Research on GPCR Biased Signaling Pathway Identification Models Based on Multimodal Pharmacodynamic Data Fusion

In the current research, we developed a new method of pharmacodynamic data fusion for predicting biased signaling pathways related to G protein-coupled receptors (GPCRs). The β2-adrenergic receptor in humans (ADRB2) was chosen as the targeted GPCR, while the stable cell line of HEK293T cells with ADRB2 overexpression was used as an assay system. Four pharmacodynamics parameters were considered for data collection, namely cAMP generation, β-arrestin2 recruitment, ERK1/2 activation, and cell viability. The set of 40 molecules was screened as ADRB2 ligands at eight concentrations in triplicate. The process of data processing included four steps sequentially: curve fitting via GraphPad Prism 10, preprocessing in Python 3.11, neural network training in PyTorch 2.2, and SHAP. According to the model outputs, this novel multimodal strategy could classify the ligands as belonging to one of the three classes of biased signaling: Gs-biased, β-arrestin-biased, and balanced. In comparison to single-modality cAMP-based and β-arrestin2-based models, the multimodal model outperformed by its ability to distinguish ligands by their biased signaling profiles, reaching accuracy of 88.7%, macro-F1 of 0.872, and ROC-AUC of 0.931. Selection of key features showed four important features: cAMP pEC50, β-arrestin2 Emax, ERK1/2 max, and pathway ratios. According to SHAP results, cAMP pEC50 and β-arrestin2 Emax were two most important features, having contribution of 21.8% and 19.6%, correspondingly.