Modern Method of Drug Discovery : Modern Method of Drug Discovery In Silico Drug Designing Presented by: Priya Ranjan Kumar 8 th Sem, Dept. of Biotechnology Background: Background Research based pharmaceutical companies, on average, spend about 20% of their sales on research and development (R&D). Despite these enormous expenditures, there has been a steady decline in the number of drugs introduced each year into human therapy. 70-100 in the 60s 60-70 in the 70s ~ 50 in the 80s ~ 40 in the 90s (“ Innovation Deficit” - coined in 1996 by Jurgen Drews , president of research at Hoffmann- LaRoche .) 9/6/2011 2 Reasons for the innovation deficit: : Reasons for the innovation deficit: Increased demand on safety for drugs. - the average number of clinical trials per new drug application (NDA ) increased from 30 in the 70s to 40 in the 80s, to 70 in the 90s. - the increased demand on safety is also reflected in a prolonged duration of the drug development process . In the 60s, total development time was 8.1 yrs In the 70s, total development time was 11.8 yrs In the 80s, total development time was 14.2 yrs In the 90s, total development time was 14.9 yrs Currently, total development time is ~16 yrs 9/6/2011 3 Drug Discovery & Development: Drug Discovery & Development Identify disease Isolate protein involved in disease (2-5 years) Find a drug effective against disease protein (2-5 years) Preclinical testing (1-3 years) Formulation & Scale-up Human clinical trials (2-10 years) FDA approval (2-3 years) File IND File NDA 4 Slide 5: 9/6/2011 5 Allocation of R&D time Slide 6: 9/6/2011 6 A new drug today costs ~$880 million and takes ~15-16 yrs to develop . Allocation of R&D funds Slide 7: About 75% of this cost ($660 million) is attributable to failure during development . 90% of all drug development candidates fail to make it to market . Methods that enhance the drug discovery process and reduce failure rates are highly desirable! 9/6/2011 7 Impact of new technology on drug discovery: Impact of new technology on drug discovery The last few years have seen a number of “revolutionary” new technologies: Gene chips, genomics and HGP Bioinformatics & Molecular biology More protein structures High-throughput screening & assays Virtual screening and library design Docking Combinatorial chemistry In-vitro ADME testing Other computational methods How do we make it all work for us? 9/6/2011 8 Slide 9: Identify disease Isolate protein Find drug Preclinical testing GENOMICS, PROTEOMICS & BIOPHARM. HIGH THROUGHPUT SCREENING MOLECULAR MODELING VIRTUAL SCREENING COMBINATORIAL CHEMISTRY IN VITRO & IN SILICO ADME MODELS Potentially producing many more targets and “personalized” targets Screening up to 100,000 compounds a day for activity against a target protein Using a computer to predict activity Rapidly producing vast numbers of compounds Computer graphics & models help improve activity Tissue and computer models begin to replace animal testing 9 Slide 10: 9/6/2011 10 1) Drug Target Identification : 1) Drug Target Identification The identification of new, clinically relevant, molecular targets is of most importance to the discovery of innovative drugs. It has been estimated that up to 10 genes contribute to multifactoral diseases. Science 287:1960-1964 (2000) Typically these “disease genes” are linked to another 5 to 10 gene products in physiological circuits which are also suitable for pharmaceutical intervention. If these numbers are multiplied with the number of diseases that pose a major medical problem in the industrial world, then there are ~5000 to 10000 potential drug targets. 9/6/2011 11 Contd.: Contd. Current therapy is based upon less than 500 molecular targets 45% of which are G-protein coupled receptors 28% are enzymes 11% are hormones and factors 5% ion channels 2% nuclear receptors Therefore, many more drug targets exist! How to identify them? Besides classical methods of cellular and molecular biology, new techniques of target identification are becoming increasingly important. These include: a) Genomics b) Bioinformatics c) Proteomics 9/6/2011 12 a) Genomics : a) Genomics Term was coined in the mid 80s. Evolved from 2 independent advances: 1) Automation – resulting in a significant increase in the number of experiments that could be constructed in a given time. (eg. DNA sequencing) 2) Informatics- the ability to transform raw data into meaningful information by applying computerized techniques for managing, analyzing, and interpreting data. The identification of new biological targets has benefited from the genomics approach: eg. The sequencing of the human genome Blueprint of all proteins Bioinformatics methods are used to transform the raw sequence into meaningful information (eg. genes and their encoded proteins) and to compare whole genomes (disease vs. not). Drug Discovery Today 5:135-143 2000 9/6/2011 13 b) Bioinformatics : b) Bioinformatics The in silico identification of novel drug targets is now feasible by systematically searching for paralogs (related proteins within an organism) of known drug targets (eg. may be able to modify an existing drug to bind to the paralog ). Can compare the entire genome of pathogenic and nonpathogenic strains of a microbe and identify genes/proteins associated with pathogenism . Current Opin. Microbiol 1:572-579 1998 Using gene expression microarrays and gene chip technologies, a single device can be used to evaluate and compare the expression of up to 20000 genes of healthy and diseased individuals at once. Trends Biotechnol 19:412-415 2001 9/6/2011 14 c) Proteomics : c) Proteomics It is becoming increasingly evident that the complexity of biological systems lies at the level of the proteins, and that genomics alone will not suffice to understand these systems. It is also at the protein level that disease processes become manifest, and at which most (91%) drugs act. Therefore, the analysis of proteins (including protein-protein, protein-nucleic acid, and protein- ligand interactions) will be most importance to target discovery. 9/6/2011 15 2) Target Validation : 2) Target Validation Involves demonstrating the relevance of the target protein in a disease process/ pathogenicity and ideally requires both gain and loss of function studies. This is accomplished primarily with knock-out or knock-in animal models, small molecule inhibitors/agonists/antagonists, antisense nucleic acid constructs, ribozymes, and neutralizing antibodies. Since strong interactions between a protein and its ligand are characterized by a high degree of complementarity , knowledge of the protein three dimensional structure will enable the prediction of “ druggability ” of the protein. 9/6/2011 16 3) Lead Compound Identification : 3) Lead Compound Identification Compounds are identified which interact with the target protein and modulate its activity. Hundreds- thousands of chemical compounds are made and tested in an effort to find one that can achieve desirable results . only one in about 10,000 compounds can ever reach market . Compounds are mainly identified using random (screening) or rational (design) approaches. 9/6/2011 17 A) High-throughput Screening : A) High-throughput Screening Used to test large numbers of compounds for their ability to affect the activity of target proteins. Natural product and synthetic compound libraries with millions of compounds are screened using a test assay. There are concerns with the “numbers approach” to screening for a lead molecule. In theory generating the entire ‘chemical space’ for drug molecules and testing them would be an elegant approach to drug discovery. In practice, this isn’t feasible. Therefore, concepts are needed to synthesize and select biologically relevant compounds. 9/6/2011 18 Contd.: Contd. One solution may be to accumulate as much knowledge as possible on biological targets (eg. structure, function, interactions, ligands ) and choose targeted approaches to chemical synthesis. Another crucial point for reliable high-throughput screening results is the robustness and quality of the biological test assays. 9/6/2011 19 B) Structure Based Drug Design : B) Structure Based Drug Design Three dimensional structures of compounds from virtual or physically existing libraries are docked into binding sites of target proteins with known or predicted structure. Scoring functions evaluate the steric and electrostatic complementarity between compounds and the target protein. The highest ranked compounds are then suggested for biological testing. Drug Discovery Today 7:64-70 2002 9/6/2011 20 Contd.: Contd. Once hits (compounds that elicit a positive response in an assay) have been identified via the screening approach, these are validated by re-testing them and checking the purity and structure of the compounds. Only if the hits fulfill certain criteria are then regarded as leads. The criteria can originate from: 1) Pharmacodynamic properties - efficacy, potency, selectivity 2) Physiochemical properties - water solubility, chemical stability, Lipinski’s “rule-of-five”. 3) Pharmacokinetic properties - metabolic stability and toxological aspects. 4) Chemical optimization potential - ease of chemical synthesis. 5) Patentability 9/6/2011 21 4) Lead Optimization : 4) Lead Optimization Molecules are chemically modified and subsequently characterized in order to obtain compounds with suitable properties to become a drug. Leads are characterized with respect to pharmacodynamic properties such as efficacy and potency in vitro and in vivo, physiochemical properties, pharmacokinetic properties, and toxicological aspects. 9/6/2011 22 Efficacy vs Potency : Efficacy vs Potency Potency refers to the amount of drug required for its specific effect to occur; it is measured simply as the inverse of the EC50 for that drug. Efficacy measures the maximum strength of the effect itself, at saturating drug concentrations. Drug Red exceeds drug Black in potency, while the opposite is true of the efficacy. 9/6/2011 23 Slide 24: Pharmacokinetics determining the fate of xenobiotics. (“what the body does to the drug.”) Often divided into areas examining the extent and rate of adsorption, distribution, metabolism, and excretion (ADME). Pharmacodynamics determining the biochemical and physiological effects of drugs, the mechanism of drug action, and the relationship between drug concentration and effect. (“what the drug does to the body”) 9/6/2011 24 Contd.: Contd. This process ideally requires the simultaneous optimization of multiple parameters and is thus a time consuming and costly step. This is often the tightest bottleneck in drug discovery. Hints on how to modify a lead compound can originate from molecular modeling, quantitative structure-activity relationships, and from structural biology (structure-based drug design). In parallel to compound characterization with respect to potency and selectivity, in vitro assays for the prediction of pharmacokinetic properties should be performed. Once compounds with desirable in vitro profiles have been identified, these are characterized using in vivo models. 9/6/2011 25 5) Preclinical and Clinical Development : 5) Preclinical and Clinical Development Preclinical studies involve in vitro studies and trials on animal populations. Wide ranging dosages of the compounds are introduced to the cell line or animal in order to obtain preliminary efficacy and pharmacokinetic information. After successful completion of Preclinical studies, the drug goes through the 4 phages of Clinical trials during which the safety and efficacy of the drug on human being is to be examine. Once a drug has proven acceptable, the trial results are combined into a large document which includes a comprehensive description of manufacturing procedures, formulation details, shelf life, etc. This document is submitted to the FDA for review. 9/6/2011 26 Softwares for Drug designing :
Softwares for Drug designing Sanjeevini A complete drug design software. Drug-DNA Interaction Energy
Calculates the Drug-DNA interaction energy. Binding Affinity Prediction of Protein- Ligand Server(BAPPL) Computes the binding free energy of a protein- ligand complex. ParDOCK - Automated Server for Rigid Docking Predicts the binding mode of the ligand in receptor target site. Active Site Prediction 9/6/2011 27
Contd: Contd 6. Lipinski Filters Checks whether a drug satisfies the 5 Lipinski rules. 7. Molecular Volume Calculator Calculates the volume of a molecule 8. DNA Sequence to Structure Generates double helical secondary structure of DNA using conformational parameters taken from experimental fiber-diffraction studies. 9. RASPD for Preliminary Screening of Drugs Preliminary screening of ligand molecules based on physico -chemical properties of the ligand and the active site of the protein. This will predict binding energy of drug/target at a preliminary stage. 9/6/2011 28 Slide 29: 9/6/2011 29 Discovery Studio Life Science Modeling and Simulations Tool Summary : Summary R&D in the pharmaceutical industry is undergoing a lot of technological changes, and there is pressure to make the investment pay off There is a big need to sensibly use the large amounts of chemical and biological-related information produced in the process Thoughtful use of new technologies, methods and softwares are becoming crucial to the success of drug discovery An in silico revolution is emerging in this field that can alter the conduct of early drug development. The use of computers and computational methods permeates all aspects of drug discovery today and forms the core of structure-based drug design. 9/6/2011 30 References: References Aurther M. Lesk . Introduction to Bioinformatics. Oxford University Press, USA (May 9, 2002) | ISBN-10: 0199251967 Madsen, Ulf Krogsgaard -Larsen, Povl Liljefors , Tommy (2002). Textbook of Drug Design and Discovery”. Third Edition, Washington, DC: Taylor & Francis. Alexander Zien , Robert Küffner , Theo Mevissen , Ralf Zimmer and Thomas Lengauer . Identification of Drug Target Proteins. ERCIM News No.43 - October 2000. Lata N., Jayaram B. A binding affinity based computational pathway for active site directed lead molecule design: Some promises and prespective . 2005, Drug Design Reviews-Online , 2(2), 145. The Drug Development Company Database and the Alzheimer's Drug Discovery Foundation (ADDF) and the Alzheimer Research Forum. Retrieved April 4, 2011, from http://www.alzforum.org/drg/tut/tutorial.asp/. Supercomputing Facility for Bioinformatics & Computational Biology, IIT Delhi. Retrieved April 5, 2011, from http://www.scfbio-iitd.res.in/ Ludwig Institute for Cancer Research (2010, February 4). New computational tool for cancer treatment. Science Daily . Retrieved April 5, 2011, from http://www.sciencedaily.com/releases/2010/01/100129151756.htm 9/6/2011 31 Slide 32: 9/6/2011 32 THANK U….