Lead Discovery Programs Research Articles

Drug rings database with web interface. A tool for identifying alternative chemical rings in lead discovery programs.

This paper describes the development of a drug rings database and Web-based search tools. The database contains ring structures from both corporate and commercial databases, along with characteristic descriptors including frequency of occurrence as an indicator of synthetic accessibility and calculated property and geometric parameters. Analysis of the rings in several major databases is described, with illustrations of applications of the database in lead discovery programs where bioisosteres and geometric isosteres are sought.

Comparison of ranking methods for virtual screening in lead-discovery programs.

This paper discusses the use of several rank-based virtual screening methods for prioritizing compounds in lead-discovery programs, given a training set for which both structural and bioactivity data are available. Structures from the NCI AIDS data set and from the Syngenta corporate database were represented by two types of fragment bit-string and by sets of high-level molecular features. These representations were processed using binary kernel discrimination, similarity searching, substructural analysis, support vector machine, and trend vector analysis, with the effectiveness of the methods being judged by the extent to which active test set molecules were clustered toward the top of the resultant rankings. The binary kernel discrimination approach yielded consistently superior rankings and would appear to have considerable potential for chemical screening applications.

A FRET-Based Assay Platform for Ultra-High Density Drug Screening of Protein Kinases and Phosphatases

Protein phosphorylation is one of the major regulatory mechanisms involved in signal-induced cellular events, including cell proliferation, apoptosis, and metabolism. Because many facets of biology are regulated by protein phosphorylation, aberrant kinase and/or phosphatase activity forms the basis for many different types of pathology. The disease relevance of protein kinases and phosphatases has led many pharmaceutical and biotechnology companies to expend significant resources in lead discovery programs for these two target classes. The existence of >500 kinases and phosphatases encoded by the human genome necessitates development of methodologies for the rapid screening for novel and specific compound inhibitors. We describe here a fluorescence-based, molecular assay platform that is compatible with robotic, ultra-high throughput screening systems and can be applied to virtually all tyrosine and serine/threonine protein kinases and phosphatases. The assay has a coupled-enzyme format, utilizing the differential protease sensitivity of phosphorylated versus nonphosphorylated peptide substrates. In addition to screening individual kinases, the assay can be formatted such that kinase pathways are re-created in vitro to identify compounds that specifically interact with inactive kinases. Miniaturization of this assay format to the 1-microl scale allows for the rapid and accurate compound screening of a host of kinase and phosphatase targets, thereby facilitating the hunt for new leads for these target classes.

Chemical library subset selection algorithms: a unified derivation using spatial statistics.

If similar compounds have similar activity, rational subset selection becomes superior to random selection in screening for pharmacological lead discovery programs. Traditional approaches to this experimental design problem fall into two classes: (i) a linear or quadratic response function is assumed (ii) some space filling criterion is optimized. The assumptions underlying the first approach are clear but not always defendable; the second approach yields more intuitive designs but lacks a clear theoretical foundation. We model activity in a bioassay as realization of a stochastic process and use the best linear unbiased estimator to construct spatial sampling designs that optimize the integrated mean square prediction error, the maximum mean square prediction error, or the entropy. We argue that our approach constitutes a unifying framework encompassing most proposed techniques as limiting cases and sheds light on their underlying assumptions. In particular, vector quantization is obtained, in dimensions up to eight, in the limiting case of very smooth response surfaces for the integrated mean square error criterion. Closest packing is obtained for very rough surfaces under the integrated mean square error and entropy criteria. We suggest to use either the integrated mean square prediction error or the entropy as optimization criteria rather than approximations thereof and propose a scheme for direct iterative minimization of the integrated mean square prediction error. Finally, we discuss how the quality of chemical descriptors manifests itself and clarify the assumptions underlying the selection of diverse or representative subsets.

Novel trends in orphan market drug discovery: amyotrophic lateral sclerosis as a case study

As new lead discovery technologies of high throughput screening and rational drug design have been incorporated into pharmaceutical and biotechnology drug discovery programs, researchers have focused on the applying these new technologies in diseases traditionally neglected by for-profit drug discovery efforts. This article reviews general trends in orphan disease lead discovery, identifies best practices of orphan market drug discovery and provides an overview of recent ALS lead discovery programs and drug development according to these metrics. Best practices in orphan market drug discovery embodied by programs like the NIH Anticonvulsant Screening Program include the (1) management of timelines and priorities, (2) engagement of for-profit partners, (3) creative application of technology, (4) collaboration, and (5) flexibility. Recent trends in ALS lead discovery have been shaped not only by the predominance of animal models of disease over in vitro models, but also by the successes and best practices of these earlier orphan market drug discovery programs. The ALS Treatment Initiative, the Johns Hopkins Center for ALS Research, the ALS Association, and the ALS Therapy Development Foundation have all initiated lead discovery programs in the past several years which seek to utilize existing experimental models of the disease and challenge assumptions about the linear nature of the lead discovery and development process. The compounds currently in clinical evaluation for ALS were identified as leads from a variety of sources, further reinforcing the transforming effect these new lead discovery programs have had on drug discovery and development in ALS. We conclude our review with an overview of the challenges and opportunities lead discovery in ALS currently faces, ultimately concluding that ALS lead discovery, and indeed orphan market drug discovery in general, would most benefit from more centralized lead discovery management, expanded national access to core facilities for lead discovery, and matrixed simultaneous screening of multiple compounds for multiple neglected diseases.

Novel trends in orphan market drug discovery: amyotrophic lateral sclerosis as a case study.

As new lead discovery technologies of high throughput screening and rational drug design have been incorporated into pharmaceutical and biotechnology drug discovery programs, researchers have focused on the applying these new technologies in diseases traditionally neglected by for-profit drug discovery efforts. This article reviews general trends in orphan disease lead discovery, identifies best practices of orphan market drug discovery and provides an overview of recent ALS lead discovery programs and drug development according to these metrics. Best practices in orphan market drug discovery embodied by programs like the NIH Anticonvulsant Screening Program include the (1) management of timelines and priorities, (2) engagement of for-profit partners, (3) creative application of technology, (4) collaboration, and (5) flexibility. Recent trends in ALS lead discovery have been shaped not only by the predominance of animal models of disease over in vitro models, but also by the successes and best practices of these earlier orphan market drug discovery programs. The ALS Treatment Initiative, the Johns Hopkins Center for ALS Research, the ALS Association, and the ALS Therapy Development Foundation have all initiated lead discovery programs in the past several years which seek to utilize existing experimental models of the disease and challenge assumptions about the linear nature of the lead discovery and development process. The compounds currently in clinical evaluation for ALS were identified as leads from a variety of sources, further reinforcing the transforming effect these new lead discovery programs have had on drug discovery and development in ALS. We conclude our review with an overview of the challenges and opportunities lead discovery in ALS currently faces, ultimately concluding that ALS lead discovery, and indeed orphan market drug discovery in general, would most benefit from more centralized lead discovery management, expanded national access to core facilities for lead discovery, and matrixed simultaneous screening of multiple compounds for multiple neglected diseases.

Rapid Quantification of Molecular Diversity for Selective Database Acquisition

There is an increasing need to expand the structural diversity of the molecules investigated in lead-discovery programs. One way in which this can be achieved is by acquiring external datasets that will enhance an existing database. This paper describes a rapid procedure for the selection of external datasets using a measure of structural diversity that is calculated from sums of pairwise intermolecular structural similarities.

Designing Chemical Libraries for Lead Discovery

The selection of compounds for use in high throughput biological assays is one of the critical factors that dictates the likelihood of detecting exploitable biological properties. In this paper, we present a process designed to deliver molecules that contain chemical functionality of immediate value in a lead discovery program, molecules that are sufficiently different from each other to ensure that redundancy of effort is avoided. The design process has already been implemented and used to add tens of thousands of reaction products to the Optiverse™ library