Discovery, Optimization, and Biological Evaluation of Arylpyridones as Cbl-b Inhibitors.

Casitas B-lymphoma proto-oncogene-b (Cbl-b), a member of the Cbl family of RING finger E3 ubiquitin ligases, has been demonstrated to play a central role in regulating effector T-cell function. Multiple studies using gene-targeting approaches have provided direct evidence that Cbl-b negatively regulates T, B, and NK cell activation via a ubiquitin-mediated protein modulation. Thus, inhibition of Cbl-b ligase activity can lead to immune activation and has therapeutic potential in immuno-oncology. Herein, we describe the discovery and optimization of an arylpyridone series as Cbl-b inhibitors by structure-based drug discovery to afford compound 31. This compound binds to Cbl-b with an IC50 value of 30 nM and induces IL-2 production in T-cells with an EC50 value of 230 nM. Compound 31 also shows robust intracellular target engagement demonstrated through inhibition of Cbl-b autoubiquitination, inhibition of ubiquitin transfer to ZAP70, and the cellular modulation of phosphorylation of a downstream signal within the TCR axis.

Discovery of a Novel Benzodiazepine Series of Cbl-b Inhibitors for the Enhancement of Antitumor Immunity.

Casitas B-lineage lymphoma proto-oncogene-b (Cbl-b) is a RING finger E3 ligase that is responsible for repressing T-cell, natural killer (NK) cell, and B-cell activation. The robust antitumor activity observed in Cbl-b deficient mice arising from elevated T-cell and NK-cell activity justified our discovery effort toward Cbl-b inhibitors that might show therapeutic promise in immuno-oncology, where activation of the immune system can drive the recognition and killing of cancer cells. We undertook a high-throughput screening campaign followed by structure-enabled optimization to develop a novel benzodiazepine series of potent Cbl-b inhibitors. This series displayed nanomolar levels of biochemical potency, as well as potent T-cell activation. The functional activity of this class of Cbl-b inhibitors was further corroborated with ubiquitin-based cellular assays.

DockStream: a docking wrapper to enhance de novo molecular design.

Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream .

De novo design with deep generative models based on 3D similarity scoring.

We have demonstrated the utility of a 3D shape and pharmacophore similarity scoring component in molecular design with a deep generative model trained with reinforcement learning. Using Dopamine receptor type 2 (DRD2) as an example and its antagonist haloperidol 1 as a starting point in a ligand based design context, we have shown in a retrospective study that a 3D similarity enabled generative model can discover new leads in the absence of any other information. It can be efficiently used for scaffold hopping and generation of novel series. 3D similarity based models were compared against 2D QSAR based, indicating a significant degree of orthogonality of the generated outputs and with the former having a more diverse output. In addition, when the two scoring components are combined together for training of the generative model, it results in more efficient exploration of desirable chemical space compared to the individual components.

New Associations between Drug-Induced Adverse Events in Animal Models and Humans Reveal Novel Candidate Safety Targets.

To improve our ability to extrapolate preclinical toxicity to humans, there is a need to understand and quantify the concordance of adverse events (AEs) between animal models and clinical studies. In the present work, we discovered 3011 statistically significant associations between preclinical and clinical AEs caused by drugs reported in the PharmaPendium database of which 2952 were new associations between toxicities encoded by different Medical Dictionary for Regulatory Activities terms across species. To find plausible and testable candidate off-target drug activities for the derived associations, we investigated the genetic overlap between the genes linked to both a preclinical and a clinical AE and the protein targets found to interact with one or more drugs causing both AEs. We discuss three associations from the analysis in more detail for which novel candidate off-target drug activities could be identified, namely, the association of preclinical mutagenicity readouts with clinical teratospermia and ovarian failure, the association of preclinical reflexes abnormal with clinical poor-quality sleep, and the association of preclinical psychomotor hyperactivity with clinical drug withdrawal syndrome. Our analysis successfully identified a total of 77% of known safety targets currently tested in in vitro screening panels plus an additional 431 genes which were proposed for investigation as future safety targets for different clinical toxicities. This work provides new translational toxicity relationships beyond AE term-matching, the results of which can be used for risk profiling of future new chemical entities for clinical studies and for the development of future in vitro safety panels.

Uncertainty quantification in drug design.

Machine learning and artificial intelligence are increasingly being applied to the drug-design process as a result of the development of novel algorithms, growing access, the falling cost of computation and the development of novel technologies for generating chemically and biologically relevant data. There has been recent progress in fields such as molecular de novo generation, synthetic route prediction and, to some extent, property predictions. Despite this, most research in these fields has focused on improving the accuracy of the technologies, rather than on quantifying the uncertainty in the predictions. Uncertainty quantification will become a key component in autonomous decision making and will be crucial for integrating machine learning and chemistry automation to create an autonomous design-make-test-analyse cycle. This review covers the empirical, frequentist and Bayesian approaches to uncertainty quantification, and outlines how they can be used for drug design. We also outline the impact of uncertainty quantification on decision making.

Information-Derived Mechanistic Hypotheses for Structural Cardiotoxicity.

Adverse events resulting from drug therapy can be a cause of drug withdrawal, reduced and or restricted clinical use, as well as a major economic burden for society. To increase the safety of new drugs, there is a need to better understand the mechanisms causing the adverse events. One way to derive new mechanistic hypotheses is by linking data on drug adverse events with the drugs' biological targets. In this study, we have used data mining techniques and mutual information statistical approaches to find associations between reported adverse events collected from the FDA Adverse Event Reporting System and assay outcomes from ToxCast, with the aim to generate mechanistic hypotheses related to structural cardiotoxicity (morphological damage to cardiomyocytes and/or loss of viability). Our workflow identified 22 adverse event-assay outcome associations. From these associations, 10 implicated targets could be substantiated with evidence from previous studies reported in the literature. For two of the identified targets, we also describe a more detailed mechanism, forming putative adverse outcome pathways associated with structural cardiotoxicity. Our study also highlights the difficulties deriving these type of associations from the very limited amount of data available.

Prospectively Validated Proteochemometric Models for the Prediction of Small-Molecule Binding to Bromodomain Proteins.

The bromodomain-containing proteins are a ligandable family of epigenetic readers, which play important roles in oncological, cardiovascular, and inflammatory diseases. Achieving selective inhibition of specific bromodomains is challenging, due to the limited understanding of compound and target selectivity features. In this study we build and benchmark proteochemometric (PCM) classification models on bioactivity data for 15,350 data points across 31 bromodomains, using both compound fingerprints and binding site protein descriptors as input variables, achieving a maximum performance as measured by the Matthew's Correlation Coefficient (MCC) of 0.83 on the external test set. We also find that histone peptide binding data can be used as a target descriptor to build a high performing PCM model (MCC 0.80), showing the transferability of peptide interaction information to modeling small-molecule bioactivity. 1,139 compounds were selected for prospective experimental testing by performing a virtual screen using model predictions and implementing conformal prediction, which resulted in 319 correctly predicted compound-target pair actives and the correct prediction for certain selectivity profile combinations of the four bromodomains tested against. We identify that conformal prediction can be used to fine-tune the balance between hit retrieval and hit structural diversity in a virtual screening setting. PCM can be applied to future virtual screening and compound design, including off-target prediction for bromodomains.

Is epilepsy a preventable disorder? New evidence from animal models.

Epilepsy accounts for 0.5% of the global burden of disease, and primary prevention of epilepsy represents one of the three 2007 NINDS Epilepsy Research Benchmarks. In the past decade, efforts to understand and intervene in the process of epileptogenesis have yielded fruitful preventative strategies in animal models.This article reviews the current understanding of epileptogenesis, introduces the concept of a "critical period" for epileptogenesis, and examines strategies for epilepsy prevention in animal models of both acquired and genetic epilepsies. We discuss specific animal models, which may yield important insights into epilepsy prevention including kindling, poststatus epilepticus, prolonged febrile seizures, traumatic brain injury, hypoxia, the tuberous sclerosis mouse model, and the WAG/Rij rat model of primary generalized epilepsy. Hopefully, further investigation of antiepileptogenesis in animal models will soon enable human therapeutic trials to be initiated, leading to long-term epilepsy prevention and improved patient quality of life.