Generating Selective Leads for Mer Kinase Inhibitors-Example of a Comprehensive Lead-Generation Strategy

Hit me with your best shot: Integrated hit discovery for the next generation of drug targets

Identification of high-quality hit chemical matter is of vital importance to the success of drug discovery campaigns. However, this goal is becoming ever harder to achieve as the targets entering the portfolios of pharmaceutical and biotechnology companies are increasingly trending towards novel and traditionally challenging to drug. This demand has fuelled the development and adoption of numerous new screening approaches, whereby the contemporary hit identification toolbox comprises a growing number of orthogonal and complementary technologies including high-throughput screening, fragment-based ligand design, affinity screening (affinity-selection mass spectrometry, differential scanning fluorimetry, DNA-encoded library screening), as well as increasingly sophisticated computational predictive approaches. Herein we describe how an integrated strategy for hit discovery, whereby multiple hit identification techniques are tactically applied, selected in the context of target suitability and resource priority, represents an optimal and often essential approach to maximise the likelihood of identifying quality starting points from which to develop the next generation of medicines.

Building Block-Centric Approach to DNA-Encoded Library Design

DNA-encoded library technology grants access to nearly infinite opportunities to explore the chemical structure space for drug discovery. Successful navigation depends on the design and synthesis of libraries with appropriate physicochemical properties (PCPs) and structural diversity while aligning with practical considerations. To this end, we analyze combinatorial library design constraints including the number of chemistry cycles, bond construction strategies, and building block (BB) class selection in pursuit of ideal library designs. We compare two-cycle library designs (amino acid + carboxylic acid, primary amine + carboxylic acid) in the context of PCPs and chemical space coverage, given different BB selection strategies and constraints. We find that broad availability of amines and acids is essential for enabling the widest exploration of chemical space. Surprisingly, cost is not a driving factor, and virtually, the same chemical space can be explored with "budget" BBs.

Reversible male contraception by targeted inhibition of serine/threonine kinase 33

Men or mice with homozygous serine/threonine kinase 33 (STK33) mutations are sterile owing to defective sperm morphology and motility. To chemically evaluate STK33 for male contraception with STK33-specific inhibitors, we screened our multibillion-compound collection of DNA-encoded chemical libraries, uncovered potent STK33-specific inhibitors, determined the STK33 kinase domain structure bound with a truncated hit CDD-2211, and generated an optimized hit CDD-2807 that demonstrates nanomolar cellular potency (half-maximal inhibitory concentration = 9.2 nanomolar) and favorable metabolic stability. In mice, CDD-2807 exhibited no toxicity, efficiently crossed the blood-testis barrier, did not accumulate in brain, and induced a reversible contraceptive effect that phenocopied genetic STK33 perturbations without altering testis size. Thus, STK33 is a chemically validated, nonhormonal contraceptive target, and CDD-2807 is an effective tool compound.

Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors

Protein kinases become an important source of potential drug targets. Developing new, efficient, and safe small-molecule kinase inhibitors has become an important topic in the field of drug research and development. In contrast with traditional wet experiments which are time-consuming and expensive, machine learning-based approaches for predicting small molecule inhibitors for protein kinases are time-saving and cost-effective, which are highly desired for us. However, the issue of sample scarcity (known active and inactive compounds are usually limited for most kinases) poses a challenge to the research and development of machine learning-based kinase inhibitors' active prediction methods. To alleviate the data scarcity problem in the prediction of kinase inhibitors, in this study, we present a novel Meta-learning-based inductive logistic matrix completion method for the Prediction of Kinase Inhibitors (MetaILMC). MetaILMC adopts a meta-learning framework to learn a well-generalized model from tasks with sufficient samples, which can fast adapt to new tasks with limited samples. As MetaILMC allows the effective transfer of the prior knowledge learned from kinases with sufficient samples to kinases with a small number of samples, the proposed model can produce accurate predictions for kinases with limited data. Experimental results show that MetaILMC has excellent performance for prediction tasks of kinases with few-shot samples and is significantly superior to the state-of-the-art multi-task learning in terms of AUC, AUPR, etc., various performance metrics. Case studies also provided for two drugs to predict Kinase Inhibitory scores, further validating the proposed method's effectiveness and feasibility. SCIENTIFIC CONTRIBUTION: Considering the potential correlation between activity prediction tasks for different kinases, we propose a novel meta learning algorithm MetaILMC, which learns a prior of strong generalization capacity during meta-training from the tasks with sufficient training samples, such that it can be easily and quickly adapted to the new tasks of the kinase with scarce data during meta-testing. Thus, MetaILMC can effectively alleviate the data scarcity problem in the prediction of kinase inhibitors.

Screening Ultra-Large Encoded Compound Libraries Leads to Novel Protein-Ligand Interactions and High Selectivity

The DNA-encoded library (DEL) discovery platform has emerged as a powerful technology for hit identification in recent years. It has become one of the major parallel workstreams for small molecule drug discovery along with other strategies such as HTS and data mining. For many researchers working in the DEL field, it has become increasingly evident that many hits and leads discovered via DEL screening bind to target proteins with unique and unprecedented binding modes. This Perspective is our attempt to analyze reports of DEL screening with the purpose of providing a rigorous and useful account of the binding modes observed for DEL-derived ligands with a focus on binding mode novelty.

Small-molecule discovery through DNA-encoded libraries

The development of bioactive small molecules as probes or drug candidates requires discovery platforms that enable access to chemical diversity and can quickly reveal new ligands for a target of interest. Within the past 15 years, DNA-encoded library (DEL) technology has matured into a widely used platform for small-molecule discovery, yielding a wide variety of bioactive ligands for many therapeutically relevant targets. DELs offer many advantages compared with traditional screening methods, including efficiency of screening, easily multiplexed targets and library selections, minimized resources needed to evaluate an entire DEL and large library sizes. This Review provides accounts of recently described small molecules discovered from DELs, including their initial identification, optimization and validation of biological properties including suitability for clinical applications.

Privileged heterocycles for DNA-encoded library design and hit-to-lead optimization

It is well known that heterocyclic compounds play a key role in improving drug activity, target selectivity, physicochemical properties as well as reducing toxicity. In this review, we summarized the representative heterocyclic structures involved in hit compounds which were obtained from DNA-encoded library from 2013 to 2021. In some examples, the state of the art in heterocycle-based DEL synthesis and hit-to-lead optimization are highlighted. We hope that more and more novel heterocycle-based DEL toolboxes and in-depth pharmaceutical research on these lead compounds can be developed to accelerate the discovery of new drugs.

Discovery of Highly Potent and BMPR2-Selective Kinase Inhibitors Using DNA-Encoded Chemical Library Screening

The discovery of monokinase-selective inhibitors for patients is challenging because the 500+ kinases encoded by the human genome share highly conserved catalytic domains. Until now, no selective inhibitors unique for a single transforming growth factor β (TGFβ) family transmembrane receptor kinase, including bone morphogenetic protein receptor type 2 (BMPR2), have been reported. This dearth of receptor-specific kinase inhibitors hinders therapeutic options for skeletal defects and cancer as a result of an overactivated BMP signaling pathway. By screening 4.17 billion "unbiased" and "kinase-biased" DNA-encoded chemical library molecules, we identified hits CDD-1115 and CDD-1431, respectively, that were low-nanomolar selective kinase inhibitors of BMPR2. Structure-activity relationship studies addressed metabolic lability and high-molecular-weight issues, resulting in potent and BMPR2-selective inhibitor analogs CDD-1281 (IC50 = 1.2 nM) and CDD-1653 (IC50 = 2.8 nM), respectively. Our work demonstrates that DNA-encoded chemistry technology (DEC-Tec) is reliable for identifying novel first-in-class, highly potent, and selective kinase inhibitors.

Pharmacological inhibition of MERTK induces in vivo retinal degeneration: a multimodal imaging ocular safety assessment

The receptor tyrosine kinase, MERTK, plays an essential role in homeostasis of the retina via efferocytosis of shed outer nuclear segments of photoreceptors. The Royal College of Surgeons rat model of retinal degeneration has been linked to loss-of-function of MERTK, and together with the MERTK knock-out mouse, phenocopy retinitis pigmentosa in humans with MERTK mutations. Given recent efforts and interest in MERTK as a potential immuno-oncology target, development of a strategy to assess ocular safety at an early pre-clinical stage is critical. We have applied a state-of-the-art, multi-modal imaging platform to assess the in vivo effects of pharmacological inhibition of MERTK in mice. This involved the application of mass spectrometry imaging (MSI) to characterize the ocular spatial distribution of our highly selective MERTK inhibitor; AZ14145845, together with histopathology and transmission electron microscopy to characterize pathological and ultra-structural change in response to MERTK inhibition. In addition, we assessed the utility of a human retinal in vitro cell model to identify perturbation of phagocytosis post MERTK inhibition. We identified high localized total compound concentrations in the retinal pigment epithelium (RPE) and retinal lesions following 28 days of treatment with AZ14145845. These lesions were present in 4 of 8 treated animals, and were characterized by a thinning of the outer nuclear layer, loss of photoreceptors (PR) and accumulation of photoreceptor outer segments at the interface of the RPE and PRs. Furthermore, the lesions were very similar to that shown in the RCS rat and MERTK knock-out mouse, suggesting a MERTK-induced mechanism of PR cell death. This was further supported by the observation of reduced phagocytosis in the human retinal cell model following treatment with AZ14145845. Our study provides a viable, translational strategy to investigate the pre-clinical toxicity of MERTK inhibitors but is equally transferrable to novel chemotypes.

Optimization of an Imidazo[1,2-a]pyridine Series to Afford Highly Selective Type I1/2 Dual Mer/Axl Kinase Inhibitors with In Vivo Efficacy

Inhibition of Mer and Axl kinases has been implicated as a potential way to improve the efficacy of current immuno-oncology therapeutics by restoring the innate immune response in the tumor microenvironment. Highly selective dual Mer/Axl kinase inhibitors are required to validate this hypothesis. Starting from hits from a DNA-encoded library screen, we optimized an imidazo[1,2-a]pyridine series using structure-based compound design to improve potency and reduce lipophilicity, resulting in a highly selective in vivo probe compound 32. We demonstrated dose-dependent in vivo efficacy and target engagement in Mer- and Axl-dependent efficacy models using two structurally differentiated and selective dual Mer/Axl inhibitors. Additionally, in vivo efficacy was observed in a preclinical MC38 immuno-oncology model in combination with anti-PD1 antibodies and ionizing radiation.

Discovery of Novel, Potent Inhibitors of Hydroxy Acid Oxidase 1 (HAO1) Using DNA-Encoded Chemical Library Screening

Inhibition of hydroxy acid oxidase 1 (HAO1) is a strategy to mitigate the accumulation of toxic oxalate that results from reduced activity of alanine-glyoxylate aminotransferase (AGXT) in primary hyperoxaluria 1 (PH1) patients. DNA-Encoded Chemical Library (DECL) screening provided two novel chemical series of potent HAO1 inhibitors, represented by compounds 3-6. Compound 5 was further optimized via various structure-activity relationship (SAR) exploration methods to 29, a compound with improved potency and absorption, distribution, metabolism, and excretion (ADME)/pharmacokinetic (PK) properties. Since carboxylic acid-containing compounds are often poorly permeable and have potential active glucuronide metabolites, we undertook a brief, initial exploration of acid replacements with the aim of identifying non-acid-containing HAO1 inhibitors. Structure-based drug design initiated with Compound 5 led to the identification of a nonacid inhibitor of HAO1, 31, which has weaker potency and increased permeability.

Design concepts for DNA-encoded library synthesis

An approach of building block (BB) inclusivity and atom efficient library schemes deliver the quality and diversity of DNA-encoded libraries best suited for small molecule drug discovery. In this Perspective, we offer key learnings in DEL design from a decade's worth of DEL-driven screening.