Biomedical subjects

T Lundstedt

Publications and source records attributed to T Lundstedt.

6 recordsLinked to original sources

PubMed2002

Detection and quantification of ochratoxin A and deoxynivalenol in barley grains by GC-MS and electronic nose.

Mycotoxin contamination of cereal grains can be detected and quantified using complex extraction procedures and analytical techniques. Normally, the grain odour, i.e. the presence of non-grain volatile metabolites, is used for quality classification of grain. We have investigated the possibility of using fungal volatile metabolites as indicators of mycotoxins in grain. Ten barley samples with normal odour, and 30 with some kind of off-odour were selected from Swedish granaries. The samples were evaluated with regard to moisture content, fungal contamination, ergosterol content, and levels of ochratoxin A (OA) and deoxynivalenol (DON). Volatile compounds were also analysed using both an electronic nose and gas chromatography combined with mass spectrometry (GC-MS). Samples with normal odour had no detectable ochratoxin A and average DON contents of 16 microg kg(-1) (range 0-80), while samples with off-odour had average OA contents of 76 microg kg(-1) (range 0-934) and DON contents of 69 microg kg(-1) (range 0-857). Data were evaluated by multivariate data analysis using projection methods such as principal component analysis (PCA) and partial least squares (PLS). The results show that it was possible to classify the OA level as below or above the maximum limit of 5 microg kg(-1) cereal grain established by the Swedish National Food Administration, and that the DON level could be estimated using PLS. Samples with OA levels below 5 microg kg(-1) had higher concentration of aldehydes (nonanal, 2-hexenal) and alcohols (1-penten-3-ol, 1-octanol). Samples with OA levels above 5 microg kg(-1) had higher concentrations of ketones (2-hexanone, 3-octanone). The GC-MS system predicted OA concentrations with a higher accuracy than the electronic nose, since the GC-MS misclassified only 3 of 37 samples and the electronic nose 7 of 37 samples. No correlation was found between odour and OA level, as samples with pronounced or strong off-odours had OA levels both below and above 5 microg kg(-1). We were able to predict DON levels in the naturally contaminated barley samples using the volatile compounds detected and quantified by either GC-MS or the electronic nose. Pentane, methylpyrazine, 3-pentanone, 3-octene-2-ol and isooctylacetate showed a positive correlation with DON, while ethylhexanol, pentadecane, toluene, 1-octanol, 1-nonanol, and 1-heptanol showed a negative correlation with DON. The root mean square error of estimation values for prediction of DON based on GC-MS and electronic nose data were 16 and 25 microg kg(-1), respectively.

Carcinogens↗

PubMed2002

Investigation of flow properties of powders by means of a uniaxial tester, in relation to direct tablet compression.

Unexpected poor flowability during commercial production of a direct compression tablet formulation initiated an investigation of the flow properties of the powder mixture and its components by means of a uniaxial tester. The failure function--a curve describing the strength of the powder bed as a junction of the maximum main stress that has consolidated the bed--of the powder mixture and its components was determined. The drug was more cohesive than the filler, which was somewhat more cohesive than the powder mixture. Three excipients--a hinder, a glidant and a lubricant constituting 3.5 w/w% of the formulation improved the flowability of the mixture of active ingredient and filler. The failure function discriminated powder mixtures with poor flow from mixtures with medium or good flow. However, it was not possible to discriminate medium from good flow by means of the failure function. Attempts to correlate univariately the flow property parameters of the powder mixtures with particle size data or flow property data of included active ingredient and filler batches failed. Therefore a multivariate approach was tested. Principal component analysis (PCA) and projection to latent structures by means of partial least squares (PLS) were employed. An excellent PCA model was obtained with the flow properties of the powder mixture. A good PCA model of tableting performance--based on tablet weight variation and tablet machine speed-was obtained.

Drug Compounding↗

PubMed2001

Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions.

A novel method for the analysis of drug receptor interactions has been developed and used to explore mechanisms involved in the binding of 4-piperidyl oxazole antagonists to alpha1a-, alpha1b- and alpha1d-adrenoceptors. The method exploits affinity data for a series of organic chemical compounds binding to wild-type and artificially mutated receptors. The receptor sequences and compounds are assigned predictor variables that are correlated to the measured pharmacological activities using partial least-squares projections to latent structures. The predictor variables consist of one descriptor block derived from the chemical properties of the receptors' primary amino acid sequences and another descriptor block derived from the chemical properties of the organic compounds. The cross-terms generated from the two descriptor blocks are also derived. Using this approach, very sturdy models were generated describing the interactions of the chemical compounds with the receptors. Models are useful to predict binding affinity and receptor subtype selectivity of compounds prior to their synthesis, and may find use in rational drug design. Moreover, models also give quantitative information about the interactions of the amino acids of the receptors with the ligands, thereby giving an insight into the molecular mechanisms involved in ligand binding.

Adrenergic alpha-Antagonists↗

PubMed2001

PLS modeling of chimeric MS04/MSH-peptide and MC1/MC3-receptor interactions reveals a novel method for the analysis of ligand-receptor interactions.

A novel method has been developed for the analysis of ligand-receptor interactions. The method utilizes binding data generated from the analysis of chimeric proteins with chimeric peptides. To each chimeric part of the peptide and receptor are assigned descriptors, thus creating a matrix of X descriptors. These descriptors are then correlated with the experimentally determined interaction binding affinities for each chimeric receptor/peptide pair by use of partial least-squares projection to latent structures (PLS). The method was applied to analyze the interactions of chimeric MSH-peptides with wild-type MC1 and MC3 receptors, and MC1/MC3 receptor chimeras (in total 40 peptide-receptor combinations). Two types of PLS models could be created, one that revealed the relationships between receptor and peptide structure and peptide binding pK(i) values (i.e., affinity) (R2 and Q2 being 0.71 and 0.62, respectively), and another that revealed the relationships between peptide and receptor structure and peptide-receptor selectivity (R2 and Q2 being 0.64 and 0.57, respectively). After addition of cross-terms these models improved significantly; the R2 and Q2 being 0.93 and 0.75 for affinity, and 0.92 and 0.72 for selectivity, respectively. The analysis shows that the high affinity of the MSH-peptides is primarily achieved by interactions of the peptides' C-terminal amino acids with TM2 and TM3 of the receptor, and, to a lesser extent, by the interaction of the N-terminus with TM1, TM2 and TM3 of the receptor. However, in contrast, the MC1 receptor selectivity is primarily determined by an interaction of the peptides' N-termini with TM2/3 of the receptor. Moreover, the cross-terms of the PLS model revealed the existence of a strong interaction between TM6/7 and TM2/3 of the receptors.

Amino Acid Sequence↗

PubMed2000

Volatiles for mycological quality grading of barley grains: determinations using gas chromatography-mass spectrometry and electronic nose.

The possibility of using an electronic nose or gas chromatography combined with mass spectrometry (GC-MS) to quantify ergosterol and colony forming units (CFU) of naturally contaminated barley samples was investigated. Each sample was split into three parts for (i) ergosterol and CFU analysis, (ii) measurements with the electronic nose and (iii) identification of volatiles collected on an adsorbent with a GC-MS system. Forty samples were selected after sensory analysis to obtain 10 samples with normal odour and 30 with some degree of off-odour. The data set of volatile compounds and the data collected from the electronic nose were evaluated by multivariate analyse techniques. SIMCA classification (soft independent modelling of class analogy) was used for objective evaluation of the usefulness of the data from the GC-MS or electronic nose measurements for classification of grain samples as normal or with off-odour. The main volatile compounds of grain with normal odour were 2-hexenal, benzaldehyde and nonanal, while 3-octanone, methylheptanone and trimethylbenzene were the main volatile compounds of grain with off-odours. Using data from the electronic nose three samples of 40 were misclassified, while data analysis of the volatile compounds detected with the GC-MS, led to six misclassified samples. Regression models (partial least-squares, PLS) were built to predict ergosterol- and CFU-levels with data from the GC-MS or electronic nose measurements. PLS models based on both GC-MS and electronic nose data could be used to predict the ergosterol levels with high accuracy and with low root mean square error of prediction (RMSEP). CFU values from naturally infected grain could not be predicted with the same degree of confidence.

Ergosterol↗

PubMed2000

Multivariate methods in developing an evolutionary strategy for tablet formulation.

The aim of this study was to develop a new strategy for choosing excipients in tablet formulation. Multivariate techniques such as principal component analysis (PCA) and experimental design were combined in a multivariate design for screening experiments. Of a total 87 investigated excipients, the initial screening experiments contained 5 lubricants, 9 binders, and 5 disintegrants, and 35 experiments were carried out. Considering a reduced factorial design was used, the resulting PCA and partial least squares (PLS) models offered good insight into the possibilities of tablet formulation. It also offered solutions to the problems and clearly gave directions for optimum formulations. Further, it offered several alternatives for achieving quality formulations. Additional experiments conducted to validate and verify the usefulness of the model were successful, resulting in several tablets of good quality. The conclusion is that a multivariate strategy in tablet of formulation is efficient and can be used to reduce the number of experiments drastically. Combining multivariate characterization, physicochemical properties, experimental design, multivariate design, and PLS would lead to an evolutionary strategy for tablet formulation. Since it includes a learning strategy that continuously incorporates data for new compounds and from conducted experiments, this would be an even more powerful tool than expert systems.

Chemistry, Pharmaceutical↗