A Novel Irreversible TEAD Inhibitor, SWTX-143, Blocks Hippo Pathway Transcriptional Output and Causes Tumor Regression in Preclinical Mesothelioma Models.

The Hippo pathway and its downstream effectors, the YAP and TAZ transcriptional coactivators, are deregulated in multiple different types of human cancer and are required for cancer cell phenotypes in vitro and in vivo, while largely dispensable for tissue homeostasis in adult mice. YAP/TAZ and their main partner transcription factors, the TEAD1-4 factors, are therefore promising anticancer targets. Because of frequent YAP/TAZ hyperactivation caused by mutations in the Hippo pathway components NF2 and LATS2, mesothelioma is one of the prime cancer types predicted to be responsive to YAP/TAZ-TEAD inhibitor treatment. Mesothelioma is a devastating disease for which currently no effective treatment options exist. Here, we describe a novel covalent YAP/TAZ-TEAD inhibitor, SWTX-143, that binds to the palmitoylation pocket of all four TEAD isoforms. SWTX-143 caused irreversible and specific inhibition of the transcriptional activity of YAP/TAZ-TEAD in Hippo-mutant tumor cell lines. More importantly, YAP/TAZ-TEAD inhibitor treatment caused strong mesothelioma regression in subcutaneous xenograft models with human cells and in an orthotopic mesothelioma mouse model. Finally, SWTX-143 also selectively impaired the growth of NF2-mutant kidney cancer cell lines, suggesting that the sensitivity of mesothelioma models to these YAP/TAZ-TEAD inhibitors can be extended to other tumor types with aberrations in Hippo signaling. In brief, we describe a novel and specific YAP/TAZ-TEAD inhibitor that has potential to treat multiple Hippo-mutant solid tumor types.

Covalent inhibition of bacterial quorum sensing.

Chemical coordination of gene expression among bacteria as a function of population density is regulated by a mechanism known as 'quorum sensing' (QS). QS in Pseudomonas aeruginosa, an opportunistic pathogen that causes disease in immunocompromised patients, is mediated by binding of the transcriptional activator, LasR, to its ligand, 3-oxo-C(12)-HSL, leading to population-wide secretion of virulence factors and biofilm formation. We have targeted QS in P. aeruginosa with a set of electrophilic probes designed to covalently bind Cys79 in the LasR binding pocket, leading to specific inhibition of QS-regulated gene expression and concomitant reduction of virulence factor secretion and biofilm formation. This first example of covalent modification of a QS receptor provides a new tool to study molecular mechanisms of bacterial group behavior and could lead to new strategies for targeting bacterial virulence.

Dihydroxy-pyrimidine and N-methylpyrimidone HIV-integrase inhibitors: improving cell based activity by the quaternarization of a chiral center.

In the context of HIV-integrase, dihydroxypyrimidine and N-methyl pyrimidone inhibitors the cellular activity of this class of compounds has been optimized by the introduction of a simple methyl substituent in the alpha-position of the C-2 side chains. Enhanced passive membrane permeability has been identified as the key factor driving the observed cell-based activity improvement. The rat PK profile of the alpha-methyl derivative 26a was also improved over its des-methyl exact analog.

4-Hydroxy-5-pyrrolinone-3-carboxamide HIV-1 integrase inhibitors.

The viral enzyme integrase is essential for the replication of HIV-1 and, after the discovery of Isentress, represents a validated target for anti-retroviral therapy. Incorporation of the dihydroxycarbonyl pharmacophore into a pyrrolinone scaffold led to the discovery of 5-pyrrolinone-3-carboxamides as a structurally diverse class of HIV-1 integrase inhibitors.

Target specific compound identification using a support vector machine.

In many cases at the beginning of an HTS-campaign, some information about active molecules is already available. Often known active compounds (such as substrate analogues, natural products, inhibitors of a related protein or ligands published by a pharmaceutical company) are identified in low-throughput validation studies of the biochemical target. In this study we evaluate the effectiveness of a support vector machine applied for those compounds and used to classify a collection with unknown activity. This approach was aimed at reducing the number of compounds to be tested against the given target. Our method predicts the biological activity of chemical compounds based on only the atom pairs (AP) two dimensional topological descriptors. The supervised support vector machine (SVM) method herein is trained on compounds from the MDL drug data report (MDDR) known to be active for specific protein target. For detailed analysis, five different biological targets were selected including cyclooxygenase-2, dihydrofolate reductase, thrombin, HIV-reverse transcriptase and antagonists of the estrogen receptor. The accuracy of compound identification was estimated using the recall and precision values. The sensitivities for all protein targets exceeded 80% and the classification performance reached 100% for selected targets. In another application of the method, we addressed the absence of an initial set of active compounds for a selected protein target at the beginning of an HTS-campaign. In such a case, virtual high-throughput screening (vHTS) is usually applied by using a flexible docking procedure. However, the vHTS experiment typically contains a large percentage of false positives that should be verified by costly and time-consuming experimental follow-up assays. The subsequent use of our machine learning method was found to improve the speed (since the docking procedure was not required for all compounds from the database) and also the accuracy of the HTS hit lists (the enrichment factor).

H1PVAT is a novel and potent early-stage inhibitor of poliovirus replication that targets VP1.

A novel small molecule, H1PVAT, was identified as a potent and selective inhibitor of the in vitro replication of all three poliovirus serotypes, whereas no activity was observed against other enteroviruses. Time-of-drug-addition studies revealed that the compound interfered with an early stage of virus replication. Four independently-selected H1PVAT-resistant virus variants uniformly carried the single amino acid substitution I194F in the VP1 capsid protein. Poliovirus type 1 strain Sabin, reverse-engineered to contain this substitution, proved to be completely insensitive to the antiviral effect of H1PVAT and was cross-resistant to the capsid-binding inhibitors V-073 and pirodavir. The VP1 I194F mutant had a smaller plaque phenotype than wild-type virus, and the amino acid substitution rendered the virus more susceptible to heat inactivation. Both for the wild-type and VP1 I194F mutant virus, the presence of H1PVAT increased the temperature at which the virus was inactivated, providing evidence that the compound interacts with the viral capsid, and that capsid stabilization and antiviral activity are not necessarily correlated. Molecular modeling suggested that H1PVAT binds with high affinity in the pocket underneath the floor of the canyon that is involved in receptor binding. Introduction of the I194F substitution in the model of VP1 induced a slight concerted rearrangement of the core ß-barrel in this pocket, which disfavors binding of the compound. Taken together, the compound scaffold, to which H1PVAT belongs, may represent another promising class of poliovirus capsid-binding inhibitors next to V-073 and pirodavir. Potent antivirals against poliovirus will be essential in the poliovirus eradication end-game.

Performance of machine learning methods for ligand-based virtual screening.

Computational screening of compound databases has become increasingly popular in pharmaceutical research. This review focuses on the evaluation of ligand-based virtual screening using active compounds as templates in the context of drug discovery. Ligand-based screening techniques are based on comparative molecular similarity analysis of compounds with known and unknown activity. We provide an overview of publications that have evaluated different machine learning methods, such as support vector machines, decision trees, ensemble methods such as boosting, bagging and random forests, clustering methods, neuronal networks, naïve Bayesian, data fusion methods and others.

Assessing different classification methods for virtual screening.

How well do different classification methods perform in selecting the ligands of a protein target out of large compound collections not used to train the model? Support vector machines, random forest, artificial neural networks, k-nearest-neighbor classification with genetic-algorithm-optimized feature selection, trend vectors, naïve Bayesian classification, and decision tree were used to divide databases into molecules predicted to be active and those predicted to be inactive. Training and predicted activities were treated as binary. The database was generated for the ligands of five different biological targets which have been the object of intense drug discovery efforts: HIV-reverse transcriptase, COX2, dihydrofolate reductase, estrogen receptor, and thrombin. We report significant differences in the performance of the methods independent of the biological target and compound class. Different methods can have different applications; some provide particularly high enrichment, others are strong in retrieving the maximum number of actives. We also show that these methods do surprisingly well in predicting recently published ligands of a target on the basis of initial leads and that a combination of the results of different methods in certain cases can improve results compared to the most consistent method.