Legends for supplemental figures and tables

<p>KINOMEScan assay results for RAF709</p>

<p>Figure S1: pMEK and pERK suppression in KRAS mutant and BRAFV600E cells by dabrafenib or RAF709. Figure S2: pMEK and pERK suppression in NRAS mutant and BRAFV600E cells by dabrafenib or RAF709. Figure S3: Cell cycle and apoptotic effects of RAF709 in KRASmut cells. Figure S4: Antiproliferative activity of RAF709 in HPAFII cells.</p>

<p>Table S1: Kinase hits from KinomeScan panel. Table S4: Antiproliferative activity of RAF709 in a Ba/F3 cell kinase panel.</p>

<p>KINOMEScan assay results for RAF709</p>

<p>KiNative assay results for RAF709</p>

Predicting solubility of small molecules is a very difficult undertaking due to the lack of reliable and consistent experimental solubility data. It is well known that for a molecule in a crystal lattice to be dissolved, it must, first, dissociate from the lattice and then, second, be solvated. The melting point of a compound is proportional to the lattice energy, and the octanol-water partition coefficient (log P) is a measure of the compound's solvation efficiency. The CCDC's melting point dataset of almost one hundred thousand compounds was utilized to create widely applicable machine learning models of small molecule melting points. Using the general solubility equation, the aqueous thermodynamic solubilities of the same compounds can be predicted. The global model could be easily localized by adding additional melting point measurements for a chemical series of interest.

<p>KiNative assay results for RAF709</p>

<p>Antiproliferative effect of RAF709 compared to dabrafenib, trametinib, RAF265 and sorafenib across a tumor cell panel</p>

A method is presented for an ultrafast shape-based search workflow for the screening of large compound collections, i.e., those of vendors. The three-dimensional shape of a molecule dictates its biological activity by enabling the molecule to fit into binding pockets of proteins. Quite often, distinctly different chemical compounds that have similar shapes can bind in a similar way. OpenEye pioneered an algorithm for comparing shapes of molecules by overlaying them in a computer and measuring differences between a query molecule and a target molecule. Overlaying shapes is a computationally intensive process and represents a bottleneck in searching for similar molecules. More recent publications describe alternative methods of overlaying molecules, which are accomplished by comparing shape-based descriptors. These methods were implemented in the Open Drug Discovery Toolkit (ODDT) package. We utilized a combination of open-source software packages like ODDT and RDkit to implement a workflow for ultrafast conformer generation and matching that does not require storing precomputed conformers on the file system or in memory. Moreover, the generated descriptors could be optionally stored in MongoDB for performing searches in the future. To speed up the search, we created a set of indexes from the transformed shape-based descriptors. We are in the process of calculating descriptors for multiple vendors, including Enamine's "REAL" collection of 1.2 billion compounds. Currently, the shape similarity search on more than 70 million compounds takes less than 8 s! We exemplified our methodology with the screen of compounds that can act as putative TLR4 agonists. The search was based on a literature-known small-molecule TLR4 agonist series. In due course, we identified compounds with novel structural motifs that were active in mouse and human TLR4 reporter cell lines.

Herein, we describe the discovery and optimization of a novel series that inhibits bacterial DNA gyrase and topoisomerase IV via binding to, and stabilization of, DNA cleavage complexes. Optimization of this series led to the identification of compound 25, which has potent activity against Gram-positive bacteria, a favorable in vitro safety profile, and excellent in vivo pharmacokinetic properties. Compound 25 was found to be efficacious against fluoroquinolone-sensitive Staphylococcus aureus infection in a mouse thigh model at lower doses than moxifloxacin. An X-ray crystal structure of the ternary complex formed by topoisomerase IV from Klebsiella pneumoniae, compound 25, and cleaved DNA indicates that this compound does not engage in a water-metal ion bridge interaction and forms no direct contacts with residues in the quinolone resistance determining region (QRDR). This suggests a structural basis for the reduced impact of QRDR mutations on antibacterial activity of 25 compared to fluoroquinolones.

Profile-QSAR (pQSAR) is a massively multi-task, 2-step machine learning method with unprecedented scope, accuracy and applicability domain. In step one, a “profile” of conventional single-assay random forest regression (RFR) models are trained on a very large number of biochemical and cellular pIC50 assays using Morgan 2 sub-structural fingerprints as compound descriptors. In step 2, a panel of PLS models are built using the profile of pIC50 predictions from those RFR models as compound descriptors. Hence the name. Previously described for a panel of 728 biochemical and cellular kinase assays, we have now built an enormous pQSAR from 11,805 diverse Novartis IC50 and EC50 assays. This large number of assays, and hence of compound descriptors for PLS, dictated reducing the profile by only including RFR models whose predictions correlate with the assay being modeled. We evaluate both the RFR and pQSAR models with our “realistically novel” held-out test set whose median, average similarity to the nearest training set member across the 11,805 assays was only 0.34, thus testing a realistically large applicability domain. For the 11,805 single-assay RFR models, the median correlation of prediction with experiment was only R2ext=0.05, virtually random, and only 8% of the models achieved our standard success threshold of R2ext=0.30. For pQSAR, the median correlation was R2ext=0.53, comparable to 4-concentration experimental IC50s, and 72% of the models met our R2ext>0.30 standard, totaling 8558 successful models. The successful models included assays from all of the 51 annotated target sub-classes, as well as 4196 phenotypic assays, indicating that pQSAR can be applied to virtually any disease area. Every month, all models are updated to include new measurements, and predictions are made for 5.5 million Novartis compounds, totaling 50 billion predictions. Common uses have included virtual screening, selectivity design, toxicity and promiscuity prediction, mechanism-of-action prediction, and others.

Profile-QSAR (pQSAR) is a massively multi-task, 2-step machine learning method with unprecedented scope, accuracy and applicability domain. In step one, a “profile” of conventional single-assay random forest regression (RFR) models are trained on a very large number of biochemical and cellular pIC50 assays using Morgan 2 sub-structural fingerprints as compound descriptors. In step two, a panel of PLS models are built using the profile of pIC50 predictions from those RFR models as compound descriptors. Hence the name. Previously described for a panel of 728 biochemical and cellular kinase assays, we have now built an enormous pQSAR from 11,805 diverse Novartis IC50 and EC50 assays. This large number of assays, and hence of compound descriptors for PLS, dictated reducing the profile by only including RFR models whose predictions correlate with the assay being modeled. The RFR and pQSAR models were evaluated with our “realistically novel” held-out test set whose median average similarity to the nearest training set member across the 11,805 assays was only 0.34, thus testing a realistically large applicability domain. For the 11,805 single-assay RFR models, the median correlation of prediction with experiment was only R 2 ext=0.05, virtually random, and only 8% of the models achieved our standard success threshold of R 2 ext=0.30. For pQSAR, the median correlation was R 2 ext=0.53, comparable to 4-concentration experimental IC50s, and 72% of the models met our R 2 ext>0.30 standard, totaling 8558 successful models. The successful models included assays from all of the 51 annotated target sub-classes, as well as 4196 phenotypic assays, indicating that pQSAR can be applied to virtually any disease area. Every month, all models are updated to include new measurements, and predictions are made for 5.5 million Novartis compounds, totaling 50 billion predictions. Common uses have included virtual screening, selectivity design, toxicity and promiscuity prediction, mechanism-of-action prediction, and others.

Profile-quantitative structure-activity relationship (pQSAR) is a massively multitask, two-step machine learning method with unprecedented scope, accuracy, and applicability domain. In step one, a "profile" of conventional single-assay random forest regression models are trained on a very large number of biochemical and cellular pIC(50) assays using Morgan 2 substructural fingerprints as compound descriptors. In step two, a panel of partial least squares (PLS) models are built using the profile of pIC(50) predictions from those random forest regression models as compound descriptors (hence the name). Previously described for a panel of 728 biochemical and cellular kinase assays, we have now built an enormous pQSAR from 11 805 diverse Novartis (NVS) IC50 and EC50 assays. This large number of assays, and hence of compound descriptors for PLS, dictated reducing the profile by only including random forest regression models whose predictions correlate with the assay being modeled. The random forest regression and pQSAR models were evaluated with our "realistically novel" held-out test set, whose median average similarity to the nearest training set member across the 11 805 assays was only 0.34, comparable to the novelty of compounds actually selected from virtual screens. For the 11 805 single-assay random forest regression models, the median correlation of prediction with the experiment was only r(ext)(2) = 0.05, virtually random, and only 8% of the models achieved our standard success threshold of r(ext)(2) = 0.30. For pQSAR, the median correlation was r(ext)(2) = 0.53, comparable to four-concentration experimental IC(50)s, and 72% of the models met our r(ext)(2) > 0.30 standard, totaling 8558 successful models. The successful models included assays from all of the 51 annotated target subclasses, as well as 4196 phenotypic assays, indicating that pQSAR can be applied to virtually any disease area. Every month, all models are updated to include new measurements, and predictions are made for 5.5 million NVS compounds, totaling 50 billion predictions. Common uses have included virtual screening, selectivity design, toxicity and promiscuity prediction, mechanism-of-action prediction, and others. Several such actual applications are described.

Direct pharmacological inhibition of RAS has remained elusive, and efforts to target CRAF have been challenging due to the complex nature of RAF signaling, downstream of activated RAS, and the poor overall kinase selectivity of putative RAF inhibitors. Herein, we describe 15 (LXH254, Aversa, R.; et al. Int. Patent WO2014151616A1, 2014), a selective B/C RAF inhibitor, which was developed by focusing on drug-like properties and selectivity. Our previous tool compound, 3 (RAF709; Nishiguchi, G. A.; et al. J. Med. Chem.2017, 60, 4969), was potent, selective, efficacious, and well tolerated in preclinical models, but the high human intrinsic clearance precluded further development and prompted further investigation of close analogues. A structure-based approach led to a pyridine series with an alcohol side chain that could interact with the DFG loop and significantly improved cell potency. Further mitigation of human intrinsic clearance and time-dependent inhibition led to the discovery of 15. Due to its excellent properties, it was progressed through toxicology studies and is being tested in phase 1 clinical trials.

Abstract Resistance to the RAF inhibitor vemurafenib arises commonly in melanomas driven by the activated BRAF oncogene. Here, we report antitumor properties of RAF709, a novel ATP-competitive kinase inhibitor with high potency and selectivity against RAF kinases. RAF709 exhibited a mode of RAF inhibition distinct from RAF monomer inhibitors such as vemurafenib, showing equal activity against both RAF monomers and dimers. As a result, RAF709 inhibited MAPK signaling activity in tumor models harboring either BRAFV600 alterations or mutant N- and KRAS-driven signaling, with minimal paradoxical activation of wild-type RAF. In cell lines and murine xenograft models, RAF709 demonstrated selective antitumor activity in tumor cells harboring BRAF or RAS mutations compared with cells with wild-type BRAF and RAS genes. RAF709 demonstrated a direct pharmacokinetic/pharmacodynamic relationship in in vivo tumor models harboring KRAS mutation. Furthermore, RAF709 elicited regression of primary human tumor–derived xenograft models with BRAF, NRAS, or KRAS mutations with excellent tolerability. Our results support further development of inhibitors like RAF709, which represents a next-generation RAF inhibitor with unique biochemical and cellular properties that enables antitumor activities in RAS-mutant tumors. Significance: In an effort to develop RAF inhibitors with the appropriate pharmacological properties to treat RAS mutant tumors, RAF709, a compound with potency, selectivity, and in vivo properties, was developed that will allow preclinical therapeutic hypothesis testing, but also provide an excellent probe to further unravel the complexities of RAF kinase signaling. Cancer Res; 78(6); 1537–48. ©2018 AACR.

The mitogen-activated protein kinase (MAPK) signaling pathway is frequently activated in human cancers due to genetic alterations that can occur at multiple nodes, the most prevalent of which are mutations in RAS or BRAF. While BRAFV600 mutant tumors are responsive to RAF inhibitors such as dabrafenib and vemurafenib, these drugs are ineffective in RAS mutant cancers and tumors expressing other RAF mutations. CRAF kinase functions as a critical effector in mutant RAS and Class II/III BRAF mutant tumors and plays a role in feedback-mediated pathway reactivation following MEK inhibition. Thus, selective inhibitors that potently inhibit the activity of CRAF could be both effective in blocking mutant RAS and BRAF signaling and in inhibiting feedback-mediated activation in combination with a MEK inhibitor. LXH254 is a type II ATP-competitive inhibitor that inhibits both B- and CRAF kinase activities at picomolar concentrations with a high degree of selectivity against a panel of 456 human kinases and in cell-based assays. LXH254 not only inhibits MAPK signaling activity in tumor models harboring BRAFV600 mutation, but also inhibits mutant N- and KRAS-driven signaling due to its ability to inhibit both RAF monomers and dimers with similar potencies. LXH254 is orally bioavailable, demonstrates a direct PK/PD relationship and causes tumor regression in multiple cell line and primary human tumor derived xenograft models at well-tolerated doses. LXH254 represents a next generation RAF inhibitor that is differentiated from other RAF inhibitors in this class due to the high degree of selectivity. In preclinical efficacy and toxicology studies, LXH254 demonstrated a relatively wide therapeutic index which should enable effective interrogation of RAF inhibition in patients with decreased risk for off-target toxicity. LXH254 is currently in a Phase I trial in patients with solid tumors expressing MAPK pathway mutations. Citation Format: Darrin D. Stuart, Wenlin Shao, Yuji Mishina, Yun Feng, Giordano Caponigro, Vesselina G. Cooke, Stacey Rivera, Fang Shen, Joshua Korn, Lesley A. Mathews Griner, Giselle Nishiguchi, Benjamin Taft, Lifeng Wan, Sharadha Subramanian, Yan Lou, Lina Setti, Matthew Burger, Victor Tamez, Alice Rico, Robert Aversa, John Tellew, Jacob R. Haling, Valery Polyakov, Amy Lambert, Richard Zang, Ann Van Abbema, Mohamad Hekmat-Nejad, Payman Amiri, Mallika Singh, Nicholas Keen, Michael P. Dillon, Emma Lees, William R. Sellers, Savithri Ramurthy. Pharmacological profile and anti-tumor properties of LXH254, a highly selective RAF kinase inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr DDT01-04.

CLK2 inhibition has been proposed as a potential mechanism to improve autism and neuronal functions in Phelan-McDermid syndrome (PMDS). Herein, the discovery of a very potent indazole CLK inhibitor series and the CLK2 X-ray structure of the most potent analogue are reported. This new indazole series was identified through a biochemical CLK2 Caliper assay screen with 30k compounds selected by an in silico approach. Novel high-resolution X-ray structures of all CLKs, including the first CLK4 X-ray structure, bound to known CLK2 inhibitor tool compounds (e.g., TG003, CX-4945), are also shown and yield insight into inhibitor selectivity in the CLK family. The efficacy of the new CLK2 inhibitors from the indazole series was demonstrated in the mouse brain slice assay, and potential safety concerns were investigated. Genotoxicity findings in the human lymphocyte micronucleus test (MNT) assay are shown by using two structurally different CLK inhibitors to reveal a major concern for pan-CLK inhibition in PMDS.