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Safety pharmacology screening against a wide range of unintended vital targets using in vitro assays is crucial to understand off-target interactions with drug candidates. With the increasing demand for in vitro assays, ligand- and structure-based virtual screening approaches have been evaluated for potential utilization in safety profiling. Although ligand based approaches have been actively applied in retrospective analysis or prospectively within well-defined chemical space during the early discovery stage (i.e., HTS screening and lead optimization), virtual screening is rarely implemented in later stage of drug discovery (i.e., safety). Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2A). The resulting models, obtained from 268 chemically diverse compounds, were used to test a set of 1,897 chemically distinct drugs, simulating the real-world challenge of safety screening when presented with novel chemistry and a limited training set. Due to the unique requirements of safety screening versus discovery screening, the limitations of 3D QSAR methods (i.e., chemotypes, dependence on large training set, and prone to false positives) are less critical than early discovery screen. We demonstrated that 3D QSAR modeling can be effectively applied in safety assessment prior to in vitro assays, even with chemotypes that are drastically different from training compounds. It is also worth noting that our model is able to adequately make the mechanistic distinction between agonists and antagonists, which is important to inform subsequent in vivo studies. Overall, we present an in-depth analysis of the appropriate utilization and interpretation of pharmacophore-based 3D QSAR models for safety screening.

We present the Consensus Induced Fit Docking (cIFD) approach for adapting a protein binding site to accommodate multiple diverse ligands for virtual screening. This novel approach results in a single binding site structure that can bind diverse chemotypes and is thus highly useful for efficient structure-based virtual screening. We first describe the cIFD method and its validation on three targets that were previously shown to be challenging for docking programs (COX-2, estrogen receptor, and HIV reverse transcriptase). We then demonstrate the application of cIFD to the challenging discovery of irreversible Crm1 inhibitors. We report the identification of 33 novel Crm1 inhibitors, which resulted from the testing of 402 purchased compounds selected from a screening set containing 261,680 compounds. This corresponds to a hit rate of 8.2 %. The novel Crm1 inhibitors reveal diverse chemical structures, validating the utility of the cIFD method in a real-world drug discovery project. This approach offers a pragmatic way to implicitly account for protein flexibility without the additional computational costs of ensemble docking or including full protein flexibility during virtual screening.

BackgroundMesothelin (MSLN) is a classic tumor-associated antigen that is expressed in lung cancer and many other solid tumors. However, MSLN is also expressed in normal mesothelium which creates a significant risk of serious inflammation for MSLN-directed therapeutics. We have developed a dual-receptor (Tmod™) system that exploits the difference between tumor and normal tissue in a subset of patients with defined heterozygous gene loss (LOH) in their tumors.MethodsT cells engineered with the MSLN CAR Tmod construct described here contain (1) a novel MSLN-activated CAR and (2) an HLA-A*02-gated inhibitory receptor (blocker). A*02 binding is intended to override T-cell cytotoxicity, even in the presence of MSLN. The Tmod system is designed to treat heterozygous HLA class I patients, selected for HLA LOH. When A*02 is absent from tumors selected for LOH, the MSLN Tmod cells are predicted to mediate potent killing of the MSLN(+)A*02(−) malignant cells.ResultsThe sensitivity of the MSLN Tmod cells is comparable with a benchmark MSLN CAR-T that was active but toxic in the clinic. Unlike MSLN CAR-T cells, the Tmod system robustly protects surrogate “normal” cells even in mixed-cell populations in vitro and in a xenograft model. The MSLN CAR can also be paired with other HLA class I blockers, supporting extension of the approach to patients beyond A*02 heterozygotes.ConclusionsThe Tmod mechanism exemplified by the MSLN CAR Tmod construct provides an alternative route to leverage solid-tumor antigens such as MSLN in safer, more effective ways than previously possible.

Cell therapy using T cell receptors (TCRs) and chimeric antigen receptors (CARs) represents a new wave of immunotherapies garnering considerable attention and investment. Further progress in this area of medicine depends in part on improving the functional capabilities of the engineered components, while maintaining the overall size of recombinant constructs to ensure their compatibility with existing gene delivery vehicles. We describe a single-variable-domain TCR (svd TCR) that utilizes only the variable domain of the β chain (Vβ). This Vβ module not only works in TCR and CAR formats, but also can be used to create single-chain bispecific CARs and TCRs. Comparison of individual ligand-binding Vβ domains in different formats suggests that the lone Vβ sequence controls the sensitivity and a major part of the specificity of the CAR or TCR construct, regardless of signaling format, in Jurkat and primary T cells.

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the PREDICT method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 \"virtual hit\" compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves,$K_{{\\rm i}}<5\\ \\mu {\\rm M}$. In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.

BackgroundRecurrent cancer-specific targets are rare. Given the pace of genomic research over the past three decades, few are likely to lie yet undiscovered. In 2013 an innovative MAGE-A3-directed cancer therapeutic of great potential value was terminated in the clinic because of neurotoxicity.1 The safety problems were hypothesized to originate from off-target TCR activity against a closely related MAGE-A12 peptide.MethodsA combination of published and new data led us to test this hypothesis with current technology, including RNA hybridization in situ and further analysis of the clinical TCR’s specificity to MAGE-A12 and other antigens.ResultsWe find that a key prediction of the MAGE-A12 toxicity hypothesis, the existence of rare, high-MAGE-A12-expressing cells in the brain, is not supported by the data. Our results imply that an alternative related peptide from the EPS8L2 protein is more likely responsible for the toxicity. Therefore, it may be valuable to reconsider MAGE-A3 as a cancer target using HLA-A*02-restricted-TCRs or CARs. As a step in this direction, we isolated MAGE-A3 pMHC-directed CARs, targeting the same peptide as the clinical TCR. These CARs have high selectivity, and avoid cross-reaction with the EPS8L2 peptide that represents a significant risk for MAGE-A3-targeted therapeutics.ConclusionsGiven the qualities of MAGE-A3 as an onco-testis antigen widely expressed in tumors and largely absent from normal adult tissues, our findings suggest that MAGE-A3 may deserve further consideration as a cancer target. We have identified CARs with selectivity profiles consistent with a cell therapeutic directed against HLA-A*02-positive, MAGE-A3-expressing cancers. The relative merits of TCRs and CARs for this target will be discussed.ReferenceMorgan RA, Chinnasamy N, Abate-Daga D, Gros A, Robbins PF, Zheng Z, Dudley ME, Feldman SA, Yang JC, Sherry RM, et al. Cancer regression and neurological toxicity following anti-MAGE-A3 TCR gene therapy. J Immunother 2013;36:133–151, doi:10.1097/CJI.0b013e3182829903.

PART A: Backbone fluctuations play an important role in protein function. Site-directed spin labeling combined with Electron Paramagnetic Resonance spectroscopy (SDSL/EPR) is a potentially useful approach for advancing the understanding of protein structure and dynamics. SDSL involves introducing a paramagnetic nitroxide side chain via site-directed mutagenesis. The EPR spectrum of a spin labeled protein reflects the motion of the nitroxide, on a nanosecond time scale. Clarifying the intrinsic internal motion of the Rl (R1 = [-CH2-S-S-CH2-3([2,2,5,5, tetramethylpyrroline-l-oxyl]) side chain and how it relates to the rotamers of R1 is important for quantitative interpretation of the spectra in terms of protein structure. X-ray crystallographic studies of Rl in T4 Lysozyme showed that nitroxide side chains adopt specific rotamers with respect to the first two dihedral angles (X1, X2). In order to determine the factors that stabilize these {X1, X 2} rotamers, quantum mechanical calculations were performed. The lowest energy conformations obtained coincided with those observed experimentally. A major determinant of the stability of these rotamers is the backbone sulfur interaction. The Rl side chain shows internal motion of high amplitude. Restricting these motions is essential for expanding the dynamic range for detection of backbone motion. Systematic chemical changes have been made to Rl to restrain its internal motion. Several novel nitroxide side chains have been identified with more restricted internal dynamic modes compared to R1 at solvent exposed α-helical sites. Computational and X-ray crystallographic studies have been used to elucidate the structural basis of this hindered motion. The results suggest that one of the novel side chains, V1, is strongly immobilized by a specific intra-residue N···S interaction. This new side chain extends the dynamic range for detection of backbone motion and is also useful for distance measurements by EPR. PART B: Computational studies have been used to study the dynamics of the methanethiosulfonate (MTS) cross-linking reagents, the possible changes cross-linking induces on actin and the interactions of actin with cofilin. Molecular dynamics simulations of the MTS cross-linkers were performed to assess the distance span of these molecules. Molecular modeling of the cross-linked actin revealed structural changes in the actin hydrophobic loop (262-274) caused by the cross-linkers. Protein-protein docking studies, employing distance constraints derived from cross-linking data, yielded a model of the G-actin-cofilin complex structure, with cofilin bound to the hydrophobic cleft between subdomains 1 and 3 on actin.

Safety pharmacology screening against a wide range of unintended vital targets using in vitro assays is crucial to understand off-target interactions with drug candidates. With the increasing demand for in vitro assays, ligand-and structure-based virtual screening approaches have been evaluated for potential utilization in safety profiling. Although ligand based approaches have been actively applied in retrospective analysis or prospectively within well-defined chemical space during the early discovery stage (i.e., HTS screening and lead optimization), virtual screening is rarely implemented in later stage of drug discovery (i.e., safety). Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2aR). The resulting models, obtained from 268 chemically diverse compounds, were used to test a set of 1,897 chemically distinct drugs, simulating the real-world challenge of safety screening when presented with novel chemistry and a limited training set. Due to the unique requirements of safety screening versus discovery screening, the limitations of 3D QSAR methods (i.e., chemotypes, dependence on large training set, and prone to false positives) are less critical than early discovery screen. We demonstrated that 3D QSAR modelling can be effectively applied in safety assessment prior to in vitro assays, even with chemotypes that are drastically different from training compounds. It is also worth noting that our model is able to adequately make the mechanistic distinction between agonists and antagonists, which is important to inform subsequent in vivo studies. Overall, we present an in-depth analysis of the appropriate utilization and interpretation of pharmacophore-based 3D QSAR models for safety screening.

Safety pharmacology screening against a wide range of unintended vital targets using in vitro assays is crucial to understand off-target interactions with drug candidates. With the increasing demand for in vitro assays, ligand- and structure-based virtual screening approaches have been evaluated for potential utilization in safety profiling. Although ligand based ap-proaches have been actively applied in retrospective analysis or prospectively within well-defined chemical space during the early discovery stage (i.e., HTS screening and lead optimization), virtual screening is rarely implemented in later stage of drug discovery (i.e., safety). Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2aR). The resulting models, obtained from 268 chemically diverse compounds, were used to test a set of 1,897 chemically distinct drugs, simulating the real-world challenge of safety screening when presented with novel chemistry and a limited training set. Due to the unique requirements of safety screening versus discovery screening, the limitations of 3D QSAR methods (i.e., chemotypes, dependence on large training set, and prone to false positives) are less critical than early discovery screen. We demonstrated that 3D QSAR modelling can be effectively applied in safety assessment prior to in vitro assays, even with chemotypes that are drastically different from training com-pounds. It is also worth noting that our model is able to adequately make the mechanistic distinction between agonists and antagonists, which is important to inform subsequent in vivo studies. Overall, we present an in-depth analysis of the appropriate utilization and interpretation of pharmacophore-based 3D QSAR models for safety screening