Establishment of a novel, cell-based autotaxin assay.

Autotaxin (ATX) plays an important role in (patho-)physiological lysophosphatidic acid (LPA) signaling. Here we describe the establishment of novel cell-based ATX assay formats. ATX-mediated LPA generation is detected by using a stable LPA receptor reporter cell line. In a first assay variant, ATX-mediated LPA generation is started in the absence of cells and the reaction mix is transferred to the reporter cells after stopping the reaction (two-tube assay). In a second assay variant, ATX is added to the reporter cells expressing the known autotaxin binding partners integrin ß1, integrin ß3 and the LPA receptor 1. LPA generation is started in the presence of cells and is detected in real-time (one-tube assay). Structurally diverse ATX inhibitors with different binding modes were characterized in both cell-based assay variants and were also tested in the well-established biochemical choline release assay. ATX inhibitors displayed similar potencies, regardless if the assay was performed in the absence or presence of cells, and comparable results were obtained in all three assay formats. In summary, our novel cell-based ATX assay formats are well-suited for sensitive detection of enzyme activity as well as for the characterization of ATX inhibitors in the presence and absence of cells.

Predictive Modeling of PROTAC Cell Permeability with Machine Learning.

Approaches for predicting proteolysis targeting chimera (PROTAC) cell permeability are of major interest to reduce resource-demanding synthesis and testing of low-permeable PROTACs. We report a comprehensive investigation of the scope and limitations of machine learning-based binary classification models developed using 17 simple descriptors for large and structurally diverse sets of cereblon (CRBN) and von Hippel-Lindau (VHL) PROTACs. For the VHL PROTAC set, kappa nearest neighbor and random forest models performed best and predicted the permeability of a blinded test set with >80% accuracy (k ≥ 0.57). Models retrained by combining the original training and the blinded test set performed equally well for a second blinded VHL set. However, models for CRBN PROTACs were less successful, mainly due to the imbalanced nature of the CRBN datasets. All descriptors contributed to the models, but size and lipophilicity were the most important. We conclude that properly trained machine learning models can be integrated as effective filters in the PROTAC design process.

Structure-Permeability Relationship of Semipeptidic Macrocycles-Understanding and Optimizing Passive Permeability and Efflux Ratio.

We herein report the first thorough analysis of the structure-permeability relationship of semipeptidic macrocycles. In total, 47 macrocycles were synthesized using a hybrid solid-phase/solution strategy, and then their passive and cellular permeability was assessed using the parallel artificial membrane permeability assay (PAMPA) and Caco-2 assay, respectively. The results indicate that semipeptidic macrocycles generally possess high passive permeability based on the PAMPA, yet their cellular permeability is governed by efflux, as reported in the Caco-2 assay. Structural variations led to tractable structure-permeability and structure-efflux relationships, wherein the linker length, stereoinversion, N-methylation, and peptoids site-specifically impact the permeability and efflux. Extensive nuclear magnetic resonance, molecular dynamics, and ensemble-based three-dimensional polar surface area (3D-PSA) studies showed that ensemble-based 3D-PSA is a good predictor of passive permeability.