Drug DesignDrug design, also sometimes referred to as rational drug design, is the inventive processof finding new medications based on the knowledge of the target. The drug is mostcommonly an organic small molecule which activates or inhibits the function of abiomolecule such as a protein which in turn results in a therapeutic benefit to the patient.In the most basic sense, drug design involves design of small molecules that arecomplementary in shape and charge to the bimolecular target to which they interact andtherefore will bind to it. Drug design frequently but not necessarily relies on computermodeling techniques. This type of modeling is often referred to as computer-aided drugdesign.TypesThere are two major types of drug design. The first is referred to as ligand-based drugdesign and the second, structure-based drug design. Flow charts of two strategies of structure-based drug design
Ligand basedLigand-based drug design (or indirect drug design) relies on knowledge of othermolecules that bind to the biological target of interest. These other molecules may beused to derive a pharmacophore that defines the minimum necessary structuralcharacteristics a molecule must possess in order to bind to the target. In other words, amodel of the biological target may be built based on the knowledge of what binds to itand this model in turn may be used to design new molecular entities that interact with thetarget.Structure basedStructure-based drug design (or direct drug design) relies on knowledge of the threedimensional structure of the biological target obtained through methods such as x-raycrystallography or NMR spectroscopy. If an experimental structure of a target is notavailable, it may be possible to create a homology model of the target based on theexperimental structure of a related protein. Using the structure of the biological target,candidate drugs that are predicted to bind with high affinity and selectivity to the targetmay be designed using interactive graphics and the intuition of a medicinal chemist.Alternatively various automated computational procedures may be used to suggest newdrug candidates.Active site identificationActive site identification is the first step in this program. It analyzes the protein to findthe binding pocket, derives key interaction sites within the binding pocket, and thenprepares the necessary data for Ligand fragment link. The basic inputs for this step arethe 3D structure of the protein and a pre-docked ligand in PDB format, as well as theiratomic properties. Both ligand and protein atoms need to be classified and their atomicproperties should be defined, basically, into four atomic types: • Hydrophobic atom: all carbons in hydrocarbon chains or in aromatic groups. • H-bond donor: Oxygen and nitrogen atoms bonded to hydrogen atom(s).
• H-bond acceptor: Oxygen and sp2 or sp hybridized nitrogen atoms with lone electron pair(s). • Polar atom: Oxygen and nitrogen atoms that are neither H-bond donor nor H- bond acceptor, sulfur, phosphorus, halogen, metal and carbon atoms bonded to hetero-atom(s).Computer-assisted drug designComputer-assisted drug design uses computational chemistry to discover, enhance, orstudy drugs and related biologically active molecules. The most fundamental goal is topredict whether a given molecule will bind to a target and if so how strongly. Molecularmechanics or molecular dynamics are most often used to predict the conformation of thesmall molecule and to model conformational changes in the biological target that mayoccur when the small molecule binds to it. Semi-empirical, ab initio quantum chemistrymethods, or density functional theory are often used to provide optimized parameters forthe molecular mechanics calculations and also provide an estimate of the electronicproperties (electrostatic potential, polarizability, etc.) of the drug candidate which willinfluence binding affinity.Molecular mechanics methods may also be used to provide semi-quantitative predictionof the binding affinity. Alternatively knowledge based scoring function may be used toprovide binding affinity estimates. These methods use linear regression, machinelearning, neural nets or other statistical techniques to derive predictive binding affinityequations by fitting experimental affinities to computationally derived interactionenergies between the small molecule and the target.Ideally the computational method should be able to predict affinity before a compound issynthesized and hence in theory only one compound needs to be synthesized. The realityhowever is that present computational methods provide at best only qualitative accurateestimates of affinity. Therefore in practice it still takes several iterations of design,synthesis, and testing before an optimal molecule is discovered. On the other hand,
computational methods have accelerated discovery by reducing the number of iterationsrequired and in addition have often provided more novel small molecule structures.Drug design with the help of computers may be used at any of the following stages ofdrug discovery: 1. hit identification using virtual screening (structure- or ligand-based design) 2. hit-to-lead optimization of affinity and selectivity (structure-based design, QSAR, etc.) 3. lead optimization optimization of other pharmaceutical properties while maintaining affinity.ExamplesA particular example of rational drug design involves the use of three-dimensionalinformation about biomolecules obtained from such techniques as x-ray crystallographyand NMR spectroscopy. This approach to drug discovery is sometimes referred to asstructure-based drug design. The first unequivocal example of the application ofstructure-based drug design leading to an approved drug is the carbonic anhydraseinhibitor dorzolamide which was approved in 1995.Another important case study in rational drug design is imatinib, a tyrosine kinaseinhibitor designed specifically for the bcr-abl fusion protein that is characteristic forPhiladelphia chromosome-positive leukemias (chronic myelogenous leukemia andoccasionally acute lymphocytic leukemia). Imatinib is substantially different fromprevious drugs for cancer, as most agents of chemotherapy simply target rapidly dividingcells, not differentiating between cancer cells and other tissues.Additional examples include: • Many of the atypical antipsychotics • Cimetidine, the prototypical H2-receptor antagonist from which the later members of the class were developed
• Selective COX-2 inhibitor NSAIDs • Dorzolamide, a carbonic anhydrase inhibitor used to treat glaucoma • Enfuvirtide, a peptide HIV entry inhibitor • Nonbenzodiazepines like zolpidem and zopiclone • Probenecid • SSRIs (selective serotonin reuptake inhibitors), a class of antidepressants • Zanamivir, an antiviral drug • Isentress, HIV Integrase inhibitor.CADD CENTRE & Mission Statement Of ThemThe Computer-Aided Drug Design (CADD) Center was created to foster collaborativeresearch between biologists, biophysicists, structural biologists and computationalscientists at the University of Maryland, Baltimore and beyond.The major goal of the CADD Center is to initiate these collaborations leading to theestablishment of research projects to discover novel chemical entities with the potential tobe developed into novel therapeutic agents.The rapid identification of novel therapeutic agents for specific disease states ispotentially the greatest step forward in health care that will occur in the 21st century. Thispotential is largely predicated by the availability of the human genome, along with thegenomes of other organisms, including pathogenic species. The fields of genomics andproteomics will allow for this information to be used to identify novel molecular targetsfor the treatment of a wide variety of disease states.Fueling this process are developmentsin the biological, chemical and physical sciences. Advances in biochemistry, cell biologyand molecular biology facilitate the identification of novel biological target molecules,and, importantly, the means to experimentally measure the activity of those targetmolecules. Advances in structural biology have allowed for the determination of the 3-dimensional (3D) structures of biological target molecules via the techniques of nuclearmagnetic resonance (NMR) or X-ray crystallography, with over 16,000 3D structurescurrently available in the Protein Data Bank.
Computer-aided drug design (CADD) approaches can use the information in the 3Dstructures of biological target molecules to identify chemicals with a high potential forbinding to the biological target molecules. These chemicals may then be obtained andexperimentally assayed to select those with the desired biological activity. The selectedcompounds are referred to as lead compounds and may then be subjected to additionalstructural optimization via structural biology, CADD and novel organic syntheticmethods to obtain compounds with improved activities. Both the lead compounds andtheir optimized analogs represent chemical entities with a high probability of beingdeveloped into therapeutic agents and, therefore, are of great interest to pharmaceuticalcompanies.ConclusionThe CADD Center provides collaborative opportunities for biologists to apply CADDapproaches to their research programs. These efforts focus on: 1. The identification of chemical compounds with the desired biological activity 2. Structural optimization of the identified compounds to enhance their desiredbiological activityChemical compounds created from these steps will have the potential to be developedinto research tools and/or therapeutic agents. Successful outcomes of this approach willinclude publication in scholarly journals and patent submissions on the biologicallyactive compounds, laying the foundation for external funding via federal, private orindustrial sources.Drug discovery typically starts with an analysis of binding sites in target proteins, or anidentification of structural motifs common to active compounds. It ends with thegeneration of small molecule "leads" suitable to further chemical synthetic work. Focalpoints of our session included, but were not limited to, modeling and analysis of protein-ligand complexes (database searching, docking, and de novo design), quantitative
assessment of binding interactions (free energy calculations and scoring functions),development of pharmacophores, and analog design.Computer-aided drug design (CADD) is an exciting and diverse discipline where variousaspects of applied and basic research merge and stimulate each other. The latesttechnological advances (QSAR/QSPR, structure-based design, combinatorial librarydesign, cheminformatics & bioinformatics); the growing number of chemical andbiological databases; and an explosion in currently available software tools are providinga much improved basis for the design of ligands and inhibitors with desired specificity.ReferencesAmari S, Aizawa M, Zhang J, Fukuzawa K, Mochizuki Y, Iwasawa Y, Nakata K,Chuman H, Nakano T (2006). "VISCANA: visualized cluster analysis of protein-ligandinteraction based on the ab initio fragment molecular orbital method for virtual ligandscreening". J Chem Inf Model 46 (1): 221–30.Cohen, N. Claude (1996). Guidebook on Molecular Modeling in Drug Design. Boston:Academic Press.Guner, Osman F. (2000). Pharmacophore Perception, Development, and use in Drug Design. La Jolla, Calif: International University Line.Greer J, Erickson JW, Baldwin JJ, Varney MD (April 1994). "Application of the three-dimensional structures of protein target molecules in structure-based drug design".Journal of Medicinal Chemistry 37 (8): 1035–54.Hendrik Timmerman; Klaus Gubernator; Hans-Joachim Böhm; Raimund Mannhold;Hugo Kubinyi (1998). Structure-based Ligand Design (Methods and Principles inMedicinal Chemistry). Weinheim: Wiley-VCH.In Reviews in Computational Chemistry, VCH Publishers, 1990, vol. 1, Kenny B. Lipkowitz and Donald B. Boyd (editors), pages 295-320.Madsen, Ulf; Krogsgaard-Larsen, Povl; Liljefors, Tommy (2002). Textbook of DrugDesign and Discovery. Washington, DC: Taylor & Francis.Roderick E Hubbard, Can drugs be designed? [Review article], Current Opinion InBiotechnology 1997, 8:696-700.