A systematic study of the effect of low pH acid treatment on anti-drug antibodies specific for a domain antibody therapeutic: Impact on drug tolerance, assay sensitivity and post-validation method assessment of ADA in clinical serum samples.

We developed a homogeneous bridging anti-drug antibody (ADA) assay on an electro chemiluminescent immunoassay (ECLIA) platform to support the immunogenicity evaluation of a dimeric domain antibody (dAb) therapeutic in clinical studies. During method development we evaluated the impact of different types of acid at various pH levels on polyclonal and monoclonal ADA controls of differing affinities and on/off rates. The data shows for the first time that acids of different pH can have a differential effect on ADA of various affinities and this in turn impacts assay sensitivity and drug tolerance as defined by these surrogate controls. Acid treatment led to a reduction in signal of intermediate and low affinity ADA, but not high affinity or polyclonal ADA. We also found that acid pretreatment is a requisite for dissociation of drug bound high affinity ADA, but not for low affinity ADA-drug complexes. Although we were unable to identify an acid that would allow a 100% retrieval of ADA signal post-treatment, use of glycine pH3.0 enabled the detection of low, intermediate and high affinity antibodies (Abs) to various extents. Following optimization, the ADA assay method was validated for clinical sample analysis. Consistencies within various parameters of the clinical data such as dose dependent increases in ADA rates and titers were observed, indicating a reliable ADA method. Pre- and post-treatment ADA negative or positive clinical samples without detectable drug were reanalyzed in the absence of acid treatment or presence of added exogenous drug respectively to further assess the effectiveness of the final acid treatment procedure. The overall ADA results indicate that assay conditions developed and validated based on surrogate controls sufficed to provide a reliable clinical data set. The effect of low pH acid treatment on possible pre-existing ADA or soluble multimeric target in normal human serum was also evaluated, and preliminary data indicate that acid type and pH also affect drug-specific signal differentially in individual samples. The results presented here represent the most extensive analyses to date on acid treatment of a wide range of ADA affinities to explore sensitivity and drug tolerance issues. They have led to a refinement of our current best practices for ADA method development and provide a depth of data to interrogate low pH mediated immune complex dissociation.

Aggregation-induced emissive nanoparticles for fluorescence signaling in a low cost paper-based immunoassay.

Low cost paper based immunoassays are receiving interest due to their fast performance and small amounts of biomolecules needed for developing an immunoassay complex. In this work aggregation-induced emissive (AIE) nanoparticles, obtained from a diastereoisomeric mixture of 1,2-di-(4-hydroxyphenyl)-1,2-diphenylethene (TPEDH) in a one-step top-down method, are characterized through Dynamic Light Scattering (DLS), Scanning Electron Microscopy (SEM), and Zeta potential. By measuring the Zeta potential before and after labeling the nanoparticles with antibodies we demonstrate that the colloidal system is stable in a wide pH-range. The AIE-active nanoparticles are deposited on chitosan and glutaraldehyde modified paper pads overcoming the common aggregation-caused quenching (ACQ) effect. Analyte concentrations from 1000ng and below are applied in a model immunocomplex using Goat anti-Rabbit IgG and Rabbit IgG. In the range of 7.81ng-250ng, linear trends with a high R(2) are observed, which leads to a strong increase of the blue fluorescence from the TPEDH nanoparticles.

Homology modeling by the ICM method.

Five models have been built by the ICM method for the Comparative Modeling section of the Meeting on the Critical Assessment of Techniques for Protein Structure Prediction. The targets have homologous proteins with known three-dimensional structure with sequence identity ranging from 25 to 77%. After alignment of the target sequence with the related three-dimensional structure, the modeling procedure consists of two subproblems: side-chain prediction and loop prediction. The ICM method approaches these problems with the following steps: (1) a starting model is created based on the homologous structure with the conserved portion fixed and the nonconserved portion having standard covalent geometry and free torsion angles; (2) the Biased Probability Monte Carlo (BPMC) procedure is applied to search the subspaces of either all the nonconservative side-chain torsion angles or torsion angles in a loop backbone and surrounding side chains. A special algorithm was designed to generate low-energy loop deformations. The BPMC procedure globally optimizes the energy function consisting of ECEPP/3 and solvation energy terms. Comparison of the predictions with the NMR or crystallographic solutions reveals a high proportion of correctly predicted side chains. The loops were not correctly predicted because imprinted distortions of the backbone increased the energy of the near-native conformation and thus made the solution unrecognizable. Interestingly, the energy terms were found to be reliable and the sampling of conformational space sufficient. The implications of this finding for the strategies of future comparative modeling are discussed.