Improving structure-based virtual screening by multivariate analysis of scoring data

Three different multivariate statistical methods, PLS discriminant analysis, rule-based methods, and Bayesian classification, have been applied to multidimensional scoring data from four different target proteins: estrogen receptor alpha (ERalpha), matrix metalloprotease 3 (MMP3), factor Xa (fXa), and acetylcholine esterase (AChE). The purpose was to build classifiers able to discriminate between active and inactive compounds, given a structure-based virtual screen. Seven different scoring functions were used to generate the scoring matrices. The classifiers were compared to classical consensus scoring and single scoring functions. The classifiers show a superior performance, with rule-based methods being most effective. The precision of correctly predicting an active compound is about 90% for three of the targets and about 25% for acetylcholine esterase. On the basis of these results, a new two-stage approach is suggested for structure-based virtual screening where limited activity information is available.     

An experimental design based quantitative structure-activity relationship study on beta-adrenergic blocking agents using PLS.

Quantitative structure-activity relationships for some beta-adrenergic blocking agents of phenoxyaminopropanol type are established with the PLS method. The basic model is based on a carefully selected training set using experimental design techniques.     

Trends in body mass index during early pregnancy in Swedish women 1978-2001

OBJECTIVE: To study the body mass index (BMI) in women seeking maternity health care during early pregnancy in Sweden, and to show trends for a period of more than 20 years. STUDY DESIGN: Register study. METHODS: Data from the maternity health programme on consecutively delivered women in two Swedish hospitals were collected for the years 1978, 1986, 1992, 1997 and 2001. All women were weighed at their first midwife visit between 8 and 10 weeks of gestation and height was also measured. RESULTS: Data on 4883 women were collected. Data on weight were available for 4490 (92%) women and data on BMI were available for 4378 (90%) women. The age-adjusted average weight increased from 59.5 kg in 1978 to 68.2 kg in 2001, and the BMI increased from 21.7 in 1978 to 24.7 in 2001. In 2001, 38.6% of the women had a BMI >25 compared with 11.2 in 1978. In 2001, 11.6% of the women were obese compared with 2.2% in 1978. CONCLUSIONS: During the last two decades, an alarming increase in weight has occurred in Swedish women of childbearing age.     

Microflora changes with norfloxacin and pivmecillinam in women with recurrent urinary tract infection

Similar changes in the periurethral and vaginal microflora were observed in 19 women with recurrent urinary tract infection following treatment with norfloxacin (NOR) or pivmecillinam (PIV). Escherichia coli strains were suppressed by both treatments. Staphylococcus spp. and enterococci colony counts increased following PIV treatment in the periurethral flora but remained stable with NOR.     

Age related changes in brain metabolites observed by 1H MRS in APP/PS1 mice

Translational biomarkers in Alzheimer's disease based on non-invasive in vivo methods are highly warranted. ¹H magnetic resonance spectroscopy (MRS) is non-invasive and applicable in vivo in both humans and experimental animals. In vivo ¹H MRS and 3D MRI were performed on brains of double transgenic (tg) mice expressing a double mutant human β-amyloid precursor protein APP(K670N,M671L) and human mutated presenilin gene PS1M146L, and wild-type (wt) littermates at 2.5, 6.5 and 9 months of age using a 9.4 T magnet. For quantification, LCModel™ was used, and the data were analyzed using multivariate data analysis (MVDA). MVDA evidenced a significant separation, which became more pronounced with age, between tg and wt mice at all time points. While myo-inositol and guanidoacetate were important for group separation in young mice, N-acetylaspartate, glutamate and macrolipids were important for separation of aged tg and wt mice. Volume segmentation revealed that brain and hippocampus were readily smaller in tg as compared to wt mice at the age of 2.5 months. Amyloid plaques were seen in 6.5 and 9 months, but not in 2.5 months old animals.In conclusion, differences in brain metabolites could be accurately depicted in tg and wt mice in vivo by combining MRS with MVDA. First differences in metabolite content were readily seen at 2.5 months, when volume defects in tg mice were present, but no amyloid plaques.     

Prediction and identification of drug interactions with the human ATP-binding cassette transporter multidrug-resistance associated protein 2 (MRP2; ABCC2)

The chemical space of registered oral drugs was explored for inhibitors of the human multidrug-resistance associated protein 2 (MRP2; ABCC2), using a data set of 191 structurally diverse drugs and drug-like compounds. The data set included a new reference set of 75 compounds, for studies of hepatic drug interactions with transport proteins, CYP enzymes, and compounds associated with liver toxicity. The inhibition of MRP2-mediated transport of estradiol-17beta-D-glucuronide was studied in inverted membrane vesicles from Sf9 cells overexpressing human MRP2. A total of 27 previously unknown MRP2 inhibitors were identified, and the results indicate an overlapping but narrower inhibitor space for MRP2 compared with the two other major ABC efflux transporters P-gp (ABCB1) and BCRP (ABCG2). In addition, 13 compounds were shown to stimulate the transport of estradiol-17beta-D-glucuronide. The experimental results were used to develop a computational model able to discriminate inhibitors from noninhibitors according to their molecular structure, resulting in a predictive power of 86% for the training set and 72% for the test set. The inhibitors were in general larger and more lipophilic and presented a higher aromaticity than the noninhibitors. The developed computational model is applicable in an early stage of the drug discovery process and is proposed as a tool for prediction of MRP2-mediated hepatic drug interactions and toxicity.     

Chemical space,diversity and activity landscape analysis of estrogen receptor binders

Understanding the structure–activity relationships (SAR) of endocrine-disrupting chemicals has a major importance in toxicology. Despite the fact that classifiers and predictive models have been developed for estrogens for the past 20 years, to the best of our knowledge, there are no studies of their activity landscape or the identification of activity cliffs. Herein, we report the first SAR of a public dataset of 121 chemicals with reported estrogen receptor binding affinities using activity landscape modeling. To this end, we conducted a systematic quantitative and visual analysis of the chemical space of the 121 chemicals. The global diversity of the dataset was characterized by means of Consensus Diversity Plot, a recently developed method. Adding pairwise activity difference information to the chemical space gave rise to the activity landscape of the data set uncovering a heterogeneous SAR, in particular for some structural classes. At least eight compounds were identified with high propensity to form activity cliffs. The findings of this work further expand the current knowledge of the underlying SAR of estrogenic compounds and can be the starting point to develop novel and potentially improved predictive models.

Global diversity and activity landscape analysis of endocrine-disrupting chemicals identifies activity cliffs that are rationalized at the structure level.