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ABSTRACT: Assessment of disease activity in patients with rheumatoid arthritis (RA) is of importance in the evaluation of treatment. The most important measure of disease activity is the Disease Activity Score counted in 28 joints (DAS28). In this study, we evaluated whether metabolic profiling could complement current measures of disease activity. Fifty-six patients, in two separate studies, were followed for two years after commencing anti-TNF therapy. DAS28 was assessed, and metabolic profiles were recorded at defined time points. Correlations between metabolic profile and DAS28 scores were analyzed using multivariate statistics. The metabolic responses to lowering DAS28 scores varied in different patients but could predict DAS28 scores at the individual and subgroup level models. The erythrocyte sedimentation rate (ESR) component in DAS28 was most correlated to the metabolite data, pointing to inflammation as the primary effect driving metabolic profile changes. Patients with RA had differing metabolic response to changes in DAS28 following anti-TNF therapy. This suggests that discovery of new metabolic biomarkers for disease activity will derive from studies at the individual and subgroup level. Increased inflammation, measured as ESR, was the main common effect seen in metabolic profiles from periods associated with high DAS28.
Journal of Proteome Research 05/2012; 11(7):3796-804. · 5.11 Impact Factor
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ABSTRACT: We found recently that thyrotropin-relasing hormone (TRH) acts as a selective agonist on the melanocortin MC1 receptor. While the TRH was capable of fully activating the MC1 receptor, it did not interact with any of the other MSH peptide binding G-protein coupled melanocortin receptor subtypes
MC3–5. The MC1 receptor is a promising target for the development of anti-inflammatory and immuno-modulatory drugs, and it was of wide interest
to explore the binding site of the TRH in this receptor. Here we have investigated the binding of TRH to MC1/MC3 chimeric receptors and used a partial least squares (PLS) modelling approach for the data evaluation. Statistically valid
PLS models (R2=0.80; Q2=0.66) were obtained explaining the contribution of the amino acid sequence parts of the receptor chimeras for the binding
of TRH. By using the variable importances in the projection (VIPs) deduced from the PLS-model, it was revealed that the trnsmembrane
(TM) regions TM1 and TM2/TM3 contribute the most to the TRH binding. The TM6/TM7 also had a significant influence on the binding.
Moreover, it was revealed that an interaction between TM1 and TM6/TM7 of the receptor contributed to the binding of TRH. The
data are in full agreement with a 3D model of a TRH peptide and MC1 receptor complex and validates the location of the TRH ligand binding pocket between the TM1, TM2 and TM7 of the receptor.
Letters in Peptide Science 04/2012; 7(4):225-228.
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ABSTRACT: Spinal cord injury (SCI) is a serious clinical situation for which no suitable drug therapy exists. SCI often results in paraplegia or quadriplegia and, apart from the personal trauma leads to huge costs to society for rehabilitation or day-to-day life support. Sensory motor dysfunction following SCI is mainly a consequence of the slowly progressing cord pathology after primary injury that worsens over tine. Thus, almost all sensory and motor nerve control and pathways passing through spinal cord and reflexes are compromised in SCI patients. As a result their peripheral nervous system, autonomic nervous function and central nervous system regulations are adversely affected. Experiments carried out in our laboratory show that various therapeutic agents, if given within 10 to 30 minutes after primary SCI could correct morphological changes to a certain extent. In these rat models of SCI reduction in cord pathology, e.g., bloodspinal cord barrier (BSCB) breakdown, edema formation and cell injury by the neuroprotective agents that also limited sensory motor dysfunction and improved functional behavior. However, these drugs if given beyond 30 minutes after SCI showed a markedly reduced neuroprotective efficacy. Thus, new strategies are needed to enhance neuroprotection in SCI to prevent structural and functional changes over longer periods of time. To that end our laboratory has initiated a series of investigations in which nanowired delivery of various neurotherapeutic agents are applied after different time periods of SCI, that resulted in a much better outcome than with the parent compounds under identical conditions. The superior neuroprotective activity of nanowired compound delivery could be due to a reduced metabolism of active compounds in the central nervous system (CNS) or by sustained release of the drug for longer times. In addition, nanowired drugs may penetrate the CNS faster and could reach widespread areas once entering the spinal cord. Thus, nanowired drug delivery to treat SCI may have potential therapeutic value. These aspects of nanowired drug delivery to enhance neuroprotection in SCI are discussed in this review based on our own investigations.
CNS & neurological disorders drug targets 02/2012; 11(1):86-95. · 3.57 Impact Factor
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ABSTRACT: The aim of this study was to assess the feasibility of diagnosing early rheumatoid arthritis (RA) by measuring selected metabolic biomarkers.
We 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.
RA 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.
Metabolite 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.
Arthritis research & therapy 02/2011; 13(1):R19. · 4.27 Impact Factor
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ABSTRACT: To find and ascertain phenotypic differences, minimal variation between biological replicates is always desired. Variation between the replicates can originate from genetic transformation but also from environmental effects in the greenhouse. Design of experiments (DoE) has been used in field trials for many years and proven its value but is underused within functional genomics including greenhouse experiments. We propose a strategy to estimate the effect of environmental factors with the ultimate goal of minimizing variation between biological replicates, based on DoE. DoE can be analyzed in many ways. We present a graphical solution together with solutions based on classical statistics as well as the newly developed OPLS methodology. In this study, we used DoE to evaluate the influence of plant specific factors (plant size, shoot type, plant quality, and amount of fertilizer) and rotation of plant positions on height and section area of 135 cloned wild type poplar trees grown in the greenhouse. Statistical analysis revealed that plant position was the main contributor to variability among biological replicates and applying a plant rotation scheme could reduce this variation.
Analytica chimica acta 01/2011; 685(2):127-31. · 4.31 Impact Factor
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ABSTRACT: 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.
Analytica chimica acta 02/2010; 659(1-2):23-33. · 4.31 Impact Factor
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ABSTRACT: The possibility that drugs attached to nanowires enhance their therapeutic efficacy was examined in a rat model of spinal cord injury (SCI). Three Acure compounds AP-173, AP-713 and AP-364 were tagged with TiO(2)-based nanowires (50-60 nm) and applied over the traumatized cord either 5 or 60 min after SCI in rats produced by a longitudinal incision into the right dorsal horn of the T10-11 segments under equithesin anaesthesia. Normal compounds were used for comparison. After 5 h SCI, behavioral outcome, blood-spinal cord barrier (BSCB) permeability, edema formation and cell injury were examined. Topical application of nanowired compound AP-713 (10 microg in 20 microL) when applied either 5 or 60 min after injury markedly attenuated behavioral dysfunction at 2-3 h after SCI and reduces BSCB disruption, edema formation and cord pathology at 5 h compared to other compounds. Whereas normal compounds applied at 5 min after injury (but not after 60 min) had some significant but less beneficial effects compared to their nanowired combinations. On the other hand, nanowires alone did not influence spinal cord pathology or motor function after SCI. Taken together, our results indicate that the nanowired-drug-delivery enhances the neuroprotective efficacy of drugs in SCI and reduces functional outcome compared to normal compounds even applied at a later stage following trauma, not reported earlier.
Acta neurochirurgica. Supplement 01/2010; 106:343-50.
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ABSTRACT: Recently nano-drug delivery to the central nervous system (CNS) has been shown to be more effective than the parent compound by itself. An increased availability of the drug for longer periods to the brain or spinal cord and/or a decrease in the drug metabolism altogether could lead to potentiation of the pharmacological activity of the nano-delivered compounds. However, it is still unclear whether the nanocarriers used to deliver the drugs may itself has any potential neurotoxic activity. Although, nanodrug-delivery appears to be a quite promising therapeutic tool for the future clinical therapy, its advantages and limitations for the routine use of patients still needs to be elucidated. Our laboratory is engaged to study a plethora of potential neuroprotective novel compounds delivered to the CNS using nanowiring techniques following brain or spinal cord trauma. Our investigations show that nanowired drugs, if delivered locally following spinal cord injury achieve better neuroprotection than the parent compounds. This effect of nano-drug delivery appears to be very selective in nature. Thus, a clear differentiation based on the compounds used for nano-drug delivery can be seen on various pathological parameters in spinal cord injury. These observations suggest that nanowiring may itself do not induce neuroprotection, but enhance the neuroprotective ability of compounds after trauma. This review describes some recent advances in nano-drug delivery to the CNS in relation to novel neuroprotective strategies with special emphasis on spinal cord trauma based on our own observations and recent findings from our laboratory investigations.
Journal of Nanoscience and Nanotechnology 08/2009; 9(8):5014-37. · 1.56 Impact Factor
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05/2008: pages 1134 - 1166; , ISBN: 9783527618279
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ABSTRACT: Modelling 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.
A 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.
The 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.
BMC Bioinformatics 02/2008; 9:105. · 2.75 Impact Factor
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Francois-Pierre J Martin,
Yulan Wang,
Norbert Sprenger,
Ivan K S Yap, Torbjörn Lundstedt,
Per Lek,
Serge Rezzi,
Ziad Ramadan,
Peter van Bladeren,
Laurent B Fay,
Sunil Kochhar,
John C Lindon,
Elaine Holmes,
Jeremy K Nicholson
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ABSTRACT: 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.
Molecular Systems Biology 02/2008; 4:157. · 8.63 Impact Factor
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ABSTRACT: Mycobacterium tuberculosis ribonucleotide reductase (RNR) is a potential target for new antitubercular drugs. Herein we describe the synthesis and evaluation of peptide inhibitors of RNR derived from the C-terminus of the small subunit of M. tuberculosis RNR. An N-terminal truncation, an alanine scan and a novel statistical molecular design (SMD) approach based on the heptapeptide Ac-Glu-Asp-Asp-Asp-Trp-Asp-Phe-OH were applied in this study. The alanine scan showed that Trp5 and Phe7 were important for inhibitory potency. A quantitative structure relationship (QSAR) model was developed based on the synthesized peptides which showed that a negative charge in positions 2, 3, and 6 is beneficial for inhibitory potency. Finally, in position 5 the model coefficients indicate that there is room for a larger side chain, as compared to Trp5.
Journal of Peptide Science 01/2008; 13(12):822-32. · 1.80 Impact Factor
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Hari Shanker Sharma,
Syed F Ali,
W Dong,
Z Ryan Tian,
R Patnaik,
S Patnaik,
Aruna Sharma,
Arne Boman,
Per Lek,
Elisabeth Seifert, Torbjörn Lundstedt
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ABSTRACT: 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 TiO(2)-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 microg in 20 microL) attenuated behavioral dysfunction (3 h after trauma), edema formation, and cell injury, as well as reducing BSCB permeability to Evans blue albumin and (131)I. 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.
Annals of the New York Academy of Sciences 01/2008; 1122:197-218. · 3.15 Impact Factor
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ABSTRACT: Abstract
Background
Modelling 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.
Results
A 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.
Conclusion
The 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.
BMC Bioinformatics. 01/2008;
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ABSTRACT: A novel design strategy based on the hierarchical design of experiments (HDoE) method named focused hierarchical design of experiments (FHDoE) is presented. FHDoE combine two design layers and use focused substitutions to increase the probability of obtaining active peptides when designing libraries through a selection of compounds biased towards a lead structure. Increasing the number of peptides with measurable activity will increase the information gained and the likelihood of constructing good quantitative structure–activity relationship (QSAR) models. The utility of the novel design method is verified using two different approaches. First, a library designed with the novel FHDoE method was compared with libraries generated from classical positional scanning techniques (e.g., alanine scan) as well as with general and centered minimum analog peptide sets (MAPS) libraries by using an example found in the literature. Secondly, the same design strategies were applied to a dataset of 58 angiotensin converting enzyme (ACE) dipeptide inhibitors. QSAR models were generated from designed sublibraries and the activities of the remaining compounds were predicted. These two examples show that the use of FHDoE renders peptide libraries close in physicochemical space to the native ligand, yielding a more thorough screening of the area of interest as compared to the classical positional scans and fractional factorial design (FFD). It is also shown that an FHDoE library of six dipeptides could produce a QSAR model that better described the requisites of high activity ACE inhibitors than could QSAR models built from either a nine-dipeptide library designed with MAPS or a 58-dipeptide library. Copyright © 2007 John Wiley & Sons, Ltd.
Journal of Chemometrics 08/2007; 21(10‐11):486 - 495. · 1.95 Impact Factor
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ABSTRACT: A combinatorial chemical library is a (usually large) set of compounds made to contain all possible structures of a certain
type. The library is often made in order to find a lead compound for a specific drug action or for the optimisation of a lead.
Because of the large number of synthesised compounds in the library, their biological activity is usually measured by rapid
and simple tests, i.e. “high throughput screening” (HTS), giving crude answers, for instance “active” or “not”. Combinatorial chemistry (CombC)
comprises a chain of parts linked by the objective of finding lead compounds for further development. Sometimes the objective
is to optimise an existing lead compound, but this is not much discussed in this chapter. An analysis of this CombC chain
indicates that the biological testing is the weakest part of the chain. This is due to the difficulty in performing an in-depth
biological testing of any set of compounds exceeding a couple of hundred members. Hence there is a strong motivation to decrease
the size of libraries to a size that allows in-depth biological testing.
We discuss how the size of a library can be drastically reduced without loss of information or decreases in the chances of
finding a lead compound. The approach is based on the use of statistical molecular design (SMD) for the selection of library
compounds to synthesise and test, followed by the use of quantitative structure activity relationships (QSARs) for the evaluation
of the resulting test data.The use of SMD and QSAR is, in turn, critically dependent on an appropriate translation of the
molecular structure to numerical descriptors, the recognition of inhomogeneities (clusters) in both the structural and biological
data spaces, and the ability to analyse and interpret relationships between multidimensional data sets.
We present a strategy for constructing a library with optimal information while still taking synthetic feasibility into account.
The objective is to provide optimal chemical diversity with a moderate number of compounds, plus adequate depth and width
of the biological testing. The strategy is based on a multivariate characterisation of the synthesis starting materials (building
blocks), Principal Component Analysis (PCA), multivariate design, and Multivariate Quantitative Structure-Activity Relationships
(M-QSAR). The strategy applies both to solid phase synthesis as well as libraries in solution.
08/2007: pages 197-220;
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ABSTRACT: SummaryA chemical (combinatorial) library is a, usually large, set of compounds made with the purpose to contain all possible compounds of a certain type. The library is often made in order to find a lead compound for a specific drug action or for the optimization of a lead. Because of the large number of synthesized compounds in the library, their biological activity is usually measured by rapid and simple tests, “high throughput screening” (HTS), giving crude answers, for instance “active” or “not”.Except for very simple libraries, however, it is impossible to synthesize all possible compounds. Hence a selection must be made, either according to synthetic feasibility, or according to some other principles. Moreover, the risk is substantial to miss interesting compounds due to the crude biological testing. Finally, there is a large risk for many false positives due to the large number of tested compounds and their individual evaluation.We here present a strategy for constructing a library with optimal information while still taking synthetic feasibility into account. The objective is to provide high (optimal) chemical diversity with a moderate number of compounds, plus adequate depth and width of the biological testing.The strategy is based on a multivariate characterization of the synthesis starting materials (building blocks, BB.s), principal component analysis, multivariate design, and multivariate QSAR. The strategy applies both to solid phase synthesis as well as libraries in solution.
05/2007: pages 189 - 208; , ISBN: 9783906390406
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ABSTRACT: The aim of this project is to establish models to predict the biomagnification of contaminants present in Baltic Sea biota. In this paper a quantitative model that we term QSBMR—Quantitative Structure Biomagnification Relationships is presented. This model describes the relationship between the biomagnification factors (BMFs) for several organochlorines (OCs) and brominated flame retardants (BFRs), for example, polychlorinated biphenyls (PCBs), polybrominated diphenylethers (PBDEs) and hexabromocyclododecane (HBCD), and their descriptors, for example, physico-chemical properties and structural descriptors.The concentrations of contaminants in herring (Clupea harengus) muscle and guillemot (Uria aalge) egg from the Baltic Sea were used. The BMFs were calculated with the randomly sampled ratios (RSR) method that denotes the BMFs with a measure of the variation. In order to describe the physico-chemical properties and chemical structures, approximately 100 descriptors for the contaminants were generated: (a), by using the software (TSAR); (b) finding log Kow values from the literature, and (c) creating binary fingerprint variables that described the position of the chlorine and bromine for the respective PCB and PBDE molecules. Partial least squares (PLS) regression was used to model the relationship between the contaminants' BMF and the descriptors and the resulting QSBMR revealed that more than 20 descriptors in combination were important for the biomagnification of OCs and BFRs between herring and guillemot.The model including all contaminants (R2X = 0.73, R2Y = 0.87 and Q2 = 0.63, three components) explained approximately as much of the variation as the model with the PCBs alone (R2X = 0.83, R2Y = 0.87 and Q2 = 0.58, two components). The model with the BFRs alone (R2X = 0.68, R2Y = 0.88 and Q2 = 0.41, two components) had a slightly lower Q2 than the model including all contaminants.For validation, a training set of seven contaminants was selected by multivariate design (MVD) and a model was established. This model was then used to predict the BMFs of the test set (seven contaminants not included in the model). The resulting R2 for the regression Observed BMF versus Predicted BMF was high (0.65). The good models showed that descriptors important for the biomagnification of OCs and BFRs had been used. These types of models will be useful for in silico predictions of the biomagnification of new, not yet investigated, compounds as an aid in risk assessments. Copyright © 2007 John Wiley & Sons, Ltd.
Journal of Chemometrics 04/2007; 20(8‐10):392 - 401. · 1.95 Impact Factor
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ABSTRACT: We provide an overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies. Four steps are demonstrated: (1) definition of the aim, (2) selection of objects, (3) sample preparation and characterization, and (4) evaluation of the collected data. This includes the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS (OPLS), and dynamic extensions thereof. This is illustrated by examples from the literature.
Journal of Proteome Research 03/2007; 6(2):469-79. · 5.11 Impact Factor
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Christian Sköld,
Susanne Winiwarter,
Johan Wernevik,
Fredrik Bergström,
Leif Engström,
Ruth Allen,
Karl Box,
John Comer,
Jon Mole,
Anders Hallberg,
Hans Lennernäs, Torbjörn Lundstedt,
Anna-Lena Ungell,
Anders Karlén
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ABSTRACT: A multivariate analysis of drugs on the Swedish market was the basis for the selection of a small, physicochemically diverse set of 24 drug compounds. Factors such as structural diversity, commercial availability, price, and a suitable analytical technique for quantification were considered in the selection. Lipophilicity, pKa, solubility, and permeability across human Caco-2 cell monolayers were measured for the compiled data set. The results show that, by use of a physicochemically diverse data set, experimental responses over a wide range were obtained. The paper also shows how experimental difficulties due to the diversity of the data set can be overcome. We anticipate that this data set can serve as a benchmark set for validation of new experimental techniques or in silico models. It can also be used as a diverse starting data set for the development of new computational models.
Journal of Medicinal Chemistry 12/2006; 49(23):6660-71. · 5.25 Impact Factor
