QSAR Models for the Prediction of Plasma Protein Binding.

QSAR models for the prediction of plasma protein binding.

Bioimpacts. 2013; 3(1): 21-7
Ghafourian T, Amin Z

The prediction of plasma protein binding (ppb) is of paramount importance in the pharmacokinetics characterization of drugs, as it causes significant changes in volume of distribution, clearance and drug half life. This study utilized Quantitative Structure – Activity Relationships (QSAR) for the prediction of plasma protein binding.Protein binding values for 794 compounds were collated from literature. The data was partitioned into a training set of 662 compounds and an external validation set of 132 compounds. Physicochemical and molecular descriptors were calculated for each compound using ACD labs/logD, MOE (Chemical Computing Group) and Symyx QSAR software packages. Several data mining tools were employed for the construction of models. These included stepwise regression analysis, Classification and Regression Trees (CART), Boosted trees and Random Forest.Several predictive models were identified; however, one model in particular produced significantly superior prediction accuracy for the external validation set as measured using mean absolute error and correlation coefficient. The selected model was a boosted regression tree model which had the mean absolute error for training set of 13.25 and for validation set of 14.96.Plasma protein binding can be modeled using simple regression trees or multiple linear regressions with reasonable model accuracies. These interpretable models were able to identify the governing molecular factors for a high ppb that included hydrophobicity, van der Waals surface area parameters, and aromaticity. On the other hand, the more complicated ensemble method of boosted regression trees produced the most accurate ppb estimations for the external validation set. HubMed – drug


A holistic evolutionary and structural study of flaviviridae provides insights into the function and inhibition of HCV helicase.

PeerJ. 2013; 1: e74
Vlachakis D, Koumandou VL, Kossida S

Viral RNA helicases are involved in duplex unwinding during the RNA replication of the virus. It is suggested that these helicases represent very promising antiviral targets. Viruses of the flaviviridae family are the causative agents of many common and devastating diseases, including hepatitis, yellow fever and dengue fever. As there is currently no available anti-Flaviviridae therapy, there is urgent need for the development of efficient anti-viral pharmaceutical strategies. Herein, we report the complete phylogenetic analysis across flaviviridae alongside a more in-depth evolutionary study that revealed a series of conserved and invariant amino acids that are predicted to be key to the function of the helicase. Structural molecular modelling analysis revealed the strategic significance of these residues based on their relative positioning on the 3D structures of the helicase enzymes, which may be used as pharmacological targets. We previously reported a novel series of highly potent HCV helicase inhibitors, and we now re-assess their antiviral potential using the 3D structural model of the invariant helicase residues. It was found that the most active compound of the series, compound C4, exhibited an IC50 in the submicromolar range, whereas its stereoisomer (compound C12) was completely inactive. Useful insights were obtained from molecular modelling and conformational search studies via molecular dynamics simulations. C12 tends to bend and lock in an almost “U” shape conformation, failing to establish vital interactions with the active site of HCV. On the contrary, C4 spends most of its conformational time in a straight, more rigid formation that allows it to successfully block the passage of the oligonucleotide in the ssRNA channel of the HCV helicase. This study paves the way and provides the necessary framework for the in-depth analysis required to enable the future design of new and potent anti-viral agents. HubMed – drug


No Association between the Response to Methylphenidate and DRD4 Gene Polymorphism in Korean Attention Deficit Hyperactivity Disorder: A Case Control Study.

Clin Psychopharmacol Neurosci. 2013 Apr; 11(1): 13-7
Ji HS, Paik KC, Park WS, Lim MH

Recently the relationship between alleles frequency distribution, drug response and the attention deficit hyperactivity disorder (ADHD), has been actively researched. We investigated the association between the genetic type, alleles and drug response for the dopamine receptor D4 (DRD4) gene in ADHD patients in Korea.One hundred fourteen patients diagnosed with ADHD according to the the Diagnostic and Statistical Manual of Mental Disorders version IV (DSM-IV) diagnostic criteria were selected for the study. The clinical features of patients were confirmed by Korean version of Conners’ parent rating scale, Attention deficit Diagnostic System, Korean version of Spielberger state-trait anxiety scale. Blood samples were taken from the 198 subjects. DNA was extracted from blood lymphocytes, PCR was performed for DRD4 Polymorphism. Alleles, genotype frequencies, the Clinical Global Impression (CGI) improvement score were compared using the chi-square test. Korean ADHD Rating Scale (K-ARS) and CGI severity scores were compared using the t-test.In comparing the ADHD with 4/4 repeats group and without the ADHD with 4/4 repeats group, no significant difference was seen between the DRD4 genetic type, alleles distribution, and CGI drug response.As a result, it is viewed that there is no relationship between ADHD and DRD4, but final decision is indefinite. Follow up studies with larger patient or pure subgroups are expected. HubMed – drug