Design of rigid protein-protein interaction inhibitors enables targeting of undruggable Mcl-1.

The structure-based design of small-molecule inhibitors targeting protein-protein interactions (PPIs) remains a huge challenge as the drug must bind typically wide and shallow protein sites. A PPI target of high interest for hematological cancer therapy is myeloid cell leukemia 1 (Mcl-1), a prosurvival guardian protein from the Bcl-2 family. Despite being previously considered undruggable, seven small-molecule Mcl-1 inhibitors have recently entered clinical trials. Here, we report the crystal structure of the clinical-stage inhibitor AMG-176 bound to Mcl-1 and analyze its interaction along with clinical inhibitors AZD5991 and S64315. Our X-ray data reveal high plasticity of Mcl-1 and a remarkable ligand-induced pocket deepening. Nuclear Magnetic Resonance (NMR)-based free ligand conformer analysis demonstrates that such unprecedented induced fit is uniquely achieved by designing highly rigid inhibitors, preorganized in their bioactive conformation. By elucidating key chemistry design principles, this work provides a roadmap for targeting the largely untapped PPI class more successfully.

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.

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.

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.

Predicting the relative binding affinity of mineralocorticoid receptor antagonists by density functional methods.

In drug discovery, prediction of binding affinity ahead of synthesis to aid compound prioritization is still hampered by the low throughput of the more accurate methods and the lack of general pertinence of one method that fits all systems. Here we show the applicability of a method based on density functional theory using core fragments and a protein model with only the first shell residues surrounding the core, to predict relative binding affinity of a matched series of mineralocorticoid receptor (MR) antagonists. Antagonists of MR are used for treatment of chronic heart failure and hypertension. Marketed MR antagonists, spironolactone and eplerenone, are also believed to be highly efficacious in treatment of chronic kidney disease in diabetes patients, but is contra-indicated due to the increased risk for hyperkalemia. These findings and a significant unmet medical need among patients with chronic kidney disease continues to stimulate efforts in the discovery of new MR antagonist with maintained efficacy but low or no risk for hyperkalemia. Applied on a matched series of MR antagonists the quantum mechanical based method gave an R(2) = 0.76 for the experimental lipophilic ligand efficiency versus relative predicted binding affinity calculated with the M06-2X functional in gas phase and an R(2) = 0.64 for experimental binding affinity versus relative predicted binding affinity calculated with the M06-2X functional including an implicit solvation model. The quantum mechanical approach using core fragments was compared to free energy perturbation calculations using the full sized compound structures.

Assessment of a Cambridge Structural Database-driven overlay program.

We recently published an improved methodology for overlaying multiple flexible ligands and an extensive data set for validating pharmacophore programs. Here, we combine these two developments and present evidence of the effectiveness of the new overlay methodology at predicting correct superimpositions for systems with varying levels of complexity. The overlay program was able to generate correct predictions for 95%, 73%, and 39% of systems classified as easy, moderate, and hard, respectively.

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 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.

An extensive and diverse set of molecular overlays for the validation of pharmacophore programs.

The pharmacophore hypothesis plays a central role in both the design and optimization of drug-like ligands. Pharmacophore patterns are invoked to explain the binding affinity of ligands and to enable the design of chemically distinct scaffolds that show affinity for a protein target of interest. The importance of pharmacophores in rationalizing ligand affinity has led to numerous algorithms that seek to overlay ligands based on their pharmacophoric features. All such algorithms must be validated with respect to known ligand overlays, usually by extracting ligand overlay sets from the Protein Data Bank (PDB). This validation step creates the problem of which of the known overlays to select and from which proteins. The large number of structures and protein families in the PDB makes it difficult to establish a definitive overlay set; as a result, validation studies have rarely employed the same data sets. We have therefore undertaken an exhaustive analysis of the RCSB PDB to identify 121 distinct ligand overlay sets. We have defined a robust protein overlay protocol, which is free from subjective interpretation over which residues to include, and we have analyzed each overlay set on the basis of whether they provide evidence for the pharmacophore hypothesis. Our final data set spans a broad range of structural types and degrees of difficulty and includes overlays that any algorithm should be able to reproduce, as well as some for which there is very weak evidence for a conserved pharmacophore at all. We provide this set in the hope that it will prove definitive, at least until the PDB is greatly enriched with further structures or with radically different protein folds and families. Upon publication, the data set will be available for free download from the Web site of the Cambridge Crystallographic Data Centre.

Quantification of free ligand conformational preferences by NMR and their relationship to the bioactive conformation.

Accurate unbound solution 3D-structures of ligands provide unique opportunities for medicinal chemistry and, in particular, a context to understand binding thermodynamics and kinetics. Previous methods of deriving these 3D-structures have had neither the accuracy nor resolution needed for drug design and have not yet realized their potential. Here, we describe and apply a NMR methodology to the aminoglycoside streptomycin that can accurately quantify accessible 3D-space and rank the occupancy of observed conformers to a resolution that enables medicinal chemistry understanding and design. Importantly, it is based upon conventional small molecule NMR techniques and can be performed in physiologically-relevant solvents. The methodology uses multiple datasets, an order of magnitude more experimental data than previous NMR approaches and a dynamic model during refinement, is independent of computational chemistry and avoids the problem of virtual conformations. The refined set of solution 3D-shapes for streptomycin can be grouped into two major families, of which the most populated is almost identical to the 30S ribosomal subunit bioactive shape. We therefore propose that accurate unbound ligand solution conformations may, in some cases, provide a subsidiary route to bioactive shape without crystallography. This experimental technique opens up new opportunities for drug design and more so when complemented with protein co-crystal structures, SAR data and pharmacophore modeling.

High throughput screening of 0.5 million compounds against CRAF using Alpha CETSAⓇ.

The cellular thermal shift assay (CETSA®) has increasingly been used in early drug discovery to provide a measure of cellular target engagement. Traditionally, CETSA has been employed for bespoke questions with small to medium throughput and has predominantly been applied during hit validation rather than in hit identification. Using a CETSA screen versus the kinase CRAF, we assessed 3 key questions: (1) technical feasibility - could the CETSA methodology technically be applied at truly high throughput scale? (2) relevance - could hits suitable for further optimisation be identified? (3) reliability - would the approach identify known chemical equity. Here, we describe the first large scale AlphaLISA SureFire based CETSA (Alpha CETSA) approach allowing us to screen a large library of almost 0.5 million compounds. We discuss the issues overcome in automating and executing the screen and describe the resulting screen output.

Identification, optimisation and in vivo evaluation of oxadiazole DGAT-1 inhibitors for the treatment of obesity and diabetes.

A novel series of DGAT-1 inhibitors was discovered from an oxadiazole amide high throughput screening (HTS) hit. Optimisation of potency and ligand lipophilicity efficiency (LLE) resulted in a carboxylic acid containing clinical candidate 53 (AZD3988), which demonstrated excellent DGAT-1 potency (0.6 nM), good pharmacokinetics and pre-clinical in vivo efficacy that could be rationalised through a PK/PD relationship.

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.

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 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.

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.

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.

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.

Use of quantitative (1)H NMR chemical shift changes for ligand docking into barnase.

(1)H NMR complexation-induced changes in chemical shift (CIS) of HN protons have been used to characterize the complexes of barnase with the deoxyoligonucleotides d(GC) and d(CGAC). Quantitative shift changes are used not only to locate the most probable binding site (using ring-current shifts), but also to determine the orientation of the ligand within the binding site, based on a more complete shift calculation including bond magnetic anisotropies and electric field effects. For both ligands, the guanine is in the same binding site cleft, in the same position as identified in the crystal structure of the d(CGAC) complex. By contrast, a previous X-ray crystal structure of the d(GC) complex showed the ligand in the mouth of the active site, rather than at the guanyl-specific site, implying that the location may be an artifact of the crystallisation process.