What is Active site directed Drug Design?

As structures of more and more protein targets become available through crystallography, NMR and bioinformatics methods has bring a major drive in the computational methods to use the structure of the protein target as a route to discover novel lead compounds. The methods include de novo design, virtual screening and fragment based discovery.

Virtual screening and de novo design play an important role within the pharmaceutical industry in lead discovery process. Virtual screening refers to computational screening of large libraries of chemicals for compounds that complement targets of known structure which could be tested experimentally. Since, the virtual screening takes place in the three-dimensional active site of the target, it is also called as structure-based virtual screening.

De novo design attemps to use the unliganded structure of the protein to generate novel chemical structure that can bind. There are varying algorithms, most of which depend on identifying initial putative sites of interaction that are grown into complete ligands.

Fragment based discovery is based on the premise that most ligands that bind stongly to a protein active site can be considered as a number of smaller fragments or functionalities. Fragmnents are identified by screening a relatively small library of molecule(400-20,000) by X-ray crystallography, NMR spectroscopy.These structues of the fragment binding to the protein can be used to design new ligands by adding functionality to the fragments or by incorporating features of the fragment onto existing ligands.

In silico ADME/T prediction

The phrase “drug-like” generally means molecules which contain functional groups and/or have properties consistent with the majority of known drugs. Lead structures are ligands that typically exhibit suboptimal target binding affinity. Studies have shown that there exists a difference between leads and drugs which can be expressed as follows: Lead structures exhibit, on average, less molecular complexity (less molecular weight, less number of rings and rotatable bonds), are less hydrophobic (lower ClogP and LogD74) and have lower polarizability (less CMR). Leads should display the following properties to be considered for further development in the drug discovery process or to be called as “drug-like”:

(1) relatively simple chemical features, amenable for combinatorial and medicinal chemistry optimization efforts;

(2) membership to a well established SAR (structure-activity relationship) series, wherein compounds with similar structures exhibit similar target binding affinity;

(3) favorable patent situation; and

(4) good ADME (absorption, distribution, metabolism and excretion) properties.

Leads discovered using virtual screening and de novo design methodologies needs to be optimized to produce candidates with improved bioavailability and low toxicity. Studies have indicated that poor pharmacokinetics and toxicity are the most important causes of high attrition-rates in drug development and it has been widely accepted that these areas should be considered as early as possible in the drug discovery process, thus improving the efficiency and cost-effectiveness of the industry. Resolving the pharmacokinetic and toxicological properties of drug candidates remains a key challenge for drug developers. Evaluation of drug-likeness involves prediction of ADMET (absorption, distribution, metabolism, excretion, toxicity) properties and these predictions can be attempted at several levels:

In silico prediction of drug-likeness at an early stage involves evaluation of various ADMET properties using computational approaches like QSAR or molecular modeling. A number of studies have been performed to find out the properties which make a drug distinct from other chemicals. Availability of large databases of drug or drug-like molecules, e.g. CMC (Comprehensive Medicinal Chemistry), MDDR (MACCS-II Drug Data Report), WDI (World Drug Index) provides useful information about the properties of drugs.

The most influential study of “Lipinski’s rule-of-five” identifies several critical properties that should be considered for compounds with oral delivery as concern. A deeper understanding of the relationships between important ADME parameters and molecular structure and properties is needed to develop better in silico models to predict ADMET properties. Some of the ADME properties evaluated using in silico models are; intestinal permeability, aqueous solubility, human intestinal absorption, human oral bioavailability, active transport, efflux by P-glycoprotein, blood-brain barrier permeation, plasma protein binding, metabolic stability, interactions with cytochrome P450s and toxicity.

To calculate the ADMET properties various pharmaceutical, biotech or software companies and some academic research laboratories have launched their software products like; C2-ADME (www.accelrys.com), TOPKAT (www.accelrys.com), CLOGP (www.biobyte.com), DrugMatrix (www.iconixpharm.com), AbSolv (www.sirius-analytical.com), Bioprint (www.cerep.fr), GastroPlus (www.simulations-plus.com).