Biospectra Analysis:  Model Proteome Characterizations for Linking Molecular Structure and Biological Response

Abstract

Establishing quantitative relationships between molecular structure and broad biological effects has been a long-standing goal in drug discovery. Evaluation of the capacity of molecules to modulate protein functions is a prerequisite for understanding the relationship between molecular structure and in vivo biological response. A particular challenge in these investigations is to derive quantitative measurements of a molecule's functional activity pattern across different proteins. Herein we describe an operationally simple probabilistic structure-activity relationship (SAR) approach, termed biospectra analysis, for identifying agonist and antagonist effect profiles of medicinal agents by using pattern similarity between biological activity spectra (biospectra) of molecules as the determinant. Accordingly, in vitro binding data (percent inhibition values of molecules determined at single high drug concentration in a battery of assays representing a cross section of the proteome) are useful for identifying functional effect profile similarity between medicinal agents. To illustrate this finding, the relationship between biospectra similarity of 24 molecules, identified by hierarchical clustering of a 1567 molecule dataset as being most closely aligned with the neurotransmitter dopamine, and their agonist or antagonist properties was probed. Distinguishing the results described in this study from those obtained with affinity-based methods, the observed association between biospectra and biological response profile similarity remains intact even upon removal of putative drug targets from the dataset (four dopaminergic [D 1 /D 2 /D 3 /D 4 ] and two adrenergic [R 1 and R 2 ] receptors). These findings indicate that biospectra analysis provides an unbiased new tool for forecasting structure-response relationships and for translating broad biological effect information into chemical structure design.

Opportunities and Challenges of Psychiatric Drug Discovery: Roles for Scientists in Academic, Industry, and Government Settings

“…One approach that has promise for these efforts is mining the receptorome, or broad screening of the effects of a clinically active compounds against a broad array of potential drug receptors (Armbruster and Roth, 2005). As previously mentioned, we and others have proposed that comprehensively screening the 'druggable genome' (Hopkins and Groom, 2002; o3000 molecular targets) using an array of approved medications, metabolites and candidate medications would likely lead to insights into many of the molecular targets responsible for the efficacies and side effects of drugs in humans (O'Connor and Roth, 2005;Fliri et al, 2005b).…”

Structure‐Activity Relationship Studies of a Number of α1‐Adrenoceptor Antagonists and Antiarrhythmic Agents

“…The structure-function studies enables consideration of response determinants and does not require representation of response as a linear combination of independent relationships. [26,35,36] Of course we need a method capable of verification of obtained models. Ideal tool, in our opinion, is the multiple logistic regression analysis.…”

“…An aryl moiety bound to the nitrogen atom at the 4-position of the piperazine ring is the second structural feature of these compounds which is necessary for interacting with the receptor. [26][27][28] We have previously reported that a series of 4-arylpiperazin-1-yl-propyl-pyrrolidin-2-one or 3-alkyl-3-phenylpyrrolidin-2-one derivatives possess affinity for a 1 -and a 2 -ARs and show marked hypertensive and AA activities. [29][30][31][32][33] Current drug discovery methods estimate biological response of potential medicinal agents by constructing independent and linear models.…”

Chemogenomics: Systematization of Drug Discovery

“…The same similarity relationships were observed by hierarchical clustering that provides an unbiased mean for establishing quantitative relationships between chemical structures and biological activity spectra. The same data set was used in a follow-up study to investigate the relationships between biospectra similarities of 24 molecules being by hierarchical clustering most closely aligned with the neurotransmitter dopamine [85]. The agonist or antagonist properties were probed and successfully distinguished.…”