Biomedical subjects

Michael J Sorich

Publications and source records attributed to Michael J Sorich.

9 recordsLinked to original sources

PubMed2004

Rapid prediction of chemical metabolism by human UDP-glucuronosyltransferase isoforms using quantum chemical descriptors derived with the electronegativity equalization method.

This study aimed to evaluate in silico models based on quantum chemical (QC) descriptors derived using the electronegativity equalization method (EEM) and to assess the use of QC properties to predict chemical metabolism by human UDP-glucuronosyltransferase (UGT) isoforms. Various EEM-derived QC molecular descriptors were calculated for known UGT substrates and nonsubstrates. Classification models were developed using support vector machine and partial least squares discriminant analysis. In general, the most predictive models were generated with the support vector machine. Combining QC and 2D descriptors (from previous work) using a consensus approach resulted in a statistically significant improvement in predictivity (to 84%) over both the QC and 2D models and the other methods of combining the descriptors. EEM-derived QC descriptors were shown to be both highly predictive and computationally efficient. It is likely that EEM-derived QC properties will be generally useful for predicting ADMET and physicochemical properties during drug discovery.

Electricity↗

PubMed2004

Multiple pharmacophores for the investigation of human UDP-glucuronosyltransferase isoform substrate selectivity.

The UDP-glucuronosyltransferase (UGT) enzyme 'superfamily' contributes to the metabolism of a myriad of drugs, nondrug xenobiotic agents, and endogenous compounds. Although the individual UGT isoforms exhibit distinct but overlapping substrate selectivities, structural features of substrates that confer selectivity remain largely unknown. Using methods developed for pharmacophore fingerprinting combined with optimization and pattern recognition techniques, subsets of pharmacophores associated with the substrates and nonsubstrates of 12 human UGT isoforms were selected to generate predictive models of substrate selectivity and to elucidate the chemical and structural features associated with substrates and nonsubstrates. For all 12 UGT isoforms, the pharmacophore model generated showed predictive ability, as determined by a test set comprising 30% of the available data for each isoform. Models for UGT1A6, -1A7, -1A9, and -2B4 displayed the best predictive ability (>75% of test set predicted correctly) and were further analyzed to interpret the pharmacophores selected as important. The individual pharmacophores differed among isoforms but generally represented relatively simple structural and chemical features. For example, an aromatic ring attached to the nucleophilic group was found to increase the likelihood of glucuronidation by UGT1A6, UGT1A7 and UGT1A9. A large hydrophobic region close to the nucleophile and a hydrogen bond acceptor 10 A from the nucleophile were found to be common to most UGT2B4 substrates. The pharmacophores further suggest that the environment immediately adjacent to the nucleophilic site of conjugation is an important determinant of metabolism by a particular UGT.

Glucuronosyltransferase↗

PubMed2004

Predicting human drug glucuronidation parameters: application of in vitro and in silico modeling approaches.

Cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT), which both exist as enzyme "superfamilies," are together responsible for the metabolism of most hepatically cleared drugs. There is currently intense interest in the development of techniques that permit identification of the CYP and UGT isoform(s) involved in the metabolism of a newly discovered drug, and hence prediction of factors likely to alter elimination in vivo. In addition, the quantitative scaling of kinetic parameters for a metabolic pathway assumes importance for identifying newly discovered drugs with undesirable in vivo pharmacokinetic properties. Although qualitative and quantitative in vitro-in vivo correlation based on data generated using human liver tissue or recombinant enzymes have been applied successfully to many drugs eliminated by CYP, these strategies have proved less definitive for glucuronidated compounds. Computational (in silico) modeling techniques that potentially provide a facile and economic alternative to the in vitro methods are now emerging. This review assesses the utility of in vitro and in silico approaches for the qualitative and quantitative prediction of drug glucuronidation parameters and the challenges facing the development of generalizable models.

Cytochrome P-450 Enzyme System↗

PubMed2003

Pharmacophore and quantitative structure-activity relationship modeling: complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity.

Pharmacophore, two-dimensional (2D), and three-dimensional (3D) quantitative structure-activity relationship (QSAR) modeling techniques were used to develop and test models capable of rationalizing and predicting human UDP-glucuronosyltransferase 1A4 (UGT1A4) substrate selectivity and binding affinity (as K(m,app)). The dataset included 24 structurally diverse UGT1A4 substrates, with 18 of these comprising the training set and 6 an external prediction set. A common features pharmacophore was generated with the program Catalyst after overlapping the sites of conjugation using a novel, user-defined "glucuronidation" feature. Pharmacophore-based 3D-QSAR (r(2) = 0.88) and molecular-field-based 3D-QSAR (r(2) = 0.73) models were developed using Catalyst and self-organizing molecular field analysis (SOMFA) software, respectively. In addition, a 2D-QSAR (r(2) = 0.80, CV r(2) = 0.73) was generated using partial least-squares (PLS) regression and variable selection using an unsupervised forward selection (UFS) algorithm. Both UGT1A4 pharmacophores included two hydrophobic features and the glucuronidation site. The 2D-QSAR showed the best overall predictivity and highlighted the importance of hydrophobicity (as log P) in substrate-enzyme binding.

Catalysis↗

PubMed2002

Pharmacophore and quantitative structure activity relationship modelling of UDP-glucuronosyltransferase 1A1 (UGT1A1) substrates.

UDP-glucuronosyltransferase 1A1 (UGT1A1) is a polymorphic enzyme responsible for the glucuronidation of structurally diverse drugs, non-drug xenobiotics and endogenous compounds (e.g. bilirubin). Thus, definition of UGT1A1 substrate and inhibitor selectivities and binding affinities assumes importance for the identification of compounds whose elimination may be impaired in subjects with variant genotypes, and for the prediction of potentially inhibitory interactions involving xenobiotics and endogenous compounds metabolized by UGT1A1. We report the generation of two- and three-dimensional (2D and 3D) quantitative structure activity relationships (QSAR) and pharmacophore models for 23 known UGT1A1 substrates with diverse structure and binding affinity. Initially, a simple procedure was developed to determine apparent inhibition constants (Ki,app) for these compounds. Eighteen substrates were subsequently used to construct models and the remaining five to validate the predictive ability of the models. Three different models were constructed: (i) three feature pharmacophore model able to predict the Ki,app on the basis of the degree to which a substrate can fit to the arrangement of 3D features (r2 = 0.87, Ki,app for all five test substrates predicted within log unit); (ii) 3D-QSAR using a 'common features' pharmacophore to align the substrates (r2 = 0.71, Ki,app for four out of five test substrates predicted within one log unit); (iii) 2D-QSAR constructed with six chemical descriptors (r2 = 0.92, Ki,app of all five test substrates predicted within one log unit). The common features pharmacophore demonstrated the importance of two hydrophobic domains separated from the glucuronidation site by 4 A and 7 A, respectively. These models, which represent the first generalized predictive models for a UGT isoform, complement each other and are an important first step towards computer based (in silico) models of UGT1A1 for high throughput prediction of metabolism.

Glucuronosyltransferase↗

PubMedDate not supplied

Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP-glucuronosyltransferase isoforms.

Partial least squares discriminant analysis (PLSDA), Bayesian regularized artificial neural network (BRANN), and support vector machine (SVM) methodologies were compared by their ability to classify substrates and nonsubstrates of 12 isoforms of human UDP-glucuronosyltransferase (UGT), an enzyme "superfamily" involved in the metabolism of drugs, nondrug xenobiotics, and endogenous compounds. Simple two-dimensional descriptors were used to capture chemical information. For each data set, 70% of the data were used for training, and the remainder were used to assess the generalization performance. In general, the SVM methodology was able to produce models with the best predictive performance, followed by BRANN and then PLSDA. However, a small number of data sets showed either equivalent or better predictability using PLSDA, which may indicate relatively linear relationships in these data sets. All SVM models showed predictive ability (>60% of test set predicted correctly) and five out of the 12 test sets showed excellent prediction (>80% prediction accuracy). These models represent the first use of pattern recognition methods to discriminate between substrates and nonsubstrates of human drug metabolizing enzymes and the first thorough assessment of three classification algorithms using multiple metabolic data sets.

Algorithms↗

PubMedDate not supplied

Systematic statistical comparison of comparative molecular similarity indices analysis molecular fields for computer-aided lead optimization.

Comparative molecular similarity indices analysis (CoMSIA) is a 3D quantitative structure-activity relationship technique used to determine structural and electronic features influencing biological activity. This proves particularly useful for facilitating lead optimization projects. This study aimed to compare CoMSIA models produced using different subsets of the CoMSIA molecular fields (steric, electrostatic, hydrophobic, hydrogen-bond donor, and hydrogen-bond acceptor) in a systematic and statistically valid manner. A total of 23 data sets sourced from the literature were used to compare molecular field contribution and model predictivity using leave-one-out cross-validated R2 values. Predictive ability varied in a highly statistically significant manner depending on the set of CoMSIA molecular fields used. In general, the greater the number of CoMSIA molecular fields included in the analysis, the better the model predictivity was. There is great redundancy in the information contained in the different CoMSIA molecular fields. When all five CoMSIA molecular fields are included, the hydrophobic and electrostatic fields had the largest and the steric field the smallest contribution. Data sets were clustered into four groups on the basis of the utility of molecular field sets to generate predictive models.

Computer-Aided Design↗

PubMedDate not supplied

Molecular modeling approaches for the prediction of the nonspecific binding of drugs to hepatic microsomes.

Molecular modeling approaches for the prediction of the nonspecific binding of drugs to hepatic microsomes were examined using a published database of 56 compounds. Models generated were evaluated using an independent test set of 13 compounds. A pharmacophore approach identified structural features of drugs associated with nonspecific binding. A side-chain amino group and complementary hydrophobic domain were the principal features noted. The use of shape overlays, based on the pharmacophore, in conjunction with a chemical force field in the program ROCS, yielded discrimination between molecules classified as strong binders (experimental fraction unbound in microsomes<0.50) and those with a lower degree of binding (experimental fraction unbound in microsomes>0.50). In the initial data set of 56 molecules, 18 were classified as strong binders (on the basis of the above criteria), and all of those were recovered in the top 22 molecular hits from ROCS. Additionally, computationally generated values of log P were shown to provide a reasonable estimate of the fraction unbound in microsomes, providing the compounds were in their basic form at physiological pH.

Amino Acid Motifs↗

PubMedDate not supplied

The importance of local chemical structure for chemical metabolism by human uridine 5'-diphosphate-glucuronosyltransferase.

The uridine 5'-diphosphate- (UDP-)glucuronosyltransferase (UGT) family of enzymes catalyzes the conjugation of chemicals containing a suitable nucleophilic atom with glucuronic acid. Despite the importance of glucuronidation as an elimination and detoxification mechanism for drugs, environmental chemicals, and endogenous compounds, the structural features of substrates that confer isoform selectivity are poorly understood. The relationship between the local molecular structure of nucleophilic atoms of chemicals and the ability of UGT isoforms to glucuronidate the nucleophilic atoms was investigated here. The proximity of an aromatic ring to the nucleophilic atom was highly associated with a greater likelihood of glucuronidation by most UGT isoforms. Similarly, most UGT isoforms were found to have a statistically significant preference for oxygen over nitrogen as the nucleophilic atom. The converse was established only for UGT1A4. Naïve Bayes models were trained to predict the site of glucuronidation for eight UGT isoforms on the basis of the partial charge and Fukui function of the nucleophilic atom and whether an aromatic ring was attached to the nucleophilic atom. On average, the cross-validated sensitivity and specificity of the models were approximately 75-80%. For all but UGT2B7, the area under the receiver operating characteristics curve of the model was greater than 0.8, indicating strong predictive ability. A chemical diversity analysis of the currently available data indicates bias toward chemicals containing phenolic groups, and it is likely that the availability of chemical data sets with greater diversity will facilitate further insights into the structural features of substrates that confer enzyme selectivity.

Bayes Theorem↗