Liteng Shen

Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

Predicting the biological properties of molecules is crucial in computer-aided drug development, yet it's often impeded by data scarcity and imbalance in many practical applications. Existing approaches are based on self-supervised learning or 3D data and using an increasing number of parameters to improve performance. These approaches may not take full advantage of established chemical knowledge and could inadvertently introduce noise into the respective model. In this study, we introduce a more elegant transformer-based framework with focused attention for molecular representation (TransFoxMol) to improve the understanding of artificial intelligence (AI) of molecular structure property relationships. TransFoxMol incorporates a multi-scale 2D molecular environment into a graph neural network + Transformer module and uses prior chemical maps to obtain a more focused attention landscape compared to that obtained using existing approaches. Experimental results show that TransFoxMol achieves state-of-the-art performance on MoleculeNet benchmarks and surpasses the performance of baselines that use self-supervised learning or geometry-enhanced strategies on small-scale datasets. Subsequent analyses indicate that TransFoxMol's predictions are highly interpretable and the clever use of chemical knowledge enables AI to perceive molecules in a simple but rational way, enhancing performance.

Bystander-killing payloads can significantly overcome the tumor heterogeneity issue and enhance the clinical potential of antibody-drug conjugates (ADC), but the rational design and identification of effective bystander warheads constrain the broader implementation of this strategy. Here, graph attention networks (GAT) are constructed for a rational bystander killing scoring model and ADC construction workflow for the first time. To generate efficient bystander-killing payloads, this model is utilized for score-directed exatecan derivatives design. Among them, Ed9, the most potent payload with satisfactory permeability and bioactivity, is further used to construct ADC. Through linker optimization and conjugation, novel ADCs are constructed that perform excellent anti-tumor efficacy and bystander-killing effect in vivo and in vitro. The optimal conjugate T-VEd9 exhibited therapeutic efficacy superior to DS-8201 against heterogeneous tumors. These results demonstrate that the effective scoring approach can pave the way for the discovery of novel ADC with promising bystander payloads to combat tumor heterogeneity.

BRD9 is a pivotal epigenetic factor involved in cancers and inflammatory diseases. Still, the limited selectivity and poor phenotypic activity of targeted agents make it an atypically undruggable target. PROTAC offers an alternative strategy for overcoming the issue. In this study, we explored diverse E3 ligase ligands for the contribution of BRD9 PROTAC degradation. Through molecular docking, binding affinity analysis, and structure–activity relationship study, we identified a highly potent PROTAC E5, with excellent BRD9 degradation (DC50 = 16 pM) and antiproliferation in MV4–11 cells (IC50 = 0.27 nM) and OCI-LY10 cells (IC50 = 1.04 nM). E5 can selectively degrade BRD9 and induce cell cycle arrest and apoptosis. Moreover, the therapeutic efficacy of E5 was confirmed in xenograft tumor models, accompanied by further RNA-seq analysis. Therefore, these results may pave the way and provide the reference for the discovery and investigation of highly effective PROTAC degraders.

Ferroptosis is a promising therapeutic target for injury-related diseases, yet diversity in ferroptosis inhibitors remains limited. In this study, initial structure optimization led us to focus on the bond dissociation enthalpy (BDE) of the N–H bond and the residency time of radical scavengers in a phospholipid bilayer, which may play an important role in ferroptosis inhibition potency. This led to the discovery of compound D1, exhibiting potent ferroptosis inhibition, high radical scavenging, and moderate membrane permeability. D1 demonstrated significant neuroprotection in an oxygen glucose deprivation/reoxygenation (OGD/R) model and reduced infarct volume in an in vivo stroke model upon intravenous treatment. Further screening based on this strategy identified NecroX-7 and Eriodictyol-7-O-glucoside as novel ferroptosis inhibitors with highly polar structural characteristics. This approach bridges the gap between free radical scavengers and ferroptosis inhibitors, providing a foundation for research and insights into novel ferroptosis inhibitor development.