A collaborative hit-to-lead investigation leveraging medicinal chemistry expertise with high throughput library design, synthesis and purification capabilities.

High throughput screening (HTS) campaigns, where laboratory automation is used to expose biological targets to large numbers of materials from corporate compound collections, have become commonplace within the lead generation phase of pharmaceutical discovery. Advances in genomics and related fields have afforded a wealth of targets such that screening facilities at larger organizations routinely execute over 100 hit-finding campaigns per year. Often, 10(5) or 10(6) molecules will be tested within a campaign/cycle to locate a large number of actives requiring follow-up investigation. Due to resource constraints at every organization, traditional chemistry methods for validating hits and developing structure activity relationships (SAR) become untenable when challenged with hundreds of hits in multiple chemical families per target. To compound the issue, comparison and prioritization of hits versus multiple screens, or physical chemical property criteria, is made more complex by the informatics issues associated with handling large data sets. This article describes a collaborative research project designed to simultaneously leverage the medicinal chemistry and drug development expertise of the Novartis Institutes for Biomedical Research Inc. (NIBRI) and ArQule Inc.'s high throughput library design, synthesis and purification capabilities. The work processes developed by the team to efficiently design, prepare, purify, assess and prioritize multiple chemical classes that were identified during high throughput screening, cheminformatics and molecular modeling activities will be detailed.

Do we need lipid clinics? Shifting the balance between secondary and primary care.

BACKGROUND: Increasing activity in cholesterol lowering is placing increasing demands on lipid clinics to be able to cope with the increase in demand. A combination of interventions has been used to improve laboratory testing, focus interpretative results and provide educational and advisory facilities for general practitioners in order to increase shared care of many potential clinic patients. METHODS: Retrospective study of clinic waiting times, results in patients managed on a shared care basis, and overall clinical activity over a three-and-a-half-year intervention period between March 2001 and August 2004 in a single-consultant, single-centre secondary care clinic serving three primary care trusts, covering a population of approximately 270,000 people. The interventions involved a change to the laboratory request form, outreach educational meetings and promotion of use of "written advice only" for certain patients as an alternative to direct clinic referral. The main outcome measures were percentages of patients followed up in primary care, change in cholesterol and triglyceride results after advice, and clinic waiting time. RESULTS: A total of 520 patients were referred over three and a half years, either to be seen or for written advice only. About 291 of these were handled by advisory letters. In all, 90% of these patients were already receiving or had received lipid-lowering therapy at the time of referral. 98% of patients were followed up by their general practice post-advice. Cholesterol and triglyceride concentrations fell by 23% and 39%, respectively, post-advice. Waiting times fell from a peak of 35 weeks before the interventions to an average of three weeks after. This fall has been maintained over the three-and-a-half years the intervention has run. CONCLUSIONS: The interventions have resulted in a large fall in clinic waiting times, improvement in lipid results, and high rate of general practice follow-up, all of which have been sustained in the long term.

Applications of random sampling to virtual screening of combinatorial libraries.

We describe statistical techniques for effective evaluation of large virtual combinatorial libraries (> 10(10) potential compounds). The methods described are used for computationally evaluating templates (prioritization of candidate libraries for synthesis and screening) and for the design of individual combinatorial libraries (e.g., for a given diversity site, reagents can be selected based on the estimated frequency with which they appear in products that pass a computational filter). These statistical methods are powerful because they provide a simple way to estimate the properties of the overall library without explicitly enumerating all of the possible products. In addition, they are fast and simple, and the amount of sampling required to achieve a desired precision is calculable. In this article, we discuss the computational methods that allow random product selection from a combinatorial library and the statistics involved in estimating errors from quantities obtained from such samples. We then describe three examples: (1) an estimate of average molecular weight for the several billion possible products in a four-component Ugi reaction, a quantity that can be calculated exactly for comparison; (2) the prioritization of four templates for combinatorial synthesis using a computational filter based on four-point pharmacophores; and (3) selection of reagents for the four-component Ugi reaction based on their frequency of occurrence in products that pass a pharmacophore filter.

Use of augmented Lagrangians in the calculation of molecular conformations by distance geometry.

Distance geometry is a technique widely used to find atomic coordinates that agree with given upper and lower bounds on the interatomic distances. It is successful because it chooses at random some relatively good "trial coordinates" that take into account the whole molecule and all constraints at once. Customarily, these trial coordinates must be refined by minimizing a penalty function until the structure agrees with the original bounds. Here we present an alternative to minimizing the penalty function, which has the advantage of more precisely satisfying the bounds, showing more clearly when the bounds are mutually contradictory, and simultaneously optimizing an objective function subject to precise satisfaction of the bounds.

Fast drug-receptor mapping by site-directed distances: a novel method of predicting new pharmacological leads.

The searching and characterization of large chemical databases has recently provoked much interest, particularly with respect to the question of whether any of the compounds in the database could serve as new leads to a compound of pharmacological interest. This paper introduces a fast and novel method of determining whether any of a given series of compounds are able, on geometrical grounds, to interact with an active site of interest. The C program written to implement the method is able to make a qualitative prediction for a given compound in about 1 s per structure (for drug-sized molecules), while still permitting the compound complete conformational freedom. However, the algorithm is sufficiently flexible to permit distance constraints to be placed on the molecules while docking. The test system studied was a family of Baker's triazines docking into the active site of dihydrofolate reductase (DHFR), as defined by a methotrexate/NADPH complex.

Identification of common functional configurations among molecules.

A new algorithm for identifying three-dimensional configurations of chemical features common to a set of molecules is described. The algorithm scores each configuration based both on the degree to which it is common to the input set and its estimated rarity. The algorithm can be applied to molecules with large (several hundred) conformational models. Results from the application of this algorithm to three data sets are discussed: PAF antagonists, HIV reverse transcriptase inhibitors, and HIV protease inhibitors. Of particular interest is a common configuration identified for a set of HIV reverse transcriptase inhibitors; this configuration is shared by two new, potent inhibitors that were recently described in the literature.