A generalized model and high throughput data analysis system for functional modulation of receptor-agonist systems suitable for use in drug discovery.

Positive allosteric modulators (PAMs) of receptors represent a class of pharmacologic agents having the desirable property of acting only in the presence of cognate ligands. Discovery and optimization of the structure activity relationships of PAMs is complicated by the requirement of a second ligand to manifest their action, and by the need to quantify both affinity and intrinsic efficacy. Multivariate regression analysis is a statistical method capable of simultaneously obtaining affinity and intrinsic efficacy parameters from curve fits of multiple agonist dose-response functions generated in the presence of varying concentrations of PAMs. Capitalizing on the advantages of multivariate regression analysis for PAM optimization requires a theoretical framework and a system that facilitates efficient flow of information from data generation through data analysis, storage, and retrieval. We describe here the experimental design, mathematical model and informatics workflow enabling a multivariate regression approach for rapidly obtaining affinity and intrinsic efficacy values for PAMs in a drug discovery setting.

New informatics and automated infrastructure to accelerate new leads discovery by high throughput screening (HTS).

The Lankenau Institute for Medical Research Chemical Genomics Center, Inc. has developed a new (patents issued and pending) Nanotube Automated Repository System (NARS) for dynamic storage of millions of 'single-shot' samples stored in a new monolithic microtiter-storage tube plate of our own design we call 'nanotubes.' We have integrated the NARS with customized software to efficiently access up to 10,000,000 samples stored continuously frozen (-20°C) in a dehumidified enclosure and sealed in a new microtiter NARS plate that is SBS compliant. Additional software was developed to analyze HTS data from orthogonally pooled compound libraries. Following 'de-convolution' of pooled HTS data, the software designates confirmatory retest samples to be 'cherry-picked' using the NARS. The application of a new, fully-integrated infrastructure for new leads discovery is described in detail. Other applications for our technologies and new infrastructure are discussed.