Optimization and Mechanistic Investigations of Novel Allosteric Activators of PKG1α.

Activation of PKG1α is a compelling strategy for the treatment of cardiovascular diseases. As the main effector of cyclic guanosine monophosphate (cGMP), activation of PKG1α induces smooth muscle relaxation in blood vessels, lowers pulmonary blood pressure, prevents platelet aggregation, and protects against cardiac stress. The development of activators has been mostly limited to cGMP mimetics and synthetic peptides. Described herein is the optimization of a piperidine series of small molecules to yield activators that demonstrate in vitro phosphorylation of vasodilator-stimulated phosphoprotein as well as antiproliferative effects in human pulmonary arterial smooth muscle cells. Hydrogen/deuterium exchange mass spectrometry experiments with the small molecule activators revealed a mechanism of action consistent with cGMP-induced activation, and an X-ray co-crystal structure with a construct encompassing the regulatory domains illustrated a binding mode in an allosteric pocket proximal to the low-affinity cyclic nucleotide-binding domain.

Structural basis for isoform-specific inhibition of human CTPS1.

Cytidine triphosphate synthase 1 (CTPS1) is necessary for an effective immune response, as revealed by severe immunodeficiency in CTPS1-deficient individuals [E. Martin et al], [Nature] [510], [288-292] ([2014]). CTPS1 expression is up-regulated in activated lymphocytes to expand CTP pools [E. Martin et al], [Nature] [510], [288-292] ([2014]), satisfying increased demand for nucleic acid and lipid synthesis [L. D. Fairbanks, M. Bofill, K. Ruckemann, H. A. Simmonds], [J. Biol. Chem. ] [270], [29682-29689] ([1995]). Demand for CTP in other tissues is met by the CTPS2 isoform and nucleoside salvage pathways [E. Martin et al], [Nature] [510], [288-292] ([2014]). Selective inhibition of the proliferative CTPS1 isoform is therefore desirable in the treatment of immune disorders and lymphocyte cancers, but little is known about differences in regulation of the isoforms or mechanisms of known inhibitors. We show that CTP regulates both isoforms by binding in two sites that clash with substrates. CTPS1 is less sensitive to CTP feedback inhibition, consistent with its role in increasing CTP levels in proliferation. We also characterize recently reported small-molecule inhibitors, both CTPS1 selective and nonselective. Cryo-electron microscopy (cryo-EM) structures reveal these inhibitors mimic CTP binding in one inhibitory site, where a single amino acid substitution explains selectivity for CTPS1. The inhibitors bind to CTPS assembled into large-scale filaments, which for CTPS1 normally represents a hyperactive form of the enzyme [E. M. Lynch et al], [Nat. Struct. Mol. Biol.] [24], [507-514] ([2017]). This highlights the utility of cryo-EM in drug discovery, particularly for cases in which targets form large multimeric assemblies not amenable to structure determination by other techniques. Both inhibitors also inhibit the proliferation of human primary T cells. The mechanisms of selective inhibition of CTPS1 lay the foundation for the design of immunosuppressive therapies.

Discovery of the First Non-cGMP Mimetic Small Molecule Activators of cGMP-Dependent Protein Kinase 1 α (PKG1α).

PKG1α is a central node in cGMP signaling. Current therapeutics that look to activate this pathway rely on elevation of cGMP levels and subsequent activation of PKG1α. Direct activation of PKG1α could potentially drive additional efficacy without associated side effects of blanket cGMP elevation. We undertook a high-throughput screen to identify novel activators. After triaging through numerous false positive hits, attributed to compound mediated oxidation and activation of PKG1α, a piperidine series of compounds was validated. The hit 1 was a weak activator with EC50 = 47 µM. The activity could be improved to single digit micromolar, as seen in compounds 21 and 25 (7.0 and 3.7 µM, respectively). Several compounds were tested in a pVASP cell-based assay, and for compounds with moderate permeability, good agreement was observed between the biochemical and functional assays. These compounds will function as efficient tools to further interrogate PKG1α biology.

MAIP: a web service for predicting blood-stage malaria inhibitors.

Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/ . MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.

Development of indazole mineralocorticoid receptor antagonists and investigation into their selective late-stage functionalization.

The derivatization of pharmaceuticals is a core activity in the discovery and development of new medicines. Late-stage functionalization via modern CH functionalization chemistry has emerged as a powerful technique with which to diversify advanced pharmaceutical intermediates. We report herein a case study in late-stage functionalization towards the development of a new class of indazole-based mineralocorticoid receptor antagonists (MRA). An effort to modify the electronics of the core indazole heterocycle inspired the use of modern CH borylation chemistry. New reactivity patterns were revealed and studied computationally. Ultimately, a de novo synthesis delivered a key 6-fluoroindazole compound 26, a potent MRA with excellent metabolic stability.

Accelerating the discovery of DGAT1 inhibitors through the application of parallel medicinal chemistry (PMC).

The parallel medicinal chemistry (PMC) was effectively applied to accelerate the optimization of diacylglycerol O-acyltransferase I (DGAT-1) inhibitors. Through a highly collaborative and iterative library design, synthesis and testing, a benzimidazole lead was rapidly and systematically advanced to a highly potent, selective and bioavailable DGAT1 inhibitor with the potential for further development.

Benzimidazole-based DGAT1 inhibitors with a [3.1.0] bicyclohexane carboxylic acid moiety.

Previously disclosed benzimidazole-based DGAT1 inhibitors containing a cyclohexane carboxylic acid moiety suffer from isomerization at the alpha position of the carboxylic acid group, generating active metabolites which exhibit DGAT1 inhibition comparable to the corresponding parent compounds. In this report, we describe the design, synthesis and profiling of benzimidazole-based DGAT1 inhibitors with a [3.1.0] bicyclohexane carboxylic acid moiety. Our results show that single isomer 3A maintains in vitro and in vivo inhibition against DGAT1. In contrast to previous lead compounds, 3A does not undergo isomerization during in vitro hepatocyte incubation study or in vivo mouse study.

Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016.

We describe the performance of multiple pose prediction methods for the D3R 2016 Grand Challenge. The pose prediction challenge includes 36 ligands, which represent 4 chemotypes and some miscellaneous structures against the FXR ligand binding domain. In this study we use a mix of fully automated methods as well as human-guided methods with considerations of both the challenge data and publicly available data. The methods include ensemble docking, colony entropy pose prediction, target selection by molecular similarity, molecular dynamics guided pose refinement, and pose selection by visual inspection. We evaluated the success of our predictions by method, chemotype, and relevance of publicly available data. For the overall data set, ensemble docking, visual inspection, and molecular dynamics guided pose prediction performed the best with overall mean RMSDs of 2.4, 2.2, and 2.2 Å respectively. For several individual challenge molecules, the best performing method is evaluated in light of that particular ligand. We also describe the protein, ligand, and public information data preparations that are typical of our binding mode prediction workflow.

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

Is Multitask Deep Learning Practical for Pharma?

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles.

High-throughput screening (HTS) has enabled millions of compounds to be assessed for biological activity, but challenges remain in the prioritization of hit series. While biological, absorption, distribution, metabolism, excretion, and toxicity (ADMET), purity, and structural data are routinely used to select chemical matter for further follow-up, the scarcity of historical ADMET data for screening hits limits our understanding of early hit compounds. Herein, we describe a process that utilizes a battery of in-house quantitative structure-activity relationship (QSAR) models to generate in silico ADMET profiles for hit series to enable more complete characterizations of HTS chemical matter. These profiles allow teams to quickly assess hit series for desirable ADMET properties or suspected liabilities that may require significant optimization. Accordingly, these in silico data can direct ADMET experimentation and profoundly impact the progression of hit series. Several prospective examples are presented to substantiate the value of this approach.

Discovery of indazole aldosterone synthase (CYP11B2) inhibitors as potential treatments for hypertension.

We report the discovery and hit-to-lead optimization of a structurally novel indazole series of CYP11B2 inhibitors. Benchmark compound 34 from this series displays potent inhibition of CYP11B2, high selectivity versus related steroidal and hepatic CYP targets, and lead-like physical and pharmacokinetic properties. On the basis of these and other data, the indazole series was progressed to lead optimization for further refinement.

Microscale High-Throughput Experimentation as an Enabling Technology in Drug Discovery: Application in the Discovery of (Piperidinyl)pyridinyl-1H-benzimidazole Diacylglycerol Acyltransferase 1 Inhibitors.

Miniaturization and parallel processing play an important role in the evolution of many technologies. We demonstrate the application of miniaturized high-throughput experimentation methods to resolve synthetic chemistry challenges on the frontlines of a lead optimization effort to develop diacylglycerol acyltransferase (DGAT1) inhibitors. Reactions were performed on ∼1 mg scale using glass microvials providing a miniaturized high-throughput experimentation capability that was used to study a challenging SNAr reaction. The availability of robust synthetic chemistry conditions discovered in these miniaturized investigations enabled the development of structure-activity relationships that ultimately led to the discovery of soluble, selective, and potent inhibitors of DGAT1.

Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition.

The development of new antimalarial therapies is essential, and lowering the barrier of entry for the screening and discovery of new lead compound classes can spur drug development at organizations that may not have large compound screening libraries or resources to conduct high-throughput screens. Machine learning models have been long established to be more robust and have a larger domain of applicability with larger training sets. Screens over multiple data sets to find compounds with potential malaria blood stage inhibitory activity have been used to generate multiple Bayesian models. Here we describe a method by which Bayesian quantitative structure-activity relationship models, which contain information on thousands to millions of proprietary compounds, can be shared between collaborators at both for-profit and not-for-profit institutions. This model-sharing paradigm allows for the development of consensus models that have increased predictive power over any single model and yet does not reveal the identity of any compounds in the training sets.

Discovery of Chromane Propionic Acid Analogues as Selective Agonists of GPR120 with in Vivo Activity in Rodents.

GPR120 (FFAR4) is a fatty acid sensing G protein coupled receptor (GPCR) that has been identified as a target for possible treatment of type 2 diabetes. A selective activator of GPR120 containing a chromane scaffold has been designed, synthesized, and evaluated in vivo. Results of these efforts suggest that chromane propionic acid 18 is a suitable tool molecule for further animal studies. Compound 18 is selective over the closely related target GPR40 (FFAR1), has a clean off-target profile, demonstrates suitable pharmacokinetic properties, and has been evaluated in wild-type/knockout GPR120 mouse oGTT studies.

Discovery of Spirocyclic Aldosterone Synthase Inhibitors as Potential Treatments for Resistant Hypertension.

Herein we report the discovery and hit-to-lead optimization of a series of spirocyclic piperidine aldosterone synthase (CYP11B2) inhibitors. Compounds from this series display potent CYP11B2 inhibition, good selectivity versus related CYP enzymes, and lead-like physical and pharmacokinetic properties.

Discovery and Pharmacology of a Novel Class of Diacylglycerol Acyltransferase 2 Inhibitors.

DGAT2 plays a critical role in hepatic triglyceride production, and data suggests that inhibition of DGAT2 could prove to be beneficial in treating a number of disease states. This article documents the discovery and optimization of a selective small molecule inhibitor of DGAT2 as well as pharmacological proof of biology in a mouse model of triglyceride production.

Discovery of Triazole CYP11B2 Inhibitors with in Vivo Activity in Rhesus Monkeys.

Hit-to-lead efforts resulted in the discovery of compound 19, a potent CYP11B2 inhibitor that displays high selectivity vs related CYPs, good pharmacokinetic properties in rat and rhesus, and lead-like physical properties. In a rhesus pharmacodynamic model, compound 19 displays robust, dose-dependent aldosterone lowering efficacy, with no apparent effect on cortisol levels.

Discovery of Benzimidazole CYP11B2 Inhibitors with in Vivo Activity in Rhesus Monkeys.

We report the discovery of a benzimidazole series of CYP11B2 inhibitors. Hit-to-lead and lead optimization studies identified compounds such as 32, which displays potent CYP11B2 inhibition, high selectivity versus related CYP targets, and good pharmacokinetic properties in rat and rhesus. In a rhesus pharmacodynamic model, 32 produces dose-dependent aldosterone lowering efficacy, with no apparent effect on cortisol levels.

Pyrazoles as non-classical bioisosteres in prolylcarboxypeptidase (PrCP) inhibitors.

Bioisosteres are integral components of modern pharmaceutical research that allow structural optimization to maximize in vivo efficacy and minimize adverse effects by selectively modifying pharmacodynamic, pharmacokinetic and physicochemical properties. A recent medicinal chemistry campaign focused on identifying small molecule inhibitors of prolylcarboxypeptidase (PrCP) initiated an investigation into the use of pyrazoles as bioisosteres for amides. The results indicate that pyrazoles are suitable bioisosteric replacements of amide functional groups. The study is an example of managing bioisosteric replacement by incorporating subsequent structural modifications to maintain potency against the selected target. A heuristic model for an embedded pharmacophore is also described.