Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp3-Enriched Fragment Library.

Fragment-based drug discovery has played an important role in medicinal chemistry and pharmaceutical research. Despite numerous demonstrated successes, the limited diversity and overrepresentation of planar, sp2-rich structures in commercial libraries often hamper the full potential of this approach. Hence, the thorough design of screening libraries inevitably determines the probability for meaningful hits and subsequent structural elaboration. Against this background, we present the generation of an exclusive fragment library based on iterative entry nomination by a specifically designed computational workflow: "Fragtory". Following a pharmacophore diversity-driven approach, we used Fragtory in an interdisciplinary academic setting to guide both tailored synthesis efforts and the implementation of in-house compounds to build a curated 288-member library of sp3-enriched fragments. Subsequent NMR screens against a model protein and hit validation by protein crystallography led to the identification of structurally novel ligands that were further characterized by isothermal titration calorimetry, demonstrating the applicability of our experimental approach.

VSFlow: an open-source ligand-based virtual screening tool.

Ligand-based virtual screening is a widespread method in modern drug design. It allows for a rapid screening of large compound databases in order to identify similar structures. Here we report an open-source command line tool which includes a substructure-, fingerprint- and shape-based virtual screening. Most of the implemented features fully rely on the RDKit cheminformatics framework. VSFlow accepts a wide range of input file formats and is highly customizable. Additionally, a quick visualization of the screening results as pdf and/or pymol file is supported.

Targeting oncogenic KRasG13C with nucleotide-based covalent inhibitors.

Mutations within Ras proteins represent major drivers in human cancer. In this study, we report the structure-based design, synthesis, as well as biochemical and cellular evaluation of nucleotide-based covalent inhibitors for KRasG13C, an important oncogenic mutant of Ras that has not been successfully addressed in the past. Mass spectrometry experiments and kinetic studies reveal promising molecular properties of these covalent inhibitors, and X-ray crystallographic analysis has yielded the first reported crystal structures of KRasG13C covalently locked with these GDP analogues. Importantly, KRasG13C covalently modified with these inhibitors can no longer undergo SOS-catalysed nucleotide exchange. As a final proof-of-concept, we show that in contrast to KRasG13C, the covalently locked protein is unable to induce oncogenic signalling in cells, further highlighting the possibility of using nucleotide-based inhibitors with covalent warheads in KRasG13C-driven cancer.

Picomolar FKBP inhibitors enabled by a single water-displacing methyl group in bicyclic [4.3.1] aza-amides.

Methyl groups can have profound effects in drug discovery but the underlying mechanisms are diverse and incompletely understood. Here we report the stereospecific effect of a single, solvent-exposed methyl group in bicyclic [4.3.1] aza-amides, robustly leading to a 2 to 10-fold increase in binding affinity for FK506-binding proteins (FKBPs). This resulted in the most potent and efficient FKBP ligands known to date. By a combination of co-crystal structures, isothermal titration calorimetry (ITC), density-functional theory (DFT), and 3D reference interaction site model (3D-RISM) calculations we elucidated the origin of the observed affinity boost, which was purely entropically driven and relied on the displacement of a water molecule at the protein-ligand-bulk solvent interface. The best compounds potently occupied FKBPs in cells and enhanced bone morphogenic protein (BMP) signaling. Our results show how subtle manipulation of the solvent network can be used to design atom-efficient ligands for difficult, solvent-exposed binding pockets.

Pseudonatural Products Occur Frequently in Biologically Relevant Compounds.

A new methodology for classifying fragment combinations and characterizing pseudonatural products (PNPs) is described. The source code is based on open-source tools and is organized as a Python package. Tasks can be executed individually or within the context of scalable, robust workflows. First, structures are standardized and duplicate entries are filtered out. Then, molecules are probed for the presence of predefined fragments. For molecules with more than one match, fragment combinations are classified. The algorithm considers the pairwise relative position of fragments within the molecule (fused atoms, linkers, intermediary rings), resulting in 18 different possible fragment combination categories. Finally, all combinations for a given molecule are assembled into a fragment combination graph, with fragments as nodes and combination types as edges. This workflow was applied to characterize PNPs in the ChEMBL database via comparison of fragment combination graphs with natural product (NP) references, represented by the Dictionary of Natural Products. The Murcko fragments extracted from 2000 structures previously described were used to define NP fragments. The results indicate that ca. 23% of the biologically relevant compounds listed in ChEMBL comply to the PNP definition and that, therefore, PNPs occur frequently among known biologically relevant small molecules. The majority (>95%) of PNPs contain two to four fragments, mainly (>95%) distributed in five different combination types. These findings may provide guidance for the design of new PNPs.

Resistance to Avapritinib in PDGFRA-Driven GIST Is Caused by Secondary Mutations in the PDGFRA Kinase Domain.

Gastrointestinal stromal tumors (GIST) harboring activating mutations of PDGFRA respond to imatinib, with the notable exception of the most common mutation, D842V. Avapritinib is a novel, potent KIT/PDGFRA inhibitor with substantial clinical activity in patients with the D842V genotype. To date, only a minority of PDGFRA-mutant patients treated with avapritinib have developed secondary resistance. Tumor and plasma biopsies in 6 of 7 patients with PDGFRA primary mutations who progressed on avapritinib or imatinib had secondary resistance mutations within PDGFRA exons 13, 14, and 15 that interfere with avapritinib binding. Secondary PDGFRA mutations causing V658A, N659K, Y676C, and G680R substitutions were found in 2 or more patients each, representing recurrent mechanisms of PDGFRA GIST drug resistance. Notably, most PDGFRA-mutant GISTs refractory to avapritinib remain dependent on the PDGFRA oncogenic signal. Inhibitors that target PDGFRA protein stability or inhibition of PDGFRA-dependent signaling pathways may overcome avapritinib resistance. SIGNIFICANCE: Here, we provide the first description of avapritinib resistance mechanisms in PDGFRA-mutant GIST.This article is highlighted in the In This Issue feature, p. 1.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects.

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.

Machine learning meets pK a.

We present a small molecule pK a prediction tool entirely written in Python. It predicts the macroscopic pK a value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r 2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

Light-Activatable TET-Dioxygenases Reveal Dynamics of 5-Methylcytosine Oxidation and Transcriptome Reorganization.

Ten-eleven-translocation (TET) dioxygenases catalyze the oxidation of 5-methylcytosine (5mC), the central epigenetic regulator of mammalian DNA. This activity dynamically reshapes the epigenome and transcriptome by depositing oxidized 5mC derivatives and initiating active DNA demethylation. However, studying this dynamic is hampered by the inability to selectively activate individual TETs with temporal control in cells. We report activation of TETs in mammalian cells by incorporation of genetically encoded 4,5-dimethoxy-2-nitrobenzyl-l-serine as a transient active-site block, and its subsequent deprotection with light. Our approach enables precise insights into the impact of cancer-associated TET2 mutations on the kinetics of TET2 catalysis in vivo, and allows time-resolved monitoring of target gene activation and transcriptome reorganization. This sets a basis for dissecting the order and kinetics of chromatin-associated events triggered by TET catalysis, ranging from DNA demethylation to chromatin and transcription regulation.

Applications of machine learning in drug discovery and development.

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.

Structural and chemical insights into the covalent-allosteric inhibition of the protein kinase Akt.

The Ser/Thr kinase Akt (Protein Kinase B/PKB) is a master switch in cellular signal transduction pathways. Its downstream signaling influences cell proliferation, cell growth, and apoptosis, rendering Akt a prominent drug target. The unique activation mechanism of Akt involves a change of the relative orientation of its N-terminal pleckstrin homology (PH) and the kinase domain and makes this kinase suitable for highly specific allosteric modulation. Here we present a unique set of crystal structures of covalent-allosteric interdomain inhibitors in complex with full-length Akt and report the structure-based design, synthesis, biological and pharmacological evaluation of a focused library of these innovative inhibitors.

Binding Pose Flip Explained via Enthalpic and Entropic Contributions.

The anomalous binding modes of five highly similar fragments of TIE2 inhibitors, showing three distinct binding poses, are investigated. We report a quantitative rationalization for the changes in binding pose based on molecular dynamics simulations. We investigated five fragments in complex with the transforming growth factor ß receptor type 1 kinase domain. Analyses of these simulations using Grid Inhomogeneous Solvation Theory (GIST), pKA calculations, and a tool to investigate enthalpic differences upon binding unraveled the various thermodynamic contributions to the different binding modes. While one binding mode flip can be rationalized by steric repulsion, the second binding pose flip revealed a different protonation state for one of the ligands, leading to different enthalpic and entropic contributions to the binding free energy. One binding pose is stabilized by the displacement of entropically unfavored water molecules (binding pose determined by solvation entropy), ligands in the other binding pose are stabilized by strong enthalpic interactions, overcompensating the unfavorable water entropy in this pose (binding pose determined by enthalpic interactions). This analysis elucidates unprecedented details determining the flipping of the binding modes, which can elegantly explain the experimental findings for this system.

OCEAN: Optimized Cross rEActivity estimatioN.

The prediction of molecular targets is highly beneficial during the drug discovery process, be it for off-target elucidation or deconvolution of phenotypic screens. Here, we present OCEAN, a target prediction tool exclusively utilizing publically available ChEMBL data. OCEAN uses a heuristics approach based on a validation set containing almost 1000 drug ← → target relationships. New ChEMBL data (ChEMBL20 as well as ChEMBL21) released after the validation was used for a prospective OCEAN performance check. The success rates of OCEAN to predict correctly the targets within the TOP10 ranks are 77% for recently marketed drugs and 62% for all new ChEMBL20 compounds and 51% for all new ChEMBL21 compounds. OCEAN is also capable of identifying polypharmacological compounds; the success rate for molecules simultaneously hitting at least two targets is 64% to be correctly predicted within the TOP10 ranks. The source code of OCEAN can be found at http://www.github.com/rdkit/OCEAN.

Structure-Based Optimization of Potent, Selective, and Orally Bioavailable CDK8 Inhibitors Discovered by High-Throughput Screening.

The mediator complex-associated cyclin dependent kinase CDK8 regulates ß-catenin-dependent transcription following activation of WNT signaling. Multiple lines of evidence suggest CDK8 may act as an oncogene in the development of colorectal cancer. Here we describe the successful optimization of an imidazo-thiadiazole series of CDK8 inhibitors that was identified in a high-throughput screening campaign and further progressed by structure-based design. In several optimization cycles, we improved the microsomal stability, potency, and kinase selectivity. The initial imidazo-thiadiazole scaffold was replaced by a 3-methyl-1H-pyrazolo[3,4-b]-pyridine which resulted in compound 25 (MSC2530818) that displayed excellent kinase selectivity, biochemical and cellular potency, microsomal stability, and is orally bioavailable. Furthermore, we demonstrated modulation of phospho-STAT1, a pharmacodynamic biomarker of CDK8 activity, and tumor growth inhibition in an APC mutant SW620 human colorectal carcinoma xenograft model after oral administration. Compound 25 demonstrated suitable potency and selectivity to progress into preclinical in vivo efficacy and safety studies.

Perturbation Approaches for Exploring Protein Binding Site Flexibility to Predict Transient Binding Pockets.

Simulations of the long-time scale motions of a ligand binding pocket in a protein may open up new perspectives for the design of compounds with steric or chemical properties differing from those of known binders. However, slow motions of proteins are difficult to access using standard molecular dynamics (MD) simulations and are thus usually neglected in computational drug design. Here, we introduce two nonequilibrium MD approaches to identify conformational changes of a binding site and detect transient pockets associated with these motions. The methods proposed are based on the rotamerically induced perturbation (RIP) MD approach, which employs perturbation of side-chain torsional motion for initiating large-scale protein movement. The first approach, Langevin-RIP (L-RIP), entails a series of short Langevin MD simulations, each starting with perturbation of one of the side-chains lining the binding site of interest. L-RIP provides extensive sampling of conformational changes of the binding site. In less than 1 ns of MD simulation with L-RIP, we observed distortions of the α-helix in the ATP binding site of HSP90 and flipping of the DFG loop in Src kinase. In the second approach, RIPlig, a perturbation is applied to a pseudoligand placed in different parts of a binding pocket, which enables flexible regions of the binding site to be identified in a small number of 10 ps MD simulations. The methods were evaluated for four test proteins displaying different types and degrees of binding site flexibility. Both methods reveal all transient pocket regions in less than a total of 10 ns of simulations, even though many of these regions remained closed in 100 ns conventional MD. The proposed methods provide computationally efficient tools to explore binding site flexibility and can aid in the functional characterization of protein pockets, and the identification of transient pockets for ligand design.

2,8-Disubstituted-1,6-Naphthyridines and 4,6-Disubstituted-Isoquinolines with Potent, Selective Affinity for CDK8/19.

We demonstrate a designed scaffold-hop approach to the discovery of 2,8-disubstituted-1,6-naphthyridine- and 4,6-disubstituted-isoquinoline-based dual CDK8/19 ligands. Optimized compounds in both series exhibited rapid aldehyde oxidase-mediated metabolism, which could be abrogated by introduction of an amino substituent at C5 of the 1,6-naphthyridine scaffold or at C1 of the isoquinoline scaffold. Compounds 51 and 59 were progressed to in vivo pharmacokinetic studies, and 51 also demonstrated sustained inhibition of STAT1(SER727) phosphorylation, a biomarker of CDK8 inhibition, in an SW620 colorectal carcinoma human tumor xenograft model following oral dosing.

Discovery of potent and selective CDK8 inhibitors from an HSP90 pharmacophore.

Here we describe the discovery and optimization of 3-benzylindazoles as potent and selective inhibitors of CDK8, also modulating CDK19, discovered from a high-throughput screening (HTS) campaign sampling the Merck compound collection. The primary hits with strong HSP90 affinity were subsequently optimized to potent and selective CDK8 inhibitors which demonstrate inhibition of WNT pathway activity in cell-based assays. X-ray crystallographic data demonstrated that 3-benzylindazoles occupy the ATP binding site of CDK8 and adopt a Type I binding mode. Medicinal chemistry optimization successfully led to improved potency, physicochemical properties and oral pharmacokinetics. Modulation of phospho-STAT1, a pharmacodynamic biomarker of CDK8, was demonstrated in an APC-mutant SW620 human colorectal carcinoma xenograft model following oral administration.

Discovery of Potent, Selective, and Orally Bioavailable Small-Molecule Modulators of the Mediator Complex-Associated Kinases CDK8 and CDK19.

The Mediator complex-associated cyclin-dependent kinase CDK8 has been implicated in human disease, particularly in colorectal cancer where it has been reported as a putative oncogene. Here we report the discovery of 109 (CCT251921), a potent, selective, and orally bioavailable inhibitor of CDK8 with equipotent affinity for CDK19. We describe a structure-based design approach leading to the discovery of a 3,4,5-trisubstituted-2-aminopyridine series and present the application of physicochemical property analyses to successfully reduce in vivo metabolic clearance, minimize transporter-mediated biliary elimination while maintaining acceptable aqueous solubility. Compound 109 affords the optimal compromise of in vitro biochemical, pharmacokinetic, and physicochemical properties and is suitable for progression to animal models of cancer.

A selective chemical probe for exploring the role of CDK8 and CDK19 in human disease.

There is unmet need for chemical tools to explore the role of the Mediator complex in human pathologies ranging from cancer to cardiovascular disease. Here we determine that CCT251545, a small-molecule inhibitor of the WNT pathway discovered through cell-based screening, is a potent and selective chemical probe for the human Mediator complex-associated protein kinases CDK8 and CDK19 with >100-fold selectivity over 291 other kinases. X-ray crystallography demonstrates a type 1 binding mode involving insertion of the CDK8 C terminus into the ligand binding site. In contrast to type II inhibitors of CDK8 and CDK19, CCT251545 displays potent cell-based activity. We show that CCT251545 and close analogs alter WNT pathway-regulated gene expression and other on-target effects of modulating CDK8 and CDK19, including expression of genes regulated by STAT1. Consistent with this, we find that phosphorylation of STAT1(SER727) is a biomarker of CDK8 kinase activity in vitro and in vivo. Finally, we demonstrate in vivo activity of CCT251545 in WNT-dependent tumors.

Selection of fragments for kinase inhibitor design: decoration is key.

In fragment-based screening, the choice of the best suited fragment hit among the detected hits is crucial for success. In our study, a kinase lead compound was fragmented, the hinge-binding motif extracted as a core fragment, and a minilibrary of five similar compounds with fragment-like properties was selected from our proprietary compound database. The structures of five fragments in complex with transforming growth factor ß receptor type 1 kinase domain were determined by X-ray crystallography. Three different binding modes of the fragments are observed that depend on the position and the type of the substitution at the core fragment. The influence of different substituents on the preferred fragment pose was analyzed by various computational approaches. We postulate that the replacement of water molecules leads to the different binding modes.