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Results for “External validity”

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At least 1,099 records · Page 61Linked to original sources
PubMed2003

Does clinical trial subject selection restrict the ability to generalize use and cost of health services to "real life" subjects?

OBJECTIVES: To explore one aspect of the external validity of the randomized controlled trial (RCT), specifically how being selected for inclusion in a trial and having participated has influenced the use and cost of asthma-related health services. METHODS: Services used by asthmatic users of inhaled corticosteroids (iCSTs) having previously participated in an RCT (TS, n = 46) were compared with individuals who had never participated (NS, n = 51). RESULTS: TS were more likely to use higher (> or = 400 microg) daily doses of iCSTs than NS (OR, 3.3; 95% Cl, 1.1-8.3) but less likely to visit emergency departments (OR, 0.3; 95% Cl, 0.1-0.7). Total asthma-related costs did not differ significantly. CONCLUSIONS: Subject differences may impede generalizing from RCTs to real life.

Adrenal Cortex Hormones↗
PubMed1992

Toward an understanding of cognitive functioning in geriatric depression.

This paper addresses the questions of whether depression compromises cognition in the elderly and whether discernable patterns of cognitive performances could be differentiated between patients with severe depression and those with organic dementia. Published data on geriatric depression and cognitive functioning are divided in demonstrating a depression effect. Further examination and external validation by new data show that the depression effect on discrete cognitive tasks is (1) small, and (2) sensitive to the confounding of sampling and task variables. Future research must take these factors into account. Patterns of cognitive functioning in depression and dementia are different and can be differentiated using a variety of measures. This review finds the term pseudodementia inappropriate and misleading and recommends that it be abandoned.

Aged↗
PubMed2003

Neuropsychological assessment and telemedicine: a preliminary study examining the reliability of neuropsychology services performed via telecommunication.

Within the last decade, various applications of two-way audio-visual telecommunication have been implemented in the Norwegian health care system to facilitate the delivery of medical services to patients located in rural areas away from the hospitals. This technology may also be useful to extend neuropsychological services to underserved areas and thereby reduce the patients' travel time and costs. In the current study, a total of 12 visual, verbal and performance tests were administered face-to-face and via videophones to 32 volunteer participants to examine the consistency and stability of test-scores when delivered via different formats. The obtained reliability coefficients ranged from .37 to .86 with a median value of .74. When testing for mean differences, the measures on verbal learning (WMS-Logical Memory I) and auditory attention (Seashore Rhythm Test) differed significantly due to administration format (t = 2.34, p = .024 and t = 2.37, p = .025, respectively). The findings imply that administration format does not appear to affect the reliability of measurement but neuropsychological test performance is significantly higher for the measures of attention and memory when delivered via videophone. Additional research on these cognitive domains is required, and if the observed differences due to testing format persist, separate normative data via telecommunication will be required before assessments are carried out. Also, since only normal participants were included in this study, the findings may have external validity for normal populations, but similar controlled trials with patients need to be completed before remote assessments can be implemented in regular clinical practice.

Adolescent↗
PubMed2002

Use of a Plackett-Burman design with multivariate calibration for the analysis of polycyclic aromatic hydrocarbons in micellar media by synchronous fluorescence.

This work describes the use of a multilevel Plackett-Burman design (PB) for the creation of a calibration set for partial least square regression (PLS). The PB calibration set was compared to a collinear analogue by testing these two PLS models for the analysis of six polycyclic aromatic hydrocarbons (PAHs). These compounds were analyzed in micellar media by synchronous fluorescence after determination of the experimental conditions (choice of surfactant, analytical conditions such as deltalambda, step, and scan range). The external validation shows that the collinear set is inappropriate to quantify PAH in real samples, but the PB calibration set affords optimal results.

Calibration↗
PubMed2003

Calibration transfer for solving the signal instability in quantitative headspace-mass spectrometry.

It is reported that calibration transfer is able to compensate the variations in sensitivity in direct coupling of a headspace sampler to a mass spectrometer when used for quantification purposes using multivariate calibration techniques. This strategy of signal stability compensation allows the use of models constructed from large calibration standard sets without having to repeat their measurement even though variations occur in sensitivity, which may or may not be constant along the mass range. This technique offers advantages over the use of internal standards in this methodology and only requires the measurement of a small number of transfer samples with each set of unknown samples. The results obtained in the determination of six volatile organic compounds-benzene, toluene, ethylbenzene, and m-xylene (BTEX), methyl tert-butyl ether (MTBE), and mesitylene-are reported. To obtain an appropriate calibration set, a Plackett-Burman design with five levels of concentration for each component was employed. A PLS multivariate calibration model was constructed with a group of 25 samples. For selection of the optimum number of principal components, an external validation set (5 samples) was used and the prediction capacity of this set was checked with an additional group of samples that had not been used either in the construction or in the validation of the model. The results obtained can be considered highly satisfactory, and the methodology was successfully tested with natural matrixes (river and tap water).

Journal Article↗
PubMedDate not supplied

Critical evaluation of models developed for monitoring an industrial submerged bioprocess for antibiotic production using near-infrared spectroscopy.

Near-infrared spectroscopy (NIRS) is known to have potential for cost-effective monitoring of bioprocesses. Although this has been demonstrated in many instances and several models have been reported, information regarding the complexity of models required and their utility over extended periods of time is lacking. In the present study, the complexity of the models required for the NIRS prediction of substrate (oil) and product (tylosin) concentration in an industrial bioprocess that employs a physicochemically heterogeneous medium for antibiotic production was assessed. Measurements made by both the diffuse reflectance and transmittance modes were investigated. SEP values for the prediction of the analytes averaged 5% or less, for the successful models, when evaluated using an external validation set, 2 years after the initial model development exercise. Diffuse reflectance measurements showed poorer results, compared to transmittance measurements, especially for monitoring tylosin. In general, this investigation provides evidence to support the fact that models built for the prediction of analytes in a commercial bioprocess that employs a physicochemically complex production medium can be robust in performance over an extended period of time and that simple models based on fewer terms or latent variables can perform well, even in the context of matrices that are relatively complex. It also indicates that sample presentation is likely to be a critical factor in the successful application of NIRS in bioprocess monitoring, which merits further detailed investigation.

Anti-Bacterial Agents↗
PubMedDate not supplied

Drugs and nondrugs: an effective discrimination with topological methods and artificial neural networks.

A set of topological and structural descriptors has been used to discriminate general pharmacological activity. To that end, we selected a group of molecules with proven pharmacological activity including different therapeutic categories, and another molecule group without any activity. As a method for pharmacological activity discrimination, an artificial neural network was used, dividing molecules into active and inactive, to train the network and externally validate it. The following plot frequency distribution diagrams were used: a function of the number of drugs within a value interval, and the output value of the neural network versus these values. Pharmacological distribution diagrams (PDD) were used as a visualizing technique for the identification of drug and nondrug molecules. The results confirmed the discriminative capacity of the topological descriptors proposed.

Data Display↗
PubMedDate not supplied

Predicting the genotoxicity of secondary and aromatic amines using data subsetting to generate a model ensemble.

Binary quantitative structure-activity relationship (QSAR) models are developed to classify a data set of 334 aromatic and secondary amine compounds as genotoxic or nongenotoxic based on information calculated solely from chemical structure. Genotoxic endpoints for each compound were determined using the SOS Chromotest in both the presence and absence of an S9 rat liver homogenate. Compounds were considered genotoxic if assay results indicated a positive genotoxicity hit for either the S9 inactivated or S9 activated assay. Each compound in the data set was encoded through the calculation of numerical descriptors that describe various aspects of chemical structure (e.g. topological, geometric, electronic, polar surface area). Furthermore, five additional descriptors that focused on the secondary and aromatic nitrogen atoms in each molecule were calculated specifically for this study. Descriptor subsets were examined using a genetic algorithm search engine interfaced with a k-Nearest Neighbor fitness evaluator to find the most information-rich subsets, which ultimately served as the final predictive models. Models were chosen for their ability to minimize the total number of misclassifications, with special attention given to those models that possessed fewer occurrences of positive toxicity hits being misclassified as nontoxic (false negatives). In addition, a subsetting procedure was used to form an ensemble of models using different combinations of compounds in the training and prediction sets. This was done to ensure that consistent results could be obtained regardless of training set composition. The procedure also allowed for each compound to be externally validated three times by different training set data with the resultant predictions being used in a "majority rules" voting scheme to produce a consensus prediction for each member of the data set. The individual models produced an average training set classification rate of 71.6% and an average prediction set classification rate of 67.7%. However, the model ensemble was able to correctly classify the genotoxicity of 72.2% of all prediction set compounds.

Algorithms↗
PubMedDate not supplied

Modeling the octanol-water partition coefficients by an optimized molecular connectivity index.

A procedure that makes it possible to generate a coherent model for prediction of the octanol-water partition coefficient within the molecular connectivity formalism was put forward. The method is based on the optimization of weights for corresponding skeletal atoms and is similar to the method for calculation of a variable connectivity index proposed by Randić. In contrast to Randić's method, we incorporate in the algorithm the possibility that the contribution of a term describing a carbon-heteroatom bond may be negative. When tested on a set of about 300 structurally diverse organic molecules, our procedure proved to be superior to the standard valence connectivity method. External validation on a smaller set of compounds confirmed the superiority of our procedure with respect to the standard one. Intramolecular interactions, which are operative in more complex compounds, are treated in a similar fashion to that in the Hansch-Leo or Rekker methods, by inclusion of empirical correction factors.

Algorithms↗
PubMedDate not supplied

A stepwise approach for defining the applicability domain of SAR and QSAR models.

A stepwise approach for determining the model applicability domain is proposed. Four stages are applied to account for the diversity and complexity of the current SAR/QSAR models, reflecting their mechanistic rationality (including metabolic activation of chemicals) and transparency. General parametric requirements are imposed in the first stage, specifying in the domain only those chemicals that fall in the range of variation of the physicochemical properties of the chemicals in the training set. The second stage defines the structural similarity between chemicals that are correctly predicted by the model. The structural neighborhood of atom-centered fragments is used to determine this similarity. The third stage in defining the domain is based on a mechanistic understanding of the modeled phenomenon. Here, the model domain combines the reliability of specific reactive groups hypothesized to cause the effect and the domain of explanatory variables determining the parametric requirements in order for functional groups to elicit their reactivity. Finally, the reliability of simulated metabolism (metabolites, pathways, and maps) is taken into account in assessing the reliability of predictions, if metabolic activation of chemicals is a part of the (Q)SAR model. Some of the stages of the proposed approach for defining the model domain can be eliminated depending on the availability and quality of the experimental data used to derive the model, the specificity of (Q)SARs, and the goals of their ultimate application. The performance of the proposed definition of the model domain is tested using several examples of (Q)SARs that have been externally validated, including models for predicting acute toxicity, skin sensitization, and biodegradation. The results clearly showed that credibility in predictions of QSAR models for chemicals belonging to their domain is much higher than for chemicals outside this domain.

Animals↗
PubMedDate not supplied

GA strategy for variable selection in QSAR studies: application of GA-based region selection to a 3D-QSAR study of acetylcholinesterase inhibitors.

Comparative molecular field analysis (CoMFA) with partial least squares (PLS) is one of the most frequently used tools in three-dimensional quantitative structure-activity relationships (3D-QSAR) studies. Although many successful CoMFA applications have proved the value of this approach, there are some problems in its proper application. Especially, the inability of PLS to handle the low signal-to-noise ratio (sample-to-variable ratio) has attracted much attention from QSAR researchers as an exciting research target, and several variable selection methods have been proposed. More recently, we have developed a novel variable selection method for CoMFA modeling (GARGS: genetic algorithm-based region selection), and its utility has been demonstrated in the previous paper (Kimura, T., et al. J. Chem. Inf. Comput. Sci. 1998, 38, 276-282). The purpose of this study is to evaluate whether GARGS can pinpoint known molecular interactions in 3D space. We have used a published set of acetylcholinesterase (AChE) inhibitors as a test example. By applying GARGS to a data set of AChE inhibitors, several improved models with high internal prediction and low number of field variables were obtained. External validation was performed to select a final model among them. The coefficient contour maps of the final GARGS model were compared with the properties of the active site in AChE and the consistency between them was evaluated.

Acetylcholinesterase↗
PubMed2003

Acid detergent fiber analysis in oilseed Brassicas by near-infrared spectroscopy.

The potential of near infrared spectroscopy (NIRS) for determining the acid detergent fiber (ADF) in the seed of oilseed Brassica (fam. Brassicaceae) was assessed. One hundred and fifty accessions belonging to the species Indian mustard (Brassica juncea L. Czern.& Coss.), Ethiopian mustard (B. carinata A. Braun) and rapeseed (B. napus L.) were scanned by NIRS as intact and ground seed, and their ADF values were regressed against different spectra transformations by modified partial least squares regression. The coefficients of determination in the external validation (r(2)) for intact and ground seed were 0.83 and 0.85, respectively. The standard deviation to standard error of prediction ratio and range to standard error of prediction ratio were 2.40 and 10.75 for intact seed and 2.62 and 11.76 for ground seed. No significant differences in the prediction were found for both sample presentations. Effects of the C-H and O-H groups of lipids and water, respectively, as well as protein and chlorophyll, were most important in modeling these equations.

Brassica↗
PubMed2004

Modulation of binding strength in several classes of active site inhibitors of acetylcholinesterase studied by comparative binding energy analysis.

The comparative binding energy (COMBINE) methodology has been used to identify the key residues that modulate the inhibitory potencies of three structurally different classes of acetylcholinesterase inhibitors (tacrines, huprines, and dihydroquinazolines) targeting the catalytic active site of this enzyme. The extended set of energy descriptors and the partial least-squares methodology used by COMBINE analysis on a unique training set containing all the compounds yielded an interpretable model that was able to fit and predict the activities of the whole series of inhibitors reasonably well (r2 = 0.91 and q2 = 0.76, 4 principal components). A more robust model (q2 = 0.81 and SDEP = 0.25, 3 principal components) was obtained when the same chemometric analysis was applied to the huprines set alone, but the method was unable to provide predictive models for the other two families when they were treated separately from the rest. This finding appears to indicate that the enrichment in chemical information brought about by the inclusion of different classes of compounds into a single training set can be beneficial when an internally consistent set of pharmacological data can be derived. The COMBINE model was externally validated when it was shown to predict the activity of an additional set of compounds that were not employed in model construction. Remarkably, the differences in inhibitory potency within the whole series were found to be finely tuned by the electrostatic contribution to the desolvation of the binding site and a network of secondary interactions established between the inhibitor and several protein residues that are distinct from those directly involved in the anchoring of the ligand. This information can now be used to advantage in the design of more potent inhibitors.

Algorithms↗
PubMed2006

Skin permeation rate as a function of chemical structure.

Multilinear and nonlinear QSAR models were built for the skin permeation rate (Log K(p)) of a set of 143 diverse compounds. Satisfactory models were obtained by three approaches applied: (i) CODESSA PRO, (ii) Neural Network modeling using large pools of theoretical molecular descriptors, and (iii) ISIDA modeling based on fragment descriptors. The predictive abilities of the models were assessed by internal and external validations. The descriptors involved in the equations are discussed from the physicochemical point of view to illuminate the factors that influence skin permeation.

Computer Simulation↗
PubMed2006

Docking and three-dimensional quantitative structure-activity relationship (3D QSAR) analyses of nonsteroidal progesterone receptor ligands.

We report a docking and comparative molecular similarity indices analysis (CoMSIA) study of progesterone receptor (PR) ligands with an emphasis on nonsteroids including tanaproget. The ligand alignment generation, a critical part of model building, comprised two stages. First, thorough conformational sampling of docking poses within the PR binding pocket was made with the program GOLD. Second, a strategy to select representative poses for CoMSIA was developed utilizing the FlexX scoring function. After manual replacement of five poses where this approach had problems, a significant correlation (r(2) = 0.878) between the experimental affinities and electrostatic, hydrophobic, and hydrogen bond donor properties of the aligned ligands was found. Extensive model validation was made using random-group cross-validations, external test set predictions (r(pred)(2) = 0.833), and consistency check between the CoMSIA model and the PR binding site structure. Robustness, predictive ability, and automated alignment generation make the model a potential tool for virtual screening.

Binding Sites↗
PubMed1998

Antileishmanial chalcones: statistical design, synthesis, and three-dimensional quantitative structure-activity relationship analysis.

A large number of substituted chalcones have been synthesized and tested for antileishmanial and lymphocyte-suppressing activities. A subset of the chalcones was designed by using statistical methods. 3D-QSAR analyses using 67 (antileishmanial activity) and 63 (lymphocyte-suppressing activity) of the compounds for the training sets and 9 compounds as an external validation set were performed by using the GRID/GOLPE methodology. The Smart Region Definition procedure with subsequent region selection as implemented in GOLPE reduced the number of variables to approximately 1300 yielding 3D-QSAR models of high quality (lymphocyte-suppressing model, R2 = 0. 90, Q2 = 0.80; antileishmanial model, R2 = 0.73, Q2 = 0.63). The coefficient plots indicate that steric interactions between the chalcones and the target are of major importance for the potencies of the compounds. A comparison of the coefficient plots for the antileishmanial effect and the lymphocyte-suppressing activity discloses significant differences which should make it possible to design chalcones having a high antileishmanial activity without suppressing the proliferation of lymphocytes.

Animals↗
PubMed1998

Correlation of human jejunal permeability (in vivo) of drugs with experimentally and theoretically derived parameters. A multivariate data analysis approach.

The effective permeability (Peff) in the human jejunum (in vivo) of 22 structurally diverse compounds was correlated with both experimentally determined lipophilicity values and calculated molecular descriptors. The permeability data were previously obtained by using a regional in vivo perfusion system in the proximal jejunum in humans as part of constructing a biopharmaceutical classification system for oral immediate-release products. pKa, log P, and, where relevant, log Pion values were determined using the pH-metric technique. On the basis of these experiments, log D values were calculated at pH 5.5, 6.5, and 7.4. Multivariate data analysis was used to derive models that correlate passive intestinal permeability to physicochemical descriptors. The best model obtained, based on 13 passively transcellularly absorbed compounds, used the variables HBD (number of hydrogen bond donors), PSA (polar surface area), and either log D5.5 or log D6.5 (octanol/water distribution coefficient at pH 5.5 and 6.5, respectively). Statistically good models for prediciting human in vivo Peff values were also obtained by using only HBD and PSA or HBD, PSA, and CLOGP. These models can be used to predict passive intestinal membrane diffusion in humans for compounds that fit within the defined property space. We used one of the models obtained above to predict the log Peff values for an external validation set consisting of 34 compounds. A good correlation with the absorption data of these compounds was found.

Humans↗
PubMed2000

A 3D-QSAR study on the structural requirements for binding to CB(1) and CB(2) cannabinoid receptors.

A 3D-QSAR study was carried out on 20 cannabinoids for which the binding affinities (K(i)) with respect to CB(1) and CB(2) receptors, determined in the same cell line, were available. For the first time three series of significantly different chemical structures such as Delta(9)-THC analogues, anandamides, and indoles were included in a single 3D-QSAR model, to obtain information on the interactions of all ligands with both CB(1) and CB(2) receptors and on their receptor selectivity. Delta(9)-THC was chosen as the structural template for alignment. The 3D-structure-activity correlation obtained by the GOLPE procedure provided a partial least squares (PLS) model with a very good predictive ability for the CB(1) receptor affinity of all compounds. The model allowed us to identify seven different regions in the space that contribute to explain the above binding affinities. External validation of the interpretation of the 3D-QSAR model was derived from a response-independent procedure such as principal components analysis (PCA). The CB(2) receptor model evidenced, besides the seven regions found for the CB(1) receptor, a new characteristic region for the CB(2) receptor. Another PCA, using 10 GRID probes, provided further evidence of receptor selectivity regions. One region opposite to the amidic NH of CB(1) selective O585 appears to be responsible for the CB(1) selectivity, while an interaction region opposite to the carbonyl of CB(2) selective JWH-015 appears to be involved in the CB(2) binding selectivity.

Arachidonic Acids↗