Computational drug discovery approaches identify mebendazole as a candidate treatment for autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is a rare genetic disorder characterised by numerous renal cysts, the progressive expansion of which can impact kidney function and lead eventually to renal failure. Tolvaptan is the only disease-modifying drug approved for the treatment of ADPKD, however its poor side effect and safety profile necessitates the need for the development of new therapeutics in this area. Using a combination of transcriptomic and machine learning computational drug discovery tools, we predicted that a number of existing drugs could have utility in the treatment of ADPKD, and subsequently validated several of these drug predictions in established models of disease. We determined that the anthelmintic mebendazole was a potent anti-cystic agent in human cellular and in vivo models of ADPKD, and is likely acting through the inhibition of microtubule polymerisation and protein kinase activity. These findings demonstrate the utility of combining computational approaches to identify and understand potential new treatments for traditionally underserved rare diseases.

A novel direct adenosine monophosphate kinase activator ameliorates disease progression in preclinical models of Autosomal Dominant Polycystic Kidney Disease.

Autosomal dominant polycystic kidney disease (ADPKD) mainly results from mutations in the PKD1 gene, which encodes polycystin 1. It is the most common inherited kidney disease and is characterized by a progressive bilateral increase in cyst number and size, often leading to kidney failure. The cellular energy sensor and regulator adenosine monophosphate stimulated protein kinase (AMPK) has been implicated as a promising new therapeutic target. To address this hypothesis, we determined the effects of a potent and selective clinical stage direct allosteric AMPK activator, PXL770, in canine and patient-derived 3D cyst models and an orthologous mouse model of ADPKD. PXL770 induced AMPK activation and dose-dependently reduced cyst growth in principal-like Madin-Darby Canine Kidney cells stimulated with forskolin and kidney epithelial cells derived from patients with ADPKD stimulated with desmopressin. In an inducible, kidney epithelium-specific Pkd1 knockout mouse model, PXL770 produced kidney AMPK pathway engagement, prevented the onset of kidney failure (reducing blood urea by 47%), decreased cystic index by 26% and lowered the kidney weight to body weight ratio by 35% compared to untreated control Pkd1 knockout mice. These effects were accompanied by a reduction of markers of cell proliferation (-48%), macrophage infiltration (-53%) and tissue fibrosis (-37%). Thus, our results show the potential of direct allosteric AMPK activation in the treatment of ADPKD and support the further development of PXL770 for this indication.

Renal cyst growth is attenuated by a combination treatment of tolvaptan and pioglitazone, while pioglitazone treatment alone is not effective.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is one of the most common monogenic disorders, characterized by the progressive formation of fluid-filled cysts. Tolvaptan is an approved drug for ADPKD patients, but is also associated with multiple side effects. The peroxisome proliferator-activator receptor gamma (PPARγ) agonist pioglitazone slows disease progression in the PCK rat model for PKD. Here, we tested whether a combination treatment of relevant doses of tolvaptan and pioglitazone leads to improved efficacy in an adult-onset PKD mouse model. Tolvaptan indeed slowed PKD progression, but the combination treatment was not more effective than tolvaptan alone. In addition, although pioglitazone raised plasma levels of its surrogate drug marker adiponectin, the drug unexpectedly failed to slow PKD progression. The pioglitazone target PPARγ was expressed at surprisingly low levels in mouse, rat and human kidneys. Other pioglitazone targets were more abundantly expressed, but this pattern was comparable across various species. The data suggest that several potential pharmacokinetic and pharmacodynamic (PK/PD) differences between different species may underlie whether or not pioglitazone is able to slow PKD progression. The ongoing phase II clinical trial with low-dose pioglitazone treatment (NCT02697617) will show whether pioglitazone is a suitable drug candidate for ADPKD treatment.

In vitro 3D phenotypic drug screen identifies celastrol as an effective in vivo inhibitor of polycystic kidney disease.

Polycystic kidney disease (PKD) is a prevalent genetic disorder, characterized by the formation of kidney cysts that progressively lead to kidney failure. The currently available drug tolvaptan is not well tolerated by all patients and there remains a strong need for alternative treatments. The signaling rewiring in PKD that drives cyst formation is highly complex and not fully understood. As a consequence, the effects of drugs are sometimes difficult to predict. We previously established a high throughput microscopy phenotypic screening method for quantitative assessment of renal cyst growth. Here, we applied this 3D cyst growth phenotypic assay and screened 2320 small drug-like molecules, including approved drugs. We identified 81 active molecules that inhibit cyst growth. Multi-parametric phenotypic profiling of the effects on 3D cultured cysts discriminated molecules that showed preferred pharmacological effects above genuine toxicological properties. Celastrol, a triterpenoid from Tripterygium Wilfordii, was identified as a potent inhibitor of cyst growth in vitro. In an in vivo iKspCre-Pkd1lox,lox mouse model for PKD, celastrol inhibited the growth of renal cysts and maintained kidney function.

Prioritization of novel ADPKD drug candidates from disease-stage specific gene expression profiles.

BACKGROUND: Autosomal Dominant Polycystic Kidney Disease (ADPKD) is one of the most common causes of end-stage renal failure, caused by mutations in PKD1 or PKD2 genes. Tolvaptan, the only drug approved for ADPKD treatment, results in serious side-effects, warranting the need for novel drugs. METHODS: In this study, we applied RNA-sequencing of Pkd1cko mice at different disease stages, and with/without drug treatment to identify genes involved in ADPKD progression that were further used to identify novel drug candidates for ADPKD. We followed an integrative computational approach using a combination of gene expression profiling, bioinformatics and cheminformatics data. FINDINGS: We identified 1162 genes that had a normalized expression after treating the mice with drugs proven effective in preclinical models. Intersecting these genes with target affinity profiles for clinically-approved drugs in ChEMBL, resulted in the identification of 116 drugs targeting 29 proteins, of which several are previously linked to Polycystic Kidney Disease such as Rosiglitazone. Further testing the efficacy of six candidate drugs for inhibition of cyst swelling using a human 3D-cyst assay, revealed that three of the six had cyst-growth reducing effects with limited toxicity. INTERPRETATION: Our data further establishes drug repurposing as a robust drug discovery method, with three promising drug candidates identified for ADPKD treatment (Meclofenamic Acid, Gamolenic Acid and Birinapant). Our strategy that combines multiple-omics data, can be extended for ADPKD and other diseases in the future. FUNDING: European Union's Seventh Framework Program, Dutch Technology Foundation Stichting Technische Wetenschappen and the Dutch Kidney Foundation.

High-Throughput Phenotypic Screening of Kinase Inhibitors to Identify Drug Targets for Polycystic Kidney Disease.

Polycystic kidney disease (PKD) is a prevalent disorder characterized by renal cysts that lead to kidney failure. Various signaling pathways have been targeted to stop disease progression, but most interventions still focus on alleviating PKD-associated symptoms. The mechanistic complexity of the disease, as well as the lack of functional in vitro assays for compound testing, has made drug discovery for PKD challenging. To identify modulators of PKD, Pkd1-/- kidney tubule epithelial cells were applied to a scalable and automated 3D cyst culture model for compound screening, followed by phenotypic profiling to determine compound efficacy. We used this screening platform to screen a library of 273 kinase inhibitors to probe various signaling pathways involved in cyst growth. We show that inhibition of several targets, including aurora kinase, CDK, Chk, IGF-1R, Syk, and mTOR, but, surprisingly, not PI3K, prevented forskolin-induced cyst swelling. Additionally, we show that multiparametric phenotypic classification discriminated potentially undesirable (i.e., cytotoxic) compounds from molecules inducing the desired phenotypic change, greatly facilitating hit selection and validation. Our findings show that a pathophysiologically relevant 3D cyst culture model of PKD coupled to phenotypic profiling can be used to identify potentially therapeutic compounds and predict and validate molecular targets for PKD.