Humanization and Characterization of an Anti-Human TNF-α Murine Monoclonal Antibody

A murine monoclonal antibody, m357, showing the highly neutralizing activities for human tumor necrosis factor (TNF-α) was chosen to be humanized by a variable domain resurfacing approach. The non-conserved surface residues in the framework regions of both the heavy and light chain variable regions were identified via a molecular modeling of m357 built by computer-assisted homology modeling. By replacing these critical surface residues with the human counterparts, a humanized version, h357, was generated. The humanized h357 IgG1 was then stably expressed in a mammalian cell line and the purified antibody maintained the high antigen binding affinity as compared with the parental m357 based on a soluble TNF-α neutralization bioassay. Furthermore, h357 IgG1 possesses the ability to mediate antibody-dependent cell-mediated cytotoxicity and complement dependent cytotoxicity upon binding to cells bearing the transmembrane form of TNF-α. In a mouse model of collagen antibody-induced arthritis, h357 IgG significantly inhibited disease progression by intra-peritoneal injection of 50 µg/mouse once-daily for 9 consecutive days. These results provided a basis for the development of h357 IgG as therapeutic use

Single Cycle Structure-Based Humanization of an Anti-Nerve Growth Factor Therapeutic Antibody

Most forms of chronic pain are inadequately treated by present therapeutic options. Compelling evidence has accumulated, demonstrating that Nerve Growth Factor (NGF) is a key modulator of inflammatory and nociceptive responses, and is a promising target for the treatment of human pathologies linked to chronic and inflammatory pain. There is therefore a growing interest in the development of therapeutic molecules antagonising the NGF pathway and its nociceptor sensitization actions, among which function-blocking anti-NGF antibodies are particularly relevant candidates

Absence of polysialylated NCAM is an unfavorable prognostic phenotype for advanced stage neuroblastoma

<p>Abstract</p> <p>Background</p> <p>The expression of a neural crest stem cell marker, polysialic acid (polySia), and its main carrier, neural cell adhesion molecule (NCAM), have been detected in some malignant tumors with high metastatic activity and unfavorable prognosis, but the diagnostic and prognostic value of polySia-NCAM in neuroblastoma is unclear.</p> <p>Methods</p> <p>A tumor tissue microarray (TMA) of 36 paraffin-embedded neuroblastoma samples was utilized to detect polySia-NCAM expression with a polySia-binding fluorescent fusion protein, and polySia-NCAM expression was compared with clinical stage, age, <it>MYCN </it>amplification status, histology (INPC), and proliferation index (PI).</p> <p>Results</p> <p>PolySia-NCAM-positive neuroblastoma patients had more often metastases at diagnosis, and polySia-NCAM expression associated with advanced disease (<it>P </it>= 0.047). Most interestingly, absence of polySia-NCAM-expressing tumor cells in TMA samples, however, was a strong unfavorable prognostic factor for overall survival in advanced disease (<it>P </it>= 0.0004), especially when <it>MYCN </it>was not amplified. PolySia-NCAM-expressing bone marrow metastases were easily detected in smears, aspirates and biopsies.</p> <p>Conclusion</p> <p>PolySia-NCAM appears to be a new clinically significant molecular marker in neuroblastoma, hopefully with additional value in neuroblastoma risk stratification.</p

Development of a Humanized Antibody with High Therapeutic Potential against Dengue Virus Type 2

Dengue virus (DENV) infection remains a serious health threat despite the availability of supportive care in modern medicine. Monoclonal antibodies (mAbs) of DENV would be powerful research tools for antiviral development, diagnosis and pathological investigations. Here we described generation and characterization of seventeen mAbs with high reactivity for E protein of DENV. Four of these mAbs showed high neutralizing activity against DENV-2 infection in mice. The monoclonal antibody mAb DB32-6 showed the strongest neutralizing activity against diverse DENV-2 and protected DENV-2-infected mice against mortality in therapeutic models. We identified neutralizing epitopes of DENV located at residues K310 and E311 of viral envelope protein domain III (E-DIII) through the combination of biological and molecular strategies. Comparing the strong neutralizing activity of mAbs targeting A-strand with mAbs targeting lateral ridge, we found that epitopes located in A-strand induced stronger neutralizing activity than those located on the lateral ridge. DB32-6 humanized version was successfully developed. Humanized DB32-6 variant retained neutralizing activity and prevented DENV infection. Understanding the epitope-based antibody-mediated neutralization is crucial to controlling dengue infection. Additionally, this study also introduces a novel humanized mAb as a candidate for therapy of dengue patients

Specific Binding of the Pathogenic Prion Isoform: Development and Characterization of a Humanized Single-Chain Variable Antibody Fragment

Murine monoclonal antibody V5B2 which specifically recognizes the pathogenic form of the prion protein represents a potentially valuable tool in diagnostics or therapy of prion diseases. As murine antibodies elicit immune response in human, only modified forms can be used for therapeutic applications. We humanized a single-chain V5B2 antibody using variable domain resurfacing approach guided by computer modelling. Design based on sequence alignments and computer modelling resulted in a humanized version bearing 13 mutations compared to initial murine scFv. The humanized scFv was expressed in a dedicated bacterial system and purified by metal-affinity chromatography. Unaltered binding affinity to the original antigen was demonstrated by ELISA and maintained binding specificity was proved by Western blotting and immunohistochemistry. Since monoclonal antibodies against prion protein can antagonize prion propagation, humanized scFv specific for the pathogenic form of the prion protein might become a potential therapeutic reagent

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

<p>Abstract</p> <p>Background</p> <p>All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.</p> <p>Results</p> <p>The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%.</p> <p>Conclusions</p> <p>This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.</p