Ventilatory responses of healthy subjects to intravenous combinations of morphine and oxycodone under imposed hypercapnic and hypoxaemic conditions

Aims Previous isobolographic analysis revealed that coadministration of morphine and oxycodone produces synergistic antinociception in laboratory rodents. As both opioids can produce ventilatory depression, this study was designed to determine whether their ventilatory effects were synergistic when coadministered to healthy human subjects. Methods A placebo-controlled, randomized, crossover study was performed in 12 male volunteers. Ventilatory responses to hypoxaemia and hypercapnia were determined from 1-h intravenous infusions of saline ('placebo'), 15 mg morphine sulphate (M), 15 mg oxycodone hydrochloride (O), and their combination in the dose ratios of 1 : 2, 1 : 1, 2 : 1. Drug and metabolite concentrations in serial peripheral venous blood samples were measured by high-performance liquid chromatography-MS/MS. Results 'Placebo' treatment was without significant ventilatory effects. There were no systematic differences between active drug treatments on either the slopes or intercepts of the hypoxaemic and hypercapnia ventilation responses. During drug treatment, the mean minute ventilation at PETCO2 = 55 mmHg (V-E55) decreased to 74% of the subjects' before treatment values (95% confidence interval 62, 87), 68% (57, 80), 69% (59, 79), 68% (63, 73), and 61% (52, 69) for M15, M10/O5, M7.5/O7.5, M5/O10 and O15, respectively. Recovery was more prolonged with increasing oxycodone doses, corresponding to its greater potency and lower clearance compared with morphine. Conclusions Although adverse ventilatory effects of these drugs were found as expected, no unexpected or disproportionate effects of any of the morphine and oxycodone treatments were found that might impede their use in combination for pain management.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.