In vitro RNA SELEX for the generation of chemically-optimized therapeutic RNA drugs.

Aptamers are single-stranded DNA or RNA oligonucleotides that can bind with exquisitely high affinity and specificity to target molecules and are thus often referred to as 'nucleic acid' antibodies. Oligonucleotide aptamers are derived through a process of directed chemical evolution called SELEX (Systematic Evolution of Ligands by Exponential enrichment). This chemical equivalent of Darwinian evolution was first described in 1990 by Tuerk & Gold and Ellington & Szostak and has since yielded aptamers for a wide-range of applications, including biosensor technologies, in vitro diagnostics, biomarker discovery, and therapeutics. Since the inception of the original SELEX method, numerous modifications to the protocol have been described to fit the choice of target, specific conditions or applications. Technologies such as high-throughput sequencing methods and microfluidics have also been adapted for SELEX. In this chapter, we outline key steps in the SELEX process for enabling the rapid identification of RNA aptamers for in vivo applications. Specifically, we provide a detailed protocol for the selection of chemically-optimized RNA aptamers using the original in vitro SELEX methodology. In addition, methods for performing next-generation sequencing of the RNAs from each round of selection, based on Illumina sequencing technology, are discussed.

Targeted inhibition of prostate cancer metastases with an RNA aptamer to prostate-specific membrane antigen.

Cell-targeted therapies (smart drugs), which selectively control cancer cell progression with limited toxicity to normal cells, have been developed to effectively treat some cancers. However, many cancers such as metastatic prostate cancer (PC) have yet to be treated with current smart drug technology. Here, we describe the thorough preclinical characterization of an RNA aptamer (A9g) that functions as a smart drug for PC by inhibiting the enzymatic activity of prostate-specific membrane antigen (PSMA). Treatment of PC cells with A9g results in reduced cell migration/invasion in culture and metastatic disease in vivo. Importantly, A9g is safe in vivo and is not immunogenic in human cells. Pharmacokinetic and biodistribution studies in mice confirm target specificity and absence of non-specific on/off-target effects. In conclusion, these studies provide new and important insights into the role of PSMA in driving carcinogenesis and demonstrate critical endpoints for the translation of a novel RNA smart drug for advanced stage PC.

Synthesis and radiolabeling of chelator-RNA aptamer bioconjugates with copper-64 for targeted molecular imaging.

Ribonucleic acid (RNA) aptamers with high affinity and specificity for cancer-specific cell-surface antigens are promising reagents for targeted molecular imaging of cancer using positron emission tomography (PET). For this application, aptamers must be conjugated to chelators capable of coordinating PET-radionuclides (e.g., copper-64, (64)Cu) to enable radiolabeling for in vivo imaging of tumors. This study investigates the choice of chelator and radiolabeling parameters such as pH and temperature for the development of (64)Cu-labeled RNA-based targeted agents for PET imaging. The characterization and optimization of labeling conditions are described for four chelator-aptamer complexes. Three commercially available bifunctional macrocyclic chelators (1,4,7,10-tetraazacyclododecane-1,4,7-triacetic acid mono N-hydroxysuccinimide [DOTA-NHS]; S-2-(4-isothiocyanatobenzyl)-1,4,7-triazacyclononane-1,4,7-triacetic acid [p-SCN-Bn-NOTA]; and p-SCN-Bn-3,6,9,15-tetraazabicyclo [9.3.1]pentadeca-1(15),11,13-triene-3,6,9-triacetic acid [p-SCN-Bn-PCTA]), as well as the polyamino-macrocyclic diAmSar (3,6,10,13,16,19-hexaazabicyclo[6.6.6] icosane-1,8-diamine) were conjugated to A10-3.2, a RNA aptamer which has been shown to bind specifically to a prostate cancer-specific cell-surface antigen (PSMA). Although a commercial bifunctional version of diAmSar was not available, RNA conjugation with this chelator was achieved in a two-step reaction by the addition of a disuccinimidyl suberate linker. Radiolabeling parameters (e.g., pH, temperature, and time) for each chelator-RNA conjugate were assessed in order to optimize specific activity and RNA stability. Furthermore, the radiolabeled chelator-coupled RNA aptamers were evaluated for binding specificity to their target antigen. In summary, key parameters were established for optimal radiolabeling of RNA aptamers for eventual PET imaging with (64)Cu.

Structure selection for protein kinase docking and virtual screening: homology models or crystal structures?

There is currently far more sequence information than structural information available, and the ability to use homology models for virtual screening applications is desirable in many cases where structures have not yet been solved. This review focuses on the application of protein kinase homology models for virtual screening use. In addition to reviewing previous cases in which kinase homology models have been used in inhibitor design, we present new data - useful for template selection in homology modeling applications - indicating that the template structure with the highest sequence or structural similarity with the target structure may not always be the best choice. This new work explored the simple hypothesis that better results might be obtained for docking a ligand to a target receptor using a homology model of the target created from a different kinase template co-crystallized with the ligand, than from a crystal structure of the actual kinase target that is unliganded or bound to an unrelated ligand. This hypothesis was tested in docking studies of staurosporine with eight different kinases: AutoDock was used to dock staurosporine to homology models of each kinase created from staurosporine-bound template structures, and the results were compared with docking staurosporine to crystal structures of the target kinase that were obtained in complex with a non-staurosporine ligand or no ligand. It was found that the homology models performed as well as or better than the crystal structures, suggesting that using a homology model created from a template crystallized with a representative ligand may in some cases be a preferred approach, especially in virtual screening experiments that focus on enriching for members of a particular inhibitor class.

Rapid computational identification of the targets of protein kinase inhibitors.

This review focuses primarily on computational methods for predicting the selectivity of small-molecule inhibitors of protein kinases. A detailed discussion is presented of two computational studies that have attempted to make inhibitor selectivity predictions on a kinome-wide scale, and studies describing other methodologies that might potentially be applied to this area are also outlined. As the availability of reliable experimental data measuring inhibitor binding affinities (as opposed to the degree of inhibition of the kinase reaction) is important for the development and validation of most computational methods, recent advances in the large-scale experimental acquisition of selectivity data are also discussed, and a comparison of recently published computational selectivity predictions against a large-scale experimental screen is provided.

Rapid computational identification of the targets of protein kinase inhibitors.

We describe a method for rapidly computing the relative affinities of an inhibitor for all individual members of a family of homologous receptors. The approach, implemented in a new program, SCR, models inhibitor-receptor interactions in full atomic detail with an empirical energy function and includes an explicit account of flexibility in homology-modeled receptors through sampling of libraries of side chain rotamers. SCR's general utility was demonstrated by application to seven different protein kinase inhibitors: for each inhibitor, relative binding affinities with panels of approximately 20 protein kinases were computed and compared with experimental data. For five of the inhibitors (SB203580, purvalanol B, imatinib, H89, and hymenialdisine), SCR provided excellent reproduction of the experimental trends and, importantly, was capable of identifying the targets of inhibitors even when they belonged to different kinase families. The method's performance in a predictive setting was demonstrated by performing separate training and testing applications, and its key assumptions were tested by comparison with a number of alternative approaches employing the ligand-docking program AutoDock (Morris et al. J. Comput. Chem. 1998, 19, 1639-1662). These comparison tests included using AutoDock in nondocking and docking modes and performing energy minimizations of inhibitor-kinase complexes with the molecular mechanics code GROMACS (Berendsen et al. Comput. Phys. Commun. 1995, 91, 43-56). It was found that a surprisingly important aspect of SCR's approach is its assumption that the inhibitor be modeled in the same orientation for each kinase: although this assumption is in some respects unrealistic, calculations that used apparently more realistic approaches produced clearly inferior results. Finally, as a large-scale application of the method, SB203580, purvalanol B, and imatinib were screened against an almost full complement of 493 human protein kinases using SCR in order to identify potential new targets; the predicted targets of SB203580 were compared with those identified in recent proteomics-based experiments. These kinome-wide screens, performed within a day on a small cluster of PCs, indicate that explicit computation of inhibitor-receptor binding affinities has the potential to promote rapid discovery of new therapeutic targets for existing inhibitors.

Progress toward virtual screening for drug side effects.

The development and application of a computational protocol for conducting virtual screens of drug side interactions is described. A conventional drug-docking algorithm (AutoDock) is used to conduct two separate studies. First, a series of docking simulations is performed by using guanosine diphosphate and adenosine diphosphate as prototype drugs with the goal of successfully differentiating known receptors from a large number of bait receptors. Using the electrostatic energy of the purine ring as a basis for discrimination allows the correct identification of receptors in blind studies with 100% specificity and 94% sensitivity. In a second study, similar methodology is used to investigate the binding of clinically relevant inhibitors (Gleevec, purvalanol A, and hymenialdisine) to a variety of protein kinase targets. Overall, excellent agreement between experimental and predicted preferences for kinase targets is obtained. An important conclusion from the latter study is that homology-modeled structures of putative receptors may reasonably be used as targets for docking when directly solved crystal structures are not available. The prospects for routine application of the methodology as a means of identifying potential side interactions of candidate drugs are discussed.

Identification of RNA aptamers that internalize into HPV-16 E6/E7 transformed tonsillar epithelial cells.

Human papillomavirus type 16 (HPV-16) associated oropharyngeal cancers are on a significant increase and better therapeutic strategies are needed. The HPV-16 oncogenes E6 and E7 are expressed in HPV-associated cancers and are able to transform human tonsillar epithelial cells (HTECs). We used cell-Systematic Evolution of Ligands by Exponential Enrichment (SELEX) to select for RNA aptamers that entered into HPV-16 E6/E7-HTECs. After 12 rounds of cell-SELEX, a pool of aptamers was obtained that had significantly greater internalization capacity (~5-fold) into E6/E7-HTECs as compared to primary HTECs or fibroblasts. Analysis of individual aptamers from the pool indicated variable internalization into E6/E7-HTECs (1-8-fold as compared to a negative control). Most of the individual aptamers internalized into E6/E7 and primary HTECs with similar efficiency, while one aptamer exhibited ~3-fold better internalization into E6/E7-HTECs. Aptamers that internalize into cells may be useful for delivering therapeutic agents to HPV-16 associated malignancies.

The dosimetric impact of vaginal balloon-packing on intracavitary high-dose-rate brachytherapy for gynecological cancer.

PURPOSE: We perform a clinical retrospective study to determine whether a vaginal balloon-packing system provides a dosimetric reduction to organs at risk (OARs) versus traditional gauze packing for gynecological high-dose-rate brachytherapy (HDR-BT). We also test various balloon filling materials for optimizing imaging quality. MATERIAL AND METHODS: Filling materials for balloon-packing were evaluated based on imaging quality with X-ray, computerized tomography, and magnetic resonance imaging modalities. We then retrospectively reviewed 45 HDR-BT plans of 18 patients performed with gauze packing and 39 plans of 16 patients performed with balloon-packing. Twelve patients received both gauze and balloon-packing. HDR-BT was delivered with an iridium-192 afterloader and a Fletcher-Suit-Declos-style T&O applicator. At each fraction, 3D imaging was obtained. The D2cc values of OARs were calculated, as well as ICRU-defined point doses. RESULTS: In the 84 HDR fractions reviewed, vaginal balloon-packing provides statistically equivalent doses to rectum, bladder, and sigmoid compared to gauze packing. On average balloon-packing produced average reductions of 3.3% and 6.9% in the rectal and sigmoid D2cc doses and an increase of 3.2% to the bladder D2cc dose (normalized to prescription dose), although none of these values were statistically significant for the twelve patients who received both gauze and balloon-packing (32 and 40 total fractions, respectively). CONCLUSIONS: In the 84 HDR fractions analyzed, vaginal balloon-packing is as effective as gauze packing for dose sparing to the rectum, bladder, and sigmoid. A 1: 1 solution of saline and contrast for filling material enables easy contouring for image-guided HDR with minimal artefacts.

Rational truncation of an RNA aptamer to prostate-specific membrane antigen using computational structural modeling.

RNA aptamers represent an emerging class of pharmaceuticals with great potential for targeted cancer diagnostics and therapy. Several RNA aptamers that bind cancer cell-surface antigens with high affinity and specificity have been described. However, their clinical potential has yet to be realized. A significant obstacle to the clinical adoption of RNA aptamers is the high cost of manufacturing long RNA sequences through chemical synthesis. Therapeutic aptamers are often truncated postselection by using a trial-and-error process, which is time consuming and inefficient. Here, we used a "rational truncation" approach guided by RNA structural prediction and protein/RNA docking algorithms that enabled us to substantially truncateA9, an RNA aptamer to prostate-specific membrane antigen (PSMA),with great potential for targeted therapeutics. This truncated PSMA aptamer (A9L; 41mer) retains binding activity, functionality, and is amenable to large-scale chemical synthesis for future clinical applications. In addition, the modeled RNA tertiary structure and protein/RNA docking predictions revealed key nucleotides within the aptamer critical for binding to PSMA and inhibiting its enzymatic activity. Finally, this work highlights the utility of existing RNA structural prediction and protein docking techniques that may be generally applicable to developing RNA aptamers optimized for therapeutic use.

Nucleotide bias observed with a short SELEX RNA aptamer library.

Systematic evolution of ligands by exponential enrichment (SELEX) is a powerful in vitro selection process used for over 2 decades to identify oligonucleotide sequences (aptamers) with desired properties (usually high affinity for a protein target) from randomized nucleic acid libraries. In the case of RNA aptamers, several highly complex RNA libraries have been described with RNA sequences ranging from 71 to 81 nucleotides (nt) in length. In this study, we used high-throughput sequencing combined with bioinformatics analysis to thoroughly examine the nucleotide composition of the sequence pools derived from several selections that employed an RNA library (Sel2N20) with an abbreviated variable region. The Sel2N20 yields RNAs 51 nt in length, which unlike longer RNAs, are more amenable to large-scale chemical synthesis for therapeutic development. Our analysis revealed a consistent and early bias against inclusion of adenine, resulting in aptamers with lower predicted minimum free energies (ΔG) (higher structural stability). This bias was also observed in control, "nontargeted" selections in which the partition step (against the target) was omitted, suggesting that the bias occurred in 1 or more of the amplification and propagation steps of the SELEX process.