Discovery and Optimization of Potent, Efficacious and Selective Inhibitors Targeting EGFR Exon20 Insertion Mutations.

Herein, we report the identification and optimization of a series of potent inhibitors of EGFR Exon20 insertions with significant selectivity over wild-type EGFR. A strategically designed HTS campaign, multiple iterations of structure-based drug design (SBDD), and tactical linker replacement led to a potent and wild-type selective series of molecules and ultimately the discovery of 36. Compound 36 is a potent and selective inhibitor of EGFR Exon20 insertions and has demonstrated encouraging efficacy in NSCLC EGFR CRISPR-engineered H2073 xenografts that carry an SVD Exon20 insertion and reduced efficacy in a H2073 wild-type EGFR xenograft model compared to CLN-081 (5), indicating that 36 may have lower EGFR wild-type associated toxicity.

Characterisation of high throughput screening outputs for small molecule degrader discovery.

Targeted protein degradation is an important mechanism carried out by the cellular machinery, one that is gaining momentum as an exploitable strategy for the development of drug-like compounds. Molecules which are able to induce proximity between elusive therapeutic targets of interest and E3 ligases which subsequently leads to proteasomal degradation of the target are beginning to decrease the percentage of the human proteome described as undruggable. Therefore, having the ability to screen for, and understand the mechanism of, such molecules is becoming an increasingly attractive scientific focus. We have established a number of cascade experiments including cell-based assays and orthogonal triage steps to provide annotation to the selectivity and mechanism of action for compounds identified as putative degraders from a primary high throughput screen against a high value oncology target. We will describe our current position, using PROTACs as proof-of-concept, on the analysis of these novel outputs and highlight challenges encountered.

Discovery and characterisation of quinazolines and 8-Azaquinazolines as NLRP3 agonists with oral bioavailability in mice.

The NLRP3 inflammasome is a multiprotein complex that plays a critical role in activating the immune system in response to danger signals. Small molecule agonists of NLRP3 may offer clinical benefits in cancer immunology either as a monotherapy or in combination with checkpoint blockade, where it is hypothesised that their application can help to initiate an antitumor immune response. In this study, we report the discovery of quinazolines and 8-azaquinazolines as NLRP3 agonists and their chemical optimization to afford compounds with oral bioavailability in mice. We confirm that these compounds engage the NLRP3 inflammasome by verifying their dependence upon lipopolysaccharide (LPS) priming for cytokine release and the activation of Caspase-1. We further demonstrate pathway engagement through loss of activity in an NLRP3-knockout THP1 cell line. Based on their pharmacokinetic profile and biological activity, these compounds represent valuable tools to evaluate the therapeutic potential of NLRP3 activation in a pre-clinical setting.

Bifocal stance theory, the transmission metaphor, and institutional reality.

Biologists have replaced the metaphor of "genetic transmission" with a detailed account of the molecular mechanisms underlying the phenomenon which Darwin referred to as "like produces like." Cultural evolution theorists, in contrast, continue to appeal to "imitation" or "copying." The notion of ritual and instrumental stances does not resolve this issue, and ignores the institutions in which people live.

NMR Molecular Replacement Provides New Insights into Binding Modes to Bromodomains of BRD4 and TRIM24.

Structure-based drug discovery (SBDD) largely relies on structural information from X-ray crystallography because traditional NMR structure calculation methods are too time consuming to be aligned with typical drug discovery timelines. The recently developed NMR molecular replacement (NMR2) method dramatically reduces the time needed to generate ligand-protein complex structures using published structures (apo or holo) of the target protein and treating all observed NOEs as ambiguous restraints, bypassing the laborious process of obtaining sequence-specific resonance assignments for the protein target. We apply this method to two therapeutic targets, the bromodomain of TRIM24 and the second bromodomain of BRD4. We show that the NMR2 methodology can guide SBDD by rationalizing the observed SAR. We also demonstrate that new types of restraints and selective methyl labeling have the potential to dramatically reduce "time to structure" and extend the method to targets beyond the reach of traditional NMR structure elucidation.

Discovery of 5-{4-[(7-Ethyl-6-oxo-5,6-dihydro-1,5-naphthyridin-3-yl)methyl]piperazin-1-yl}-N-methylpyridine-2-carboxamide (AZD5305): A PARP1-DNA Trapper with High Selectivity for PARP1 over PARP2 and Other PARPs.

Poly-ADP-ribose-polymerase (PARP) inhibitors have achieved regulatory approval in oncology for homologous recombination repair deficient tumors including BRCA mutation. However, some have failed in combination with first-line chemotherapies, usually due to overlapping hematological toxicities. Currently approved PARP inhibitors lack selectivity for PARP1 over PARP2 and some other 16 PARP family members, and we hypothesized that this could contribute to toxicity. Recent literature has demonstrated that PARP1 inhibition and PARP1-DNA trapping are key for driving efficacy in a BRCA mutant background. Herein, we describe the structure- and property-based design of 25 (AZD5305), a potent and selective PARP1 inhibitor and PARP1-DNA trapper with excellent in vivo efficacy in a BRCA mutant HBCx-17 PDX model. Compound 25 is highly selective for PARP1 over other PARP family members, with good secondary pharmacology and physicochemical properties and excellent pharmacokinetics in preclinical species, with reduced effects on human bone marrow progenitor cells in vitro.

Free energy perturbation in the design of EED ligands as inhibitors of polycomb repressive complex 2 (PRC2) methyltransferase.

Free Energy Perturbation (FEP) calculations can provide high-confidence predictions of the interaction strength between a ligand and its protein target. We sought to explore a series of triazolopyrimidines which bind to the EED subunit of the PRC2 complex as potential anticancer therapeutics, using FEP calculations to inform compound design. Combining FEP predictions with a late-stage functionalisation (LSF) inspired synthetic approach allowed us to rapidly evaluate structural modifications in a previously unexplored region of the EED binding site. This approach generated a series of novel triazolopyrimidine EED ligands with improved physicochemical properties and which inhibit PRC2 methyltransferase activity in a cancer-relevant G401 cell line.

Nuclear magnetic resonance free ligand conformations and atomic resolution dynamics.

Knowledge of free ligand conformational preferences (energy minima) and conformational dynamics (rotational energy barriers) of small molecules in solution can guide drug design hypotheses and help rank ideas to bias syntheses towards more active compounds. Visualization of conformational exchange dynamics around torsion angles, by replica exchange with solute tempering molecular dynamics (REST-MD), gives results in agreement with high-resolution 1H nuclear magnetic resonance (NMR) spectra and complements free ligand conformational analyses. Rotational energy barriers around individual bonds are comparable between calculated and experimental values, making the in-silico method relevant to ranking prospective design ideas in drug discovery programs, particularly across a series of analogs. Prioritizing design ideas, based on calculations and analysis of measurements across a series, efficiently guides rational discovery towards the "right molecules" for effective medicines.

Discovery and pharmacological characterization of AZD3229, a potent KIT/PDGFRα inhibitor for treatment of gastrointestinal stromal tumors.

Gastrointestinal stromal tumor (GIST) is the most common human sarcoma driven by mutations in KIT or platelet-derived growth factor α (PDGFRα). Although first-line treatment, imatinib, has revolutionized GIST treatment, drug resistance due to acquisition of secondary KIT/PDGFRα mutations develops in a majority of patients. Second- and third-line treatments, sunitinib and regorafenib, lack activity against a plethora of mutations in KIT/PDGFRα in GIST, with median time to disease progression of 4 to 6 months and inhibition of vascular endothelial growth factor receptor 2 (VEGFR2) causing high-grade hypertension. Patients with GIST have an unmet need for a well-tolerated drug that robustly inhibits a range of KIT/PDGFRα mutations. Here, we report the discovery and pharmacological characterization of AZD3229, a potent and selective small-molecule inhibitor of KIT and PDGFRα designed to inhibit a broad range of primary and imatinib-resistant secondary mutations seen in GIST. In engineered and GIST-derived cell lines, AZD3229 is 15 to 60 times more potent than imatinib in inhibiting KIT primary mutations and has low nanomolar activity against a wide spectrum of secondary mutations. AZD3229 causes durable inhibition of KIT signaling in patient-derived xenograft (PDX) models of GIST, leading to tumor regressions at doses that showed no changes in arterial blood pressure (BP) in rat telemetry studies. AZD3229 has a superior potency and selectivity profile to standard of care (SoC) agents-imatinib, sunitinib, and regorafenib, as well as investigational agents, avapritinib (BLU-285) and ripretinib (DCC-2618). AZD3229 has the potential to be a best-in-class inhibitor for clinically relevant KIT/PDGFRα mutations in GIST.

IMPRESSION - prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy.

The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information Of Nuclei) machine learning system provides an efficient and accurate method for the prediction of NMR parameters from 3-dimensional molecular structures. Here we demonstrate that machine learning predictions of NMR parameters, trained on quantum chemical computed values, can be as accurate as, but computationally much more efficient (tens of milliseconds per molecular structure) than, quantum chemical calculations (hours/days per molecular structure) starting from the same 3-dimensional structure. Training the machine learning system on quantum chemical predictions, rather than experimental data, circumvents the need for the existence of large, structurally diverse, error-free experimental databases and makes IMPRESSION applicable to solving 3-dimensional problems such as molecular conformation and stereoisomerism.

Utilizing Grand Canonical Monte Carlo Methods in Drug Discovery.

The concepts behind targeting waters for potency and selectivity gains have been well documented and explored, although maximizing such potential gains can prove to be challenging. This problem is exacerbated in cases where there are multiple interacting waters, wherein perturbation of one water can affect the free energy landscape of the remaining waters. Knowing the right modification a priori is challenging, and computational approaches are ideally suited to help answer the key question of which substitution is best to try. Here, we use Grand Canonical Monte Carlo and the recent Grand Canonical Alchemical Perturbation methods to both understand and predict the effect of ligand-mediated water displacement when more than one water molecule is involved, as well as to understand how exploiting water networks can help govern selectivity.

PELE-MSM: A Monte Carlo Based Protocol for the Estimation of Absolute Binding Free Energies.

In this study, we present a fully automatic platform based on our Monte Carlo algorithm, the Protein Energy Landscape Exploration method (PELE), for the estimation of absolute protein-ligand binding free energies, one of the most significant challenges in computer aided drug design. Based on a ligand pathway approach, an initial short enhanced sampling simulation is performed to identify reasonable starting positions for more extended sampling. This stepwise approach allows for a significant faster convergence of the free energy estimation using the Markov State Model (MSM) technique. PELE-MSM was applied on four diverse protein and ligand systems, successfully ranking compounds for two systems. Based on the results, current limitations and challenges with physics-based methods in computational structural biology are discussed. Overall, PELE-MSM constitutes a promising step toward computing absolute binding free energies and in their application into drug discovery projects.

Free Ligand 1D NMR Conformational Signatures To Enhance Structure Based Drug Design of a Mcl-1 Inhibitor (AZD5991) and Other Synthetic Macrocycles.

The three-dimensional conformations adopted by a free ligand in solution impact bioactivity and physicochemical properties. Solution 1D NMR spectra inherently contain information on ligand conformational flexibility and three-dimensional shape, as well as the propensity of the free ligand to fully preorganize into the bioactive conformation. Herein we discuss some key learnings, distilled from our experience developing potent and selective synthetic macrocyclic inhibitors, including Mcl-1 clinical candidate AZD5991. Case studies have been selected from recent oncology research projects, demonstrating how 1D NMR conformational signatures can complement X-ray protein-ligand structural information to guide medicinal chemistry optimization. Learning to extract free ligand conformational information from routinely available 1D NMR signatures has proven to be fast enough to guide medicinal chemistry decisions within design cycles for compound optimization.

Alignment-Free Molecular Shape Comparison Using Spectral Geometry: The Framework.

A framework is presented for the calculation of novel alignment-free descriptors of molecular shape. The methods are based on the technique of spectral geometry which has been developed in the field of computer vision where it has shown impressive performance for the comparison of deformable objects such as people and animals. Spectral geometry techniques encode shape by capturing the curvature of the surface of an object into a compact, information-rich representation that is alignment-free while also being invariant to isometric deformations, that is, changes that do not distort distances over the surface. Here, we adapt the technique to the new domain of molecular shape representation. We describe a series of parametrization steps aimed at optimizing the method for this new domain. Our focus here is on demonstrating that the basic approach is able to capture a molecular shape into a compact and information-rich descriptor. We demonstrate improved performance in virtual screening over a more established alignment-free method and impressive performance compared to a more accurate, but much more computationally demanding, alignment-based approach.

Discovery of Mcl-1-specific inhibitor AZD5991 and preclinical activity in multiple myeloma and acute myeloid leukemia.

Mcl-1 is a member of the Bcl-2 family of proteins that promotes cell survival by preventing induction of apoptosis in many cancers. High expression of Mcl-1 causes tumorigenesis and resistance to anticancer therapies highlighting the potential of Mcl-1 inhibitors as anticancer drugs. Here, we describe AZD5991, a rationally designed macrocyclic molecule with high selectivity and affinity for Mcl-1 currently in clinical development. Our studies demonstrate that AZD5991 binds directly to Mcl-1 and induces rapid apoptosis in cancer cells, most notably myeloma and acute myeloid leukemia, by activating the Bak-dependent mitochondrial apoptotic pathway. AZD5991 shows potent antitumor activity in vivo with complete tumor regression in several models of multiple myeloma and acute myeloid leukemia after a single tolerated dose as monotherapy or in combination with bortezomib or venetoclax. Based on these promising data, a Phase I clinical trial has been launched for evaluation of AZD5991 in patients with hematological malignancies (NCT03218683).

Discovery of N-(4-{[5-Fluoro-7-(2-methoxyethoxy)quinazolin-4-yl]amino}phenyl)-2-[4-(propan-2-yl)-1 H-1,2,3-triazol-1-yl]acetamide (AZD3229), a Potent Pan-KIT Mutant Inhibitor for the Treatment of Gastrointestinal Stromal Tumors.

While the treatment of gastrointestinal stromal tumors (GISTs) has been revolutionized by the application of targeted tyrosine kinase inhibitors capable of inhibiting KIT-driven proliferation, diverse mutations to this kinase drive resistance to established therapies. Here we describe the identification of potent pan-KIT mutant kinase inhibitors that can be dosed without being limited by the tolerability issues seen with multitargeted agents. This effort focused on identification and optimization of an existing kinase scaffold through the use of structure-based design. Starting from a series of previously reported phenoxyquinazoline and quinoline based inhibitors of the tyrosine kinase PDGFRα, potency against a diverse panel of mutant KIT driven Ba/F3 cell lines was optimized, with a particular focus on reducing activity against a KDR driven cell model in order to limit the potential for hypertension commonly seen in second and third line GIST therapies. AZD3229 demonstrates potent single digit nM growth inhibition across a broad cell panel, with good margin to KDR-driven effects. Selectivity over KDR can be rationalized predominantly by the interaction of water molecules with the protein and ligand in the active site, and its kinome selectivity is similar to the best of the approved GIST agents. This compound demonstrates excellent cross-species pharmacokinetics, shows strong pharmacodynamic inhibition of target, and is active in several in vivo models of GIST.

Correction to "Structure Based Design of Non-Natural Peptidic Macrocyclic Mcl-1 Inhibitors".

[This corrects the article DOI: 10.1021/acsmedchemlett.6b00464.].