Mini Reviews in Medicinal Chemistry - Volume 12, Issue 6, 2012

I am very much thrilled to see the complete version of my target as the Guest Editor for Hot Topic issue entitled, “QSAR and Computer Aided Drug Design” for the journal ‘Mini-Reviews in Medicinal Chemistry (MRMC)’. Under this umbrella I had the aim to cover most important areas of structure- (SB) and ligand-based (LB) computer-aided drug design including, recent developments in rational drug design, molecular modelling, quantitative structure-activity relationship (QSAR), QRAR (Quantitative Retention-Activity Relationship), pharmacophore modelling, etc., targeting different diseases & disease conditions, like Alzheimer's disease (AD), HIV, neurodegenerative disorders, depression, malaria, etc. The volume composed of twelve articles from the experts in the field. In the 1st article Ibrahim and co-workers explained different QSAR and molecular modelling approaches on developing some fullerene derivatives as anti-HIV protease inhibitors. Prado and Garcia, in their article discussed about different approaches of QSAR, like CoMFA, CoMSIA, etc., and molecular modeling, like docking, of the molecules against Alzheimer's disease (AD) particular targeting β and γ-secretase inhibitors. Authors in their article also reviewed about the similar approaches utilized for the targets like GSK-3α and GSK-3β....

A Fullerene based system is modified in order to increase its solubility and enhance its ability to carry a protein-like structure. The modified structure, which is proposed to act as HIV-1 protease inhibitor, is [C60-C2H4N-(2,4- XCOCH2OH)C6H4], where the X atom is either O, S or Se. The geometry optimization, vibrational spectra and thermodynamics were performed using semiempirical quantum mechanical PM3 method in order to study the proposed compounds. Furthermore, the quantitative structure activity relationship (QSAR) properties of the compounds are calculated at the same level of theory. Results indicate a possible use of the investigated structures as HIV-1 protease inhibitors. The compounds containing oxygen is more stable as compared to the other two compounds.

Alzheimer's disease (AD) is characterized by several pathologies, this disease is a neuropathological lesion in brain. Indeed, a wealth of evidence suggests that β-amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. AD is the most prevalent form of dementia, and current indications show that twenty-nine million people live with AD worldwide, a figure expected rise exponentially over the coming decades. Clearly, blocking disease progression or, in the best-case scenario, preventing AD altogether would be of benefit in both social and economic terms. However, current AD therapies are merely palliative and only temporarily slow cognitive decline, and treatments that address the underlying pathologic mechanisms of AD are completely lacking. While familial AD (FAD) is caused by autosomal dominant mutations in either amyloid precursor protein (APP) or the presenilin (PS1, PS2) genes. First, we revised 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking of β and γ-secretase inhibitors. Next, we review 2D QSAR, 3D QSAR, CoMFA, CoMSIA and docking for GSK-3α and GSK-3β with different compound to find out the structural requirements.

Antidepressants are psychiatric agents used for the treatment of different types of depression, being at present amongst the most commonly prescribed drugs, while their effectiveness and adverse effects are still the subject of many studies. To reduce the inefficiency of known antidepressants caused by their side-effects, many research efforts have recently focused on the development of improved strategies for new antidepressants drug design. For this reason it is necessary to apply very fast and precise techniques, such as QSAR (Quantitative Structure-Activity Relationships) and QRAR (Quantitative Retention-Activity Relationship), which are capable to analyze and predict the biological activity for these structures, taking in account the possible changes of the molecular structures and chromatographic parameters. We discuss the pharmaceutical descriptors (van der Waals, electrostatic, hydrophobicity, hydrogen donor/acceptor bond, Verloop's parameters, polar area) involved in QSAR and also chromatographic parameters involved in QRAR studies of antidepressants. Antidepressant activities of alkanol piperazine, acetamides, arylpiperazines, thienopyrimidinone derivatives (as preclinical antidepressants) and also the antidepressants already used in clinical practice are mentioned.

We synthesized various dimer forms of 2-methoxyphenols and 2-tert-butylphenols, as dimers such as curcumin exhibit potent antioxidant and anti-inflammatory activity. We investigated the QSARs between the cytotoxicity and independent variables; kinetic parameters (inhibition rate constant (kinh/kp), stoichiometric factor (n)) or DFT-based theoretical parameters (i.e. phenolic O-H bond dissociation enthalpy (BDE), ionization potential according to Koopman’s theorem (IP), LUMO, absolute hardness (η), electronegativity (χ) and electrophilicity (ω)) for 2-methoxyphenols and 2- tert- or 2,6-di-tert-butylphenols. The cytotoxicity of these phenols against human tumor cells (HSG, HL60) and/or human gingival fibroblasts (HGF) showed a marked negative linear relationship to kinh/kp, suggesting that the cytotoxicity of phenols may be related to radical reactions. By contrast, a linear relationship between the cytotoxicity and η-term was demonstrated; 2-methoxyphenols showed a negative slope, whereas 2-tert- or 2,6-di-tert-butylphenols showed a positive slope. Also, the cytotoxicity of tert-butylphenols was linearly dependent on the LUMO-term, showing a positive slope. The cytotoxicity of methoxy-substituted monophenols toward both HSG and HGF cells was related to both log P and η- terms. Also, that of X-phenols toward murine L-1210 cells was related to both log P and η or IP-terms, determined from a dataset reported by Zhang et al., 1998. It was concluded that the phenol-induced cytotoxicity was attributable to radical reactions resulting from the terms (kinh/kp, IP, η, and LUMO) in QSAR. The LUMO-dependent cytotoxicity of 2-tert- or 2,6-di-tert-butylphenols may be related to their quinone oxidation products. Experimental and theoretical parameters provide a useful approach for analysis of the cytotoxicity for phenolic compounds.

Validation of quantitative structure-activity relationship (QSAR) models plays a key role for the selection of robust and predictive models that may be employed for further activity prediction of new molecules. Traditionally, QSAR models are validated based on classical metrics for internal (Q2) and external validation (R2 pred). Recently, it has been shown that for data sets with wide range of the response variable, these traditional metrics tend to achieve high values without truly reflecting absolute differences between the observed and predicted response values, as in both cases the reference for comparison of the predicted residuals is the deviations of the observed values from the training set mean. Roy et al. have recently developed a new parameter, modified r2 (rm 2), which considers the actual difference between the observed and predicted response data without consideration of training set mean thereby serving as a more stringent measure for assessment of model predictivity compared to the traditional validation parameters (Q2 and R2 pred). The rm 2 parameter has three different variants: (i) rm 2 (LOO) for internal validation, (ii) rm 2 (test) for external validation and (iii) rm 2 (overall) for analyzing the overall performance of the developed model considering predictions for both internal and external validation sets. Thus, the rm 2 metrics strictly judge the ability of a QSAR model to predict the activity/toxicity of untested molecules. The present review provides a survey of the development of different rm 2 metrics followed by their applications in modeling studies for selection of the best QSAR models in different reports made by several workers.

Quantitative structure-activity relationship (QSAR) analysis is a practical approach by which chemical structure is quantitatively correlated with biological activity or chemical reactivity. Human ABC transporter ABCG2 exhibits broad substrate specificity toward structurally diverse compounds. To gain insight into the relationship between the molecular structures of compounds and the interaction with ABCG2, we have developed an algorithm that analyzes QSAR to evaluate ABCG2-drug interactions. In addition, to support QSAR analysis, we developed a high-speed screening method for analyzing the drug-drug interactions of ABCG2. Based on both experimental results and computational QSAR analysis data, we propose a hypothetical mechanism underlying ABC-mediated drug transport and its interaction with drugs.

In the present paper 25 known thermolysin inhibitors were docked into thermolysin using the Internal Coordinate Mechanics (ICM™) software. Pharmacophore models based on thermolysin binding modes and activity profiles were generated using the LigandScout™ program. The docking studies indicated that all 25 inhibitors coordinated the catalytic zinc in bidentate or monodentate geometry. A ‘three-point’ pharmacophore model was proposed which consisted of a hydrophobic group, a negative ionizable group and a hydrogen bond acceptor group. Finally the pharmacophore model has been tested against a small compound library containing 18 highly, moderately, less active as well as inactive compounds. The screening indicated that the pharmacophore model could, identify highly active compounds in front of inactive or less active ones.

During the last decade the technological advances in drug discovery changed the absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles of New Chemical Entities (NCEs). Among ADMET processes, absorption plays an important role in the research and development of more effective orally administered drugs. Although significant progress has been made in in vitro, in situ and in vivo experimental determinations of absorption, the development of in silico methodologies has emerged as a cheaper and fast alternative to predict them. Even though several in silico models have been described in the literature to predict oral bioavailability and related properties, the prediction accuracy and their potential use is still limited. The low precision and high variability of data, the lack of a complete experimental and theoretical validation of in silico approach, and above all, the multi-factorial nature of the oral absorption term, make the development of predictive in silico models a thorny task. The present review discusses several important advances regarding the QSPR approaches used in the development of predictive oral bioavailability models. The importance of fixing the problem associated with data resource, as well as improving the reliability of in silico results is highlighted. Optimization of individual properties along the absorption process must be integrated in a multi-objective scenario for studying oral bioavailability behavior in the early drug discovery and development.

Since Hansch's extra thermodynamic multi-parameter approach, originally coined as Linear Free Energy Relationship, great efforts in medicinal chemistry have been made to properly estimate the binding free energy. Despite the often small amount, its value is however very critical in determining a successful binding. As a result, its correct estimation may provide a guide for a prospective rational drug design. The calculation of the absolute binding free energies is however a very challenging task as it requires a rigorous treatment of a number of physical terms that are both very time demanding and to some extent not immediately interpretable. In view of this, the introduction of some numerical approximations has permitted to develop the so called Linear Interaction Energy method that, at present, constitutes the best compromise among accuracy, speed of computation and easy interpretation. The initially developed Linear Interaction Energy method was subsequently revisited and several important improvements have been made. Significant examples are the Extended Linear Response, the surface generalized Born LIE, the molecular mechanics generalized Born surface area, the linear interaction energy in continuum electrostatics as well as its quantum mechanics variant. Principles and selected applications of these methods will be herein reviewed.

Malaria is one of the most dangerous diseases in developing countries. The chemotherapy of malaria has been based on drugs developed more than half a century ago. These drugs are continuously losing their efficacy, mainly due to multi-drug resistance developed by the malaria-causing parasite. In the last three decades, artemisinin and artemisinin-like compounds have proven to be efficient alternatives to the chemotherapeutic control of malaria. These facts have led to an increasing interest in the development of Quantitative Structure Activity Relantioship (QSAR) models for these compounds. This work presents a critical view on some QSAR models, and shows that, due to lack of a rigorous selection of the descriptors entering the models, most of them are unable to accurately indicate the molecular cause of biological activity. Some reasons for the weakness of the published models are discussed.

In modern drug discovery process, ADME/Tox properties should be determined as early as possible in the test cascade to allow a timely assessment of their property profiles. To help medicinal chemists in designing new compounds with improved pharmacokinetics, the knowledge of the soft spot position or the site of metabolism (SOM) is needed. In recent years, large number of in silico approaches for metabolism prediction have been developed and reported. Among these methods, QSAR models and combined quantum mechanics/molecular mechanics (QM/MM) methods for predicting drug metabolism have undergone significant advances. This review provides a perspective of the utility of QSAR and QM/MM approaches on drug metabolism prediction, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive dementia and loss of cognitive abilities. Until now, AD remains incurable. The principal biological target for AD therapy is acetylcholinesterase (AChE). Thus, the search for new drug candidates like AChE inhibitors constitutes an essential part for the discovery of more potent anti-AD agents. In general terms, rational drug design methodologies have played a decisive role. The present work is focused on the current state of the Ligand-Based Drug Design (LBDD) methods which have been applied to the elucidation of new molecular entities with high anti-AChE activity. Also, as a contribution to this field, we suggest a promising fragment-based approach for the search and prediction of new AChE inhibitors and for the fast and efficient extraction of substructural alerts which are responsible for the anti-AChE activity.