Discovery of WRN inhibitor HRO761 with synthetic lethality in MSI cancers.

The Werner syndrome RecQ helicase WRN was identified as a synthetic lethal target in cancer cells with microsatellite instability (MSI) by several genetic screens1-6. Despite advances in treatment with immune checkpoint inhibitors7-10, there is an unmet need in the treatment of MSI cancers11-14. Here we report the structural, biochemical, cellular and pharmacological characterization of the clinical-stage WRN helicase inhibitor HRO761, which was identified through an innovative hit-finding and lead-optimization strategy. HRO761 is a potent, selective, allosteric WRN inhibitor that binds at the interface of the D1 and D2 helicase domains, locking WRN in an inactive conformation. Pharmacological inhibition by HRO761 recapitulated the phenotype observed by WRN genetic suppression, leading to DNA damage and inhibition of tumour cell growth selectively in MSI cells in a p53-independent manner. Moreover, HRO761 led to WRN degradation in MSI cells but not in microsatellite-stable cells. Oral treatment with HRO761 resulted in dose-dependent in vivo DNA damage induction and tumour growth inhibition in MSI cell- and patient-derived xenograft models. These findings represent preclinical pharmacological validation of WRN as a therapeutic target in MSI cancers. A clinical trial with HRO761 (NCT05838768) is ongoing to assess the safety, tolerability and preliminary anti-tumour activity in patients with MSI colorectal cancer and other MSI solid tumours.

Challenges for the Discovery of Non-Covalent WRN Helicase Inhibitors.

The Werner Syndrome RecQ helicase (WRN) is a synthetic lethal target of interest for the treatment of cancers with microsatellite instability (MSI). Different hit finding approaches were initially tested. The identification of WRN inhibitors proved challenging due to a high propensity for artefacts via protein interference, i. e., hits inhibiting WRN enzymatic activities through multiple, unspecific mechanisms. Previously published WRN Helicase inhibitors (ML216, NSC19630 or NSC617145) were characterized in an extensive set of biochemical and biophysical assays and could be ruled out as specific WRN helicase probes. More innovative screening strategies need to be developed for successful drug discovery of non-covalent WRN helicase inhibitors.

PAX8 and MECOM are interaction partners driving ovarian cancer.

The transcription factor PAX8 is critical for the development of the thyroid and urogenital system. Comprehensive genomic screens furthermore indicate an additional oncogenic role for PAX8 in renal and ovarian cancers. While a plethora of PAX8-regulated genes in different contexts have been proposed, we still lack a mechanistic understanding of how PAX8 engages molecular complexes to drive disease-relevant oncogenic transcriptional programs. Here we show that protein isoforms originating from the MECOM locus form a complex with PAX8. These include MDS1-EVI1 (also called PRDM3) for which we map its interaction with PAX8 in vitro and in vivo. We show that PAX8 binds a large number of genomic sites and forms transcriptional hubs. At a subset of these, PAX8 together with PRDM3 regulates a specific gene expression module involved in adhesion and extracellular matrix. This gene module correlates with PAX8 and MECOM expression in large scale profiling of cell lines, patient-derived xenografts (PDXs) and clinical cases and stratifies gynecological cancer cases with worse prognosis. PRDM3 is amplified in ovarian cancers and we show that the MECOM locus and PAX8 sustain in vivo tumor growth, further supporting that the identified function of the MECOM locus underlies PAX8-driven oncogenic functions in ovarian cancer.

Multidimensional de†novo design reveals 5-HT2B receptor-selective ligands.

We report a multi-objective de novo design study driven by synthetic tractability and aimed at the prioritization of computer-generated 5-HT2B receptor ligands with accurately predicted target-binding affinities. Relying on quantitative bioactivity models we designed and synthesized structurally novel, selective, nanomolar, and ligand-efficient 5-HT2B modulators with sustained cell-based effects. Our results suggest that seamless amalgamation of computational activity prediction and molecular design with microfluidics-assisted synthesis enables the swift generation of small molecules with the desired polypharmacology.

Efficient search of chemical space: navigating from fragments to structurally diverse chemotypes.

We introduce a novel strategy to sample bioactive chemical space, which follows-up on hits from fragment campaigns without the need for a crystal structure. Our results strongly suggest that screening a few hundred or thousand fragments can substantially improve the selection of small-molecule screening subsets. By combining fragment-based screening with virtual fragment linking and HTS fingerprints, we have developed an effective strategy not only to expand from low-affinity hits to potent compounds but also to hop in chemical space to substantially novel chemotypes. In benchmark calculations, our approach accessed subsets of compounds that were substantially enriched in chemically diverse hit compounds for various activity classes. Overall, half of the hits in the screening collection were found by screening only 10% of the library. Furthermore, a prospective application led to the discovery of two structurally novel histone deacetylase 4 inhibitors.

Identification of the C3a receptor (C3AR1) as the target of the VGF-derived peptide TLQP-21 in rodent cells.

TLQP-21, a peptide derived from VGF (non-acronymic) by proteolytic processing, has been shown to modulate energy metabolism, differentiation, and cellular response to stress. Although extensively investigated, the receptor for this endogenous peptide has not previously been described. This study describes the use of a series of studies that show G protein-coupled receptor-mediated biological activity of TLQP-21 signaling in CHO-K1 cells. Unbiased genome-wide sequencing of the transcriptome from responsive CHO-K1 cells identified a prioritized list of possible G protein-coupled receptors bringing about this activity. Further experiments using a series of defined receptor antagonists and siRNAs led to the identification of complement C3a receptor-1 (C3AR1) as a target for TLQP-21 in rodents. We have not been able to demonstrate so far that this finding is translatable to the human receptor. Our results are in line with a large number of physiological observations in rodent models of food intake and metabolic control, where TLQP-21 shows activity. In addition, the sensitivity of TLQP-21 signaling to pertussis toxin is consistent with the known signaling pathway of C3AR1. The binding of TLQP-21 to C3AR1 not only has effects on signaling but also modulates cellular functions, as TLQP-21 was shown to have a role in directing migration of mouse RAW264.7 cells.

Discovery of NVP-BYL719 a potent and selective phosphatidylinositol-3 kinase alpha inhibitor selected for clinical evaluation.

Phosphatidylinositol-3-kinase α (PI3Kα) is a therapeutic target of high interest in anticancer drug research. On the basis of a binding model rationalizing the high selectivity and potency of a particular series of 2-aminothiazole compounds in inhibiting PI3Kα, a medicinal chemistry program has led to the discovery of the clinical candidate NVP-BYL719.

Reducing safety-related drug attrition: the use of in vitro pharmacological profiling.

In vitro pharmacological profiling is increasingly being used earlier in the drug discovery process to identify undesirable off-target activity profiles that could hinder or halt the development of candidate drugs or even lead to market withdrawal if discovered after a drug is approved. Here, for the first time, the rationale, strategies and methodologies for in vitro pharmacological profiling at four major pharmaceutical companies (AstraZeneca, GlaxoSmithKline, Novartis and Pfizer) are presented and illustrated with examples of their impact on the drug discovery process. We hope that this will enable other companies and academic institutions to benefit from this knowledge and consider joining us in our collaborative knowledge sharing.

Large-scale prediction and testing of drug activity on side-effect targets.

Discovering the unintended 'off-targets' that predict adverse drug reactions is daunting by empirical methods alone. Drugs can act on several protein targets, some of which can be unrelated by conventional molecular metrics, and hundreds of proteins have been implicated in side effects. Here we use a computational strategy to predict the activity of 656 marketed drugs on 73 unintended 'side-effect' targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 µM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a drug-target-adverse drug reaction network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic oestrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme cyclooxygenase-1. The clinical relevance of this inhibition was borne out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.

Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis.

We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.

Mapping adverse drug reactions in chemical space.

We present a novel method to better investigate adverse drug reactions in chemical space. By integrating data sources about adverse drug reactions of drugs with an established cheminformatics modeling method, we generate a data set that is then visualized with a systems biology tool. Thereby new insights into undesired drug effects are gained. In this work, we present a global analysis linking chemical features to adverse drug reactions.

In vitro safety pharmacology profiling: what else beyond hERG?

One of the main reasons for drug failures in clinical development, or postmarket launch, is lacking or compromised safety margins at therapeutic doses. Organ toxicity with poorly defined mechanisms and adverse drug reactions associated with on- and off-target effects are the major contributors to safety-related shortfalls of many clinical drug candidates. Therefore, to avoid high attrition rates in clinical trials, it is imperative to test compounds for potential adverse reactions during early drug discovery. Beyond a small number of targets associated with clinically acknowledged adverse drug reactions, there is little consensus on other targets that are important to consider at an early stage for in vitro safety pharmacology assessment. We consider here a limited number of safety-related targets, from different target families, which were selected as part of in vitro safety pharmacology profiling panels integrated in the drug-development process at Novartis. The best way to assess these targets, using a biochemical or a functional readout, is discussed. In particular, the importance of using cell-based profiling assays for the characterization of an agonist action at some GPCRs is highlighted. A careful design of in vitro safety pharmacology profiling panels allows better prediction of potential adverse effects of new chemical entities early in the drug-discovery process. This contributes to the selection of the best candidate for clinical development and, ultimately, should contribute to a decreased attrition rate.

Modeling promiscuity based on in vitro safety pharmacology profiling data.

This study describes a method for mining and modeling binding data obtained from a large panel of targets (in vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naïve Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high-throughput screening data or for lead optimization.

Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure.

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.

Clustering and rule-based classifications of chemical structures evaluated in the biological activity space.

Classification methods for data sets of molecules according to their chemical structure were evaluated for their biological relevance, including rule-based, scaffold-oriented classification methods and clustering based on molecular descriptors. Three data sets resulting from uniformly determined in vitro biological profiling experiments were classified according to their chemical structures, and the results were compared in a Pareto analysis with the number of classes and their average spread in the profile space as two concurrent objectives which were to be minimized. It has been found that no classification method is overall superior to all other studied methods, but there is a general trend that rule-based, scaffold-oriented methods are the better choice if classes with homogeneous biological activity are required, but a large number of clusters can be tolerated. On the other hand, clustering based on chemical fingerprints is superior if fewer and larger classes are required, and some loss of homogeneity in biological activity can be accepted.

High-throughput in vitro profiling assays: lessons learnt from experiences at Novartis.

This article reviews the use of a selection of in vitro assays implemented at Novartis and intends to address exposure and safety in early drug discovery. The authors' own experience, based on a large number of 'real' drug discovery compounds, is described to reflect on what has worked, where improvement is needed and how to best use the data for decision making. Possible strategies are discussed, and guidelines are provided on how to organise assays, extract value and contribute knowledge from the data.

Alkylamides from Echinacea are a new class of cannabinomimetics. Cannabinoid type 2 receptor-dependent and -independent immunomodulatory effects.

Alkylamides (alkamides) from Echinacea modulate tumor necrosis factor alpha mRNA expression in human monocytes/macrophages via the cannabinoid type 2 (CB2) receptor (Gertsch, J., Schoop, R., Kuenzle, U., and Suter, A. (2004) FEBS Lett. 577, 563-569). Here we show that the alkylamides dodeca-2E,4E,8Z,10Z-tetraenoic acid isobutylamide (A1) and dodeca-2E,4E-dienoic acid isobutylamide (A2) bind to the CB2 receptor more strongly than the endogenous cannabinoids. The Ki values of A1 and A2 (CB2 approximately 60 nM; CB1 >1500 nM) were determined by displacement of the synthetic high affinity cannabinoid ligand [3H]CP-55,940. Molecular modeling suggests that alkylamides bind in the solvent-accessible cavity in CB2, directed by H-bonding and pi-pi interactions. In a screen with 49 other pharmacologically relevant receptors, it could be shown that A1 and A2 specifically bind to CB2 and CB1. A1 and A2 elevated total intracellular Ca2+ in CB2-positive but not in CB2-negative promyelocytic HL60 cells, an effect that was inhibited by the CB2 antagonist SR144528. At 50 nM, A1, A2, and the endogenous cannabinoid anandamide (CB2 Ki >200 nM) up-regulated constitutive interleukin (IL)-6 expression in human whole blood in a seemingly CB2-dependent manner. A1, A2, anandamide, the CB2 antagonist SR144528 (Ki <10 nM), and also the non-CB2-binding alkylamide undeca-2E-ene,8,10-diynoic acid isobutylamide all significantly inhibited lipopolysaccharide-induced tumor necrosis factor alpha, IL-1beta, and IL-12p70 expression (5-500 nM) in a CB2-independent manner. Alkylamides and anandamide also showed weak differential effects on anti-CD3-versus anti-CD28-stimulated cytokine expression in human whole blood. Overall, alkylamides, anandamide, and SR144528 potently inhibited lipopolysaccharide-induced inflammation in human whole blood and exerted modulatory effects on cytokine expression, but these effects are not exclusively related to CB2 binding.

Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development.

Broad-scale in vitro pharmacology profiling of new chemical entities during early phases of drug discovery has recently become an essential tool to predict clinical adverse effects. Modern, relatively inexpensive assay technologies and rapidly expanding knowledge about G-protein coupled receptors, nuclear receptors, ion channels and enzymes have made it possible to implement a large number of assays addressing possible clinical liabilities. Together with other in vitro assays focusing on toxicology and bioavailability, they provide a powerful tool to aid drug development. In this article, we review the development of this tool for drug discovery, its appropriate use and predictive value.

Structure-based design and synthesis of novel non-zinc chelating MMP-12 inhibitors.

A new class of MMP-12 inhibitors was discovered and optimized using structure-based drug design methods. Modeling studies using a known MMP-12 crystal structure identified a new interaction mode for these new MMP-12 inhibitors. Further optimization resulted in the discovery of a compound displaying nanomolar activity against MMP-12 and which was co-crystallized with MMP-12.