Biomedical subjects
Publications and source records attributed to Will R Pitt.
A novel series of aminopyrimidine IKK2 inhibitors have been developed which show excellent in vitro inhibition of this enzyme and good selectivity over the IKK1 isoform. The relative potency and selectivity of these compounds has been rationalized using QSAR and structure-based modelling.
Structure-based drug design methods were used to search for novel inhibitors of herpes simplex virus type 1 (HSV-1) thymidine kinase and Mycobacterium tuberculosis thymidylate kinase. The method involved the use of crystal structure complexes to guide database searching for potential inhibitors. A number of weak inhibitors of HSV-2 were identified, one of which was found to inhibit HSV-1 TK and HSV-1 TK-deficient viral strains. Each compound tested against M. tuberculosis thymidylate kinase was found to have some activity. The best of these compounds was only 4.6-fold less potent than 3'-azido-3'-deoxythymidine-5'-monophosphate (AZTMP). This study demonstrates the utility of structure-based drug design methods in the search for novel enzyme inhibitors.
A series of neural networks has been trained, using consensus methods, to recognize compounds that act at biological targets belonging to specific gene families. The MDDR database was used to provide compounds targeted against gene families and sets of randomly selected molecules. BCUT parameters were employed as input descriptors that encode structural properties and information relevant to ligand-receptor interactions. In each case, the networks identified over 80% of the compounds targeting a gene family. The technique was applied to purchasing compounds from external suppliers, and results from screening against one gene family demonstrated impressive abilities to predict the activity of the majority of known hit compounds.
BCUT [Burden, CAS, and University of Texas] descriptors, defined as eigenvalues of modified connectivity matrices, have traditionally been applied to drug design tasks such as defining receptor relevant subspaces to assist in compound selections. In this paper we present studies of consensus neural networks trained on BCUTs to discriminate compounds with poor aqueous solubility from those with reasonable solubility. This level was set at 0.1 mg/mL on advice from drug formulation and drug discovery scientists. By applying strict criteria to the insolubility predictions, approximately 95% of compounds are classified correctly. For compounds whose predictions have a lower level of confidence, further parameters are examined in order to flag those considered to possess unsuitable biopharmaceutical and physicochemical properties. This approach is not designed to be applied in isolation but is intended to be used as a filter in the selection of screening candidates, compound purchases, and the application of synthetic priorities to combinatorial libraries.