3-D Pharmacophore Modelling

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Abstract of "3-D Pharmacophore Modelling" Project

Histamine 3 receptor involved in histamine actions and occurring in CNS and PNS is adruggable target for treatment of neurological disorders like Alzheimer’s disease, Parkinson’s disease, Schizophrenia, attention deficit-hyperactivity disorders (ADHD), epilepsy and control of feeding, appetite and body weight. Limitations existing for compounds such as AChE inhibitors to treat such diseases have provoked the search for potent H3 receptor antagonists using molecular modeling tools. In recent years, analogue based drug designing yielded the scope for improvement of H3 receptor antagonists potency as the protein does not have a crystal structure. Attempts were also made by researchers to homology model the protein and combine docking with pharmacophore models to understand the structural features responsible for interaction of the antagonists with the receptor.

In this project we have constructed quantitative 3-D pharmacophore using 30 chemically diverse H3 receptor antagonists by HypoGen (Catalyst 4.11). The model was tested for its predictiveness using a large test set containing 290 antagonists. The best model (hypo 1) consisted of 2 HBA lipids, 1 positive ionizable and 1 hydrophobic feature. And 3D-QSAR analysis of a set of 25 analogues of H3 receptor antagonist was performed by most widely used computational tool, molecular field analysis (MFA) to investigate the substitutional requirements for the favorable receptor-drug interaction and to derive a predictive model that may be used for the designing of a novel histamine receptor antagonists. Regression analysis was carried out using genetic partial least squares (G/PLS) method. A highly predictive and statistically significant model was generated. The predictive ability of the model developed was assessed using a test set of 25(r2 pred as high as 0..869).The analyzed MFA model has demonstrated a good fit, having r2 value of 0.901 and cross-validated coefficient r2 cv value as 0.899.

Histamine Receptors

Histamine is a biogenic amine involved in local immune responses, regulates physiological functions in the gut as well acts as a neurotransmitter. Physiological functions of histamine are executed through a set of 7 helical trans-membrane receptors namely histamine 1 (H1), histamine 2 (H2), histamine 3 (H3), and histamine 4 (H4) [1, 2] belonging to G protein coupled receptor family. Post-synaptically located H1 and H2 receptors are involved in allergic reactions and gastric secretion, respectively, and antagonists for these receptors are already been used in the clinic to treat diseases involving allergy and excess gastric acid production. Recently, discovered H4 receptors are involved in various immunological responses and are located in immune cells [3] like T cell, monocytes and neutrophils. The other kind of histamine receptors are H3 receptors identified by Arrang et al., in 1983 [4].

The presence of histamine H3 receptor was confirmed in 1987, through the development of the agonist R-alpha methyl histamine and the antagonist thioperamide [5]. H3 receptors are primarily located in central nervous system (CNS) and peripheral nervous system (PNS) of many species [6]. These receptors are highly constitutive in their activity and function as presynaptic auto-receptors in CNS and control the release and synthesis of histamine by a negative feed back mechanism [7,8]. Whereas, in non histaminergic neurons, H3 hetero-receptors regulate the release of other neurotransmitters such as dopamine, acetylcholine, glutamate, noradrenaline, GABA and serotonin [9]. Histamine is a biogenic amine involved in local immune responses, regulates physiological functions in the gut as well acts as a neurotransmitter. Physiological functions of histamine are executed through a set of 7 helical trans-membrane receptors namely histamine 1 (H1), histamine 2 (H2), histamine 3 (H3), and histamine 4 (H4) [1, 2] belonging to G protein coupled receptor family.

Post-synaptically located H1 and H2 receptors are involved in allergic reactions and gastric secretion, respectively, and antagonists for these receptors are already been used in the clinic to treat diseases involving allergy and excess gastric acid production. Recently, discovered H4 receptors are involved in various immunological responses and are located in immune cells [3] like T cell, monocytes and neutrophils. The other kind of histamine receptors are H3 receptors identified by Arrang et al., in 1983 [4]. The presence of histamine H3 receptor was confirmed in 1987, through the development of the agonist R-alpha methyl histamine and ]the antagonist thioperamide [5]. H3 receptors are primarily located in central nervous system (CNS) and peripheral nervous system (PNS) of many species [6]. These receptors are highly constitutive in their activity and function as presynaptic auto-receptors in CNS and control the release and synthesis of histamine by a negative feed back mechanism [7, 8].

Whereas, in non histaminergic neurons, H3 hetero-receptors regulate the release of other neurotransmitters such as dopamine, acetylcholine, glutamate, noradrenaline, GABA and serotonin [9]. Because of the high occurrence of H3 receptors in CNS and the functions regulated, it suggests that H3 receptor can be a druggable target and antagonists developed for this receptor can be used for the treatment of neurological disorders like Alzheimer’s disease, Parkinson’s disease, Schizophrenia, attention deficit-hyperactivity disorders (ADHD), epilepsy and control of feeding, appetite and body weight [10, 11, 12, 13]. Also, it has been suggested that the H3 antagonists improve cognitive function possibly via an increase of acetycholine release [14, 15]. On the other hand, clinical investigators used acetylcholinesterase (ACHE) [16] inhibitors for treating such neurological disorders. Although, ACHE inhibitors promote memory function, there tolerability and efficacy is limited and patients suffer a mild cognitive impairment and other gastrointestinal and cardiovascular side effects. These limitations provoked researchers to find new molecules with druggable properties.

Generation of Pharmacophore Hypotheses with HypoGen

A pharmacophore is a representation of generalized molecular features including 3D (hydrophobic groups, charged/ionizable moieties, and hydrogen bond donors/acceptors), 2D (substructures), and 1D (physical and biological data) aspects that are considered to be responsible for a desired biological activity. Thus, the next step in the generation of a pharmacophore model with HypoGen, after the selection of the training set, is the definition of the correct, feasible features, defined by chemical functions, locations, orientations in space, tolerance in locations, and weights. Taking into account the chemical nature of the compounds considered in this work, the following five features were selected to form the essential information in the hypothesis generation process: hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), hydrophobic aromatic (HpAr), and ring aromatic (Ar).

The Nl was used in order to broaden the search for deprotonated or protonated carboxylic groups at a physiological pH. The HypoGen algorithm was then forced to select only five features due to molecular flexibility and molecular complexity. For molecules larger than dipeptides, Catalyst will often find five feature hypotheses automatically, but for smaller molecules, three- or four-feature hypotheses might constitute the majority. Since hypotheses with a larger number of features are more likely to be stereo specific and generally more restrictive models, the minimum of total features was set to 5 in order to force Catalyst to search for five-feature hypotheses.

Finally, prior to a HypoGen run, the value for the uncertainty in biological data must be set. Indeed, Catalyst generates a chemical-feature-based model on basis of the most active compounds. These compounds are determined by performing a simple calculation based on the activity and uncertainty. As a matter of fact, the activity of the most active compound is multiplied by the uncertainty (which is set equal to 3.0 by default in the software) to establish a comparison number, "A". The activity of the next most active compound is divided by the uncertainty, and this result in "B", which is then compared to A. If B is smaller than A, the compound is included in the most active set; if not, the procedure stops.

3-D Quantitative Structure Activity Relationship

The fundamental structure activity relationship studies reveals that the structures can be easily compared, overlayed and displayed. The Quantitative Structure Activity Relationship based on physico-chemical properties describes a drug's structural, electronic and physicochemical characteristics. Data sets are produced using all available descriptors. Multivariate statistical methods are used to analyze the data sets thus produced QSAR studies are useful in the following three ways.

1. To predict biological activity in untested compounds.

2. To define the structural requirements required for a better active lead molecule.

3. To design a test set of compounds in order to maximize the amount of Information concerning structural requirements for activity from a minimum number of compounds tested.

A QSAR equation numerically defines the chemical structure and helps to predict biological Activity from physiochemical properties. Biological Activity is defined as pharmacological response usually expressed in milliomoles such as the effective dose in 50% of the subjects (ED50), the lethal dose in 50% of the subjects (LD50), or the minimum inhibitory concentration (IC50). It is common to express the Biological Activity as a reciprocal QSAR equation is similar to the equation for a straight line y = mx + c.

Conclusion

An attempt was made to present and discuss here the ongoing scenario regarding the molecular modeling approaches like pharmacophore modeling and QSAR modeling to discover novel H3 receptor antagonists. Earlier pharmacophore studies on H3 receptor antagonists were mostly qualitative and restrictive with respect to the compounds (nonimidazole derivatives) utilized for generating such models. Upon analysis of the qualitative pharmacophore models previously available for H3 receptor antagonists with the quantitative model constructed by us using 320 molecules consisting of both imidazole and non-imidazole based compounds, it is suggested that a more potent H3 receptor antagonist would be available for therapeutic interventions if it contains two hydrogen bond acceptor lipid/oxygen rich groups, a hydrophobic and two positive ionizable/basic amino groups.

Simultaneously , 3D-QSAR model for H3 histamine antagonists was developed based on steric and electrostatic descriptors to investigate the substitutional requirements for the favorable receptor-drug interaction and to predict relative inhibitory activities of 37 analogues of H3 histamine receptor antagonists. This study yielded stable and statistically significant model with high actual as well as predicted correlation coefficient. Three-dimensional features, electrostatic and steric, can be easily identified from the map developed for the best model. Significant predictive ability of the model observed for the external test set molecules supports that the derived model can be used for the designing further novel inhibitors. Overall, the present 3D-QSAR study investigates the indispensable structural features, which can be exploited for the modifications in histamine H3 receptor anatagonists in order to achieve improved histamine H3 receptor inhibitory activity.

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