QSAR, complex networks, principal components and partial order analysis of drug cardiotoxicity with proteome mass-spectra topological indices

• Type: Chapter or Part of a Book
• Date: 2012

Authors

Munteanu, CR (Author) Other; Cruz Monteagudo, M (Author) Other; Borges, F (Author) FCUP; Cordeiro, MNDS (Author) FCUP; Concud, R (Author) Other; Gonzalez Diaze, H (Author) Other

Book

Title: Recent Trends on QSAR in the Pharmaeutical Perceptions

Pages: 3-50

ISBN: 9781608054336

Indexing

ISI Web of Knowledge

Scopus - 1 Citation

Scientific classification

FOS: Natural sciences > Chemical sciences

CORDIS: Physical sciences > Chemistry > Applied chemistry > Pharmaceutical chemistry

Other information

Authenticus ID: P-008-D5V

DOI: 10.2174/978160805379711201010003

Abstract (EN): Blood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome-Early Drug Induced Cardiac Toxicity Relationships (called here Pro-EDICToRs).However, due to the thousands of proteins in the SP, a more realistic alternative representsthe identification of general Pro-EDICToRs patterns instead of a single protein marker. Inthis sense, we introduced a novel Cartesian 2D spectrum graph for SP-MS. Next, wecalculated the graph node-overlapping parameters (nopk) to numerically characterize SPMSby using them as inputs for a Quantitative Proteome-Toxicity Relationship (QPTR)classifier for Pro-EDICToRs with accuracy higher than 80%. This QPTR approach is theresult of adapting the classic blood proteome Quantitative Property-Structure Relationshipmodels (QSPR) used in Chemometrics to low-mass molecules study. Principal ComponentAnalysis (PCA) on the QPTR nopk values explains with one factor (F1) the 82.7% ofvariance. These nopk values were used to construct for the first time a Pro-EDICToRsComplex Network having samples as nodes linked by similarity between two samplesedges. We compared the topology of two sub-networks for the cardiac toxicity and controlsamples and found extreme relative differences for the re-linking (P) and Zagreb (M2)indices (9.5 and 54.2 % respectively) out of 11 parameters. We also compared the subnetworkswith the well-known ideal random networks including Barabasi-Albert,Kleinberg Small World, Erdos-Renyi, and Epsstein Power Law models. Finally, weproposed Partial Order (PO) schemes of the 115 samples based on LDA-probabilities, F1-scores and/or network node degrees. PCA-CN and LDA-PCA based POs with Tanimoto's.

Language: English

Type (Professor's evaluation): Scientific