Biomedical subjects

Paul Beroza

Publications and source records attributed to Paul Beroza.

6 recordsLinked to original sources

PubMed2005

Identification and characterization of novel benzil (diphenylethane-1,2-dione) analogues as inhibitors of mammalian carboxylesterases.

Carboxylesterases (CE) are ubiquitous enzymes responsible for the metabolism of xenobiotics. Because the structural and amino acid homology among esterases of different classes, the identification of selective inhibitors of these proteins has proved problematic. Using Telik's target-related affinity profiling (TRAP) technology, we have identified a class of compounds based on benzil (1,2-diphenylethane-1,2-dione) that are potent CE inhibitors, with K(i) values in the low nanomolar range. Benzil and 30 analogues demonstrated selective inhibition of CEs, with no inhibitory activity toward human acetylcholinesterase or butyrylcholinesterase. Analysis of structurally related compounds indicated that the ethane-1,2-dione moiety was essential for enzyme inhibition and that potency was dependent on the presence of, and substitution within, the benzene ring. 3D-QSAR analyses of these benzil analogues for three different mammalian CEs demonstrated excellent correlations of observed versus predicted K(i) (r(2) > 0.91), with cross-validation coefficients (q(2)) of 0.9. Overall, these results suggest that selective inhibitors of CEs with potential for use in clinical applications can be designed.

Acetylcholinesterase↗

PubMed2005

Target-related affinity profiling: Telik's lead discovery technology.

Target-Related Affinity Profiling (TRAP) is a computational drug discovery technology that is based on 'affinity fingerprints', which are molecular descriptors derived from the protein binding preferences of small molecules. The underlying concepts of TRAP are reviewed. Affinity fingerprints are compared to molecular descriptors derived from chemical structures and shown to be a useful alternative for lead discovery. The TRAP screening process is described and two example applications are presented: I. the discovery of novel inhibitors of human intestinal carboxylesterase, and II. the discovery of novel inhibitors of cyclooxygenase-1 through the use of the affinity fingerprints of known cyclooxygenase-1 inhibitors. A summary of the complementary advantages of TRAP screening technology compared to traditional approaches to drug lead discovery concludes the review.

Animals↗

PubMed2004

Novel cyclooxygenase-1 inhibitors discovered using affinity fingerprints.

We used protein affinity fingerprints to discover structurally novel inhibitors of cyclooxygenase-1 (COX-1) by screening a selected number of compounds, thus providing an alternative to extensive screening. From the affinity fingerprints of 19 known COX-1 inhibitors, a computational model for COX-1 inhibition was constructed and used to select candidate inhibitors from our compound library to be tested in the COX-1 assay. Subsequent refinement of the model by including affinity fingerprints of inactive compounds identified three molecules that were more potent than ibuprofen, a commonly used COX-1 inhibitor. These compounds are structurally distinct from those used to build the model and were discovered by testing only 62 library compounds. The discovery of these leads demonstrates the efficiency with which affinity fingerprints can identify novel bioactive chemotypes from known drugs.

Anti-Inflammatory Agents, Non-Steroidal↗

PubMed2004

Discovery of novel selective inhibitors of human intestinal carboxylesterase for the amelioration of irinotecan-induced diarrhea: synthesis, quantitative structure-activity relationship analysis, and biological activity.

The dose-limiting toxicity of the highly effective anticancer agent 7-ethyl-10-[4-(1-piperidino)-1-piperidino]carbonyloxy-camptothecin (irinotecan; CPT-11) is delayed diarrhea. This is thought to be caused by either bacteria-mediated hydrolysis of the glucuronide conjugate of the active metabolite 7-ethyl-10-hydroxycamptothecin (SN-38) or direct conversion of CPT-11 to SN-38 by carboxylesterases (CE) in the small intestine. After drug administration, a very high level of CPT-11 is present in the bile; this is deposited into the duodenum, the region of the gut with the highest levels of CE activity. Hence, it is likely that direct conversion of the drug to SN-38 is partially responsible for the diarrhea associated with this agent. In an attempt to ameliorate this toxicity, we have applied Target-Related Affinity Profiling to identify novel CE inhibitors that are selective inhibitors of the human intestinal enzyme (hiCE). Seven inhibitors, all sulfonamide derivatives, demonstrated greater than 200-fold selectivity for hiCE compared with the human liver CE hCE1, and none was an inhibitor of human acetylcholinesterase or butyrylcholinesterase. Quantitative structure-activity relationship (QSAR) analysis demonstrated excellent correlations with the predicted versus experimental Ki values (r2 = 0.944) for hiCE. Additionally, design and synthesis of a tetrafluorine-substituted sulfonamide analog, which QSAR indicated would demonstrate improved inhibition of hiCE, validated the computer predictive analyses. These and other phenyl-substituted sulfonamides compounds are regarded as lead compounds for the development of effective, selective CE inhibitors for clinical applications.

Animals↗

PubMed2002

Chemoproteomics as a basis for post-genomic drug discovery.

The large number of small organic compounds now available for drug-lead screening has led to numerous methods for classifying molecular similarity and diversity, the aim being to restore a balance between the quantity and drug-like quality of compounds in small-molecule libraries. Whereas structural and physicochemical attributes continue to be emphasized in compound selection for drug-lead screening, chemoproteomics--the use of biological information to guide chemistry--offers a highly efficient alternative to small-molecule characterization that can accelerate drug discovery in the post-genomic era.

Databases, Factual↗

PubMedDate not supplied

A novel shape-feature based approach to virtual library screening.

The shape of and the chemical features of a ligand are both critical for biological activity. This paper presents a strategy that uses these descriptors to build a computational model for virtual screening of bioactive compounds. Molecules are represented in a binary shape-feature descriptor space as bit-strings, and their relative activities are used to identify the subset of the bit-string that is most relevant to bioactivity. This subset is used to score virtual libraries. We describe the computational details of the method and present an example validation experiment on thrombin inhibitors.

Computer Simulation↗