Small‐Molecule Negative Modulators of Adrenomedullin: Design, Synthesis, and 3D‐QSAR Study
Virginia RoldósSonsoles Martín‐SantamaríaMiguel JuliánAlfredo Martı́nezLaurence ChoulierDanièle AltschuhBeatriz de Pascual‐TeresaAna Ramos
8
Citation
34
Reference
10
Related Paper
Citation Trend
Abstract:
Adrenomedullin (AM) is a peptidic hormone that was isolated in 1993, the function of which is related to several diseases such as diabetes, hypertension, and cancer. Compound 1 is one of the first nonpeptidic small-molecule negative modulators of AM, identified in a high-throughput screen carried out at the National Cancer Institute. Herein we report the synthesis of a series of analogues of 1. The ability of the synthesized compounds to disrupt the binding between AM and its monoclonal antibody has been measured, together with surface plasmon resonance (SPR)-based binding assays as implemented with Biacore technology. These data were used to derive a three-dimensional quantitative structure-activity relationship (3D-QSAR) model, with a q(2) (LOO) value of 0.8240. This study has allowed us to identify relevant features for effective binding to AM: the presence of a hydrogen-bond donor group and an aromatic ring. Evaluation of the ability of selected compounds to modify cAMP production in Rat2 cells showed that the presence of a free carboxylic acid is essential for negative AM modulation.Keywords:
Adrenomedullin
High-Throughput Screening
Loo
Loo
Molecular descriptor
Cite
Citations (33)
Abstract A quantitative structure–activity relationship (QSAR) of 3‐(9‐acridinylamino)‐5‐hydroxymethylaniline (AHMA) derivatives and their alkylcarbamates as potent anticancer agents has been studied using density functional theory (DFT), molecular mechanics (MM+), and statistical methods. In the best established QSAR equation, the energy ( E NL ) of the next lowest unoccupied molecular orbital (NLUMO) and the net charges ( Q FR ) of the first atom of the substituent R , as well as the steric parameter ( MR 2 ) of subsituent R 2 are the main independent factors contributing to the anticancer activity of the compounds. A new scheme determining outliers by “leave‐one‐out” (LOO) cross‐validation coefficient ( q ) was suggested and successfully used. The fitting correlation coefficient ( R 2 ) and the “LOO” cross‐validation coefficient ( q 2 ) values for the training set of 25 compounds are 0.881 and 0.829, respectively. The predicted activities of 5 compounds in the test set using this QSAR model are in good agreement with their experimental values, indicating that this model has excellent predictive ability. Based on the established QSAR equation, 10 new compounds with rather high anticancer activity much greater than that of 34 compounds have been designed and await experimental verification. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2007
Loo
HOMO/LUMO
Molecular descriptor
Cite
Citations (21)
Caffeic Acid Phenethyl Ester (CAPE) compounds are potentially colorectal anticancer drugs. QSAR (Quantitative Structure-Activity Relationship) research on the CAPE compound class has been carried out, but the model in the previous study did not meet the goodness of fit criteria. The development of the CAPE compound QSAR model as a colorectal anticancer was carried out to obtain a model that meets the goodness of fit criteria and is valid. Eighteen CAPE compounds were used to build the QSAR model using the Multiple Linear Regression (MLR) technique. The descriptor selection was carried out using the backward elimination method and the validation test using the internal leave one out (LOO) cross-validation. The results showed that the QSAR model with four descriptors, namely MDEC22, MDEC23, JGI1, and molecular weight (BM), met the goodness of fit and Q2(LOO) criteria. The development of the QSAR model by adding the LogP descriptor resulted in the QSAR 5 descriptor model with higher goodness of fit level than the QSAR model without the LogP descriptor. Both of these QSAR models have the potential to be used as predictors in the development of a new class of CAPE compounds as anticancer agents against HT-29 cells.
Loo
Goodness of fit
Cite
Citations (0)
Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R²) > 0.7, explained variance in leave-one-out prediction (Q²LOO) and in leave-many-out prediction (Q²LMO) > 0.6, variance explained in external prediction (Q²F1, Q²F2, and Q²F3) > 0.7, and concordance correlation coefficient (CCC) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.
Loo
Concordance correlation coefficient
Cross-validation
Cite
Citations (19)
Loo
Binding affinities
Affinities
Predictability
Cite
Citations (1)
Aromatic hydrocarbons,one of the persistent organic pollutants(POPs),has been usually found in mussels,accumulated for their hard mobility and activities in harbours and estuaries.In this study,based on the 96 hr-LC50 of 12 aromatic hydrocarbons with larval sinonvaculina constricta,three-dimensional quantitative structure-activity relationship(3D-QSAR) technique:comparative molecular similarity indices analysis(CoMSIA) and 2D-QSAR technique:multiple linear regression(MLR) were described to obtain more detailed insight into the structure-activity relationships between the molecular structure and bio-activity.The results show the MLR model based on density functional theory(DFT) calculation carried out at the B3LYP/6-311** level with Gaussian 03 program yielded a very good correlation with a coefficient squared R2 of 0.716 and a cross-validated Q2 of 0.874.The dipole moment and enthalpy,as the thermodynamic parameters,were two important factors influencing pLC50.Correspondingly,CoMSIA based on the partial least-squares(PLS) methodology with steric,electrostatic,hydrophobic,H-bond donor and acceptor fields contributing simultaneously were employed and the values of R2 and the cross validation with leave-One-Out(LOO) Q2LOO were 0.585 and 0.990,respectively,which reveals the structure features,such as the electronegative substituent(nitro-group),hydrophobic groups(the benzene ring) and H-bond(nitro-group),related to the toxicity.The results of 2D-QSAR employing MLR model and 3D-QSAR employing CoMSIA model provide the useful information for predicting the toxicity of other aromatic hydrocarbons by comparing the molecular structures of similar compounds.
Loo
Cite
Citations (0)
The present work has focused on the application of the inverse-QSAR/QSPR problem for generating new structures of pesticides; this is in view of its extremely important and widespread use in several areas, particularly the agricultural field. For this reason, we implemented a methodology containing nine detailed successive steps that include a quantitative structure–activity/property relationship (QSAR/QSPR) study performed to develop a model that relate s the structures of 190 pesticides compounds to their n-octanol–water partition coefficients ( logk ow ). We used the unique atomic signatures which represent the structures and acts as independent variables while the property ( logk ow ) as the dependent variable. The model was constructed using 130 molecules as training set, and predictive ability tested using 60 compounds. Modeling of logk ow of these compounds as a function of the signatures descriptors was established by multiple linear regression (MLR) using (LOO) cross-validation. As a result, a QSAR/QSPR equation with 14 atomic signatures was hereby obtained with a R 2 =0.659273, Q 2 =0.65617 and RMSE training = 0.930192, s=1.37297 for the training set and in leave-one-out (LOO) cross-validation experiment set value, q 2 =0.605676, RMSE LOO = 1.0936 respectively. In addition to all of the above, new structures have been generated for a range of pesticides that can be included as future search topics.
Loo
Applicability domain
Molecular descriptor
Training set
Cross-validation
Cite
Citations (7)
This study was carried out elucidate the structural properties required for pyridazinyl derivatives to exhibit angiotensin II receptor activity. The best 2D-QSAR model was selected, having correlation coefficient r2 = 0.8156, cross validated squared correlation coefficient q2 = 0.7348 and predictive ability of the selected model was also confirmed by leave one out cross validation method. Further analysis was carried out using 3D-QSAR method k-nearest neighbor molecular field analysis approach; a leave-one-out crossvalidated correlation coefficient of 0.7188 and a predictivity for the external test set (0.7613) were obtained. By studying the QSAR models, one can select the suitable substituent for active compound with maximum potency.
Loo
Cite
Citations (0)
A QSAR analysis was conducted on a series of 2-arylpyrimidine and s-triazine derivatives as selective PDE4B inhibitors. Primary objective of the study is to develop predictive QSAR models for s-triazines and 2-arylpyrimidines as selective PDE4B and to id.
Loo
Molecular descriptor
Training set
Cite
Citations (3)
Benzothiazole
Loo
Molecular descriptor
Cite
Citations (21)