Metabolism-driven in vitro/in vivo disconnect of an oral ERɑ VHL-PROTAC.

Targeting the estrogen receptor alpha (ERα) pathway is validated in the clinic as an effective means to treat ER+ breast cancers. Here we present the development of a VHL-targeting and orally bioavailable proteolysis-targeting chimera (PROTAC) degrader of ERα. In vitro studies with this PROTAC demonstrate excellent ERα degradation and ER antagonism in ER+ breast cancer cell lines. However, upon dosing the compound in vivo we observe an in vitro-in vivo disconnect. ERα degradation is lower in vivo than expected based on the in vitro data. Investigation into potential causes for the reduced maximal degradation reveals that metabolic instability of the PROTAC linker generates metabolites that compete for binding to ERα with the full PROTAC, limiting degradation. This observation highlights the requirement for metabolically stable PROTACs to ensure maximal efficacy and thus optimisation of the linker should be a key consideration when designing PROTACs.

Identification of Novel, Selective Ataxia-Telangiectasia Mutated Kinase Inhibitors with the Ability to Penetrate the Blood-Brain Barrier: The Discovery of AZD1390.

The inhibition of ataxia-telangiectasia mutated (ATM) has been shown to chemo- and radio-sensitize human glioma cells in vitro and therefore might provide an exciting new paradigm in the treatment of glioblastoma multiforme (GBM). The effective treatment of GBM will likely require a compound with the potential to efficiently cross the blood-brain barrier (BBB). Starting from clinical candidate AZD0156, 4, we investigated the imidazoquinolin-2-one scaffold with the goal of improving likely CNS exposure in humans. Strategies aimed at reducing hydrogen bonding, basicity, and flexibility of the molecule were explored alongside modulating lipophilicity. These studies identified compound 24 (AZD1390) as an exceptionally potent and selective inhibitor of ATM with a good preclinical pharmacokinetic profile. 24 showed an absence of human transporter efflux in MDCKII-MDR1-BCRP studies (efflux ratio <2), significant BBB penetrance in nonhuman primate PET studies (Kp,uu 0.33) and was deemed suitable for development as a clinical candidate to explore the radiosensitizing effects of ATM in intracranial malignancies.

Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins.

We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and intermolecular interactions in determining conformer stability. To benchmark the performance of each molecular model, we evaluated their energetic, geometric, and sampling accuracies relative to quantum-mechanical data. This benchmark involved conformational analysis both in the gas phase and chloroform solution. We also assessed the performance of the aforementioned molecular models in estimating nuclear spin-spin coupling constants by comparing their predictions to experimental data available in chloroform. The results and discussion presented in this study demonstrate that ANI-2x tends to predict stronger-than-expected hydrogen bonding and overstabilize global minima and shows problems related to inadequate description of dispersion interactions. Furthermore, while ANI-2x is a viable model for modeling in the gas phase, conventional force fields still play an important role, especially for condensed-phase simulations. Overall, this study highlights the strengths and weaknesses of each model, providing guidelines for the use and future development of force fields and machine learning potentials.

Discovery of a Potent and Orally Bioavailable Zwitterionic Series of Selective Estrogen Receptor Degrader-Antagonists.

Herein, we report the optimization of a meta-substituted series of selective estrogen receptor degrader (SERD) antagonists for the treatment of ER+ breast cancer. Structure-based design together with the use of modeling and NMR to favor the bioactive conformation led to a highly potent series of basic SERDs with promising physicochemical properties. Issues with hERG activity resulted in a strategy of zwitterion formation and ultimately in the identification of 38. This compound was shown to be a highly potent SERD capable of effectively degrading ERα in both MCF-7 and CAMA-1 cell lines. The low lipophilicity and zwitterionic nature led to a SERD with a clean secondary pharmacology profile and no hERG activity. Favorable physicochemical properties resulted in good oral bioavailability in preclinical species and potent in vivo activity in a mouse xenograft model.

Targeting a Novel KRAS Binding Site: Application of One-Component Stapling of Small (5-6-mer) Peptides.

RAS proteins are central in the proliferation of many types of cancer, but a general approach toward the identification of pan-mutant RAS inhibitors has remained unresolved. In this work, we describe the application of a binding pharmacophore identified from analysis of known RAS binding peptides to the design of novel peptides. Using a chemically divergent approach, we generated a library of small stapled peptides from which we identified compounds with weak binding activity. Exploration of structure-activity relationships (SARs) and optimization of these early compounds led to low-micromolar binders of KRAS that block nucleotide exchange.

Generation of Quantum Configurational Ensembles Using Approximate Potentials.

Conformational analysis is of paramount importance in drug design: it is crucial to determine pharmacological properties, understand molecular recognition processes, and characterize the conformations of ligands when unbound. Molecular Mechanics (MM) simulation methods, such as Monte Carlo (MC) and molecular dynamics (MD), are usually employed to generate ensembles of structures due to their ability to extensively sample the conformational space of molecules. The accuracy of these MM-based schemes strongly depends on the functional form of the force field (FF) and its parametrization, components that often hinder their performance. High-level methods, such as ab initio MD, provide reliable structural information but are still too computationally expensive to allow for extensive sampling. Therefore, to overcome these limitations, we present a multilevel MC method that is capable of generating quantum configurational ensembles while keeping the computational cost at a minimum. We show that FF reparametrization is an efficient route to generate FFs that reproduce QM results more closely, which, in turn, can be used as low-cost models to achieve the gold standard QM accuracy. We demonstrate that the MC acceptance rate is strongly correlated with various phase space overlap measurements and that it constitutes a robust metric to evaluate the similarity between the MM and QM levels of theory. As a more advanced application, we present a self-parametrizing version of the algorithm, which combines sampling and FF parametrization in one scheme, and apply the methodology to generate the QM/MM distribution of a ligand in aqueous solution.

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.

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

Mer is a member of the TAM (Tyro3, Axl, Mer) kinase family that has been associated with cancer progression, metastasis, and drug resistance. Their essential function in immune homeostasis has prompted an interest in their role as modulators of antitumor immune response in the tumor microenvironment. Here we illustrate the outcomes of an extensive lead-generation campaign for identification of Mer inhibitors, focusing on the results from concurrent, orthogonal high-throughput screening approaches. Data mining, HT (high-throughput), and DECL (DNA-encoded chemical library) screens offered means to evaluate large numbers of compounds. We discuss campaign strategy and screening outcomes, and exemplify series resulting from prioritization of hits that were identified. Concurrent execution of HT and DECL screening successfully yielded a large number of potent, selective, and novel starting points, covering a range of selectivity profiles across the TAM family members and modes of kinase binding, and offered excellent start points for lead development.

Nuisance compounds in cellular assays.

Compounds that exhibit assay interference or undesirable mechanisms of bioactivity ("nuisance compounds") are routinely encountered in cellular assays, including phenotypic and high-content screening assays. Much is known regarding compound-dependent assay interferences in cell-free assays. However, despite the essential role of cellular assays in chemical biology and drug discovery, there is considerably less known about nuisance compounds in more complex cell-based assays. In our view, a major obstacle to realizing the full potential of chemical biology will not just be difficult-to-drug targets or even the sheer number of targets, but rather nuisance compounds, due to their ability to waste significant resources and erode scientific trust. In this review, we summarize our collective academic, government, and industry experiences regarding cellular nuisance compounds. We describe assay design strategies to mitigate the impact of nuisance compounds and suggest best practices to efficiently address these compounds in complex biological settings.

A-loop interactions in Mer tyrosine kinase give rise to inhibitors with two-step mechanism and long residence time of binding.

The activation loop (A-loop) plays a key role in regulating the catalytic activity of protein kinases. Phosphorylation in this region enhances the phosphoryl transfer rate of the kinase domain and increases its affinity for ATP. Furthermore, the A-loop possesses autoinhibitory functions in some kinases, where it collapses onto the protein surface and blocks substrate binding when unphosphorylated. Due to its flexible nature, the A-loop is usually disordered and untraceable in kinase domain crystal structures. The resulting lack of structural information is regrettable as it impedes the design of drug A-loop contacts, which have proven favourable in multiple cases. Here, we characterize the binding with A-loop engagement between type 1.5 kinase inhibitor 'example 172' (EX172) and Mer tyrosine kinase (MerTK). With the help of crystal structures and binding kinetics, we portray how the recruitment of the A-loop elicits a two-step binding mechanism which results in a drug-target complex characterized by high affinity and long residence time. In addition, the type 1.5 compound possesses excellent kinome selectivity and a remarkable preference for the phosphorylated over the dephosphorylated form of MerTK. We discuss these unique characteristics in the context of known type 1 and type 2 inhibitors and highlight opportunities for future kinase inhibitor design.

Discovery of AZD9833, a Potent and Orally Bioavailable Selective Estrogen Receptor Degrader and Antagonist.

Herein we report the optimization of a series of tricyclic indazoles as selective estrogen receptor degraders (SERD) and antagonists for the treatment of ER+ breast cancer. Structure based design together with systematic investigation of each region of the molecular architecture led to the identification of N-[1-(3-fluoropropyl)azetidin-3-yl]-6-[(6S,8R)-8-methyl-7-(2,2,2-trifluoroethyl)-6,7,8,9-tetrahydro-3H-pyrazolo[4,3-f]isoquinolin-6-yl]pyridin-3-amine (28). This compound was demonstrated to be a highly potent SERD that showed a pharmacological profile comparable to fulvestrant in its ability to degrade ERα in both MCF-7 and CAMA-1 cell lines. A stringent control of lipophilicity ensured that 28 had favorable physicochemical and preclinical pharmacokinetic properties for oral administration. This, combined with demonstration of potent in vivo activity in mouse xenograft models, resulted in progression of this compound, also known as AZD9833, into clinical trials.

Building Bridges in a Series of Estrogen Receptor Degraders: An Application of Metathesis in Medicinal Chemistry.

Herein we report the use of metathesis to construct a novel tetracyclic core in a series of estrogen receptor degraders. This improved the chemical stability, as assessed using an NMR-MS based assay, and gave a molecule with excellent physicochemical properties and pharmacokinetics in rat. X-ray crystallography established minimal perturbation of the bridged compounds relative to the unbridged analogues in the receptor binding pocket. Unfortunately, despite retaining excellent binding to ERα, this adversely affected the ability of the compounds to degrade the receptor.

Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies.

A significant challenge in high-throughput screening (HTS) campaigns is the identification of assay technology interference compounds. A Compound Interfering with an Assay Technology (CIAT) gives false readouts in many assays. CIATs are often considered viable hits and investigated in follow-up studies, thus impeding research and wasting resources. In this study, we developed a machine-learning (ML) model to predict CIATs for three assay technologies. The model was trained on known CIATs and non-CIATs (NCIATs) identified in artefact assays and described by their 2D structural descriptors. Usual methods identifying CIATs are based on statistical analysis of historical primary screening data and do not consider experimental assays identifying CIATs. Our results show successful prediction of CIATs for existing and novel compounds and provide a complementary and wider set of predicted CIATs compared to BSF, a published structure-independent model, and to the PAINS substructural filters. Our analysis is an example of how well-curated datasets can provide powerful predictive models despite their relatively small size.

Mapping Hidden Residual Structure within the Myc bHLH-LZ Domain Using Chemical Denaturant Titration.

Intrinsically disordered proteins (IDPs) underpin biological regulation and hence are highly desirable drug-development targets. NMR is normally the tool of choice for studying the conformational preferences of IDPs, but the association of regions with residual structure into partially collapsed states can lead to poor spectral quality. The bHLH-LZ domain of the oncoprotein Myc is an archetypal example of such behavior. To circumvent spectral limitations, we apply chemical denaturant titration (CDT)-NMR, which exploits the predictable manner in which chemical denaturants disrupt residual structure and the rapid exchange between conformers in IDP ensembles. The secondary structure propensities and tertiary interactions of Myc are determined for all bHLH-LZ residues, including those with poor NMR properties under native conditions. This reveals conformations that are not predictable using existing crystal structures. The CDT-NMR method also maps sites perturbed by the prototype Myc inhibitor, 10058-F4, to areas of residual structure.

Discovery of a Thiadiazole-Pyridazine-Based Allosteric Glutaminase 1 Inhibitor Series That Demonstrates Oral Bioavailability and Activity in Tumor Xenograft Models.

Tumors have evolved a variety of methods to reprogram conventional metabolic pathways to favor their own nutritional needs, including glutaminolysis, the first step of which is the hydrolysis of glutamine to glutamate by the amidohydrolase glutaminase 1 (GLS1). A GLS1 inhibitor could potentially target certain cancers by blocking the tumor cell's ability to produce glutamine-derived nutrients. Starting from the known GLS1 inhibitor bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide, we describe the medicinal chemistry evolution of a series from lipophilic inhibitors with suboptimal physicochemical and pharmacokinetic properties to cell potent examples with reduced molecular weight and lipophilicity, leading to compounds with greatly improved oral exposure that demonstrate in vivo target engagement accompanied by activity in relevant disease models.

Discovery and Characterization of AZD6738, a Potent Inhibitor of Ataxia Telangiectasia Mutated and Rad3 Related (ATR) Kinase with Application as an Anticancer Agent.

The kinase ataxia telangiectasia mutated and rad3 related (ATR) is a key regulator of the DNA-damage response and the apical kinase which orchestrates the cellular processes that repair stalled replication forks (replication stress) and associated DNA double-strand breaks. Inhibition of repair pathways mediated by ATR in a context where alternative pathways are less active is expected to aid clinical response by increasing replication stress. Here we describe the development of the clinical candidate 2 (AZD6738), a potent and selective sulfoximine morpholinopyrimidine ATR inhibitor with excellent preclinical physicochemical and pharmacokinetic (PK) characteristics. Compound 2 was developed improving aqueous solubility and eliminating CYP3A4 time-dependent inhibition starting from the earlier described inhibitor 1 (AZ20). The clinical candidate 2 has favorable human PK suitable for once or twice daily dosing and achieves biologically effective exposure at moderate doses. Compound 2 is currently being tested in multiple phase I/II trials as an anticancer agent.

Utility of Resazurin, Horseradish Peroxidase, and NMR Assays to Identify Redox-Related False-Positive Behavior in High-Throughput Screens.

Discerning false positives from true actives in high-throughput screening (HTS) output is fraught with difficulty as the reason of anomalous activity seen for compounds is often not clear-cut. In this study, we introduce a novel medium-throughput NMR assay for the identification of redox-cycling compounds (RCCs), which is based on detection of oxidation of a reducing agent. We compare its outcomes to those from horseradish peroxidase (HRP)/phenol red and resazurin (RZ)-based assays that are more commonly used for triaging HTS outputs. Data from NMR, RZ, and HRP redox assay are shown to correlate, with the NMR assay showing the greatest accuracy. In addition, historical data analysis was used to identify compounds frequently active in assays for redox-susceptible targets. We provide examples of compound classes found and conclude that the NMR redox assay offers a novel and reliable way of identifying RCCs at a medium throughput. The HRP and RZ assays are reasonable higher-throughput alternatives, with both showing similar sensitivity to redox-cycling and false-positive compounds. The RZ assay has a higher hit rate, reflecting its ability to pick up multiple modes of action.

Myc phosphorylation in its basic helix-loop-helix region destabilizes transient α-helical structures, disrupting Max and DNA binding.

Myelocytomatosis proto-oncogene transcription factor (Myc) is an intrinsically disordered protein with critical roles in cellular homeostasis and neoplastic transformation. It is tightly regulated in the cell, with Myc phosphorylation playing a major role. In addition to the well-described tandem phosphorylation of Thr-52 and Ser-62 in the Myc transactivation domain linked to its degradation, P21 (RAC1)-activated kinase 2 (PAK2)-mediated phosphorylation of serine and threonine residues in the C-terminal basic helix-loop-helix leucine zipper (bHLH-LZ) region regulates Myc transcriptional activity. Here we report that PAK2 preferentially phosphorylates Myc twice, at Thr-358 and Ser-373, with only a minor fraction being modified at the previously identified Thr-400 site. For transcriptional activity, Myc binds E-box DNA elements, requiring its heterodimerization with Myc-associated factor X (Max) via the bHLH-LZ regions. Using isothermal calorimetry (ITC), we found that Myc phosphorylation destabilizes this ternary protein-DNA complex by decreasing Myc's affinity for Max by 2 orders of magnitude, suggesting a major effect of phosphorylation on this complex. Phosphomimetic substitutions revealed that Ser-373 dominates the effect on Myc-Max heterodimerization. Moreover, a T400D substitution disrupted Myc's affinity for Max. ITC, NMR, and CD analyses of several Myc variants suggested that the effect of phosphorylation on the Myc-Max interaction is caused by secondary structure disruption during heterodimerization rather than by a change in the structurally disordered state of Myc or by phosphorylation-induced electrostatic repulsion in the heterodimer. Our findings provide critical insights into the effects of PAK2-catalyzed phosphorylation of Myc on its interactions with Max and DNA.

Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017-Utility and Limitations.

Pan-Assay Interference Compounds (PAINS) are very familiar to medicinal chemists who have spent time fruitlessly trying to optimize these nonprogressible compounds. Electronic filters formulated to recognize PAINS can process hundreds and thousands of compounds in seconds and are in widespread current use to identify PAINS in order to exclude them from further analysis. However, this practice is fraught with danger because such black box treatment is simplistic. Here, we outline for the first time all necessary considerations for the appropriate use of PAINS filters.