INSILICO DRUG DESIGN

What is Drug Design ? Drug discovery and development is an intense, lengthy and an interdisciplinary endeavor. Drug discovery is mostly portrayed as a linear, consecutive process that starts with target and lead discovery, followed by lead optimization and pre-clinical in vitro and in vivo studies to determine if such compounds satisfy a number of pre-set criteria for initiating clinical development. For the pharmaceutical industry, the number of years to bring a drug from discovery to market is approximately 12-14 years and costing upto $1.2 - $1.4 billion dollars. Traditionally, drugs were discovered by synthesizing compounds in a time-consuming multi-step processes against a battery of in vivo biological screens and further investigating the promising candidates for their pharmacokinetic properties, metabolism and potential toxicity. Such a development process has resulted in high attrition rates with failures attributed to poor pharmacokinetics (39%), lack of efficacy (30%), animal toxicity (11%), adverse effects in humans (10%) and various commercial and miscellaneous factors. Today, the process of drug discovery has been revolutionized with the advent of genomics, proteomics, bioinformatics and efficient technologies like, combinatorial chemistry, high throughput screening (HTS), virtual screening, de novo design, in vitro, in silicoADMET screening and structure-based drug design. What is in-silico Drug Design ? In silico methods can help in identifying drug targets via bioinformatics tools. They can also be used to analyze the target structures for possible binding/ active sites, generate candidate molecules, check for their drug likeness , dock these molecules with the target , rank them according to their binding affinites , further optimize the molecules to improve binding characteristics The use of computers and computational methods permeates all aspects of drug discovery today and forms the core of structure-based drug design. High-performance computing, data management software and internet are facilitating the access of huge amount of data generated and transforming the massive complex biological data into workable knowledge in modern day drug discovery process. The use of complementary experimental and informatics techniques increases the chance of success in many stages of the discovery process, from the identification of novel targets and elucidation of their functions to the discovery and development of lead compounds with desired properties. Computational tools offer the advantage of delivering new drug candidates more quickly and at a lower cost. Major roles of computation in drug discovery are; (1) Virtual screening & de novo design, (2) in silicoADME/T prediction and (3) Advanced methods for determining protein-ligand binding Why in-silico Drug Design is significant ? As structures of more and more protein targets become available through crystallography, NMR and bioinformatics methods, there is an increasing demand for computational tools that can identify and analyze active sites and suggest potential drug molecules that can bind to these sites specifically. Also to combat life-threatening diseases such as AIDS, Tuberculosis, Malaria etc., a global push is essential. Millions for Viagra and pennies for the diseases of the poor is the current situation of investment in Pharma R&D. Time and cost required for designing a new drug are immense and at an unacceptable level. According to some estimates it costs about $880 million and 14 years of research to develop a new drug before it is introduced in the market Intervention of computers at some plausible steps is imperative to bring down the cost and time required in the drug discovery process Structure Based Drug Design? The crystal structure of a ligand bound to a protein provides a detailed insight into the interactions made between the protein and the ligand. Structure designed can be used to identify where the ligand can be changed to modulate the physicochemical and ADME properties of the compound,by showing which parts of the compound are important to affinity and which parts can be altered without affecting the binding. The equlibrium between target and ligand is governed by the free energy of the complex compared to the free energy of the individual target and ligand. This includes not only the interaction between target and ligand but also the solvation and entropy of the three different species and the energy of the conformation of the free species. 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 vitro–in vivo using data obtained from tissue or recombinant material from human and pre-clinical species. Inter-species, in vivo-in vivo using data from pre-clinical species. In silico or computational predictions projecting in vitro or in vivo data. 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 silicomodels 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).