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Although the potential value of RNA as a target for new small molecule therapeutics is becoming increasingly credible, the physicochemical properties required for small molecules to selectively bind to RNA remain relatively unexplored. To investigate the druggability of RNAs with small molecules, we have employed affinity mass spectrometry, using the Automated Ligand Identification System (ALIS), to screen 42 RNAs from a variety of RNA classes, each against an array of chemically diverse drug-like small molecules (~50,000 compounds) and functionally annotated tool compounds (~5100 compounds). The set of RNA-small molecule interactions that was generated was compared with that for protein-small molecule interactions, and naïve Bayesian models were constructed to determine the types of specific chemical properties that bias small molecules toward binding to RNA. This set of RNA-selective chemical features was then used to build an RNA-focused set of ~3800 small molecules that demonstrated increased propensity toward binding the RNA target set. In addition, the data provide an overview of the specific physicochemical properties that help to enable binding to potential RNA targets. This work has increased the understanding of the chemical properties that are involved in small molecule binding to RNA, and the methodology used here is generally applicable to RNA-focused drug discovery efforts.

A novel series of IDO1 inhibitors have been identified with good IDO1 Hela cell and human whole blood activity. These inhibitors contain an indoline or a 3-azaindoline scaffold. Their structure-activity-relationship studies have been explored. Compounds 37 and 41 stood out as leads due to their good potency in IDO1 Hela assay, good IDO1 unbound hWB IC50s, reasonable unbound clearance, and good MRT in rat and dog PK studies.

Though phenotypic and target-based high-throughput screening approaches have been employed to discover new antibiotics, the identification of promising therapeutic candidates remains challenging. Each approach provides different information, and understanding their results can provide hypotheses for a mechanism of action (MoA) and reveal actionable chemical matter. Here, we describe a framework for identifying efficacy targets of bioactive compounds. High throughput biophysical profiling against a broad range of targets coupled with machine learning was employed to identify chemical features with predicted efficacy targets for a given phenotypic screen. We validate the approach on data from a set of 55 000 compounds in 24 historical internal antibacterial phenotypic screens and 636 bacterial targets screened in high-throughput biophysical binding assays. Models were built to reveal the relationships between phenotype, target, and chemotype, which recapitulated mechanisms for known antibacterials. We also prospectively identified novel inhibitors of dihydrofolate reductase with nanomolar antibacterial efficacy against Mycobacterium tuberculosis. Molecular modeling provided structural insight into target-ligand interactions underlying selective killing activity toward mycobacteria over human cells.

The primary objective of early drug discovery is to associate druggable target space with a desired phenotype. The inability to efficiently associate these often leads to failure early in the drug discovery process. In this proof-of-concept study, the most tractable starting points for drug discovery within the NF-κB pathway model system were identified by integrating affinity selection-mass spectrometry (AS-MS) with functional cellular assays. The AS-MS platform Automated Ligand Identification System (ALIS) was used to rapidly screen 15 NF-κB proteins in parallel against large-compound libraries. ALIS identified 382 target-selective compounds binding to 14 of the 15 proteins. Without any chemical optimization, 22 of the 382 target-selective compounds exhibited a cellular phenotype consistent with the respective target associated in ALIS. Further studies on structurally related compounds distinguished two chemical series that exhibited a preliminary structure-activity relationship and confirmed target-driven cellular activity to NF-κB1/p105 and TRAF5, respectively. These two series represent new drug discovery opportunities for chemical optimization. The results described herein demonstrate the power of combining ALIS with cell functional assays in a high-throughput, target-based approach to determine the most tractable drug discovery opportunities within a pathway.

The Myc oncogene is overexpressed in many cancers, yet targeting it for cancer therapy has remained elusive. One strategy for inhibition of Myc expression is through stabilization of the G-quadruplex (G4), a G-rich DNA secondary structure found within the Myc promoter; stabilization of G4s has been shown to halt transcription of downstream gene products. Here we used the Automated Ligand Identification System (ALIS), an affinity selection-mass spectrometry method, to identify compounds that bind to the Myc G4 out of a pool of compounds that had previously been shown to inhibit Myc expression in a reporter screen. Using an ALIS-based screen, we identified hits that bound to the Myc G4, a small subset of which bound preferentially relative to G4s from the promoters of five other genes. To determine functionality and specificity of the Myc G4-binding compounds in cell-based assays, we compared inhibition of Myc expression in cells with and without Myc G4 regulation. Several compounds inhibited Myc expression only in the Myc G4-containing line, and one compound was verified to function through Myc G4 binding. Our study demonstrates that ALIS can be used to identify selective nucleic acid-binding compounds from phenotypic screen hits, increasing the pool of drug targets beyond proteins.

Recent advances in understanding the relevance of noncoding RNA (ncRNA) to disease have increased interest in drugging ncRNA with small molecules. The recent discovery of ribocil, a structurally distinct synthetic mimic of the natural ligand of the flavin mononucleotide (FMN) riboswitch, has revealed the potential chemical diversity of small molecules that target ncRNA. Affinity-selection mass spectrometry (AS-MS) is theoretically applicable to high-throughput screening (HTS) of small molecules binding to ncRNA. Here, we report the first application of the Automated Ligand Detection System (ALIS), an indirect AS-MS technique, for the selective detection of small molecule-ncRNA interactions, high-throughput screening against large unbiased small-molecule libraries, and identification and characterization of novel compounds (structurally distinct from both FMN and ribocil) that target the FMN riboswitch. Crystal structures reveal that different compounds induce various conformations of the FMN riboswitch, leading to different activity profiles. Our findings validate the ALIS platform for HTS screening for RNA-binding small molecules and further demonstrate that ncRNA can be broadly targeted by chemically diverse yet selective small molecules as therapeutics.

The growing prevalence of drug resistant bacteria is a significant global threat to human health. The antibacterial drug rifampin, which functions by inhibiting bacterial RNA polymerase (RNAP), is an important part of the antibacterial armamentarium. Here, in order to identify novel inhibitors of bacterial RNAP, we used affinity-selection mass spectrometry to screen a chemical library for compounds that bind to Escherichia coli RNAP. We identified a novel small molecule, MRL-436, that binds to RNAP, inhibits RNAP, and exhibits antibacterial activity. MRL-436 binds to RNAP through a binding site that differs from the rifampin binding site, inhibits rifampin-resistant RNAP derivatives, and exhibits antibacterial activity against rifampin-resistant strains. Isolation of mutants resistant to the antibacterial activity of MRL-436 yields a missense mutation in codon 622 of the rpoC gene encoding the RNAP β' subunit or a null mutation in the rpoZ gene encoding the RNAP ω subunit, confirming that RNAP is the functional cellular target for the antibacterial activity of MRL-436, and indicating that RNAP β' subunit residue 622 and the RNAP ω subunit are required for the antibacterial activity of MRL-436. Similarity between the resistance determinant for MRL-436 and the resistance determinant for the cellular alarmone ppGpp suggests a possible similarity in binding site and/or induced conformational state for MRL-436 and ppGpp.

Over the past years, improvements in high-throughput screening (HTS) technology and compound libraries have resulted in a dramatic increase in the amounts of good-quality screening hits, and there is a growing need for follow-on hit profiling assays with medium throughput to further triage hits. Here the authors present such assays for the colony-stimulating factor 1 receptor (CSF1R, Fms), including tests for cellular activity and a homogeneous assay to measure affinity for inactive CSF1R. They also present a high-throughput assay to measure target residence time, which is based on competitive binding kinetics. To better fit k(off) rates, they present a modified mathematical model for competitive kinetics. In all assays, they profiled eight reference inhibitors (imatinib, sorafenib, sunitinib, tandutinib, dasatinib, GW2580, Ki20227, and J&J's pyrido[2,3-d]pyrimidin-5-one). Using the known biochemical selectivities of these inhibitors, which can be quantified using metrics such as the selectivity entropy, the authors have determined which assay readout best predicts hit selectivity. Their profiling shows surprisingly that imatinib has a preference for the active form of CSF1R and that Ki20227 has an unusually slow target dissociation rate. This confirms that follow-on hit profiling is essential to ensure that the best hits are selected for lead optimization.