A New Spin on Antibody–Drug Conjugates: Trastuzumab-Fulvestrant Colloidal Drug Aggregates Target HER2-Positive Cells

While the formation of colloidal aggregates leads to artifacts in early drug discovery, their composition makes them attractive as nanoparticle formulations for targeted drug delivery as the entire nanoparticle is composed of drug. The typical transient stability of colloidal aggregates has inhibited exploiting this property. To overcome this limitation, we investigated a series of proteins to stabilize colloidal aggregates of the chemotherapeutic, fulvestrant, including the following: bovine serum albumin, a generic human immunoglobulin G, and trastuzumab, a therapeutic human epidermal growth factor receptor 2 antibody. Protein coronas reduced colloid size to <300 nm and improved their stability to over 48 h in both buffered saline and media containing serum protein. Unlike colloids stabilized with other proteins, trastuzumab-fulvestrant colloids were taken up by HER2 overexpressing cells and were cytotoxic. This new targeted formulation reimagines antibody–drug conjugates, delivering mM concentrations of drug to a cell

Improving performance of the network as measured through Guilt-by-Association on GO.

<p>(<b>A</b>) The prediction of GO annotation terms grouped by evidence code and sub-ontology by individual and combined networks. The ChEBI subset consists of terms associated with the Chemical Entities of Biological Interest (ChEBI) ontology (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160098#pone.0160098.s006" target="_blank">S3 Table</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160098#pone.0160098.s003" target="_blank">S3 Fig</a>). Error bars represent the standard error of the mean. Combining the networks improves performance substantially (average ~0.80). (<b>B</b>) The CoExp network performance gains with increasing sample size but with diminishing returns, especially when compared with the gains obtained by combining the orthogonal chemoinformatics network. Extrapolating the aggregation curve (orange line), we predict that we would need millions of more samples to achieve similar performance with CoExp alone as with the combined chemoinformatics and CoExp networks (orange arrow).</p

Colloidal Aggregation Affects the Efficacy of Anticancer Drugs in Cell Culture

Many small molecules, including bioactive molecules and approved drugs, spontaneously form colloidal aggregates in aqueous solution at micromolar concentrations. Though it is widely accepted that aggregation leads to artifacts in screens for ligands of soluble proteins, the effects of colloid formation in cell-based assays have not been studied. Here, seven anticancer drugs and one diagnostic reagent were found to form colloids in both biochemical buffer and in cell culture media. In cell-based assays, the antiproliferative activities of three of the drugs were substantially reduced when in colloidal form as compared to monomeric form; a new formulation method ensured the presence of drug colloids versus drug monomers in solution. We also found that Evans Blue, a dye classically used to measure vascular permeability and to demonstrate the “enhanced permeability and retention (EPR) effect” in solid tumors, forms colloids that adsorb albumin, as opposed to older literature that suggested the reverse

Colloid Formation by Drugs in Simulated Intestinal Fluid

Many organic molecules form colloidal aggregates in aqueous solution at micromolar concentrations. These aggregates promiscuously inhibit soluble proteins and are a major source of false positives in high-throughput screening. Several drugs also form colloidal aggregates, and there has been speculation that this may affect the absorption and distribution of at least one drug in vivo. Here we investigate the ability of drugs to form aggregates in simulated intestinal fluid. Thirty-three Biopharmaceutics Classification System (BCS) class II and class IV drugs, spanning multiple pharmacological activities, were tested for promiscuous aggregation in biochemical buffers. The 22 that behaved as aggregators were then tested for colloid formation in simulated intestinal fluid, a buffer mimicking conditions in the small intestine. Six formed colloids at concentrations equal to or lower than the concentrations reached in the gut, suggesting that aggregation may have an effect on the absorption and distribution of these drugs, and potentially others, in vivo

Top 20 performing GO terms in the different networks.

<p>Top 20 performing GO terms in the different networks.</p

Discordance of bioinformatics and functional genomic similarity with chemoinformatic similarity.

<p>Likelihood that two proteins will be related by ligand similarity (solid line: SEA E-value < 1e-5, dashed line: SEA E-value < 1e-20) given a threshold in the (A) sequence similarity network, (B) co-expression network, and (C) extended protein-protein interaction network. The Y-axis is the likelihood that pairs of targets will have a SEA E-value better than 1e-5 (and, for sequence similarity, also 1e-20) at any given threshold of similarity on the X-axis. (D-F) Truth tables showing the correspondence of the protein-protein pairs that either are or are not related by ligand similarity <i>and</i> by sequence similarity, co-expression, or direct protein-protein interactions. In the upper left and lower right squares, the ligand-based and genomics association agree that the targets are or are not related, while in the lower left and upper right they disagree.</p

Ligand-based networks better recapitulate Gene Ontology than do PPI or co-expression networks.

<p>The (<b>A</b>) co-expression network and the (<b>B</b>) extended protein-protein interaction network are compared with the (<b>C</b>) ligand derived network for their ability to characterize gene function (defined in the Gene Ontology, GO). We assessed performance through cross-validation (area under the ROC curve, AUROC) of a neighbor-voting algorithm. Each curve represents the distribution of AUROCs across 790 GO terms. The dark grey shows the scores in cross-validation in each network, the black curves are the AUROCs after permuting the network nodes, while the light gray curves are the scores using the node degree as a generic predictor across all functional categories. The PPI network has the highest performance (<b>B</b>, dark grey, AUROC = 0.68) but this reflects node degree bias (light grey line, AUROC = 0.6). Co-expression has less bias (<b>A</b>, light grey line, AUROC = 0.52), but performs less well (dark grey line, AUROC = 0.62). The ligand network performs almost as well (<b>C</b>, dark grey line, AUROC = 0.67) as the extended PPI network with little node degree bias (light grey line, AUROC = 0.52). The random permutation of each network (black), have AUROCs between 0.48 and 0.5.</p

Stable Colloidal Drug Aggregates Catch and Release Active Enzymes

Small molecule aggregates are considered nuisance compounds in drug discovery, but their unusual properties as colloids could be exploited to form stable vehicles to preserve protein activity. We investigated the coaggregation of seven molecules chosen because they had been previously intensely studied as colloidal aggregators, coformulating them with bis-azo dyes. The coformulation reduced colloid sizes to <100 nm and improved uniformity of the particle size distribution. The new colloid formulations are more stable than previous aggregator particles. Specifically, coaggregation of Congo Red with sorafenib, tetraiodophenolphthalein (TIPT), or vemurafenib produced particles that are stable in solutions of high ionic strength and high protein concentrations. Like traditional, single compound colloidal aggregates, the stabilized colloids adsorbed and inhibited enzymes like β-lactamase, malate dehydrogenase, and trypsin. Unlike traditional aggregates, the coformulated colloid-protein particles could be centrifuged and resuspended multiple times, and from resuspended particles, active trypsin could be released up to 72 h after adsorption. Unexpectedly, the stable colloidal formulations can sequester, stabilize, and isolate enzymes by spin-down, resuspension, and release

Colloidal Aggregation Causes Inhibition of G Protein-Coupled Receptors

Colloidal aggregation is the dominant mechanism for artifactual inhibition of soluble proteins, and controls against it are now widely deployed. Conversely, investigating this mechanism for membrane-bound receptors has proven difficult. Here we investigate the activity of four well-characterized aggregators against three G protein-coupled receptors (GPCRs) recognizing peptide and protein ligands. Each of the aggregators was active at micromolar concentrations against the three GPCRs in cell-based assays. This activity could be attenuated by either centrifugation of the inhibitor stock solution or by addition of Tween-80 detergent. In the absence of agonist, the aggregators acted as inverse agonists, consistent with a direct receptor interaction. Meanwhile, several literature GPCR ligands that resemble aggregators themselves formed colloids, by both physical and enzymological tests. These observations suggest that some GPCRs may be artifactually antagonized by colloidal aggregates, an effect that merits the attention of investigators in this field

Compiler Optimizations.

<p>Compiler Optimizations.</p