Immunotoxins are an important class of antibody-based therapeutics. The potency of the immunotoxins depends on the antibody fragments as the guiding modules targeting designated molecules on cell surfaces. Phage-displayed synthetic antibody scFv libraries provide abundant antibody fragment candidates as targeting modules for the immunoconjugates, but the discovery of optimally functional immunoconjugates is limited by the scFv-payload conjugation procedure. In this work, cytotoxicity screening of non-covalently assembled immunotoxins was developed in high throughput format to discover highly functional synthetic antibody fragments for delivering toxin payloads. The principles governing the efficiency of the antibodies as targeting modules have been elucidated from large volume of cytotoxicity data: (a) epitope and paratope of the antibody-based targeting module are major determinants for the potency of the immunotoxins; (b) immunotoxins with bivalent antibody-based targeting modules are generally superior in cytotoxic potency to those with corresponding monovalent targeting module; and (c) the potency of the immunotoxins is positively correlated with the densities of the cell surface antigen. These findings suggest that screening against the target cells with a large pool of antibodies from synthetic antibody libraries without the limitations of natural antibody responses can lead to optimal potency and minimal off-target toxicity of the immunoconjugates.

The objective of this study is to test the validity of sex determination in children and adolescents using lateral radiographic cephalometry and discriminant function analysis. Fifty male and 50 female cephalograms of Taiwanese children were used (males and females with mean age of 15.52 +/- 1.38 and 15.67 +/- 1.54 years, respectively). Twenty-two cephalometric measurements were performed using computerized cephalometry. Statistical analysis shows that all measurements were sexually dimorphic (p < 0.05). Nine measurements, statistically validated and clinically relevant, were used for discriminant function analysis. A stepwise discriminant procedure selected seven of the nine variables, producing 95% accuracy in sex determination. Resubstitution classification reveals the same discriminant rate. Cross-validation classification (the leave-one-out method) reveals that the correct sex determination rate is 91%. However, the combination of four variables using both the stepwise procedure and the resubstitution method achieves a 92% accuracy rate. A cross-validation classification procedure with the same four variables resulted in a 91% accuracy rate. Therefore, this study uses four cephalometric measurements as the minimum number of traits yielding the maximum discriminant effectiveness of sex determination in children and adolescents.

Pandemic and epidemic outbreaks of influenza A virus (IAV) infection pose severe challenges to human society. Passive immunotherapy with recombinant neutralizing antibodies can potentially mitigate the threats of IAV infection. With a high throughput neutralizing antibody discovery platform, we produced artificial anti-hemagglutinin (HA) IAV-neutralizing IgGs from phage-displayed synthetic scFv libraries without necessitating prior memory of antibody-antigen interactions or relying on affinity maturation essential for in vivo immune systems to generate highly specific neutralizing antibodies. At least two thirds of the epitope groups of the artificial anti-HA antibodies resemble those of natural protective anti-HA antibodies, providing alternatives to neutralizing antibodies from natural antibody repertoires. With continuing advancement in designing and constructing synthetic scFv libraries, this technological platform is useful in mitigating not only the threats of IAV pandemics but also those from other newly emerging viral infections.

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Antibodies provide immune protection by recognizing antigens of diverse chemical properties, but elucidating the amino acid sequence-function relationships underlying the specificity and affinity of antibody-antigen interactions remains challenging. We designed and constructed phage-displayed synthetic antibody libraries with enriched protein antigen-recognition propensities calculated with machine learning predictors, which indicated that the designed single-chain variable fragment variants were encoded with enhanced distributions of complementarity-determining region (CDR) hot spot residues with high protein antigen recognition propensities in comparison with those in the human antibody germline sequences. Antibodies derived directly from the synthetic antibody libraries, without affinity maturation cycles comparable to those in in vivo immune systems, bound to the corresponding protein antigen through diverse conformational or linear epitopes with specificity and affinity comparable to those of the affinity-matured antibodies from in vivo immune systems. The results indicated that more densely populated CDR hot spot residues were sustainable by the antibody structural frameworks and could be accompanied by enhanced functionalities in recognizing protein antigens. Our study results suggest that synthetic antibody libraries, which are not limited by the sequences found in antibodies in nature, could be designed with the guidance of the computational machine learning algorithms that are programmed to predict interaction propensities to molecules of diverse chemical properties, leading to antibodies with optimal characteristics pertinent to their medical applications.