Importance of 3D structure of drugs in drug design and drug receptor interactions
By - 06/20/2007
Jalpan K. Joshi
For designing of newer and safer drugs with good potency it is necessary to understand drug receptor interactions in detail. Drug receptor interactions are governed by the stereochemistry of the drugs as well as the receptors. Increase in the dimensions in drug design will increase the chances of obtaining safer drug with good therapeutic effects.
Starting from receptors:
Receptors are proteins (functional macromolecular component of an organism) interacting with extra-cellular physiological signals and converting them into intracellular effects. The most important concept is that the receptor: Receives a signal; and Transduces the signal to An Effector mechanism.
3D structure of Drugs mainly target,
(1) Enzymes (Activators or Inhibitors)
(2) Proteins
(3) Nucleic acids (RNA, DNA)
(4) Membranes
(5) Ion channel receptors
(6) G-Protein coupled receptors
(7) Tyrosine kinase linked receptors.
Binding of drugs with these receptors is governed by bond length, bond angle, favourable conformation, torsional strain, hybridization, charge, size and nature of substituent groups, quantum mechanics and quantum dynamics. The importance of most factors affecting the 3D-shape of the drugs and receptors is illustrated in figure 1. with simple example of the oligopeptide chain.
Figure 1: Factors affecting 3D shape of drugs and receptors
Stereochemical aspects:
Different stereoisomers (Enantiomers, Diastereomers, Epimers, Geometrical isomers and Bioisosteres) show different biological activity in quantity and quality as well as different Pharmacokinetic and Pharmacodynamic pattern.
(S)-Ibuprofen is over 100-fold more potent inhibitor of Cyclo-oxygenase-I than (R)-Ibuprofen. (R)-Methadone has a 20-fold higher affinity for the µ opioid receptor than (S)-Methadone. (S)-Citalopram is over 100-fold more potent inhibitor of the Serotonin Reuptake Transporter than (R)-Citalopram. (S)-Baclofen antagonises the effects of (R)-Baclofen. The bioavailability of (R)-Verapamil is more than double than that of (S)-Verapamil due to reduced hepatic first-pass metabolism. The volume of distribution of (R)-Methadone is double than that of (S)-Methadone due to lower plasma binding and increased tissue binding. The clearance of (R)-Fluoxetine is about four times greater than (S)-Fluoxetine due to a higher rate of enzyme metabolism. The renal clearance of (R)-Pindolol is 25% less than (S)-Pindolol due to reduced renal tubular secretion. Half-life of (S)-Fluoxetine is one quarter than (R)-Fluoxetine. Different diastereomers of Ephedrine shows different activity. Trans-diethylstilbeostrol is more oestrogenic than Cis-diethylstilbeostrol. The staggered conformation of Acetylcholine interacts with muscarinic receptors and gauche conformation reacts with nicotinic receptors.
Bioisosteric replacement changes size, conformation, inductive and mesomeric effects, polarizability, H-bond formation capacity, pKa, solubility, hydrophobicity, reactivity and stability of molecules and provide greater selectivity, less side effects, decreased toxicity, improved pharmacokinetics (solubility-hydrophobicity), improved potency, patantibility and increased stability of molecules.
CoMFA in QSAR:
CoMFA (Comparative Molecular Field Analysis) is a 3D QSAR technique based on data from known active molecules and can be applied when the 3D structure of the receptor is unknown. To apply CoMFA, all that is needed are the activities and the 3D structures of the molecules. Activities have to be measured, but 3D structures can be determined either by measurement (crystal X-ray analysis) or by calculation from the 2D diagram and (optionally) subsequent optimization. The aim of CoMFA is to derive a correlation between the biological activity of a set of molecules and their 3D shape, electrostatic and hydrogen bonding characteristics. This correlation is derived from a series of superimposed conformations, one for each molecule in the set. These conformations are presumed to be the biologically active structures, overlaid in their common binding mode. Each conformation is taken in turn, and the molecular fields around it are calculated. The fields, usually electrostatic and steric (Van der Waals interactions), are measured at the lattice points of a regular Cartesian 3D grid; the lattice spacing is typically 2 Å. The "measured" interaction is between the molecule and a probe atom (a sp3-hybridized carbon with +1 charge).
In CoMFA,
(1) Active molecules are placed in a three-dimensional grid (2-Å spacing) encompassing all of the molecules.
(2) At each grid point, steric energy (Lennard-Jones potential) and electrostatic energy are measured for each molecule by a probe atom (sp3-hybridized carbon with +1 charge).
(3) To minimize domination by large steric and electrostatic energies, all energies that exceed a specified value (default 30 kcal/mol) are set to the cutoff value. CoMFA uses a partial least-squares (PLS) analysis to predict activity from energy values at the grid points.
A rival technique, CoMSIA (Comparative Molecular Similarity Index Analysis) is similar to CoMFA, instead uses Gaussian approximations to model the probe interactions which allows for much smoother sampling of the fields around the molecules, as well as incorporating new field information such as hydrophobic and hydrogen bonding fields.
About softwares:
Different softwares are available for Drug Design and Molecular Modeling like Sybyl, Alchemy, Amber, Chem. Office, MOE (molecular operating environment) and Cerius as a tool for Computer Aided Drug Design (CADD), Computer Assisted Molecular Design (CAMD) and Computer-Assisted Molecular Modelling (CAMM) which consider 3D aspects of drugs and provide,
(1) The 3-D structure of molecules
(2) The chemical and physical characteristics of molecule
(3) Comparison of the structure of one molecule with other different molecules
(4) Visualization of complex formed between different molecules
(5) Prediction about how related molecules might look
(6) Molecular mechanics
(7) Molecular dynamics
(8) Conformational searching
(9) Docking studies
References:
1.Perun, T. J. and Propst, C. L., In: Computer Aided Drug Design Methods and Application, Marcel Dekker Inc., New York, 1989, 1-13.
2.Williams, D. A., Lemke, T. L., In: Foye’s Principle of Medicinal Chemistry, Lippincott Williams & Wilkins, 5th Edition, 2002, 37-86.
3.Cohen, N. C., Blaney J. M., Humblet, C., Gund, P. and Barry, D. C., J. Med. Chem., 1990, 33, 883.
4.Ariens, E. J., Drug Design, Academic Press, New York, 1971, 1, 42-93.
5.Martin, Y., In: Quantative Drug Design, Marcell Dekkar, New York, 1978, 103-107.
6.Choplin, F., In; Hansch, C., Sammes, P. G. and Tailors, J. B., Comprehensive Medicinal Chemistry, Pergamon Press, New York , 1990, 4, 36.
7.Kubinyi, H., In; Wolff, M. E., Burger's Medicinal Chemistry and Drug Discovery: Principle and Practice, Wiley-Interscience Publication, 5th Edition, 1995, 1, 397-574.
About Authors:
Jalpan K. Joshi
M. Pharm., Senior Lecturer, Department of Pharmaceutical Chemistry, Maliba Pharmacy College, Gopal Vidhyanagar, Bardoli-Mahua Road, Tarsadi, Surat - 394 350, Gujarat, India
E-mail: joshijalpank@yahoo.com
Payal D. Shah
Final B. Pharm., Maliba Pharmacy College, Gopal Vidhyanagar, Bardoli-Mahua Road, Tarsadi, Surat - 394 350, Gujarat, India
E-mail: payaltina2003@yahoo.com