Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 3 de 3
Filter
1.
PLoS One ; 7(6): e38539, 2012.
Article in English | MEDLINE | ID: mdl-22685579

ABSTRACT

BACKGROUND: TNFα inhibitor therapy has greatly improved the treatment of patients with rheumatoid arthritis, however at least 30% do not respond. We aimed to investigate insertions and deletions (INDELS) associated with response to TNFα inhibitors in patients with rheumatoid arthritis (RA). METHODOLOGY AND PRINCIPAL FINDINGS: In the DANBIO Registry we identified 237 TNFα inhibitor naïve patients with RA (81% women; median age 56 years; disease duration 6 years) who initiated treatment with infliximab (n=160), adalimumab (n=56) or etanercept (n=21) between 1999 and 2008 according to national treatment guidelines. Clinical response was assessed at week 26 using EULAR response criteria. Based on literature, we selected 213 INDELS potentially related to RA and treatment response using the GeneVa® (Compugen) in silico database of 350,000 genetic variations in the human genome. Genomic segments were amplified by polymerase chain reaction (PCR), and genotyped by Sanger sequencing or fragment analysis. We tested the association between genotypes and EULAR good response versus no response, and EULAR good response versus moderate/no response using Fisher's exact test. At baseline the median DAS28 was 5.1. At week 26, 68 (29%) patients were EULAR good responders, while 81 (34%) and 88 (37%) patients were moderate and non-responders, respectively. A 19 base pair insertion within the CD6 gene was associated with EULAR good response vs. no response (OR=4.43, 95% CI: 1.99-10.09, p=7.211×10(-5)) and with EULAR good response vs. moderate/no response (OR=4.54, 95% CI: 2.29-8.99, p=3.336×10(-6)). A microsatellite within the syntaxin binding protein 6 (STXBP6) was associated with EULAR good response vs. no response (OR=4.01, 95% CI: 1.92-8.49, p=5.067×10(-5)). CONCLUSION: Genetic variations within CD6 and STXBP6 may influence response to TNFα inhibitors in patients with RA.


Subject(s)
Antigens, CD/genetics , Antigens, Differentiation, T-Lymphocyte/genetics , Antirheumatic Agents/therapeutic use , Arthritis, Rheumatoid/drug therapy , Arthritis, Rheumatoid/genetics , Carrier Proteins/genetics , INDEL Mutation , Tumor Necrosis Factor-alpha/antagonists & inhibitors , Adalimumab , Adult , Aged , Aged, 80 and over , Antibodies, Monoclonal/therapeutic use , Antibodies, Monoclonal, Humanized/therapeutic use , Cohort Studies , DNA Mutational Analysis , Denmark , Etanercept , Female , Genotype , Humans , Immunoglobulin G/therapeutic use , Infliximab , Male , Middle Aged , Polymerase Chain Reaction , Receptors, Tumor Necrosis Factor/therapeutic use , Treatment Outcome , Young Adult
2.
J Chem Inf Model ; 52(3): 678-85, 2012 Mar 26.
Article in English | MEDLINE | ID: mdl-22360790

ABSTRACT

Target-oriented substructure-based virtual screening (sSBVS) of molecules is a promising approach in drug discovery. Yet, there are doubts whether sSBVS is suitable also for extrapolation, that is, for detecting molecules that are very different from those used for training. Herein, we evaluate the predictive power of classic virtual screening methods, namely, similarity searching using Tanimoto coefficient (MTC) and Naive Bayes (NB). As could be expected, these classic methods perform better in interpolation than in extrapolation tasks. Consequently, to enhance the predictive ability for extrapolation tasks, we introduce the Shadow approach, in which inclusion relations between substructures are considered, as opposed to the classic sSBVS methods that assume independence between substructures. Specifically, we discard contributions from substructures included in ("shaded" by) others which are, in turn, included in the molecule of interest. Indeed, the Shadow classifier significantly outperforms both MTC (pValue = 3.1 × 10(-16)) and NB (pValue = 3.5 × 10(-9)) in detecting hits sharing low similarity with the training active molecules.


Subject(s)
Drug Evaluation, Preclinical/methods , User-Computer Interface , Bayes Theorem , ROC Curve
3.
Proc Natl Acad Sci U S A ; 106(33): 13797-801, 2009 Aug 18.
Article in English | MEDLINE | ID: mdl-19666568

ABSTRACT

Blocking conformational changes in biologically active proteins holds therapeutic promise. Inspired by the susceptibility of viral entry to inhibition by synthetic peptides that block the formation of helix-helix interactions in viral envelope proteins, we developed a computational approach for predicting interacting helices. Using this approach, which combines correlated mutations analysis and Fourier transform, we designed peptides that target gp96 and clusterin, 2 secreted chaperones known to shift between inactive and active conformations. In human blood mononuclear cells, the gp96-derived peptide inhibited the production of TNFalpha, IL-1beta, IL-6, and IL-8 induced by endotoxin by >80%. When injected into mice, the peptide reduced circulating levels of endotoxin-induced TNFalpha, IL-6, and IFNgamma by >50%. The clusterin-derived peptide arrested proliferation of several neoplastic cell lines, and significantly enhanced the cytostatic activity of taxol in vitro and in a xenograft model of lung cancer. Also, the predicted mode of action of the active peptides was experimentally verified. Both peptides bound to their parent proteins, and their biological activity was abolished in the presence of the peptides corresponding to the counterpart helices. These data demonstrate a previously uncharacterized method for rational design of protein antagonists.


Subject(s)
Computational Biology/methods , Peptides/chemistry , Animals , Antineoplastic Agents/pharmacology , Clusterin/chemistry , Female , Interleukin-1beta/metabolism , Interleukin-6/metabolism , Interleukin-8/metabolism , Leukocytes, Mononuclear/drug effects , Leukocytes, Mononuclear/metabolism , Membrane Glycoproteins/chemistry , Mice , Mice, Nude , Molecular Chaperones , Neoplasm Transplantation , Protein Conformation , Tumor Necrosis Factor-alpha/metabolism
SELECTION OF CITATIONS
SEARCH DETAIL
...