[Initiation of domiciliary non-invasive ventilation: proposals of the Casavni working party]. / Mise en route d'une ventilation non invasive au domicile: propositions du groupe de travail Casavni.

INTRODUCTION: In France, there are no good practice guidelines or a regulatory framework for the initiation of long term non-invasive ventilation (NIV). METHODS: The ANTADIR federation set up a working party to examine the feasibility of new methods of initiation of NIV and to consider the possibility of initiation in the home. Two problems were considered: (1) logistical and technical requirements, (2) the responsibilities of the professions involved and the regulatory framework. RESULTS: Clinical effectiveness, improvement in quality of life and adherence to treatment are similar whether NIV is initiated in hospital or at home. Domiciliary management is possible only when the patient is clinically stable. It should be undertaken by a competent physician or, on medical prescription, by a physiotherapist. A nurse or trained technician may check the apparatus but should not initiate NIV alone. Installation of domiciliary NIV should be considered under the following conditions: patients with spontaneous ventilation, availability of urgent assistance and appropriate family support. Close surveillance of the quality of ventilation is necessary, whether in hospital or at home. For the initiation of domiciliary NIV each professional involved needs a clear understanding of his/her role. CONCLUSION: Academic societies should propose good practice guidelines for the initiation of NIV. Domiciliary initiation is possible under certain conditions and the results are as good as those obtained in hospital.

SuperStar: improved knowledge-based interaction fields for protein binding sites.

SuperStar is an empirical method for identifying interaction sites in proteins, based entirely on experimental information about non-bonded interactions occurring in small-molecule crystal structures, taken from the IsoStar database. We describe recent modifications and additions to SuperStar, validating the results on a test set of 122 X-ray structures of protein-ligand complexes. In this validation, propensity maps are generated for all the binding sites of these proteins, using four different probes: a charged NH(+)(3) nitrogen atom, a carbonyl oxygen atom, a hydroxyl oxygen atom and a methyl carbon atom. Next, the maps are compared with the experimentally observed positions of ligand atoms of these types. A peak-searching algorithm is introduced that highlights potential interaction hot spots. For the three hydrogen-bonding probes - NH(+)(3) nitrogen atom, carbonyl oxygen atom and hydroxyl oxygen atom - the average distance from the ligand atom to the nearest SuperStar peak is 1.0-1.2 A (0.8-1.0 A for solvent-inaccessible ligand atoms). For the methyl carbon atom probe, this distance is about 1.5 A, probably because interactions to methyl groups are much less directional. The most important addition to SuperStar is the enabling of propensity maps around metal centres - Ca(2+), Mg(2+) and Zn(2+) - in protein binding sites. The results are validated on a test set of 24 protein-ligand complexes that have a metal ion in their binding site. Coordination geometries are derived automatically, using only the protein atoms that coordinate to the metal ion. The correct coordination geometry is derived in approximately 75 % of the cases. If the derived geometry is assumed during the SuperStar calculation, the average distance from a ligand atom coordinating to the metal ion to the nearest peak in the propensity map for an oxygen probe is 0.87(7) A. If the correct coordination geometry is imposed, this distance reduces to 0.59(7)A. This indicates that the SuperStar predictions around metal-binding sites are at least as good as those around other protein groups. Using clustering techniques, a non-redundant set of probes is selected from the set of probes available in the IsoStar database. The performance in SuperStar of all these probes is tested on the test set of protein-ligand complexes. With the exception of the "ether oxygen" probe and the "any NH(+)" probe, all new probes perform as well as the four probes introduced first.

Calculating the knowledge-based similarity of functional groups using crystallographic data.

A knowledge-based method for calculating the similarity of functional groups is described and validated. The method is based on experimental information derived from small molecule crystal structures. These data are used in the form of scatterplots that show the likelihood of a non-bonded interaction being formed between functional group A (the 'central group') and functional group B (the 'contact group' or 'probe'). The scatterplots are converted into three-dimensional maps that show the propensity of the probe at different positions around the central group. Here we describe how to calculate the similarity of a pair of central groups based on these maps. The similarity method is validated using bioisosteric functional group pairs identified in the Bioster database and Relibase. The Bioster database is a critical compilation of thousands of bioisosteric molecule pairs, including drugs, enzyme inhibitors and agrochemicals. Relibase is an object-oriented database containing structural data about protein-ligand interactions. The distributions of the similarities of the bioisosteric functional group pairs are compared with similarities for all the possible pairs in IsoStar, and are found to be significantly different. Enrichment factors are also calculated showing the similarity method is statistically significantly better than random in predicting bioisosteric functional group pairs.

Identification of biological activity profiles using substructural analysis and genetic algorithms.

A substructural analysis approach is used to calculate biological activity profiles, which contain weights that describe the differential occurrences of generic features (specifically, the numbers of hydrogen-bond donors and acceptors, the numbers of rotatable bonds and aromatic rings, the molecular weights, and the 2 kappa alpha descriptors) in active molecules taken from the World Drug Index and in (presumed) inactive molecules taken from the SPRESI database. Even with such simple structural descriptors, the profiles discriminate effectively between active and inactive compounds. The effectiveness of the approach is further increased by using a genetic algorithm for the calculation of the weights comprising a profile. The methods have been successfully applied to a number of different data sets.

SPROUT: recent developments in the de novo design of molecules.

SPROUT is a computer program for constrained structure generation. It is designed to generate molecules for a range of applications in molecular recognition. The program uses a number of approximations that enable a wide variety of diverse structures to be generated. Practical use of the program is demonstrated in two examples. The first demonstrates the ability of the program to generate candidate inhibitors for a receptor site of known 3D structure, specifically the GDP binding site of p21. In the second example, structures are generated to fit a pharmacophore hypothesis that models morphine agonists.

SPROUT: a program for structure generation.

SPROUT is a new computer program for constrained structure generation that is designed to generate molecules for a range of applications in molecular recognition. It uses artificial intelligence techniques to moderate the combinatorial explosion that is inherent in structure generation. The program is presented here for the design of enzyme inhibitors. Structure generation is divided into two phases: (i) primary structure generation to produce molecular graphs to fit the steric constraints; and (ii) secondary structure generation which is the process of introducing appropriate functionality to the graphs to produce molecules that satisfy the secondary constraints, e.g., electrostatics and hydrophobicity. Primary structure generation has been tested on two enzyme receptor sites; the p-amidino-phenyl-pyruvate binding site of trypsin and the acetyl pepstatin binding site of HIV-1 protease. The program successfully generates structures that resemble known substrates and, more importantly, the predictive power of the program has been demonstrated by its ability to suggest novel structures.