ADCdb: the database of antibody-drug conjugates

IMMUNOTAR - Integrative prioritization of cell surface targets for cancer immunotherapy

Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of less toxic immunotherapies; however, identifying targets for immunotherapies remains a challenge in the field. To address this challenge, we developed IMMUNOTAR, a computational tool that systematically prioritizes and identifies candidate immunotherapeutic targets. IMMUNOTAR integrates user-provided RNA-sequencing or proteomics data with quantitative features extracted from publicly available databases based on predefined optimal immunotherapeutic target criteria and quantitatively prioritizes potential surface protein targets. We demonstrate the utility and flexibility of IMMUNOTAR using three distinct datasets, validating its effectiveness in identifying both known and new potential immunotherapeutic targets within the analyzed cancer phenotypes. Overall, IMMUNOTAR enables the compilation of data from multiple sources into a unified platform, allowing users to simultaneously evaluate surface proteins across diverse criteria. By streamlining target identification, IMMUNOTAR empowers researchers to efficiently allocate resources and accelerate immunotherapy development.

Design, Construction, and Validation of a Yeast-Displayed Chemically Expanded Antibody Library

In vitro display technologies, exemplified by phage and yeast display, have emerged as powerful platforms for antibody discovery and engineering. However, the identification of antibodies that disrupt target functions beyond binding remains a challenge. In particular, there are very few strategies that support identification and engineering of either protein-based irreversible binders or inhibitory enzyme binders. Expanding the range of chemistries in antibody libraries has the potential to lead to efficient discovery of function-disrupting antibodies. In this work, we describe a yeast display-based platform for the discovery of chemically diversified antibodies. We constructed a billion-member antibody library that supports the presentation of a range of chemistries within antibody variable domains via noncanonical amino acid (ncAA) incorporation and subsequent bioorthogonal click chemistry conjugations. Use of a polyspecific orthogonal translation system enables introduction of chemical groups with various properties, including photo-reactive, proximity-reactive, and click chemistry-enabled functional groups for library screening. We established conjugation conditions that facilitate modification of the full library, demonstrating the feasibility of sorting the full billion-member library in "protein-small molecule hybrid" format in future work. Here, we conducted initial library screens after introducing O-(2-bromoethyl)tyrosine (OBeY), a weakly electrophilic ncAA capable of undergoing proximity-induced crosslinking to a target. Enrichments against donkey IgG and protein tyrosine phosphatase 1B (PTP1B) each led to the identification of several OBeY-substituted clones that bind to the targets of interest. Flow cytometry analysis on the yeast surface confirmed higher retention of binding for OBeY-substituted clones compared to clones substituted with ncAAs lacking electrophilic side chains after denaturation. However, subsequent crosslinking experiments in solution with ncAA-substituted clones yielded inconclusive results, suggesting that weakly reactive OBeY side chain is not sufficient to drive robust crosslinking in the clones isolated here. Nonetheless, this work establishes a multi-modal, chemically expanded antibody library and demonstrates the feasibility of conducting discovery campaigns in chemically expanded format. This versatile platform offers new opportunities for identifying and characterizing antibodies with properties beyond what is accessible with the canonical amino acids, potentially enabling discovery of new classes of reagents, diagnostics, and even therapeutic leads.

A humanized trivalent Nectin-4-targeting nanobody drug conjugate displays potent antitumor activity in gastric cancer

BACKGROUND

Gastric cancer represents a highly lethal malignancy with an elevated mortality rate among cancer patients, coupled with a suboptimal postoperative survival prognosis. Nectin-4, an overexpressed oncological target for various cancers, has been exploited to create antibody-drug conjugates (ADCs) to treat solid tumors. However, there is limited research on Nectin-4 ADCs specifically for gastric cancer, and conventional immunoglobulin G (IgG)-based ADCs frequently encounter binding site barriers. Based on the excellent tumor penetration capabilities inherent in nanobodies (Nbs), we developed Nectin-4-targeting Nb drug conjugates (NDCs) for the treatment of gastric cancer.

RESULTS

An immunized phage display library was established and employed for the selection of Nectin-4-specific Nbs using phage display technology. Subsequently, these Nbs were engineered into homodimers to enhance Nb affinity. To prolong in vivo half-life and reduce immunogenicity, we fused an Nb targeting human serum albumin (HSA), resulting in the development of trivalent humanized Nbs. Further, we site-specifically conjugated a monomethyl auristatin E (MMAE) at the C-terminus of the trivalent Nbs, creating Nectin-4 NDC (huNb26/Nb26-Nbh-MMAE) with a drug-to-antibody ratio (DAR) of 1. Nectin-4 NDC demonstrated excellent in vitro cell-binding activities and cytotoxic efficacy against cells with high Nectin-4 expression. Subsequent administration of Nectin-4 NDC to mice bearing NCI-N87 human gastric cancer xenografts demonstrated rapid tissue penetration and high tumor uptake through in vivo imaging. Moreover, Nectin-4 NDC exhibited noteworthy dose-dependent anti-tumor efficacy in in vivo studies.

CONCLUSION

We have engineered a Nectin-4 NDC with elevated affinity and effective tumor uptake, further establishing its potential as a therapeutic agent for gastric cancer.

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling

Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunotherapy, radiotherapy, and diverse chemotherapy approaches like drug repurposing and combination therapy are widely used depending on cancer subtype and metastasis severity. Our study revolves around an innovative drug discovery strategy targeting potential drug candidates specific to RTK signalling, a prominently targeted receptor class in cancer. To accomplish this, we have developed a multimodal deep neural network (MM-DNN) based QSAR model integrating omics datasets to elucidate genomic, proteomic expression data, and drug responses, validated rigorously. The results showcase an R2 value of 0.917 and an RMSE value of 0.312, affirming the model's commendable predictive capabilities. Structural analogs of drug molecules specific to RTK signalling were sourced from the PubChem database, followed by meticulous screening to eliminate dissimilar compounds. Leveraging the MM-DNN-based QSAR model, we predicted the biological activity of these molecules, subsequently clustering them into three distinct groups. Feature importance analysis was performed. Consequently, we successfully identified prime drug candidates tailored for each potential downstream regulatory protein within the RTK signalling pathway. This method makes the early stages of drug development faster by removing inactive compounds, providing a hopeful path in combating breast cancer.

Rational Identification of Novel Antibody-Drug Conjugate with High Bystander Killing Effect against Heterogeneous Tumors

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.

DDID: a comprehensive resource for visualization and analysis of diet-drug interactions

Diet-drug interactions (DDIs) are pivotal in drug discovery and pharmacovigilance. DDIs can modify the systemic bioavailability/pharmacokinetics of drugs, posing a threat to public health and patient safety. Therefore, it is crucial to establish a platform to reveal the correlation between diets and drugs. Accordingly, we have established a publicly accessible online platform, known as Diet-Drug Interactions Database (DDID, https://bddg.hznu.edu.cn/ddid/), to systematically detail the correlation and corresponding mechanisms of DDIs. The platform comprises 1338 foods/herbs, encompassing flora and fauna, alongside 1516 widely used drugs and 23 950 interaction records. All interactions are meticulously scrutinized and segmented into five categories, thereby resulting in evaluations (positive, negative, no effect, harmful and possible). Besides, cross-linkages between foods/herbs, drugs and other databases are furnished. In conclusion, DDID is a useful resource for comprehending the correlation between foods, herbs and drugs and holds a promise to enhance drug utilization and research on drug combinations.

FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response

Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.

Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity

Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.