A Dual Modulator of Farnesoid X Receptor and Soluble Epoxide Hydrolase To Counter Nonalcoholic Steatohepatitis

Development of multitarget agents possessing soluble epoxide hydrolase inhibitory activity

Over the last two decades polypharmacology has emerged as a new paradigm in drug discovery, even though developing drugs with high potency and selectivity toward a single biological target is still a major strategy. Often, targeting only a single enzyme or receptor shows lack of efficacy. High levels of inhibitor of a single target also can lead to adverse side effects. A second target may offer additive or synergistic effects to affecting the first target thereby reducing on- and off-target side effects. Therefore, drugs that inhibit multiple targets may offer a great potential for increased efficacy and reduced the adverse effects. In this review we summarize recent findings of rationally designed multitarget compounds that are aimed to improve efficacy and safety profiles compared to those that target a single enzyme or receptor. We focus on dual inhibitors/modulators that target the soluble epoxide hydrolase (sEH) as a common part of their design to take advantage of the beneficial effects of sEH inhibition.

Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning

The bile acid activated transcription factor farnesoid X receptor (FXR) has revealed therapeutic potential as a molecular drug target for the treatment of hepatic and metabolic disorders. Despite strong efforts in FXR ligand development, the structural diversity among the known FXR modulators is limited. Only four molecular frameworks account for more than 50 % of the FXR modulators annotated in ChEMBL. Here, we leverage machine learning methods to expand the chemical space of FXR-targeting small molecules by employing an ensemble of three complementary machine learning approaches. A counter-propagation artificial neural network, a k-nearest neighbor learner, and a three-dimensional pharmacophore descriptor were combined to retrieve novel FXR ligands from a collection of more than 3 million compounds. The ensemble machine learning model identified six new FXR modulators among ten top-ranked candidates. These active hits comprise both FXR activators and antagonists with micromolar potencies. With four novel FXR ligand scaffolds, these computationally identified bioactive compounds appreciably expand the chemical space of known FXR modulators and may serve as starting points for hit-to-lead expansion.

Hybrid Reporter Gene Assays: Versatile In Vitro Tools to Characterize Nuclear Receptor Modulators

Nuclear receptors act as ligand-activated transcription factors translating ligand signals into changes in gene expression. The 48 members of the superfamily of nuclear receptors identified so far are involved amongst others in maintenance of metabolic balance, inflammation, and cancer. Thus, they hold enormous potential for drug discovery and some nuclear receptors are experiencing considerable academic and industrial interest. Nuclear receptor modulator discovery requires reliable, robust, and economic test systems that allow for high throughput. In this chapter, we discuss the principle, strengths, and advantages of hybrid reporter gene assays for nuclear receptor focused drug discovery and describe how they can be developed, established, and validated.

Prospective for cytochrome P450 epoxygenase cardiovascular and renal therapeutics

Therapeutics for arachidonic acid pathways began with the development of non-steroidal anti-inflammatory drugs that inhibit cyclooxygenase (COX). The enzymatic pathways and arachidonic acid metabolites and respective receptors have been successfully targeted and therapeutics developed for pain, inflammation, pulmonary and cardiovascular diseases. These drugs target the COX and lipoxygenase pathways but not the third branch for arachidonic acid metabolism, the cytochrome P450 (CYP) pathway. Small molecule compounds targeting enzymes and CYP epoxy-fatty acid metabolites have evolved rapidly over the last two decades. These therapeutics have primarily focused on inhibiting soluble epoxide hydrolase (sEH) or agonist mimetics for epoxyeicosatrienoic acids (EET). Based on preclinical animal model studies and human studies, major therapeutic indications for these sEH inhibitors and EET mimics/analogs are renal and cardiovascular diseases. Novel small molecules that inhibit sEH have advanced to human clinical trials and demonstrate promise for cardiovascular diseases. Challenges remain for sEH inhibitor and EET analog drug development; however, there is a high likelihood that a drug that acts on this third branch of arachidonic acid metabolism will be utilized to treat a cardiovascular or kidney disease in the next decade.

Scaffold-Hopping from Synthetic Drugs by Holistic Molecular Representation

The discovery of novel ligand chemotypes allows to explore uncharted regions in chemical space, thereby potentially improving synthetic accessibility, potency, and the drug-likeness of molecules. Here, we demonstrate the scaffold-hopping ability of the new Weighted Holistic Atom Localization and Entity Shape (WHALES) molecular descriptors compared to seven state-of-the-art molecular representations on 30,000 compounds and 182 biological targets. In a prospective application, we apply WHALES to the discovery of novel retinoid X receptor (RXR) modulators. WHALES descriptors identified four agonists with innovative molecular scaffolds, populating uncharted regions of the chemical space. One of the agonists, possessing a rare non-acidic chemotype, revealed high selectivity on 12 nuclear receptors and comparable efficacy as bexarotene on induction of ATP-binding cassette transporter A1, angiopoietin like protein 4 and apolipoprotein E. The outcome of this research supports WHALES as an innovative tool to explore novel regions of the chemical space and to detect novel bioactive chemotypes by straightforward similarity searching.

Urate transporter inhibitor lesinurad is a selective peroxisome proliferator-activated receptor gamma modulator (sPPARγM) in vitro

Gout is the most common arthritic disease in human but was long neglected and therapeutic options are not satisfying. However, with the recent approval of the urate transporter inhibitor lesinurad, gout treatment has experienced a major innovation. Here we show that lesinurad possesses considerable modulatory potency on peroxisome proliferator-activated receptor γ (PPARγ). Since gout has a strong association with metabolic diseases such as type 2 diabetes, this side-activity appears as very valuable contributing factor to the clinical efficacy profile of lesinurad. Importantly, despite robustly activating PPARγ in vitro, lesinurad lacked adipogenic activity, which seems due to differential coactivator recruitment and is characterized as selective PPARγ modulator (sPPARγM).

Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds

Multitargeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation, or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multitarget compound development with valuable tools. In this Perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite some characteristic challenges remaining unresolved, designed polypharmacology holds enormous potential to secure future therapeutic innovation.

Scaffold hopping from synthetic RXR modulators by virtual screening and de novo design

The lack of potent subtype-selective modulators of retinoid X receptors (RXRs) has hindered their full exploitation as promising drug targets. Using computational similarity searching, target prediction and automated de novo design, we identified novel RXR ligands exhibiting innovative molecular frameworks, pronounced receptor-subtype preference and suitable properties for hit-to-lead expansion.

Human-based systems: Mechanistic NASH modelling just around the corner?

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease characterized by excessive triglyceride accumulation in the liver accompanied by inflammation, cell stress and apoptosis. It is the tipping point to the life-threatening stages of non-alcoholic fatty liver disease (NAFLD). Despite the high prevalence of NASH, up to five percent of the global population, there are currently no approved drugs to treat this disease. Animal models, mostly based on specific diets and genetic modifications, are often employed in anti-NASH drug development. However, due to interspecies differences and artificial pathogenic conditions, they do not represent the human situation accurately and are inadequate for testing the efficacy and safety of potential new drugs. Human-based in vitro models provide a more legitimate representation of the human NASH pathophysiology and can be used to investigate the dysregulation of cellular functions associated with the disease. Also in silico methodologies and pathway-based approaches using human datasets, may contribute to a more accurate representation of NASH, thereby facilitating the quest for new anti-NASH drugs. In this review, we describe the molecular components of NASH and how human-based tools can contribute to unraveling the pathogenesis of this disease and be used in anti-NASH drug development. We also propose a roadmap for the development and application of human-based approaches for future investigation of NASH.

Allosteric modulation of the farnesoid X receptor by a small molecule

The bile acid activated transcription factor farnesoid X receptor (FXR) regulates numerous metabolic processes and is a rising target for the treatment of hepatic and metabolic disorders. FXR agonists have revealed efficacy in treating non-alcoholic steatohepatitis (NASH), diabetes and dyslipidemia. Here we characterize imatinib as first-in-class allosteric FXR modulator and report the development of an optimized descendant that markedly promotes agonist induced FXR activation in a reporter gene assay and FXR target gene expression in HepG2 cells. Differential effects of imatinib on agonist-induced bile salt export protein and small heterodimer partner expression suggest that allosteric FXR modulation could open a new avenue to gene-selective FXR modulators.

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry.