines, including KATOIII, SNU16, and AN3CA (IC50 = 0.794 nM/0.207 nM/4.626 nM), comparable to parental inhibitors. At the same time, the preferred compound showed superiority over the parental inhibitor in kinase inhibitory activity against the gatekeeper mutant isoform FGFR2V564F (IC50 = 0.121 nM). In summary, we identified 28e as a novel selective degrader of FGFR2 with high potency and high potential to overcome resistance to gatekeeper mutation. The discovery of 28e provides new evidence for the strategy of pan-inhibitor-based development of selective degrading agents.

Discovery and characterization of novel FGFR1 inhibitors in triple-negative breast cancer via hybrid virtual screening and molecular dynamics simulations

The overexpression of FGFR1 is thought to significantly contribute to the progression of triple-negative breast cancer (TNBC), impacting aspects such as tumorigenesis, growth, metastasis, and drug resistance. Consequently, the pursuit of effective inhibitors for FGFR1 is a key area of research interest. In response to this need, our study developed a hybrid virtual screening method. Utilizing KarmaDock, an innovative algorithm that blends deep learning with molecular docking, alongside Schrödinger's Residue Scanning. This strategy led us to identify compound 6, which demonstrated promising FGFR1 inhibitory activity, evidenced by an IC50 value of approximately 0.24 nM in the HTRF bioassay. Further evaluation revealed that this compound also inhibits the FGFR1 V561M variant with an IC50 value around 1.24 nM. Our subsequent investigations demonstrate that Compound 6 robustly suppresses the migration and invasion capacities of TNBC cell lines, through the downregulation of p-FGFR1 and modulation of EMT markers, highlighting its promise as a potent anti-metastatic therapeutic agent. Additionally, our use of molecular dynamics simulations provided a deeper understanding of the compound's specific binding interactions with FGFR1.

Mechanisms of resistance to tyrosine kinase inhibitor-targeted therapy and overcoming strategies

Tyrosine kinase inhibitor (TKI)-targeted therapy has revolutionized cancer treatment by selectively blocking specific signaling pathways crucial for tumor growth, offering improved outcomes with fewer side effects compared with conventional chemotherapy. However, despite their initial effectiveness, resistance to TKIs remains a significant challenge in clinical practice. Understanding the mechanisms underlying TKI resistance is paramount for improving patient outcomes and developing more effective treatment strategies. In this review, we explored various mechanisms contributing to TKI resistance, including on-target mechanisms and off-target mechanisms, as well as changes in the tumor histology and tumor microenvironment (intrinsic mechanisms). Additionally, we summarized current therapeutic approaches aiming at circumventing TKI resistance, including the development of next-generation TKIs and combination therapies. We also discussed emerging strategies such as the use of dual-targeted antibodies and PROteolysis Targeting Chimeras. Furthermore, we explored future directions in TKI-targeted therapy, including the methods for detecting and monitoring drug resistance during treatment, identification of novel targets, exploration of dual-acting kinase inhibitors, application of nanotechnologies in targeted therapy, and so on. Overall, this review provides a comprehensive overview of the challenges and opportunities in TKI-targeted therapy, aiming to advance our understanding of resistance mechanisms and guide the development of more effective therapeutic approaches in cancer treatment.

Computational biophysical characterization of the effect of gatekeeper mutations on the binding of ponatinib to the FGFR kinase

Fibroblast Growth Factor Receptor (FGFR) is connected to numerous downstream signalling cascades regulating cellular behavior. Any dysregulation leads to a plethora of illnesses, including cancer. Therapeutics are available, but drug resistance driven by gatekeeper mutation impedes the treatment. Ponatinib is an FDA-approved drug against BCR-ABL kinase and has shown effective results against FGFR-mediated carcinogenesis. Herein, we undertake molecular dynamics simulation-based analysis on ponatinib against all the FGFR isoforms having Val to Met gatekeeper mutations. The results suggest that ponatinib is a potent and selective inhibitor for FGFR1, FGFR2, and FGFR4 gatekeeper mutations. The extensive electrostatic and van der Waals interaction network accounts for its high potency. The FGFR3_VM mutation has shown resistance towards ponatinib, which is supported by their lesser binding affinity than wild-type complexes. The disengaged molecular brake and engaged hydrophobic spine were believed to be the driving factors for weak protein-ligand interaction. Taken together, the inhibitory and structural characteristics exhibited by ponatinib may aid in thwarting resistance based on Val-to-Met gatekeeper mutations at an earlier stage of treatment and advance the design and development of other inhibitors targeted at FGFRs harboring gatekeeper mutations.

Overcoming Secondary Mutations of Type II Kinase Inhibitors

Type II kinase inhibitors bind in the "DFG-out" kinase conformation and are generally considered to be more potent and selective than type I inhibitors, which target a DFG-in conformation. Nine type II inhibitors are currently clinically approved, with more undergoing clinical development. Resistance-conferring secondary mutations emerged with the first series of type II inhibitors, most commonly at residues within the kinase activation loop and at the "gatekeeper" position. Recently, new inhibitors have been developed to overcome such mutations; however, mutations activating other pathways (and/or other targets) have subsequently emerged on occasion. Here, we systematically summarize the secondary mutations that confer resistance to type II inhibitors, the structural basis for resistance, newer inhibitors designed to overcome resistance, as well as the challenges and opportunities for the development of new inhibitors to overcome secondary kinase domain mutations.

Global Reactivity Profiling of the Catalytic Lysine in Human Kinome for Covalent Inhibitor Development

Advances in targeted covalent inhibitors (TCIs) have been made by using lysine-reactive chemistries. Few aminophiles possessing balanced reactivity/stability for the development of cell-active TCIs are however available. We report herein lysine-reactive activity-based probes (ABPs; 2-14) based on the chemistry of aryl fluorosulfates (ArOSO2 F) capable of global reactivity profiling of the catalytic lysine in human kinome from mammalian cells. We concurrently developed reversible covalent ABPs (15/16) by installing salicylaldehydes (SA) onto a promiscuous kinase-binding scaffold. The stability and amine reactivity of these probes exhibited a broad range of tunability. X-ray crystallography and mass spectrometry (MS) confirmed the successful covalent engagement between ArOSO2 F on 9 and the catalytic lysine of SRC kinase. Chemoproteomic studies enabled the profiling of >300 endogenous kinases, thus providing a global landscape of ligandable catalytic lysines of the kinome. By further introducing these aminophiles into VX-680 (a noncovalent inhibitor of AURKA kinase), we generated novel lysine-reactive TCIs that exhibited excellent in vitro potency and reasonable cellular activities with prolonged residence time. Our work serves as a general guide for the development of lysine-reactive ArOSO2 F-based TCIs.

Selective type II TRK inhibitors overcome xDFG mutation mediated acquired resistance to the second-generation inhibitors selitrectinib and repotrectinib

Neurotrophic receptor kinase (NTRK) fusions are actionable oncogenic drivers of multiple pediatric and adult solid tumors, and tropomyosin receptor kinase (TRK) has been considered as an attractive therapeutic target for "pan-cancer" harboring these fusions. Currently, two generations TRK inhibitors have been developed. The representative second-generation inhibitors selitrectinib and repotrectinib were designed to overcome clinic acquired resistance of the first-generation inhibitors larotrectinib or entrectinib resulted from solvent-front and gatekeeper on-target mutations. However, xDFG (TRKAG667C/A/S, homologous TRKCG696C/A/S) and some double mutations still confer resistance to selitrectinib and repotrectinib, and overcoming these resistances represents a major unmet clinical need. In this review, we summarize the acquired resistance mechanism of the first- and second-generation TRK inhibitors, and firstly put forward the emerging selective type II TRK inhibitors to overcome xDFG mutations mediated resistance. Additionally, we concluded our perspectives on new challenges and future directions in this field.

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides

The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.

Molecular dynamics simulations provide insights into ULK-101 potency and selectivity toward autophagic kinases ULK1/2

Kinase domains are highly conserved within protein kinases in both sequence and structure. Many factors, including phosphorylation, amino acid substitutions or mutations, and small molecule inhibitor binding, influence conformations of the kinase domain and enzymatic activity. The serine/threonine kinases ULK1 and ULK2 are highly conserved with N- and C-terminal domains, phosphate-binding P-loops, αC-helix, regulatory and catalytic spines, and activation loop DFG and APE motifs. Here, we performed molecular dynamics (MD) simulations to understand better the potency and selectivity of the ULK1/2 small molecule inhibitor, ULK-101. We observed stable bound states for ULK-101 to the adenosine triphosphate (ATP)-binding site of ULK2, coordinated by hydrogen bonding with the hinge backbone and the catalytic lysine sidechain. Notably, ULK-101 occupies a hydrophobic pocket associated with the N-terminus of the αC-helix. Large movements in the P-loop are also associated with ULK-101 inhibitor binding and exit from ULK2. Our data further suggests that ULK-101 could induce a folded P-loop conformation and hydrophobic pocket reflected in its nanomolar potency and kinome selectivity.

Recruitment of hexahydroquinoline as anticancer scaffold targeting inhibition of wild and mutants EGFR (EGFRWT, EGFRT790M, and EGFRL858R)

Hexahydroquinoline (HHQ) scaffold was constructed and recruited for development of new series of anticancer agents. Thirty-two new compounds were synthesised where x-ray crystallography was performed to confirm enantiomerism. Thirteen compounds showed moderate to good activity against NCI 60 cancer cell lines, with GI % mean up to 74% for 10c. Expending erlotinib as a reference drug, target compounds were verified for their inhibiting activities against EGFRWT, EGFRT790M, and EGFRL858R where compound 10d was the best inhibitor with IC50 = 0.097, 0.280, and 0.051 µM, respectively, compared to erlotinib (IC50 = 0.082 µM, 0.342 µM, and 0.055 µM, respectively). Safety profile was validated using normal human lung (IMR-90) cells. 10c and 10d disrupted cell cycle at pre-G1 and G2/M phases in lung cancer, HOP-92, and cell line. Molecular docking study was achieved to understand the potential binding interactions and affinities in the active sites of three versions of EGFRs.

RPBP: Deep Retrosynthesis Reaction Prediction Based on Byproducts

Retrosynthesis prediction is crucial in organic synthesis and drug discovery, aiding chemists in designing efficient synthetic routes for target molecules. Data-driven deep retrosynthesis prediction has gained importance due to new algorithms and enhanced computing power. Although existing models show certain predictive power on the USPTO-50K benchmark data set, no one considers the effects of byproducts during the prediction process, which may be due to the lack of byproduct information in the benchmark data set. Here, we propose a novel two-stage retrosynthesis reaction prediction framework based on byproducts called RPBP. First, RPBP predicts the byproduct involved in the reaction based on the product molecule. Then, it handles an end-to-end prediction problem based on the prediction of reactants by product and byproduct. Unlike other methods that first identify the potential reaction center and then predict reactant molecules, RPBP considers additional information from byproducts, such as reaction reagents, conditions, and sites. Interestingly, adding byproducts reduces model learning complexity in natural language processing (NLP). Our RPBP model achieves 54.7% and 66.6% top-1 retrosynthesis prediction accuracy when the reaction class is unknown and known, respectively. It outperforms existing methods for known-class reactions, thanks to the rich chemical information in byproducts. The prediction of four kinase drugs from the literature demonstrates the model's practicality and potential to accelerate drug discovery.

Structure-Based Optimization of the Third Generation Type II Macrocycle TRK Inhibitors with Improved Activity against Solvent-Front, xDFG, and Gatekeeper Mutations

The solvent-front (SF), gatekeeper, and xDFG motif mutations of tropomyosin receptor kinase (TRK) mediating acquired resistance of larotrectinib and entrectinib represent an unmet clinical need. To date, no effective drugs are being approved to overcome these mutants. Thus, a series of macrocycle compounds were designed and synthesized as new type II TRK inhibitors to combat clinically relevant mutations. The representative compound 10g exhibited excellent potency against wide type TRKA/C, TRKAG595R, TRKAG667C, and TRKAF589L with IC50 values of 5.21, 4.51, 6.77, 1.42, and 6.13 nM, respectively, and a good kinome selectivity against 378 kinases. 10g also strongly suppressed the proliferation of Ba/F3 cells transfected with SF, GK, xDFG, and others (Val to Met) single mutants with IC50 values of 1.43-47.56 nM. Moreover, 10g demonstrated ideal antitumor efficacy in both BaF3-CD74-NTRK1G595R and BaF3-CD74-NTRK1G667C xenograft models. The study provides a promising lead compound for pan-anticancer drug discovery.

Multitarget inhibitors/probes that target LRRK2 and AURORA A kinases noncovalently and covalently

Herein, we report a salicylaldehyde-based, reversible covalent inhibitor (A2) that possesses moderate cellular activity against AURKA with a prolonged residence time and shows significant non-covalent inhibition towards LRRK2. Our results indicated that this multitarget kinase inhibitor may be used as the starting point for future development of more potent, selective and dual-targeting covalent kinase inhibitors against AURKA and LRRK2 for mitophagy.

Advances in protein kinase drug discovery through targeting gatekeeper mutations

INTRODUCTION

Acquired resistance caused by gatekeeper mutations has become a major challenge for approved kinase inhibitors used in the clinic. Consequently, the development of new-generation inhibitors or degraders to overcome clinical resistance has become an important research focus for the field.

AREAS COVERED

This review summarizes the common gatekeeper mutations in druggable kinases and the constantly evolving inhibitors or degraders designed to overcome single or double mutations of gatekeeper residues. Furthermore, the authors provide their perspectives on the medicinal chemistry strategies for addressing clinical resistance with gatekeeper mutations.

EXPERT OPINION

The authors suggest optimizing kinase inhibitors to interact effectively with gatekeeper residues, altering the binding mode or binding pocket to avoid steric clashes, improving binding affinity with the target, utilizing protein degraders, and developing combination therapy. These approaches have the potential to be effective in overcoming resistance due to gatekeeper residues.

Deciphering the Shared and Specific Drug Resistance Mechanisms of Anaplastic Lymphoma Kinase via Binding Free Energy Computation

Anaplastic lymphoma kinase (ALK), a tyrosine receptor kinase, has been proven to be associated with the occurrence of numerous malignancies. Although there have been already at least 3 generations of ALK inhibitors approved by FDA or in clinical trials, the occurrence of various mutations seriously attenuates the effectiveness of the drugs. Unfortunately, most of the drug resistance mechanisms still remain obscure. Therefore, it is necessary to reveal the bottom reasons of the drug resistance mechanisms caused by the mutations. In this work, on the basis of verifying the accuracy of 2 main kinds of binding free energy calculation methodologies [end-point method of Molecular Mechanics with Poisson-Boltzmann/Generalized Born and Surface Area (MM/PB(GB)SA) and alchemical method of Thermodynamic Integration (TI)], we performed a systematic analysis on the ALK systems to explore the underlying shared and specific drug resistance mechanisms, covering the one-drug-multiple-mutation and multiple-drug-one-mutation cases. Through conventional molecular dynamics (cMD) simulation in conjunction with MM/PB(GB)SA and umbrella sampling (US) in conjunction with contact network analysis (CNA), the resistance mechanisms of the in-pocket, out-pocket, and multiple-site mutations were revealed. Especially for the out-pocket mutation, a possible transfer chain of the mutation effect was revealed, and the reason why different drugs exhibited various sensitivities to the same mutation was also uncovered. The proposed mechanisms may be prevalent in various drug resistance cases.

DoubleSG-DTA: Deep Learning for Drug Discovery: Case Study on the Non-Small Cell Lung Cancer with EGFRT790M Mutation

Drug-targeted therapies are promising approaches to treating tumors, and research on receptor-ligand interactions for discovering high-affinity targeted drugs has been accelerating drug development. This study presents a mechanism-driven deep learning-based computational model to learn double drug sequences, protein sequences, and drug graphs to project drug-target affinities (DTAs), which was termed the DoubleSG-DTA. We deployed lightweight graph isomorphism networks to aggregate drug graph representations and discriminate between molecular structures, and stacked multilayer squeeze-and-excitation networks to selectively enhance spatial features of drug and protein sequences. What is more, cross-multi-head attentions were constructed to further model the non-covalent molecular docking behavior. The multiple cross-validation experimental evaluations on various datasets indicated that DoubleSG-DTA consistently outperformed all previously reported works. To showcase the value of DoubleSG-DTA, we applied it to generate promising hit compounds of Non-Small Cell Lung Cancer harboring EGFRT790M mutation from natural products, which were consistent with reported laboratory studies. Afterward, we further investigated the interpretability of the graph-based "black box" model and highlighted the active structures that contributed the most. DoubleSG-DTA thus provides a powerful and interpretable framework that extrapolates for potential chemicals to modulate the systemic response to disease.