Torbjörn Lundstedt

Experimental design and optimization

Abstract The aim with this tutorial is to give a simple and easily understandable introduction to experimental design and optimization. The screening methods described in the paper are factorial and fractional factorial designs. Identification of significant variables are performed by normal distribution plots as well as by confidence intervals. Refinements of the models are also discussed. For optimization, the simplex method, central composite designs and the Doehlert design are discussed. The paper also gives an introduction to mixture designs. The paper contains 14 hands-on examples and if anyone needs the answers on these it is just to contact the authors.

Chemometrics in metabolomics--a review in human disease diagnosis.

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.

Probiotic modulation of symbiotic gut microbial–host metabolic interactions in a humanized microbiome mouse model

The transgenomic metabolic effects of exposure to either Lactobacillus paracasei or Lactobacillus rhamnosus probiotics have been measured and mapped in humanized extended genome mice (germ‐free mice colonized with human baby flora). Statistical analysis of the compartmental fluctuations in diverse metabolic compartments, including biofluids, tissue and cecal short‐chain fatty acids (SCFAs) in relation to microbial population modulation generated a novel top‐down systems biology view of the host response to probiotic intervention. Probiotic exposure exerted microbiome modification and resulted in altered hepatic lipid metabolism coupled with lowered plasma lipoprotein levels and apparent stimulated glycolysis. Probiotic treatments also altered a diverse range of pathways outcomes, including amino‐acid metabolism, methylamines and SCFAs. The novel application of hierarchical‐principal component analysis allowed visualization of multicompartmental transgenomic metabolic interactions that could also be resolved at the compartment and pathway level. These integrated system investigations demonstrate the potential of metabolic profiling as a top‐down systems biology driver for investigating the mechanistic basis of probiotic action and the therapeutic surveillance of the gut microbial activity related to dietary supplementation of probiotics.

Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences

We have developed an alignment‐independent method for classification of G‐protein coupled receptors (GPCRs) according to the principal chemical properties of their amino acid sequences. The method relies on a multivariate approach where the primary amino acid sequences are translated into vectors based on the principal physicochemical properties of the amino acids and transformation of the data into a uniform matrix by applying a modified autocross‐covariance transform. The application of principal component analysis to a data set of 929 class A GPCRs showed a clear separation of the major classes of GPCRs. The application of partial least squares projection to latent structures created a highly valid model (cross‐validated correlation coefficient, Q2 = 0.895) that gave unambiguous classification of the GPCRs in the training set according to their ligand binding class. The model was further validated by external prediction of 535 novel GPCRs not included in the training set. Of the latter, only 14 sequences, confined in rapidly expanding GPCR classes, were mispredicted. Moreover, 90 orphan GPCRs out of 165 were tentatively identified to GPCR ligand binding class. The alignment‐independent method could be used to assess the importance of the principal chemical properties of every single amino acid in the protein sequences for their contributions in explaining GPCR family membership. It was then revealed that all amino acids in the unaligned sequences contributed to the classifications, albeit to varying extent; the most important amino acids being those that could also be determined to be conserved by using traditional alignment‐based methods.

Diagnostic properties of metabolic perturbations in rheumatoid arthritis

IntroductionThe aim of this study was to assess the feasibility of diagnosing early rheumatoid arthritis (RA) by measuring selected metabolic biomarkers.MethodsWe compared the metabolic profile of patients with RA with that of healthy controls and patients with psoriatic arthritis (PsoA). The metabolites were measured using two different chromatography-mass spectrometry platforms, thereby giving a broad overview of serum metabolites. The metabolic profiles of patient and control groups were compared using multivariate statistical analysis. The findings were validated in a follow-up study of RA patients and healthy volunteers.ResultsRA patients were diagnosed with a sensitivity of 93% and a specificity of 70% in a validation study using detection of 52 metabolites. Patients with RA or PsoA could be distinguished with a sensitivity of 90% and a specificity of 94%. Glyceric acid, D-ribofuranose and hypoxanthine were increased in RA patients, whereas histidine, threonic acid, methionine, cholesterol, asparagine and threonine were all decreased compared with healthy controls.ConclusionsMetabolite profiling (metabolomics) is a potentially useful technique for diagnosing RA. The predictive value was without regard to the presence of antibodies against cyclic citrullinated peptides.

Preprocessing peptide sequences for multivariate sequence-property analysis

Abstract The increasing number of peptide sequences with different lengths, available from synthesised peptide libraries and sequenced proteins are potentially valuable for evaluating structure–activity relationships. However, in order to apply multivariate classification or Quantitative Structure–Activity Relationship (QSAR) analyses on such sequences, it is necessary to have a preprocessing method that translates them into a uniform set of variables. By describing each amino acid by principal properties ( z -scales) and then calculating auto cross covariances (ACCs) for each sequence, a new uniform matrix is generated, i.e., each sequence is described by a vector with equal length. The ACC approach has been used before for classification of peptides, but here, a QSAR analysis based on 20 peptide sequences of different lengths is presented. The results show that it is possible to obtain a predictive multivariate QSAR model ( R 2 Y cum =86.2%, Q cum 2 =60.3%) based on the ACC preprocessing method, together with Orthogonal Signal Correction (OSC) and Partial Least Squares (PLS). The model generated was further validated by permutation tests and found to be valid. The new variables generated by ACCs can also be interpreted, i.e., used to identify important features in the original sequences.

Drug delivery to the spinal cord tagged with nanowire enhances neuroprotective efficacy and functional recovery following trauma to the rat spinal cord

The possibility that drugs attached to innocuous nanowires enhance their delivery within the central nervous system (CNS) and thereby increase their therapeutic efficacy was examined in a rat model of spinal cord injury (SCI). Three compounds—AP173 (SCI‐1), AP713 (SCI‐2), and AP364 (SCI‐5) (Acure Pharma, Uppsala, Sweden)—were tagged with TiO2‐based nanowires using standard procedure. Normal compounds were used for comparison. SCI was produced by making a longitudinal incision into the right dorsal horn of the T10–T11 segments under Equithesin anesthesia. The compounds, either alone or tagged with nanowires, were applied topically within 5 to 10 min after SCI. In these rats, behavioral outcome, blood–spinal cord barrier (BSCB) permeability, edema formation, and cell injury were examined at 5 h after injury. Topical application of normal compounds in high quantity (10 μg in 20 μL) attenuated behavioral dysfunction (3 h after trauma), edema formation, and cell injury, as well as reducing BSCB permeability to Evans blue albumin and 131I. These beneficial effects are most pronounced with AP713 (SCI‐2) treatment. Interestingly, when these compounds were administered in identical conditions after tagging with nanowires, their beneficial effects on functional recovery and spinal cord pathology were further enhanced. However, topical administration of nanowires alone did not influence trauma‐induced spinal cord pathology or motor functions. Taken together, our results, probably for the first time, indicate that drug delivery and therapeutic efficacy are enhanced when the compounds are administered with nanowires.

Tissue sample stability: thawing effect on multi-organ samples

Correct handling of samples is essential in metabolomic studies. Improper handling and prolonged storage of samples has unwanted effects on the metabolite levels. The aim of this study was to identify the effects that thawing has on different organ samples. Organ samples from gut, kidney, liver, muscle and pancreas were analyzed for a number of endogenous metabolites in an untargeted metabolomics approach, using gas chromatography time of flight mass spectrometry at the Swedish Metabolomics Centre, Umeå University, Sweden. Multivariate data analysis was performed by means of principal component analysis and orthogonal projection to latent structures discriminant analysis. The results showed that the metabolic changes caused by thawing were almost identical for all organs. As expected, there was a marked increase in overall metabolite levels after thawing, caused by increased protein and cell degradation. Cholesterol was one of the eight metabolites found to be decreased in the thawed samples in all organ groups. The results also indicated that the muscles are less susceptible to oxidation compared to the rest of the organ samples.

Piecewise multivariate modelling of sequential metabolic profiling data

BackgroundModelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints.ResultsA supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted.ConclusionThe proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

Multivariate Methods in the Development of a New Tablet Formulation

Abstract The overall objective of this article is to use an efficient approach to find a suitable tablet formulation for direct compression. By using traditional approaches to statistical experimental design in tablet formulation, the number of experiments quickly grows when many descriptive variables or many excipients are included. To facilitate the screening process, a multivariate design, which allows a systematical evaluation of a large number of excipients with a limited number of experiments, was implemented. Formulations with acceptable values for disintegration time and crushing strength were obtained with some of the formulations in the present study. The multivariate experimental design strategy yielded PLS models that will be used to identify a region of interest for the optimization. The strategy is general and can be applied in many different areas of pharmaceutical research and development.