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Cystic fibrosis (CF) is a life-threatening recessive genetic disease caused by mutations in the gene encoding for the cystic fibrosis transmembrane conductance regulator (CFTR). With the discovery of Ivacaftor and Lumacaftor, it has been shown that administration of one or more small molecules can partially restore the CFTR function. Correctors are small molecules that enhance the amount of CFTR on the cell surface, while potentiators improve the gating function of the CFTR channel. Herein, we describe the discovery and optimization of a novel potentiator series. Scaffold hopping, focusing on retaining the different intramolecular contacts, was crucial in the whole discovery process to identify a novel series devoid of genotoxic liabilities. From this series, the clinical candidate GLPG2451 was selected based on its pharmacokinetic properties, allowing QD dosing and based on its low CYP induction potential.

Protein kinases are a protein family that plays an important role in several complex diseases such as cancer and cardiovascular and immunological diseases. Protein kinases have conserved ATP binding sites, which when targeted can lead to similar activities of inhibitors against different kinases. This can be exploited to create multitarget drugs. On the other hand, selectivity (lack of similar activities) is desirable in order to avoid toxicity issues. There is a vast amount of protein kinase activity data in the public domain, which can be used in many different ways. Multitask machine learning models are expected to excel for these kinds of data sets because they can learn from implicit correlations between tasks (in this case activities against a variety of kinases). However, multitask modeling of sparse data poses two major challenges: (i) creating a balanced train–test split without data leakage and (ii) handling missing data. In this work, we construct a protein kinase benchmark set composed of two balanced splits without data leakage, using random and dissimilarity-driven cluster-based mechanisms, respectively. This data set can be used for benchmarking and developing protein kinase activity prediction models. Overall, the performance on the dissimilarity-driven cluster-based split is lower than on random split-based sets for all models, indicating poor generalizability of models. Nevertheless, we show that multitask deep learning models, on this very sparse data set, outperform single-task deep learning and tree-based models. Finally, we demonstrate that data imputation does not improve the performance of (multitask) models on this benchmark set.

The methyl esters of L-tyrosine and D-(4-hydroxyphenyl)glycine were directly transformed into the corresponding 2-arylsulfonamido esters with arylsulfonyl chlorides, without protecting the phenolic hydroxy group. The reaction is conducted in a THF/DMF (8:1) mixture as solvent, and using lyophilized solid sodium carbonate as base. The N-arylsulfonylation takes place with good yields (62−85%) in a chemoselective fashion, without racemization of the stereogenic carbon centers. The DMF (2.6 mol/mol amino ester) specifically solvates the oxygen atom of the formed N,O-dianion, reducing its nucleophilicity and dramatically increasing the chemoselectivity of the N-substitution. In contrast, in the absence of a highly coordinating additive, the phenoxide anion competes unfavorably with the 2-amino group for the nucleophilic attack, and the N,O-disulfonyl esters are produced with relevant yields. (© Wiley-VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2003)

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease. Current treatments only slow down disease progression, making new therapeutic strategies compelling. Increasing evidence suggests that S1P2 antagonists could be effective agents against fibrotic diseases. Our compound collection was mined for molecules possessing substructure features associated with S1P2 activity. The weakly potent indole hit 6evolved into a potent phthalazone series, bearing a carboxylic acid, with the aid of a homology model. Suboptimal pharmacokinetics of a benzimidazole subseries were improved by modifications targeting potential interactions with transporters, based on concepts deriving from the extended clearance classification system (ECCS). Scaffold hopping, as a part of a chemical enablement strategy, permitted the rapid exploration of the position adjacent to the carboxylic acid. Compound 38, with good pharmacokinetics and in vitropotency, was efficacious at 10 mg/kg BID in three different in vivomouse models of fibrotic diseases in a therapeutic setting.

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