Molecular Evolutionary Models In Drug Discovery

Molecular Evolutionary Models in Drug Discovery explores the application of evolutionary molecular models in drug discovery in which secondary metabolites play a fundamental role. Secondary metabolites are not produced in isolation, they are the result of the interaction of genes, metabolism and the environment. The book examines the role of secondary metabolites as leads in drug discovery and on the development of a rational bioprospecting model for new medicines based on the evolution of secondary metabolism. These evolutionary models are part of biological systems and are the most reliable expression of the functioning of living beings. Examines the integration and application of evolutionary models in the pharmaceutical industry to create new drug development platforms Investigates the biotechnological prospecting of secondary metabolites and their potential use in the discovery of new drugs Evaluates the ecosystem of living beings and how its molecular adaptation might improve the success of therapies

Fruit flies are "little people with wings" goes the saying in the scientific community, ever since the completion of the Human Genome Project and its revelations about the similarity amongst the genomes of different organisms. It is humbling that most signalling pathways which "define" humans are conserved in Drosophila, the common fruit fly. Feed a fruit fly caffeine and it has trouble falling asleep; feed it antihistamines and it cannot stay awake. A C. elegans worm placed on the antidepressant flouxetine has increased serotonin levels in its tiny brain. Yeast treated with chemotherapeutics stop their cell division. Removal of a single gene from a mouse or zebrafish can cause the animals to develop Alzheimer’s disease or heart disease. These organisms are utilized as surrogates to investigate the function and design of complex human biological systems. Advances in bioinformatics, proteomics, automation technologies and their application to model organism systems now occur on an industrial scale. The integration of model systems into the drug discovery process, the speed of the tools, and the in vivo validation data that these models can provide, will clearly help definition of disease biology and high-quality target validation. Enhanced target selection will lead to the more efficacious and less toxic therapeutic compounds of the future. Leading experts in the field provide detailed accounts of model organism research that have impacted on specific therapeutic areas and they examine state-of-the-art applications of model systems, describing real life applications and their possible impact in the future. This book will be of interest to geneticists, bioinformaticians, pharmacologists, molecular biologists and people working in the pharmaceutical industry, particularly genomics.

Systematically examining current methods and strategies, this ready reference covers a wide range of molecular structures, from organic-chemical drugs to peptides, Proteins and nucleic acids, in line with emerging new drug classes derived from biomacromolecules. A leader in the field and one of the pioneers of this young discipline has assembled here the most prominent experts from across the world to provide first-hand knowledge. While most of their methods and examples come from the area of pharmaceutical discovery and development, the approaches are equally applicable for chemical probes and diagnostics, pesticides, and any other molecule designed to interact with a biological system. Numerous images and screenshots illustrate the many examples and method descriptions. With its broad and balanced coverage, this will be the firststop resource not only for medicinal chemists, biochemists and biotechnologists, but equally for bioinformaticians and molecular designers for many years to come. From the content: * Reaction-driven de novo design * Adaptive methods in molecular design * Design of ligands against multitarget profiles * Free energy methods in ligand design * Fragment-based de novo design * Automated design of focused and target family-oriented compound libraries * Molecular de novo design by nature-inspired computing * 3D QSAR approaches to de novo drug design * Bioisosteres in de novo design * De novo design of peptides, proteins and nucleic acid structures, including RNA aptamers and many more.

The book aims to introduce the reader to the emerging field of Evolutionary Systems Biology, which approaches classical systems biology questions within an evolutionary framework. An evolutionary approach might allow understanding the significance of observed diversity, uncover “evolutionary design principles” and extend predictions made in model organisms to others. In addition, evolutionary systems biology can generate new insights into the adaptive landscape by combining molecular systems biology models and evolutionary simulations. This insight can enable the development of more detailed mechanistic evolutionary hypotheses.

A breakthrough guide employing knowledge that unites cheminformatics and bioinformatics as innovation for the future Bridging the gap between cheminformatics and bioinformatics for the first time, Computational Approaches in Cheminformatics and Bioinformatics provides insight on how to blend these two sciences for progressive research benefits. It describes the development and evolution of these fields, how chemical information may be used for biological relations and vice versa, the implications of these new connections, and foreseeable developments in the future. Using algorithms and domains as workflow tools, this revolutionary text drives bioinformaticians to consider chemical structure, and similarly, encourages cheminformaticians to consider large biological systems such as protein targets and networks. Computational Approaches in Cheminformatics and Bioinformatics covers: Data sources available for modelling and prediction purposes Developments of conventional Quantitative Structure-Activity Relationships (QSAR) Computational tools for manipulating chemical and biological data Novel ways of probing the interactions between small molecules and proteins Also including insight from public (NIH), academic, and industrial sources (Novartis, Pfizer), this book offers expert knowledge to aid scientists through industry and academic study. The invaluable applications for drug discovery, cellular and molecular biology, enzymology, and metabolism make Computational Approaches in Cheminformatics and Bioinformatics the essential guidebook for evolving drug discovery research and alleviating the issue of chemical control and manipulation of various systems.

The central concern of this book is with the "prediction problem" in biomedical research. In particular, the authors examine the use of animal models to predict human responses in drug and disease research. The arguments discussed are drawn from both biological and biomedical theory (with numerous examples and case studies drawn from evolutionary biology, complex systems theory, oncology, teratology, and AIDS research), and analyses of empirical evidence (concerning, for example, data on intra- and inter-species differences revealed by recent results from genome analyses of various species, human population studies, and statistical studies of the predictive utility of animal models). This book comes to the unique conclusion that while animals can be successfully used for many endeavors in science such as basic and comparative research, they cannot be used to predict drug and disease response in humans. The arguments presented are rooted in the history, philosophy, and methodology of biomedical research. This book will be of interest to anyone involved, directly or indirectly, in biomedical research (including physicians, veterinarians and scientists), and anyone interested in the history, philosophy and methodology of science. In contrast to books written by and for the animal rights movement and books written by and for the animal-based research industry, this book honestly examines all sides of the scientific arguments for using animals in science and concludes that each group in turn exaggerates the flaws or strengths of using animals. There are areas in science where animals can be viably used but there are also areas where they cannot be so used. REVIEWS See Philosophies, Ethics, and Humanities in Medicine 17 August 2010

This book is aimed at, from students to advanced researchers, for anyone that is interested or works with current experimental and theoretical methods in medicinal chemistry and biological physics, with particular interest in chemoinformatics, bioinformatics, molecular modeling, QSAR, spectrometry, molecular biology and combinatorial chemistry for many therapeutic purposes. This book attempts to convey something of the fascination of working in these multidisciplinar areas, which overlap knowledge of chemistry, physics, biochemistry, biology and pharmacology. This second volume, in particular, contains 11 chapters, of which 6 are related to theoretical methods in medicinal chemistry and at least 5 deal with experimental/mixed methods. In the modern computational medicinal chemistry, quantum mechanics (QM) plays an important role since the associated methods can describe molecular energies, bond breaking or forming, charge transfer and polarization effects. Historically in drug design, QM ligand-based applications were devoted to investigations of electronic features, and they have also been routinely used in the development of quantum descriptors in quantitative structure-activity relationships (QSAR) approaches. In chapter 1, we present an overview of the state-of-the-art of quantum methods currently used in medicinal chemistry. Molecular Dynamics (MD) simulation is a sophisticated molecular modeling technique useful to describe molecular structures and macroscopic properties in very large molecular systems comprising hundreds or even thousands of atoms. In the field of drug discovery, MD simulation has been widely used to understand the biomolecule structure, drug and biomolecule interactions. The chapter 2 outlines the theory and practical details of MD approach and focuses on its application in studies of prediction of binding affinities for putative receptor-ligand complexes. In chapter 3 we discuss the important role of the homology modeling procedure in the drug discovery process. This strategy, associated with computational power and more sophisticated and robust algorithms, has been used to predict properties, energies, conformations and support the binding modes of ligands inside their receptor sites. This approach is vital in structure-based drug design (SBBD), since it can quickly predict the tertiary structure of the target whose structure has not been experimentally solved. In drug discovery research, a massive dataset of information is involved and the high throughput screening of typically millions of compounds plays an important role. Different docking protocols can be combined in order to predict binding models and affinities of a ligand with a target receptor, selecting as example the best drug-like compound candidates to further experimental assays, leading to a reduction in the time and cost of the drug discovery process. In the chapter 4, we discuss the general basis and aspects of this approach, presenting some successful cases in drug discovery. Structure-based approaches have increasingly demonstrated their value in drug design. The impact of these technologies on early discovery and lead optimization is significant. Although there is a multiplicity of different approaches being employed in early stages of drug discovery, structure-based drug design (SBDD) is one of the most powerful techniques, and has been used quite frequently by scientists in the pharmaceutical industry as well as in academic laboratories over the past twenty years. The evolution of medicinal chemistry has resulted in an increase in the number of successful applications of structure-based approaches. Some case studies are presented in chapter 5, exploring the value of structure-based virtual screening (SBVS) approaches in drug design, highlighting the identification of novel, potent and selective receptor modulators with drug like properties. Drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein-ligand interactions. The combination of available knowledge of several 3D protein structures with hundreds of thousands of commercially available small molecules has attracted the attention of scientists from all over the world for the application of structure-based pharmacophore strategies. Pharmacophore approaches offer timely and cost-effective ways to identify new drug-like ligands for a variety of biological targets, and their utility in drug design is unquestionable. In the chapter 6, the understanding and limitations of this approach in drug R&D are discussed. Modern molecular biology has inundated drug discovery organizations with countless potential novel drug targets. A foremost challenge for the researchers is to validate this asset of targets with bioactive small molecules (bioproducts can also be included). Eventually, they will be developed into drugs for the more promising targets. The difficulty of finding a good small-molecule starting point is at the beginning of the searching for a proper chemical space that is well related to biological space. Drugs that are small molecules and act at enzyme targets account for over 50% of all medicines in therapeutically use in the marketplace. It is for this reason that chapter 7 take thermodynamics of the small molecule-target enzyme interactions into account to a limited scope. So far, the main purpose of this chapter is to provide a guidance profile of biocalorimetry and its role in drug discovery and development. The chapter 8 intends to describe how proteomes can be analyzed and studied. It addresses some available databases and bioinformatics tools. The description of certain instrumentation, such as mass spectrometry is also presented, but not highly detailed. The aim of chapter 9 is to introduce the reader to the wide spectrum of tools currently available in the drug validation process. With the conclusion of the human genome sequencing, an increase demand for target validation follows the development of high throughput techniques used in the identification of potential new drugs. In vitro technology as the RNA interference (RNAi) and recombinant protein array together with advances on the in vivo technology as the development of transgenic animals, including here the humanized ones, will certainly improve the safety of future clinical trials processes and ultimately play an important role in the treatment of several human diseases. A therapeutically significant drug may have limited utilization in clinical practice because of various shortcomings like poor organoleptic properties (chloranphenicol), poor bioavailability (ampicilin), lack of site specificity (antineoplastic agents), incomplete absorption (epinephrine), poor aqueous solubility (corticosteroids), high first-pass metabolism (propranolol), low chemical stability (penicillin), high toxicity (thalidomide) or other adverse effects. Sometimes, an adequate pharmaceutical formulation can overcome these drawbacks, but often the galenic formulation is inoperant and a chemical modification of active molecule is necessary to correct its pharmacokinetic profile. This chemical formulation process, whose objective is to convert an interesting active molecule into a clinically acceptable drug, often involves the so-called prodrug design , which is extensively discussed in chapter 10. The dominant role of synthetic chemistry has been increasingly challenged by knowledge of the structure and functions of enzymes, receptors, channels, membrane pumps, nucleic acids and by the exponential growth of information about biology, genetics and pathology, giving paramount importance to the dialogue between chemists and biologists. Nevertheless, as in the old days, the development of new chemical entities is still highly dependent on the ability of chemists to obtain, with simple, reliable, fast and possibly inexpensive methods, the molecules that have been designed. Even if it is an undisputed fact that biology has become exceedingly important in drug research, it is reasonable to imagine that chemistry, and in particular synthetic organic chemistry, will continue to play a fundamental role in academic research and in the R&D departments of drug companies of the third millennium. In chapter 11, we describe synthetic routes that have been used to synthesize the structures of top drugs in current usage. This provides an ideal way of introducing students to a wide range of applied chemistry with brief descriptions of the modes of action of these drugs. Some contents of this book therefore reflect our own ideas and personal experiences, which are presented in reviews of different topics here investigated. It is interesting to consider the information described in this book as the starting point to access available and varied knowledge in Medicinal Chemistry and Biological Physics or related areas.

This book brings together drug design practitioners, all leaders in their field, who are actively advancing the field of quantitative methods to guide drug discovery, from structure-based design to empirical statistical models - from rule-based approaches to toxicology to the fields of bioinformatics and systems biology. The aim of the book is to show how various facets of the drug discovery process can be addressed in a quantitative fashion (ie: numerical analysis to enable robust predictions to be made). Each chapter includes a brief review of the topic showing the historical development of.