In view of the cardioprotective effect of high‐density lipoproteins (HDL) and the limited effects of statin and fibrate therapy on HDL cholesterol, it is clinically relevant to test whether pharmacological treatment aimed at raising HDL lowers cardiovascular risk. Cholesteryl ester transfer protein (CETP) is a new therapeutic target, because the cholesteryl ester transfer process lowers HDL cholesterol and contributes to an atherogenic lipoprotein profile, particularly when plasma triglycerides are high. Clinical evidence suggests that coronary artery calcification as well as intima media thickness is positively related to plasma cholesteryl ester transfer, and that high plasma CETP concentration is associated with increased cardiovascular risk in hypertriglyceridaemia. However, CETP could also have anti‐atherogenic potential, since it provides a potentially beneficial route for delivery of HDL‐derived cholesteryl esters to the liver. In addition, CETP could also favourably stimulate peripheral cell cholesterol removal and enhance hepatic cholesterol uptake. Recent evidence suggests that a high CETP level may confer lower cardiovascular risk in the context of low triglycerides. At maximal doses, the CETP inhibitors JTT‐705 and torcetrapib elicit a marked rise in HDL cholesterol of up to 34% and 91–106%, respectively. The effectiveness of these drugs on (intermediate) clinical outcome measures is currently being tested in large‐scale phase III clinical trials, with torcetrapib being only evaluated in combination therapy with atorvastatin. When and how to use CETP inhibitors, e.g. in combination with a statin or a fibrate, is a major challenge. We propose that low HDL cholesterol in the context of high triglycerides, such as found in type 2 diabetes mellitus, could become an important indication area for this new class of drugs.

CETP inhibition in cardiovascular risk management: a critical appraisal (vol 37, pg 90, 2007)

Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (Apis mellifera L.). Thus, a particular attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD50) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (Q
 2 and $$ \overline{r_m^2}\Big) $$
 are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD50 of 20 other pesticides. A good correlation between predicted and experimental values was found (R
 2 = 0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.

QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees ( Apis mellifera L.)

Prediction performance often depends on the cross- and test validation protocols applied Several combinations of different cross-validation variants and model-building techniques were used

Modelling methods and cross-validation variants in QSAR: a multi-level analysis$.

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies.

/pdf/lipophilicity-in-drug-design-an-overview-of-lipophilicity-3gq8ctvzps.pdf

Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies.

Background Alzheimer's disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer's disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites. Methods In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria. Results The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme. Conclusion Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors.

A new family of AChE inhibitors, N-benzylpiperidines, showed exceptional efficacy in vitro and in vivo, minimal side effects and high selectivity for acetylcholinesterase (AChE). Three regression methods were chosen in this work to develop robust predictive models, namely multiple linear regression (MLR), genetic function approximation (GFA) and multilayer perceptron network (MLP). Ten descriptors were selected for a dataset of 99 molecules, using a genetic algorithm. The best results were obtained for MLP with a 10-6-1 artificial neural network model trained with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Statistical prediction for MLR and GFA were r2 = 0.882 and r2 = 0.875, respectively. Because internal and external validation strategies play an important role, we adopted all available validation strategies to check the robustness of the models. All criteria used to validate these models revealed the superiority of the GFA model. Therefore, the models developed in this study provide an excellent prediction of the inhibitory concentration of a new family of AChE inhibitors.

QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors.

Cardiovascular disease (CVD) is one of the major causes of human death. Preliminary evidence indicates that the inhibition treatment of Cholesteryl Ester Transfer Protein (CETP) causes the most pronounced increase in HDL cholesterol reported so far. Merck has disclosed certain (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one derivatives, which show potent CETP inhibitory activity. Therefore, it would be desirable to develop computational models to facilitate the screening of these inhibitors. In the present work, quantitative structure-activity relationship (QSAR) models have been developed to predict the therapeutic potency of 108 derivatives of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one: Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Feedforward Neural Network using Particle Swarm Optimization (FNN-PSO). Six descriptors were selected using genetic algorithms, whereas, internal and external validation of the models was performed according to all available validation strategies. It was shown that CETP inhibitory activity is mainly governed by electronegativity, the structure of the molecule, and the electronic properties. The best results were obtained with the SVR model. The results obtained may assist in the design of new CETP inhibitors.

2D QSAR studies on a series of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one as CETP inhibitors.

Numerous studies show that tacrine derivatives exhibit increased inhibitory activity against butyrylcholinesterase (BuChE) and acetylcholinesterase (AChE). However, the screening assays for current...

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Papers

QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors.

2D QSAR studies on a series of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one as CETP inhibitors.

Prediction of therapeutic potency of tacrine derivatives as BuChE inhibitors from quantitative structure-activity relationship modelling.