Sequence and chemistry requirements for a novel aptameric oligonucleotide inhibitor of EGF receptor tyrosine kinase activity

We have previously identified a phosphorothioate oligonucleotide (PS-ODN) that inhibited epidermal growth factor receptor tyrosine kinase (TK) activity both in cell fractions and in intact A431 cells. Since ODN-based TK inhibitors may have anti-cancer applications and may also help understand the non-antisense mediated effects of PS-ODNs, we have further studied the sequence and chemistry requirements of the parent PS-ODN (sequence: 5′-GGA GGG TCG CAT CGC-3′) as a sequence-dependent TK inhibitor. Sequence deletion and substitution studies revealed that the 5′-terminal GGA GGG hexamer sequence in the parent compound was essential for anti-TK activity in A431 cells. Site-specific substitution of any G with a T in this 5′-terminal motif within the parent compound caused a significant loss in anti-TK activity. The fully PS-modified hexameric motif alone exhibited equipotent activity as the parent 15-mer whereas phosphodiester (PO) or 2′-O-methyl-modified versions of this motif had significantly reduced anti-TK activity. Further, T substitutions within the two 5′-terminal G residues of the hexameric PS-ODN to produce a sequence, TTA GGG, representing the telomeric repeats in human chromosomes, also did not exhibit a significant anti-TK activity. Multiple repeats of the active hexameric motif in PS-ODNs resulted in more potent inhibitors of TK activity than the parent ODN. These results suggested that PS-ODNs, but not PO or 2′-O-methyl modified ODNs, containing the GGA GGG motif can exert potent anti-TK activity which may be desirable in some anti-tumor applications. Additionally, the presence of this previously unidentified motif in antisense PS-ODN constructs may contribute to their biological effects in vitro and in vivo and should be accounted for in the design of the PS-modified antisense ODNs. © 2002 Published by Elsevier Science Inc.

Adverse drug reaction classification with deep neural networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.