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A Computational Quantitative Structure-Activity Relationship Study of Carbamate Anticonvulsants Using Quantum Pharmacological Methods

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A Computational Quantitative Structure-Activity Relationship Study of Carbamate Anticonvulsants Using Quantum Pharmacological Methods View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Seizure 1998; 7: 347-354 A computational quantitative structure-activity relationship study of carbamate anticonvulsants using quantum pharmacological methods JENNIFER L. KNIGHT* & DONALD F. WEAVER-f Department of Chemistry*, Department of Medicine? (Neurology), Queen’s University, Kingston, Ontario, Canada, K7L 3N6 Correspondence to: Donald F. Weaver, Department of Medicine (Neurology), Queen’s University, Kingston, Ontario, Canada, K7L 3N6. A pattern recognition quantitative structure-activity relationship (QSAR) study has been performed to determine the molecular features of carbamate anticonvulsants which influence biological activity. Although carbamates, such as felbamate, have been used to treat epilepsy, their mechanisms of efficacy and toxicity are not completely understood. Quantum and classical mechanics calculations have been exploited to describe 46 carbamate drugs. Employing a principal component analysis and multiple linear regression calculations, five crucial structural descriptors were identified which directly relate to the bioactivity of the carbamate family. With the resulting mathematical model, the biological activity of carbamate analogues can be predicted with 85-90% accuracy. Key words: felbamate, anticonvulsant, structure-activity. INTRODUCTION carbamate fragment capable of producing desired anticonvulsant biological effects. The pattern recognition approach to QSAR The underlying mechanisms of action for the attempts to determine reasons for biological agents used to treat epilepsy are incompletely activity in some compounds and inactivity in understood. For example, felbamate (2-phenyl- others within the same drug class. This approach 1,3-propanediol dicarbamate), a newly-developed assumes the following: (1) a wide variety of a potential anticonvulsant, has an unknown mecha- drug’s physical and chemical properties can be nism of actionim5; moreover, a number of patients reduced to numerical quantities, termed who received this medication died from either descriptors’, (2) the descriptors which influence hepatoxicity or aplastic anemia6. Thus, just as its activity can be determined and calculated’ and (3) mechanism of efficacy is unknown, felbamate’s a mathematical relationship between the descrip- mechanism of toxicity has likewise not been tors and the compound’s biological activity can be elucidated’-‘. A thorough molecular-level study described”,’ ‘. This knowledge assists in the early of felbamate and other carbamates may shed stages of research as the therapeutic potential of a light on these mechanisms. Quantitative given class of compounds is investigated. The structure-activity relationship (QSAR) studies simplicity of some of the descriptors and the are a technique which aid in identifying the availability of high-powered computers for calcu- structural features which are responsible for lating the large number of equations facilitate biological activity. This process, in turn, has the this in-depth structural analysis. In a competitive potential to uncover possible mechanisms of market, sensitive, yet economical devices are of action for efficacy and toxicity’. Therefore, the great importance. In this study, a set of focused goal of this study is to perform a pattern carbamates is thoroughly described and a recognition QSAR on carbamate anticonvulsants mathematical model is developed which predicts to identify the bioactive pharmacophore, i.e. the the activity or inactivity of individual analogues three-dimensional arrangement of atoms within a against an animal model of epilepsy. 1059-131 l/98/050347 + 08 $12.00/O 0 1998 British Epilepsy Association 348 J. L. Knight & D. F. Weaver MATERIALS AND METHODS molecular fragment (i.e. ‘the bioactive fragment’) which constituted the region of structural com- monality among active anticonvulsants. Background to methods The selection of descriptors capable of com- pletely encoding the significant and structural aspects of carbamate molecular structure was The correlation of biological activity with mole- crucial. The carbamate numbering scheme is cular structure is central to modem neurophar- shown in Fig. 1. The descriptors are listed in macology and enables an enhanced mechanistic Table 1, and are catagorized into four classes: understanding of drug action at the molecular geometric, electronic, topological and physio- level of reality. Accordingly, contemporary chemical (42 descriptors were employed in this approaches to the study and design of bioactive study). Geometric descriptors represent three- molecules are based on the notion that bioactivity dimensional properties and reflect aspects of may be related quantitatively to molecular molecular shape and size. The geometric descrip- structure through the application of QSAR tors employed in this study were derived from techniques’2-‘4. The establishment of QSAR theoretical quantum mechanics and molecular relationships for anticonvulsants has been hin- mechanics calculations. The carbamate analogues dered by the chemical diversity of the moelcules under study were conformationally and geomet- and by the complexity of the physiological and rically optimized using the AM1 Hamiltonian biochemical processes that initiate and propagate after a preliminary scanning of their conforma- seizure activity. tional space using multiple molecular mechanics A vareity of QSAR techniques are available, force field optimizations. Electronic descriptors including free energy models (Hansch such as charge densities, bond moments, orbital equation15”6), mathematical models (Free- energies and frontier electron densities, reflect Wilson equation”), statistical models” (dis- molecular properties arising from variable el- criminant analysis, cluster analysis), quantum ectron distribution through a compound’s str- mechanics and molecular mechanics methods”, uctural framework. The electronic descriptors topological methods2’ and pattern recognition used in this study were obtained from the methods2’-24. In recent years, the pattern recog- Mulliken population analyses of theoretical AM1 nition technique described by Stuper and Jurr?‘, semi-empirical quantum mechanics calculations. Kirschner and Kolalski26 and Wold et al”*24 has Topological descriptors encode aspects of mole- emerged as one of the most all-encompassing cular composition and connectivity, and describe and versatile techniques. patterns of interatomic interconnections that The initial objective of this theoretical pattern determine ultimate molecular architecture. The recognition study was to identify a theoretical topological descriptors used in this study were ‘classification rule’ capable of correctly categoriz- derived from graph theory calculations2635. In ing carbamate compounds into pharmacologically graph theory calculations, a graph G is a active and inactive classes based solely upon an examination of their structural properties. This problem was approached in two stages. First, a set (a) 04 (b) of carbamates of known biological activity was 0 selected and designated as the ‘training set’. A group of theoretical ‘descriptors’ was then gener- ated to describe each compound in this training set. Statistical techniques (regression analyses) were then employed to identify the minimal number of descriptors (i.e. the ‘essential descrip- (c) tor set’) capable of distinguishing active from inactive carbamates. This essential descriptor set -1 AN/H constitutes a classification algorithm or rule. This CH-CH.--0 classification rule was next applied to a second set Cb’ of carbamates, termed the ‘test set’, to evaluate the validity and predictability of the rule. As a Fig. 1: Carbtimate numbering scheme. Representations of corollary to developing a classification rule which the carbamates evaluated in the study. The generalized designated activity from inactivity, it was possible form is represented by A while f3 and C represent specific through complementarity arguments, to identify a analogues Cl and C2, respectively. A structure-activity relationship study of carbamate anticonvulsants 349 Table 1: List of descriptors the Schrodinger partial differential equation: Geometric descriptors (from AM1 semi-empirical W = W, where H is the Hamiltonian calculations) differential operator and Ic, is the wave function. 1-3 Bond lengths Molecular quantum mechanics calculations may NI-C2,C2-03,C2-04. be ab initio or semi-empirical. Semi-empirical 4-6 interatomic distances Nl -R, Nl - Rl, Nl - R2. calculations use a simpler Hamiltonian than the 7-9 Bond angles correct molecular Hamiltonian and incorporate Nl-C2-03,Nl-C2-04,03-C2-04. additional parameters designed to fit experimen- 10 Torsional angle tal data. In contrast, ab initio calculations use the Nl -C2-03-04. full correct Hamiltonian and attempt a solution Electronic descriptors (from AMI semi-empirical calculations) through a rigorous first principles treatment. 1 I-14 Atomic charge densities Classical mechanics force field calculation is Nl, C2, 03. 04. conceptually quite different: it is an empirical 15-16 Molecular dipole moments technique which employs multiple classical equa- 17 Heath of formation tions of motion to provide a priori geometries for 18 E HOMO-LUMO Topological descriptors (from graph theory calculations) varying conformations of large molecules. The 19-20 Zagreb topological indices sum of the individual classical equations constit- M,. M2 utes a multi-dimensional potential
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