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Slide 1-

DRUG DESIGN (AN OVERVIEW)
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Drug Design & Discovery: Introduction Drugs: Natural sources Synthetic sources Targets:
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Discovering and Developing the ‘One Drug’
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Profile of Today’s Pharmaceutical Business Time to market: 10-12 years. By contrast, a chemist develops a new adhesive in 3 months! Why? (Biochemical, animal, human trials; scaleup; approvals from FDA, EPA, OSHA)
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Administrative Support Analytical Chemistry Animal Health Anti-infective Disease Bacteriology Behavioral Sciences Biochemistry Biology Biometrics Cardiology Cardiovascular Science Clinical Research Communication Computer Science Cytogenetics Developmental Planning DNA Sequencing Diabetology Document Preparation Dosage Form Development Drug Absorption Drug Degradation Drug Delivery Electrical Engineering Electron Microscopy Electrophysiology Environmental Health & Safety Employee Resources Endocrinology Enzymology Facilities Maintenance Fermentation Finance Formulation Gastroenterology Graphic Design Histomorphology Intestinal Permeability Law Library Science Medical Services Mechanical Engineering Medicinal Chemistry Molecular Biology Molecular Genetics Molecular Models Natural Products Neurobiology Neurochemistry Neurology Neurophysiology Obesity Oncology Organic Chemistry Pathology Peptide Chemistry Pharmacokinetics Pharmacology Photochemistry Physical Chemistry Physiology Phytochemistry Planning Powder Flow Process Development Project Management Protein Chemistry Psychiatry Public Relations Pulmonary Physiology Radiochemistry Radiology Robotics Spectroscopy Statistics Sterile Manufacturing Tabletting Taxonomy Technical Information Toxicology Transdermal Drug Delivery Veterinary Science Virology X-ray Spectroscopy Over 100 Different Disciplines Working Together Pharmaceutical R&DA Multi-Disciplinary Team
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Medicinal chemists today are facing a serious challenge because of the increased cost and enormous amount of time taken to discover a new drug, and also because of fierce competition amongst different drug companies
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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 Human clinical trials (2-10 years) Scale-up FDA approval (2-3 years) File IND File NDA Drug Design Molecular Modeling Virtual Screening
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Technology is impacting this process 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
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History of Drug Discovery…. 1909 - First rational drug design. Goal: safer syphilis treatment than Atoxyl. Paul Erhlich and Sacachiro Hata wanted to maximize toxicity to pathogen and minimize toxicity to human (therapeutic index). They found Salvarsan (which was replaced by penicillin in the 1940’s) 1960 - First successful attempt to relate chemical structure to biological action quantitatively (QSAR = Quantitative structure-activity relationships). Hansch and Fujita
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History of Drug Discovery Mid to late 20th century - understand disease states, biological structures, processes, drug transport, distribution, metabolism. Medicinal chemists use this knowledge to modify chemical structure to influence a drug’s activity, stability, etc. procaine = local anaesthetic; Procainamide = antirhythmic
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Drug Discovery overview Approaches to drug discovery: Serendipity (luck) Chemical Modification Screening Rational
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Serendipity “Chance favors the prepared mind” 1928 Fleming studied Staph, but contamination of plates with airborne mold. Noticed bacteria were lysed in the area of mold. A mold product inhibited the growth of bacteria: the antibiotic penicillin
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(A) Homolog Approach: Homologs of a lead prepared (B) Molecular Disconnection /Simplification (C) Molecular Addition (D) Isosteric Replacements Chemical Modifications
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Chemical Modification…. Traditional method. An analog of a known, active compound is synthesized with a minor modification, that will lead to improved Biological Activity. Advantage and Limitation: End up with something very similar to what you start with.
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Screening Testing a random and large number of different molecules for biological activity reveals leads. Innovations have led to the automation of synthesis (combinatorial synthesis) and testing (high-throughput screening). Example: Prontosil is derived from a dye that exhibited antibacterial properties.
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Irrational, based on serendipity & Intuition Trial & error approach Time consuming with low through output No de novo design, mostly “Me Too Approach”
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Rational Drug Design - Cimetadine (Tagamet) Starts with a validated biological target and ends up with a drug that optimally interacts with the target and triggers the desired biological action. Problem: histamine triggers release of stomach acid. Want a histamine antagonist to prevent stomach acid release by histamine = VALIDATED BIOLOGICAL TARGET. Histamine analogs were synthesized with systematically varied structures (chemical modification), and SCREENED. N-guanyl-histamine showed some antagonist properties = LEAD compound.
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Rational Drug Design - Cimetadine (Tagamet) - continued b. More potent and orally active, but thiourea found to be toxic in clinical trials c. Replacement of the group led to an effective and well-tolerated product: a. Chemical modifications were made of the lead = LEAD OPTIMIZATION: d. Eventually replaced by Zantac with an improved safety profile
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First generation Rational approach in Drug design In 1970s the medicinal chemists considered molecules as topological entities in 2 dimension (2D) with associated chemical properties. QSAR concept became quite popular. It was implemented in computers and constituted first generation rational approach to drug design
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2nd generation rational drug design The acceptance by medicinal chemists of molecular modeling was favored by the fact that the QSAR was now supplemented by 3D visualization. The “lock and key” complementarity is actually supported by 3D model. Computer aided molecular design (CAMD) is expected to contribute to intelligent lead
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MECHANISM BASED DRUG-DESIGN Most rational approach employed today. Disease process is understood at molecular level & targets are well defined. Drug can then be designed to effectively bind these targets & disrupt the disease process Very complex & intellectual approach & therefore requires detailed knowledge & information retrieval. (CADD Holds Great Future) “Drug –Receptor Interaction is not merely a lock-key interaction but a dynamic & energetically favorable one”
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Evolutionary drug designing Ancient times: Natural products with biological activities used as drugs. Chemical Era: Synthetic organic compounds Rationalizing design process: SAR & Computational Chemistry based Drugs Biochemical era: To elucidate biochemical pathways and macromolecular structures as target as well as drug.
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DRUG DISCOVERY PROCESS High-Throughput Screening Target Identification and Validation Chemical Libraries, Combichem, Natural Products Lead Compounds Evaluation Clinical Trials
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New Targets
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HIGH-THROUGHPUT SCREENING FUNCTIONAL INTEGRATION OF: BIOLOGY CHEMISTRY SCREENING TECHNOLOGY INFORMATICS
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BOTTLENECKS Hundreds of “Hits” but NO “Leads” Data mining Accurate profiling of molecules for further studies.
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ALTERNATE STRATEGIES Rational Design of Chemical Libraries Molecular Modeling Approach Virtual Screening Early ADME & Toxicity Profiling
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Smart Drug Discovery platform A view of Drug Discovery road map illustrating some key multidisciplinary technologies that enable the development of (a) Breakthrough medicines from promising candidates (b) LO & generation processes that are relative to novel ligands. Tomi K Sawyer, Nature Chemical Biology 2, (12) December 2006.
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Molecular Modeling Model construction Molecular mechanics Conformational searches Molecular dynamics QSAR/3D QSAR Structure-based drug designRational drug design NMR and X-ray structure determination Combinatorial chemistry Chemical similarityChemical diversity Homology modeling Bioinformatics Chemoinformatics QM, MM methods
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What is Molecular Modeling? Molecular Graphics: Visual representation of molecules & their properties. Computational Chemistry: Simulation of atomic/molecular properties of compound through computer solvable equations. Statistical Modeling: D-R, QSAR/3-D QSAR Molecular data Information Management: Organizational databases retrieval /search & processing of properties of 1000… of compounds. MM = Computation + Visualization + Statistical modeling + Molecular Data Management
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COMPUTATIONAL TOOLS: QM/MM (A) MOLECULAR MECHANICS (MM) (B) QUANTUM MECHANICS (QM)
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COMPUTATIONAL TOOLS Quantum Mechanics (QM) Ab-initio and semi-empirical methods Considers electronic effect & electronic structure of the molecule Calculates charge distribution and orbital energies Can simulate bond breaking and formation Upper atom limit of about 100-120 atoms
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COMPUTATIONAL TOOLS Molecular Mechanics (MM) Totally empirical technique applicable to both small and macromolecular systems a molecule is described as a series of charged points (atoms) linked by springs (bonds) The potential energy of molecule is described by a mathematical function called a FORCE FIELD
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When Newton meets Schrödinger... Sir Isaac Newton (1642 - 1727) Erwin Schrödinger (1887 - 1961)
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Traditional QC Methods First-Principles Car-Parrinello MD Classical MD Simulations Mixed Quantum-Classical
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Main idea Partitioning the system into chemical active part treated by QM methods 2. Interface region 3. large environment that is modeled by a classical force field
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Main idea Partitioning the system into chemical active part treated by QM methods 2. Interface region 3. large environment that is modeled by a classical force field
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Basic modeling Strategies
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Computer Aided Drug Design Techniques Physicochemical Properties Calculations Partition Coefficient (LogP), Dissociation Constant (pKa) etc. Drug Design Ligand Based Drug Design QSARs Pharmacophore Perception Structure Based Drug Design Docking & Scoring de-novo drug design Pharmacokinetic Modeling (QSPRs) Absorption, Metabolism, Distribution and Toxicity etc. Cheminformatics Database Management Similarity / Diversity Searches All techniques joins together to form VIRTUAL SCREENING protocols
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Quantitative Structure Activity Relationships (QSAR) QSARs are the mathematical relationships linking chemical structures with biological activity using physicochemical or any other derived property as an interface. Mathematical Methods used in QSAR includes various regression and pattern recognition techniques. Physicochemical or any other property used for generating QSARs is termed as Descriptors and treated as independent variable. Biological property is treated as dependent variable. Biological Activity = f (Physico-chemical properties)
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QSAR and Drug Design
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Chemical Space Issue WHY QSAR…….?
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Figure: Stage-by-stage quality assessment to reduce costly late-stage attrition. (Ref: Nature Review-Drug Discovery vol. 2, May 2003, 369) Virtual Hit Series, Lead Series Identification, clinical candidate selection
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Types of QSARs Two Dimensional QSAR - Classical Hansh Analysis - Two dimensional molecular properties Three Dimensional QSAR - Three dimensional molecular properties - Molecular Field Analysis - Molecular Shape Analysis - Distance Geometry - Receptor Surface Analysis
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The Effect is produced by model compound and not it’s metabolites. The proposed conformation is the bioactive one. The binding site is same for all modeled compounds. The Bioactivity explain the direct interaction of molecule and target. Pharmacokinetics aspects, solvent effects, diffusion, transport are not under consideration. QSAR ASSUMPTIONS
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1. Selection of training set2. Enter biological activity data3. Generate conformations4. Calculate descriptors5. Selection of statistical method6. Generate a QSAR equation7. Validation of QSAR equation8. Predict for Unknown QSAR Generation Process
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Descriptors Structural descriptors Electronic descriptors Quantum Mech. descriptors Thermodynamic descriptors Shape descriptors Spatial descriptors Conformational descriptors Receptor descriptors Selection of Descriptors What is particularly relevant to the therapeutic target? What variation is relevant to the compound series? What property data can be readily measured? What can be readily calculated?
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QSAR EQUATION
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Molecular Field Analysis Activity = 0.947055 - 0.258821(Ele/401) + 0.085612(vdW/392) + 0.122799(Ele/391) - 0.7848(vdW/350)
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Comparative Molecular Field Analysis Electrostatic Field Steric Field Blue (electropositive) Red (electronegative) Green (favourable) Yellow (repulsive) regions
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COMFA studies on oxazolone derivatives q2 = 0.688 and r2 = 0.969 Steric Electrostatic alignment comparative molecular similarity indices (CoMSIA)
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COMSIA studies on imidazole derivatives alignment Electrostatic Hydrophobic/ hydrophilic steric q2 =0.761 and r2 = 0.945
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3D-QSAR - RECEPTOR SURFACE MODEL Hypothetical receptor surface model constructed from training set molecule’s 3D shape and activity data. The best model can be derived by optimizing various parameters like atomic partial charges and surface fit. Descriptors like van der Waals energy, electrostatic energy, and total non-bonded energy can be used to derived series of QSAR equations using G/PLS statistical method
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PHARMACOPHORE APPROCH Types of Pharmacophore Models Distance Geometry based Qualitative Common Feature Hypothesis. Quantitative Predictive Pharmacophores from a training set with known biological activities. Pharmacophore: The Spatial orientation of various functional groups or features in 3D necessary to show biological activity.
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Pharmacophore-based Drug Design Examine features of inactive small molecules (ligands) and the features of active small molecules. Generate a hypothesis about what chemical groups on the ligand are necessary for biological function; what chemical groups suppress biological function. Generate new ligands which have the same necessary chemical groups in the same 3D locations. (“Mimic” the active groups) Advantage: Don’t need to know the biological target structure
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Pharmacophore Generation Process Five Steps Training set selection. Features selection Conformation Generation Common feature Alignments Validation
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Training Set Molecules should be - Diverse in structure - Contain maximum structural information. - Most potent within series. Features should be selected on the basis of SAR studies of training set Each training set molecule should be represented by a set of low energy conformations. Conformations generation technique ensures broad coverage of conformational space. Align the active conformations of the training set molecules to find the best overlay of the corresponding features. Judge by statistical profile & visual inspection of model. Considerations/Assumptions
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Pharmacophore Features HB Acceptor & HB Donor Hydrophobic Hydrophobic aliphatic Hydrophobic aromatic Positive charge/Pos. Ionizable Negative charge/Neg. Ionizable Ring Aromatic Each feature consists of four parts: 1. Chemical function 2. Location and orientation in 3D space 3. Tolerance in location 4. Weight
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Pharmacophore Generation Evaluate Hypothesis
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Training set
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Pharmacophore Hypothesis Mapped on Active Molecule
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Receptor-based Drug Design Examine the 3D structure of the biological target (an X-ray/ NMR structure. Hopefully one where the target is complexed with a small molecule ligand (Co-crystallized) Look for specific chemical groups that could be part of an attractive interaction between the target protein and the ligand. Design a new ligands that will have sites of complementary interactions with the biological target. Advantage: Visualization allows direct design of molecules
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Docking Process Put a compound in the approximate area where binding occurs Docking algorithm encodes orientation of compound and conformations. Optimize binding to protein Minimize energy Hydrogen bonding Hydrophobic interactions Scoring
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“Docking” compounds into proteins computationally
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De Novo Drug DesignBuild compounds that are complementary to a target binding site on a protein via “random” combination of small molecular fragments to make complete molecule with better binding profile.
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Can pursue both receptor and pharmacophore-based approaches independently If the binding mode of the ligand and target is known, information from each approach can be used to help the other Ideally, identify a structural model that explains the biological activities of the known small molecules on the basis of their interactions with the 3D structure of the target protein.
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Typical projects are not purely receptor or pharmacophore-based; they use combination of information, hopefully synergistically
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Cheminformatics - Data Management Need to be able to store chemical structure and biological data for millions of data points Computational representation of 2D structure Need to be able to organize thousands of active compounds into meaningful groups Group similar structures together and relate to activity Need to learn as much information as possible from the data (data mining) Apply statistical methods to the structures and related information
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Chemical Library Issues Which R-groups to choose Which libraries to make “Fill out” existing compound collection? Targeted to a particular protein? As many compounds as possible? Computational profiling of libraries can help “Virtual libraries” can be assessed on computer
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VIRTUAL SCREENING PROTOCOL Objective - To search chemical compounds similar to active structure. Essential components of protocol are as follows Substructure Hypothesis Pharmacophore Hypothesis Shape Similarity Hypothesis Electronic Similarity Hypothesis VIRTUAL SCREENING Library of ~ 2 lac compounds was screened Initially 800 compounds were short listed applying above filters. Further 30 compounds were selected by applying diversity & similarity analysis. Compounds have been in vitro screened and found various new scaffolds
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Virtual Screening Build a computational model of activity for a particular target Use model to score compounds from “virtual” or real libraries Use scores to decide which to make and pass through a real screen We may want to virtual screen All of a company’s in-house compounds, to see which to screen first A compound collection that could be purchased A potential chemistry library, to see if it is worth making, and if so which to make
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Virtual Screening
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1970’s: no biological target structures known, so all pharmacophore-based approaches. 1990’s: recombinant DNA, cloning, etc. helped the generation of 3D structural data of biological targets. Present: plenty of structural data of biological targets, but also improved technology to increase pharmacophore-based projects.
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Drug Discovery overview (LI & LO) Lead discovery. Identification of a compound that triggers specific biological actions. Lead optimization. Properties of the lead are tested with biological assays; new molecules are designed and synthesized to obtain the desired properties
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Pharmacokinetic Modeling (QSPRs)
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In-Silico ADMET Models Computational methods can predict compound properties important to ADMET Solubility Permeability Absorption Cytochrome p450 metabolism Toxicity Estimates can be made for millions of compounds, helping reduce “attrition” – the failure rate of compounds in late stage
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Name of the drug discovered Biol. Activity 1. Erythromycin analogs Antibacterial 2. New Sulfonamide dervs. Antibacterial 3. Rifampicin dervs. Anti-T.B. 4. Napthoquinones Antimalerials 5. Mitomycins Antileukemia 6. Pyridine –2-methanol’s Spasmolytics 7. Cyclopropalamines MAO inhibitors 8. -Carbolines MAO Inhibitors 9. Phenyl oxazolidines Radioprotectives 10.Hydantoin dervs. Anti CNS-tumors 11.Quinolones Antibacterial Drug Design Successes (Fruits of QSAR)
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Drug Design Successes While we are still waiting for a drug totally designed from scratch, many drugs have been developed with major contributions from computational methods
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Drug Design Successes-2 HIV-1 protease inhibitors
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SUMMARY Drug Discovery is a multidisciplinary, complex, costly and intellect intensive process. Modern drug design techniques can make drug discovery process more fruitful & rational. Knowledge management and technique specific expertise can save time & cost, which is a paramount need of the hour.
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HARDWARE: SGI Indigo2, O2 OCTANE SOFTWARE: Cerius2 (Version 4.10) Catalyst (Version 4.10) Daylight (ClogP ver. 4.1) ACD/Lab (pKa & logD suite) TOPKAT (6.2) DATABASES: ACD, NCI, MayBridge, MiniBiobyte, CAPScreening UNIX, LINUX & (Oracle support) CADD Facility at TORRENT
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Acknowledgment Dr. C. Dutt Dr. Sunil Nadkarni Dr. Vijay Chauthaiwale Dr .Deepa Joshi Dr. R.C. Gupta Mr. Davinder Tuli Dr. Pankaj Sharma
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THANK YOU