Polymeric Microarray Patches for Enhanced Transdermal Delivery of the Poorly Soluble Drug Olanzapine.

Transdermal drug delivery is an alternative route of administration that offers avoidance of the associated drawbacks of orally and parenterally administered hydrophobics. However, owing to the extremely specific set of physicochemical characteristics required for passive transdermal drug permeation, the development of marketed transdermal products containing poorly soluble drugs has been severely limited. Microarray patches (MAPs) are a type of transdermal patch that differ from the traditional patch design due to the presence of tiny, micron-sized needles that permit enhanced drug permeation on their application surface. To date, MAPs have predominantly been used to deliver hydrophilic compounds. However, this work challenges this trend and focuses on the use of MAPs, in combination with commonly utilized solubility-enhancing techniques, to deliver the hydrophobic drug olanzapine (OLP) across the skin. Specifically, cyclodextrin (CD) complexation and particle size reduction were employed in tandem with hydrogel-forming and dissolving MAPs, respectively. In vivo experimentation using a female Sprague-Dawley rat model confirmed the successful delivery of OLP from hydrogel-forming MAPs (Cmax = 611.13 ± 153.34 ng/mL, Tmax = 2 h) and dissolving MAPs (Cmax = 690.56 ± 161.33 ng/mL, Tmax = 2 h) in a manner similar to that of oral therapy in terms of the rate and extent of drug absorption, as well as overall drug exposure and bioavailability. This work is the first reported use of polymeric MAPs in combination with the solubility-enhancing techniques of CD complexation and particle size reduction to successfully deliver the poorly soluble drug OLP via the transdermal route. Accordingly, this paper provides significant evidence to support an expansion of the library of molecules amenable to MAP-mediated drug delivery to include those that exhibit poor aqueous solubility.

The PhyloFacts FAT-CAT web server: ortholog identification and function prediction using fast approximate tree classification.

The PhyloFacts 'Fast Approximate Tree Classification' (FAT-CAT) web server provides a novel approach to ortholog identification using subtree hidden Markov model-based placement of protein sequences to phylogenomic orthology groups in the PhyloFacts database. Results on a data set of microbial, plant and animal proteins demonstrate FAT-CAT's high precision at separating orthologs and paralogs and robustness to promiscuous domains. We also present results documenting the precision of ortholog identification based on subtree hidden Markov model scoring. The FAT-CAT phylogenetic placement is used to derive a functional annotation for the query, including confidence scores and drill-down capabilities. PhyloFacts' broad taxonomic and functional coverage, with >7.3 M proteins from across the Tree of Life, enables FAT-CAT to predict orthologs and assign function for most sequence inputs. Four pipeline parameter presets are provided to handle different sequence types, including partial sequences and proteins containing promiscuous domains; users can also modify individual parameters. PhyloFacts trees matching the query can be viewed interactively online using the PhyloScope Javascript tree viewer and are hyperlinked to various external databases. The FAT-CAT web server is available at http://phylogenomics.berkeley.edu/phylofacts/fatcat/.

Making automated multiple alignments of very large numbers of protein sequences.

MOTIVATION: Recent developments in sequence alignment software have made possible multiple sequence alignments (MSAs) of >100 000 sequences in reasonable times. At present, there are no systematic analyses concerning the scalability of the alignment quality as the number of aligned sequences is increased. RESULTS: We benchmarked a wide range of widely used MSA packages using a selection of protein families with some known structures and found that the accuracy of such alignments decreases markedly as the number of sequences grows. This is more or less true of all packages and protein families. The phenomenon is mostly due to the accumulation of alignment errors, rather than problems in guide-tree construction. This is partly alleviated by using iterative refinement or selectively adding sequences. The average accuracy of progressive methods by comparison with structure-based benchmarks can be improved by incorporating information derived from high-quality structural alignments of sequences with solved structures. This suggests that the availability of high quality curated alignments will have to complement algorithmic and/or software developments in the long-term. AVAILABILITY AND IMPLEMENTATION: Benchmark data used in this study are available at http://www.clustal.org/omega/homfam-20110613-25.tar.gz and http://www.clustal.org/omega/bali3fam-26.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega.

Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.

Ensemble approach combining multiple methods improves human transcription start site prediction.

BACKGROUND: The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets. RESULTS: We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier ('Profisi Ensemble') using predictions from 7 programs, along with 2 other data sources. Support vector machines using 'full' and 'reduced' data sets are combined in an either/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool. CONCLUSIONS: Supervised learning methods are a useful way to combine predictions from diverse sources.

High DNA melting temperature predicts transcription start site location in human and mouse.

The accurate computational prediction of transcription start sites (TSS) in vertebrate genomes is a difficult problem. The physicochemical properties of DNA can be computed in various ways and a many combinations of DNA features have been tested in the past for use as predictors of transcription. We looked in detail at melting temperature, which measures the temperature, at which two strands of DNA separate, considering the cooperative nature of this process. We find that peaks in melting temperature correspond closely to experimentally determined transcription start sites in human and mouse chromosomes. Using melting temperature alone, and with simple thresholding, we can predict TSS with accuracy that is competitive with the most accurate state-of-the-art TSS prediction methods. Accuracy is measured using both experimentally and manually determined TSS. The method works especially well with CpG island containing promoters, but also works when CpG islands are absent. This result is clear evidence of the important role of the physical properties of DNA in the process of transcription. It also points to the importance for TSS prediction methods to include melting temperature as prior information.