Biomedical subjects

Michael P Lawton

Publications and source records attributed to Michael P Lawton.

3 recordsLinked to original sources

PubMed2006

Three-dimensional quantitative structure-activity relationship analysis of human CYP51 inhibitors.

CYP51 fulfills an essential requirement for all cells, by catalyzing three sequential mono-oxidations within the cholesterol biosynthesis cascade. Inhibition of fungal CYP51 is used as a therapy for treating fungal infections, whereas inhibition of human CYP51 has been considered as a pharmacological approach to treat dyslipidemia and some forms of cancer. To predict the interaction of inhibitors with the active site of human CYP51, a three-dimensional quantitative structure-activity relationship model was constructed. This pharmacophore model of the common structural features of CYP51 inhibitors was built using the program Catalyst from multiple inhibitors (n = 26) of recombinant human CYP51-mediated lanosterol 14alpha-demethylation. The pharmacophore, which consisted of one hydrophobe, one hydrogen bond acceptor, and two ring aromatic features, demonstrated a high correlation between observed and predicted IC(50) values (r = 0.92). Validation of this pharmacophore was performed by predicting the IC(50) of a test set of commercially available (n = 19) and CP-320626-related (n = 48) CYP51 inhibitors. Using predictions below 10 microM as a cutoff indicative of active inhibitors, 16 of 19 commercially available inhibitors (84%) and 38 of 48 CP-320626-related inhibitors (79.2%) were predicted correctly. To better understand how inhibitors fit into the enzyme, potent CYP51 inhibitors were used to build a Cerius(2) receptor surface model representing the volume of the active site. This study has demonstrated the potential for ligand-based computational pharmacophore modeling of human CYP51 and enables a high-throughput screening system for drug discovery and data base mining.

Amides↗

PubMed2006

Pathway analysis using random forests classification and regression.

MOTIVATION: Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers. RESULTS: In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data. AVAILABILITY: Source code written in R is available from http://bioinformatics.med.yale.edu/pathway-analysis/rf.htm.

Algorithms↗

PubMed2006

Acute drug-induced vascular injury in beagle dogs: pathology and correlating genomic expression.

Acute vascular injury that leads to vascular inflammation is a common finding in the preclinical toxicity testing of drugs in rats and dogs. However, the relevance of this finding for risk to humans is unclear. Concern about the safety of these drugs is heightened by the current lack of noninvasive clinical methods to predict the onset of vascular damage in animals or humans. Determining the relevance of this poorly understood preclinical outcome for humans requires a better understanding of the molecular mechanisms of injury in addition to the development of sensitive and specific leading biomarkers for the clinical diagnosis of acute vascular damage. Most molecular research on this toxicity has been performed in rats, but recent development of canine gene expression microarrays makes transcriptomic studies now possible in the dog. In this study, we investigated the molecular mechanisms of drug-induced vascular injury in dogs using gene arrays. After treating Beagles with toxic doses of CI-947, an adenosine receptor agonist, we profiled gene expression in the coronary arteries and correlated those changes with histopathology at 16 and 24 hours after dosing. The results demonstrated that pathobiological processes such as stimulation of the innate immune response, increased extracellular matrix turnover and oxidative stress were active at times of very early injury.

Acute Disease↗