With the emergence and rapid spread of new pandemic variants, especially variants of concern (VOCs), the development of next-generation vaccines with broad-spectrum neutralizing activities is of great importance. In this study, SCTV01C, a clinical stage bivalent vaccine based on trimeric spike extracellular domain (S-ECD) of SARS-CoV-2 variants Alpha (B.1.1.7) and Beta (B.1.351) with a squalene-based oil-in-water adjuvant was evaluated in comparison to its two corresponding (Alpha and Beta) monovalent vaccines in mouse immunogenicity studies. The two monovalent vaccines induced potent neutralizing antibody responses against the antigen-matched variants, but drastic reductions in neutralizing antibody titers against antigen-mismatched variants were observed. In comparison, the bivalent vaccine SCTV01C induced relatively higher and broad-spectrum cross-neutralizing activities against various SARS-CoV-2 variants, including the D614G variant, VOCs (B.1.1.7, B.1.351, P.1, B.1.617.2, B.1.1.529), variants of interest (VOIs) (C.37, B.1.621), variants under monitoring (VUMs) (B.1.526, B.1.617.1, B.1.429, C.36.3) and other variants (B.1.618, 20I/484Q). All three vaccines elicited potent Th1-biased T-cell immune responses. These results provide direct evidence that variant-based multivalent vaccines could play important roles in addressing the critical issue of reduced protective efficacy against the existing and emerging SARS-CoV-2 variants.

Purpose: Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies. Methods: Using machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a Retrieval-Augmented Generation (RAG) approach, we supplement each pair of bispecific targets’ machine learning prediction with important features and rationales, generating interpretable analytical reports. Results: In this study, the XGBoost model with pairwise learning was employed to predict the druggability of BsAbs. By analyzing extensive data on BsAbs and designing features from perspectives such as target activity, safety, cell type specificity, pathway mechanism, and gene embedding representation, our model is able to predict target combinations of BsAbs with high market potential. Specifically, we integrated XGBoost with the GPT model to discuss the efficacy of each bispecific target pair, thereby aiding the decision-making for drug developers. Conclusion: The novelty of this study lies in the integration of machine learning and GPT techniques to provide a novel framework for the design of BsAbs drugs. This holistic approach not only improves prediction accuracy, but also enhances the interpretability and innovativeness of drug design.

Background/Objectives: Finotonlimab (SCTI10A) is a humanized anti-PD-1 antibody tested in Phase III trials for several solid tumor types. Methods: This study characterized the in vitro and in vivo efficacy, Fc-mediated effector function, and non-clinical PK/PD properties of finotonlimab. Results: The results demonstrated that finotonlimab is effective in stimulating human T cell function in vitro and exhibits marked antitumor efficacy in vivo using both PD-1-humanized and PBMC-reconstructed mouse models. Additionally, finotonlimab exhibited minimal impact on the activation of effector cells via Fc receptor-dependent pathways, potentially facilitating PD-1⁺ T cell killing. In cynomolgus monkeys, finotonlimab exhibited a nonlinear pharmacokinetic (PK) profile in a dose-dependent manner, and a receptor occupancy rate of approximately 90% was observed at 168 h following a single administration of 1 mg/kg. Finotonlimab’s PK profile (especially C_max) was better than that of marketed antibodies. Following a 13-week successive administration of finotonlimab, a pharmacodynamic analysis revealed that a sustained mean receptor occupancy of PD-1 molecules on circulating T cells remained at or above 93% for up to 8 weeks, even at a dose of 3 mg/kg, and that there were higher antibody accumulations in different dose groups. Conclusions: Taken together, the preclinical findings are promising and provide the groundwork for evaluating the efficacy and pharmacodynamic characteristics of finotonlimab in clinical trials.

We previously developed a polysaccharide-–RBD-conjugated nanoparticle vaccine which induced protective efficacy against SARS-CoV-2 in a mouse model. Here, we newly developed a vaccine, SCTV01A, by chemically conjugating recombinant SARS-CoV-2 RBD-Fc and PPS14 (Streptococcus pneumoniae serotype type 14 capsular polysaccharide). The immunogenicity and toxicity of SCTV01A were evaluated in animal models. The PPS14 conjugation enhanced the immunogenicity of RBD-Fc in C57BL/6 mice whether formulated with SCT-VA02B or Alum adjuvant. SCTV01A also induced high opsonophagocytic activity (OPA) against S. pneumoniae serotype 14. In addition, SCTV01A stimulated potent neutralizing titers in rhesus macaques and effectively reduced lung inflammation after SARS-CoV-2 infection with neither antibody-dependent enhancement (ADE) nor vaccine-enhanced diseases (VED) phenomenon. Importantly, the long-term toxicity study of SCTV01A in rhesus macaques did not cause any abnormal toxicity and was tolerated at the highest tested dose (120 μg). The existing immunogenicity and toxicological evaluation results have demonstrated the safety and efficacy of SCTV01A, which will be a promising and feasible vaccine to protect against SARS-CoV-2 infection.