Selective activation of IFNγ-ipilimumab enhances the therapeutic effect and safety of ipilimumab.

INTRODUCTION: Immune checkpoint inhibitor therapy is a highly promising strategy for clinical treatment of cancer. Among these inhibitors, ipilimumab stands out for its ability to induce cytotoxic T cell proliferation and activation by binding to CTLA-4. However, ipilimumab also gives rise to systemic immune-related adverse effects and tumor immune evasion, limiting its effectiveness. OBJECTIVES: We developed IFNγ-ipilimumab and confirmed that the addition of INF-γ does not alter the fundamental properties of ipilimumab. RESULTS: IFNγ-ipilimumab can be activated by matrix metalloproteinases, thereby promoting the IFNγ signaling pathway and enhancing the cytotoxicity of T cells. In vivo studies demonstrated that IFNγ-ipilimumab enhances the therapeutic effect of ipilimumab against colorectal cancer by increasing CD8+ and CD4+ lymphocyte infiltration into the tumor area and inducing MHC-I expression in tumor cells. Mice treated with IFNγ-ipilimumab showed higher survival rates and body weight, as well as lower CD4+ and CD8+ lymphocyte activation rates in the blood and reduced organ damage. CONCLUSION: IFNγ-ipilimumab improved the effectiveness of ipilimumab while reducing its side effects. It is likely that future immunotherapies would rely on such antibodies to activate local cancer cells or immune cells, thereby increasing the therapeutic effectiveness of cancer treatments and ensuring their safety.

Exploring the therapeutic potential of DV-B-120 as an inhibitor of dengue virus infection.

Dengue fever, an infectious disease prevalent in subtropical and tropical regions, currently lacks effective small-molecule drugs as treatment. In this study, we used a fluorescence peptide cleavage assay to screen seven compounds to assess their inhibition of the dengue virus (DENV) NS2B-NS3 protease. DV-B-120 demonstrated superior inhibition of NS2B-NS3 protease activity and lower toxicity compared to ARDP0006. The selectivity index of DV-B-120 was higher than that of ARDP0006. In vivo assessments of the antiviral efficacy of DV-B-120 against DENV replication demonstrated delayed mortality of suckling mice treated with the compound, with 60-80% protection against life-threatening effects, compared to the outcomes of DENV-infected mice treated with saline. The lower clinical scores of DENV-infected mice treated with DV-B-120 indicated a reduction in acute-progressive illness symptoms, underscoring the potential therapeutic impact of DV-B-120. Investigations of DV-B-120's ability to restore the antiviral type I IFN response in the brain tissue of DENV-infected ICR suckling mice demonstrated its capacity to stimulate IFN and antiviral IFN-stimulated gene expression. DV-B-120 not only significantly delayed DENV-2-induced mortality and illness symptoms but also reduced viral numbers in the brain, ultimately restoring the innate antiviral response. These findings strongly suggest that DV-B-120 holds promise as a therapeutic agent against DENV infection and highlight its potential contribution in addressing the current lack of effective treatments for this infectious disease.IMPORTANCEThe prevalence of dengue virus (DENV) infection in tropical and subtropical regions is escalating due to factors like climate change and mosquito vector expansion. With over 300 million annual infections and potentially fatal outcomes, the urgent need for effective treatments is evident. While the approved Dengvaxia vaccine has variable efficacy, there are currently no antiviral drugs for DENV. This study explores seven compounds targeting the NS2B-NS3 protease, a crucial protein in DENV replication. These compounds exhibit inhibitory effects on DENV-2 NS2B-NS3, holding promise for disrupting viral replication and preventing severe manifestations. However, further research, including animal testing, is imperative to assess therapeutic efficacy and potential toxicity. Developing safe and potent treatments for DENV infection is critical in addressing the rising global health threat posed by this virus.

Development of NS2B-NS3 protease inhibitor that impairs Zika virus replication.

Zika virus (ZIKV) is a mosquito-borne flavivirus that causes severe neurological disorders, such as microcephaly in fetuses. Most recently, an outbreak of ZIKV started in Brazil in 2015. To date, no therapeutic agents have been approved to treat ZIKV infection in the clinic. Here, we screened a small molecule inhibitor that can inhibit the function of ZIKV non-structural protein 2B (NS2B)-NS3 protease (ZIKV NS2B-NS3 protease), thereby interfering with viral replication and spread. First, we identified the half maximal inhibitory concentration (IC50) of compound 3 (14.01 µM), 8 (6.85 µM), and 9 (14.2 µM) and confirmed that they are all non-competitive inhibitors. In addition, we have used the blind molecular docking method to simulate the inhibition area of three non-competitive inhibitors (compound 3, 8, and 9) with the ZIKV NS2B-NS3 protease. The results indicated that the four allosteric binding residues (Gln139, Trp148, Leu150, and Val220) could form hydrogen bonds or non-bonding interactions most frequently with the three compounds. The interaction might induce the reaction center conformation change of NS2B-NS3 protease to reduce catalyzed efficiency. The concentration of compounds required to reduce cell viability by 50% (CC50), and the concentration of compounds required to inhibit virus-induced cytopathic effect by 50% (EC50) of three potential compounds are >200 µM, 2.15 µM (compound 3), > 200 µM, 0.52 µM (compound 8) and 61.48 µM, 3.52 µM (compound 9), and Temoporfin are 61.05 µM, 2 µM, respectively. To select candidate compounds for further animal experiments, we analyzed the selectivity index (SI) of compound 3 (93.02), 8 (384.61), 9 (17.46), and Temoporfin (30.53, FDA-approved drug against cancer). Compound 8 has the highest SI value. Therefore, compound 8 was selected for verification in animal models. In vivo, compound 8 significantly delayed ZIKV-induced lethality and illness symptoms and decreased ZIKV-induced weight loss in a ZIKV-infected suckling mouse model. We conclude that compound 8 is worth further investigation for use as a potential future therapeutic agent against ZIKV infection.

A universal in silico V(D)J recombination strategy for developing humanized monoclonal antibodies.

BACKGROUND: Humanization of mouse monoclonal antibodies (mAbs) is crucial for reducing their immunogenicity in humans. However, humanized mAbs often lose their binding affinities. Therefore, an in silico humanization method that can prevent the loss of the binding affinity of mAbs is needed. METHODS: We developed an in silico V(D)J recombination platform in which we used V(D)J human germline gene sequences to design five humanized candidates of anti-tumor necrosis factor (TNF)-α mAbs (C1-C5) by using different human germline templates. The candidates were subjected to molecular dynamics simulation. In addition, the structural similarities of their complementarity-determining regions (CDRs) to those of original mouse mAbs were estimated to derive the weighted interatomic root mean squared deviation (wRMSDi) value. Subsequently, the correlation of the derived wRMSDi value with the half maximal effective concentration (EC50) and the binding affinity (KD) of the humanized anti-TNF-α candidates was examined. To confirm whether our in silico estimation method can be used for other humanized mAbs, we tested our method using the anti-epidermal growth factor receptor (EGFR) a4.6.1, anti-glypican-3 (GPC3) YP9.1 and anti-α4ß1 integrin HP1/2L mAbs. RESULTS: The R2 value for the correlation between the wRMSDi and log(EC50) of the recombinant Remicade and those of the humanized anti-TNF-α candidates was 0.901, and the R2 value for the correlation between wRMSDi and log(KD) was 0.9921. The results indicated that our in silico V(D)J recombination platform could predict the binding affinity of humanized candidates and successfully identify the high-affinity humanized anti-TNF-α antibody (Ab) C1 with a binding affinity similar to that of the parental chimeric mAb (5.13 × 10-10). For the anti-EGFR a4.6.1, anti-GPC3 YP9.1, and anti-α4ß1 integrin HP1/2L mAbs, the wRMSDi and log(EC50) exhibited strong correlations (R2 = 0.9908, 0.9999, and 0.8907, respectively). CONCLUSIONS: Our in silico V(D)J recombination platform can facilitate the development of humanized mAbs with low immunogenicity and high binding affinities. This platform can directly transform numerous mAbs with therapeutic potential to humanized or even human therapeutic Abs for clinical use.

Fibrinogen-Like Protein 1 Serves as an Anti-Inflammatory Agent for Collagen-Induced Arthritis Therapy in Mice.

Fibrinogen-like protein 1 (FGL1) was recently identified as a major ligand of lymphocyte-activation gene-3 (LAG-3) on activated T cells and serves as an immune suppressive molecule for regulation of immune homeostasis. However, whether FGL1 has therapeutic potential for use in the T cell-induced the autoimmune disease, rheumatoid arthritis (RA), is still unknown. Here, we attempted to evaluate the effect of FGL1 protein on arthritis progression. We also evaluated potential adverse events in a collagen-induced arthritis (CIA) mouse model. We first confirmed that soluble Fgl1 protein could specifically bind to surface Lag-3 receptor on 3T3-Lag-3 cells and further inhibit interleukin (IL-2) and interferon gamma (IFNγ) secretion from activated primary mouse T cells by 95% and 43%, respectively. Intraperitoneal administration of Fgl1 protein significantly decreased the inflammatory cytokine level (i.e., IL-1ß and IL-6) in local paw tissue, and prevented joint inflammation, cellular infiltration, bone deformation and attenuated collagen-induced arthritis progression in vivo. We further demonstrated that exogenous Fgl1 does not cause obvious adverse events during treatment by monitoring body weight and liver weight, and assessing the morphology of several organs (i.e., heart, liver, spleen, lung and kidney) by pathological studies. We expect that Fgl1 protein may be suitable to serve as a potential therapeutic agent for treatment of RA or even other types of T cell-induced autoimmune or inflammatory diseases in the future.

Prediction of beta-turns in proteins using the first-order Markov models.

We present a method based on the first-order Markov models for predicting simple beta-turns and loops containing multiple turns in proteins. Sequences of 338 proteins in a database are divided using the published turn criteria into the following three regions, namely, the turn, the boundary, and the nonturn ones. A transition probability matrix is constructed for either the turn or the nonturn region using the weighted transition probabilities computed for dipeptides identified from each region. There are two such matrices constructed for the boundary region since the transition probabilities for dipeptides immediately preceding or following a turn are different. The window used for scanning a protein sequence from amino (N-) to carboxyl (C-) terminal is a hexapeptide since the transition probability computed for a turn tetrapeptide is capped at both the N- and C- termini with a boundary transition probability indexed respectively from the two boundary transition matrices. A sum of the averaged product of the transition probabilities of all the hexapeptides involving each residue is computed. This is then weighted with a probability computed from assuming that all the hexapeptides are from the nonturn region to give the final prediction quantity. Both simple beta-turns and loops containing multiple turns in a protein are then identified by the rising of the prediction quantity computed. The performance of the prediction scheme or the percentage (%) of correct prediction is evaluated through computation of Matthews correlation coefficients for each protein predicted. It is found that the prediction method is capable of giving prediction results with better correlation between the percent of correct prediction and the Matthews correlation coefficients for a group of test proteins as compared with those predicted using some secondary structural prediction methods. The prediction accuracy for about 40% of proteins in the database or 50% of proteins in the test set is better than 70%. Such a percentage for the test set is reduced to 30 if the structures of all the proteins in the set are treated as unknown.