Aromatic and heteroaromatic amines (ArNH(2)) represent a class of potential mutagens that after being metabolically activated covalently modify DNA. Activation of ArNH(2) in many cases starts with N-hydroxylation by P450 enzymes, primarily CYP1A2. Poor understanding of structure-mutagenicity relationships of ArNH(2) limits their use in drug discovery programs. Key factors that facilitate activation of ArNH(2) are revealed by exploring their reaction intermediates in CYP1A2 using DFT calculations. On the basis of these calculations and extensive analysis of structure-mutagenicity data, we suggest that mutagenic metabolites are generated by ferric peroxo intermediate, (CYP1A2)Fe(III)-OO(-), in a three-step heterolytic mechanism. First, the distal oxygen of the oxidant abstracts proton from H-bonded ArNH(2). The subsequent proximal protonation of the resulting (CYP1A2)Fe(III)-OOH weakens both the O-O and the O-H bonds of the oxidant. Heterolytic cleavage of the O-O bond leads to N-hydroxylation of ArNH(-) via S(N)2 mechanism, whereas cleavage of the O-H bond results in release of hydroperoxy radical. Thus, our proposed reaction offers a mechanistic explanation for previous observations that metabolism of aromatic amines could cause oxidative stress. The primary drivers for mutagenic potency of ArNH(2) are (i) binding affinity of ArNH(2) in the productive binding mode within the CYP1A2 substrate cavity, (ii) resonance stabilization of the anionic forms of ArNH(2), and (iii) exothermicity of proton-assisted heterolytic cleavage of N-O bonds of hydroxylamines and their bioconjugates. This leads to a strategy for designing mutagenicity free ArNH(2): Structural alterations in ArNH(2), which disrupt geometric compatibility with CYP1A2, hinder proton abstraction, or strongly destabilize the nitrenium ion, in this order of priority, prevent genotoxicity.

A class of inhibitors of mitogen activated protein kinase-activated kinase 2 (MK2) was discovered via high-throughput screening. This compound class demonstrates activity against the enzyme with sub-microM IC(50) values, and suppresses LPS-induced TNFalpha levels in THP-1 cells. MK2 inhibition kinetic measurements indicated mixed binding approaching non-ATP competitive inhibition.

The metabolic stability and selectivity of a series of CCR8 antagonists against binding to the hERG ion channel and cytochrome Cyp2D6 are studied by principal component analysis. It is demonstrated that an efficient way of increasing metabolic stability and selectivity of this series is to decrease compound lipophilicity by engineering nondesolvation related attractive interactions with CCR8, as rationalized by three-dimensional receptor models. Although such polar interactions led to increased compound selectivity, such a strategy could also jeopardize the DMPK profile of compounds. However, once increased potency is found, the lipophilicity can be readjusted by engineering hydrophobic substituents that fit to CCR8 but do not fit to hERG. Several such lipophilic fragments are identified by two-dimensional fragment-based QSAR analysis. Electrophysiological measurements and site-directed mutagenesis studies indicated that the repulsive interactions of these fragments with hERG are caused by steric hindrances with residue F656.

Multivariate analysis such as principal-components analysis (PCA) and partial-least-squares-discriminant analysis (PLS-DA) have been applied to peptidomics data from clinical urine samples subjected to LC/MS analysis. We show that it is possible to use these methods to get information from a complex set of clinical data. The aim of the work is to use this information as a first step in the further search for clinical biomarker data. It is possible to identify peptide-biomarker fingerprints related to disease diagnosis and progression. Further, we review clinical proteomics and pharmacogenomics data analyzed with the same multivariate approach.

The aim of this investigation was to analyze the structure–absorption relationships for pulmonary delivered drugs. First, the inhaled drugs on the market during 2001 were identified and a profile of the calculated physicochemical properties was made. Second, an in vivo pharmacokinetic investigation was performed in anesthetized rats. Eight selected drugs were administered by intratracheal nebulization and intravenous bolus administration and the plasma concentrations of the drugs were determined by LC‐MS‐MS. Third, an evaluation of the relationships between the absorption/bioavailability data and the drugs' physicochemical characteristics and the epithelial permeability in Caco‐2 cells, respectively, was performed. The drug absorption rate was found to correlate to the molecular polar surface area and the hydrogen bonding potential, as well as to the apparent permeability in Caco‐2 cell monolayers, which indicated that passive diffusion was the predominating mechanism of absorption in the rat lung. In contrast to the intestinal mucosa and the blood–brain barrier, the pulmonary epithelium was shown to be highly permeable to compounds with high molecular polar surface area (e.g., PSA 479 Å2). Furthermore, a high bioavailability was found for the efflux transporter substrates talinolol (81%) and losartan (92%), which provides functional evidence for a quantitatively less important role for efflux transporters, such as P‐glycoprotein, in limiting the absorption of these drugs from the rat lung. In conclusion, the pulmonary route should be regarded as a potential alternative for the delivery of drugs that are inadequately absorbed after oral administration. © 2003 Wiley‐Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 92:1216–1233, 2003

The pulmonary absorption of nine low-molecular-weight (225-430 Da) drugs (atenolol, budesonide, enalaprilat, enalapril, formoterol, losartan, metoprolol, propranolol and terbutaline) and one high-molecular-weight membrane permeability marker compound (FITC-dextran 10000 Da) was investigated using the isolated, perfused and ventilated rat lung (IPL). The relationships between pulmonary transport characteristics, epithelial permeability of Caco-2 cell monolayers and drug physicochemical properties were evaluated using multivariate data analysis. Finally, an in vitro-in vivo correlation was made using in vivo rat lung absorption data. The absorption half-life of the investigated drugs ranged from 2 to 59 min, and the extent of absorption from 21 to 94% in 2 h in the isolated perfused rat lung model. The apparent first-order absorption rate constant in IPL (ka(lung)) was found to correlate to the apparent permeability (P(app)) of Caco-2 cell monolayers (r = 0.87), cLog D(7.4) (r = 0.70), cLog P, and to the molecular polar surface area (%PSA) (r = -0.79) of the drugs. A Partial Least Squares (PLS)-model for prediction of the absorption rate (log ka(lung)) from the descriptors log P(app), %PSA and cLogD(7.4) was found (Q2 = 0.74, R2 = 0.78). Furthermore, a strong in vitro-in vivo correlation (r = 0.98) was found for the in vitro (IPL) drug absorption half-life and the pulmonary absorption half-life obtained in rats in vivo, based on a sub-set of five compounds.

A library of thrombin inhibitors has been designed using statistical molecular design. An aromatic scaffold was used, with three varied positions corresponding to three pockets at the active site of thrombin (the S-, P-, and D-pockets). The selection was performed in the building block space, and previously acquired data were included in the design procedure. The design resulted in six, four, and six building blocks for the first (S), second (P), and third (D) pockets, respectively. A second round of selection applied to the combined selected building blocks resulted in a subset of 18 compounds. The selected library was synthesized in parallel and biologically evaluated. The compounds were analyzed with respect to their inhibition (pIC(50)) of thrombin; membrane permeability, estimated by migration behavior in micellar media (CE log k') and pK(a); and specificity with respect to inhibition (K(i)) of trypsin. Multivariate QSAR studies of the responses yielded valuable results and information that could only be found using statistical molecular design in combination with multivariate analysis.

The last decade has witnessed much progress in how to characterize and describe chemical structure, how to synthesize large sets of compounds, how to make simple and fast in-vitro assays, and how to determine the structure (sequence) of our genetic material. The possible consequences of this progress for drug design are great and exciting, but also bewilderingly complicated.Fortunately, the last decade has also seen progress in how to investigate and model complicated systems, of which relationships between chemical structure and biological activity provide typical examples. These relationships are central in drug design and some related areas, notably combinatorial chemistry and bioinformatics.The essential steps in the investigation of complicated systems include the following:1. The appropriate quantitative parameterization of its parts (here the varying parts of the chemical structures I biopolymer sequences).2. The appropriate measurements of the interesting properties of the system (here the "biological effects").3. Selecting a representative set of molecules (or other systems) to investigate and make the following measurements.4. The analysis of the resulting data.5. The interpretation of the results.The use of multivariate characterization, design, and modelling in these steps will be discussed in relation to drug design, combinatorial chemistry (which compounds to make and test, and how to deal with the biological test results), and bioinformatics (how to parameterize and analyze biopolymer sequences).

A strategy for cluster analysis of chemical compounds for combinatorial chemistry is presented in this paper and applied to a set of 627 alcohols. The alcohols are characterised by 50 semi-empirical descriptors and the resulting 627 x 50 table was compressed by PCA. The method used to investigate the groupings was fuzzy clustering, using the fuzzy c-means algorithm. This technique allows a compound to belong to more than one group. Different values of the fuzziness coefficients were used and two different distances were incorporated in the algorithm, the traditional Euclidean distance and the Mahalanobis distance. The latter takes correlations within a group into account and can hence deal with elongated clusters. The resulted membership matrices were validated by PLS regression. The models created were used to verify statistical and chemical relevance of the formed clusters. The results showed that the Mahlanobis distance and a fuzziness coefficient of 1.2 should be used for an optimal clustering. The coefficients from the PLS models were further used for chemical interpretation of the groups. The four groups were chemically interpretable and consistent. The first group contained large flexible molecules, the second contained more polar compounds, the third contained molecules with two or more aromatic rings fused together, and the forth contained small and relatively reactive molecules. Molecules that did not fit into any of the groups, i.e., singletons, were flagged as outliers in the PLS models. (C) 1998 Elsevier Science B.V. All rights reserved.

Statistical experimental design provides an efficient approach for selecting the building blocks to span the structural space and increase the information content in a combinatorial library. A set of renin-inhibitors, hexapeptoids, is used to illustrate the approach. Multivariate quantitative structure-activity relationships (MQSARs) were developed relating renin inhibition to the peptoid sequences variation, parametrized by the z-scales. By using the information from the models, the number of building block sets could be reduced from six to three. Using a statistical molecular design (SMD) reduces the number of compounds from more than 100 000 down to 90. A second SMD was used for comparison, based on less prior knowledge. This gave a reduction from over 2 billion to 120 compounds.

Multivariate data analysis (MVA) has been used as an aid in the analysis and interpretation of 13C NMR spectra in the solid state. The goal of this study was to investigate the effect of some important instrumental parameters and calculation strategies on the outcome of the multivariate data analysis. The samples used were two peat forming plants, Sphagnumfuscum and Carex rostrata, incubated in four different redox environments. It was found that normalising each NMR spectrum to a constant area should be avoided. Using non-normalised data we get a slightly better class separation and the peaks in the ‘subspectra’ are sharpened. Depending on the relative size of interesting variation one should be careful when choosing the number of variables, i.e. number of data points characterising each spectrum. The line broadening technique should be used with great care in order not to obscure the information. We also suggest the use of the free induction decay (FID)/MVA directly for classification purposes. This is a new approach to analyse the output data from NMR measurements.

Several different parameters, including elemental and functional group analyses, solid state 13C NMR spectroscopy and E4/E6 ratios, were measured on the dissolved organic matter isolated and fractionated with ultrafiltration and XAD-8 techniques from two highly colored lakes in Finland. The results of the analyses are discussed in relation to the sample isolation (DOM, FA or HA). Multivariate analyses showed some trends in the average molecular masses, in the results of elemental analyses and, especially, in the characteristics of the 13C NMR spectra so far obtained.

13CCP/MAS NMR spectra were measured in order to identify and quantify various forms of carbon in decomposing litter. The Klason-lignin content was also determined by conventional techniques. Quantitatively, the NMR results showed a decrease in carbohydrates as litter decomposition proceeded. An initial increase in polymethylene resonances levelled off in highly decomposed samples. Further, it is shown that by using a multivariate data analysis method, in this case partial least squares, NMR data can be used to determine sample-specific properties, such as lignin content.

13C CP/MAS NMR spectra of birch pulp samples were measured at regular intervals during the kraft pulping process. General multivariate data analysis methods based on principal components were used to extract the spectral information content and to relate these components to chemical descriptors such as lignin content. It was shown that this approach has an improved predictive ability relative to traditional methods. Model parameters could also be used to construct subspectra with independent information.