Recent Trends on QSAR in the Pharmaceutical Perceptions


by

Mahmud T. Hassan Khan

DOI: 10.2174/97816080537971120101
eISBN: 978-1-60805-379-7, 2012
ISBN: 978-1-60805-4336



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Quantitative Structure-Activity Relationship (QSAR) is a field where true multidisciplinary approaches are being used. This volume tit...[view complete introduction]

Table of Contents

Foreword By Alexandru T. Balaban

- Pp. i-iii (3)

Alexandru T. Balaban

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Foreword By Roberto Todeschini

- Pp. iv-vi (3)

Roberto Todeschini

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Preface

- Pp. vii-ix (3)

Mahmud Tareq Hassan Khan

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List of Contributors

- Pp. x-xiii (4)

Mahmud Tareq Hassan Khan

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QSAR, Complex Networks, Principal Components and Partial Order Analysis of Drug Cardiotoxicity with Proteome Mass- Spectra Topological Indices

- Pp. 3-50 (48)

Cristian R. Munteanu, Maykel Cruz-Monteagudo, Fernanda Borges, M. Natália D. S. Cordeiro, Ricardo Concu and Humberto González-Díaz

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Calcium Channel Blockers: Past and Future

- Pp. 51-62 (12)

Corina Duda-Seiman, Speranta Avram, Daniel Duda-Seiman, Bogdan Bumbacila, Florin Borcan and Rodica Cinca

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Evaluation of the Pharmacological Descriptors Related to the Induction of Antimicrobial Activity by Using QSAR and Computational Mutagenesis

- Pp. 63-98 (36)

Speranta Avram, Catalin Buiu, Corina Duda-Seiman, Florin Borcan, Daniel Duda-Seiman and Dan Mihailescu

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Computer-Based Strategies Towards the Discovery of New Antiepileptic Agents

- Pp. 99-118 (20)

Luciana Gavernet, Alan Talevi and Luis E. Bruno-Blanch

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Bridging Chemical and Biological Space: QSAR Probing Using 3D Molecular Descriptors

- Pp. 119-193 (75)

M. Natália D.S. Cordeiro, Fernanda Borges and Aliuska Morales Helguera

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Overview of QSAR Modelling in Rational Drug Design

- Pp. 194-241 (48)

Feng Luan and M. Natália D.S. Cordeiro

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QSAR for the Cytotoxicity of tert-Butylphenols and 2- Methoxyphenols in Terms of Inhibition Rate Constant and a Theoretical Parameter

- Pp. 242-254 (13)

Seiichiro Fujisawa and Yoshinori Kadoma

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Artificial Neural Network Model Based QSAR for Oxygen Containing Heterocycles as Selective COX-2 Inhibitors

- Pp. 255-271 (17)

Ponnurengam Malliappan Sivakumar and Mukesh Doble

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Recent Studies on Similarity Measures and its Applications to Chemoinformatics and Drug Design

- Pp. 272-297 (26)

Alan Talevi, Eduardo A. Castro and Luis E. Bruno-Blanch

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QSAR-Based CMs and TOMOCOMD-CARD Approach for the Discovery of New Tyrosinase Inhibitor Chemicals

- Pp. 298-341 (44)

Gerardo M. Casañola-Martin, Huong Le-Thi-Thu, Yovani Marrero-Ponce, Francisco Torrens, Antonio Rescigno, Concepción Abad and Mahmud Tareq Hassan Khan

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QSAR and Bioinformatics for Rational Peptide Design: An Overview of the Development of New Anti-Infective Drugs and MHC Binding Peptides

- Pp. 342-359 (18)

Olivier Taboureau, Morten Nielsen, Claus Lundegaard and Ole Lund

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Quantitative Structure Activity Relationship: History, Development and Applications

- Pp. 360-391 (32)

Medhat Ibrahim, Noha A. Saleh and Wael M. Elshemey

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Foreword

Foreword By Alexandru T. Balaban

The twelve chapters of this book edited by Dr. Mahmud T. H. Khan have been written by 38 authors in a truly international collaborative effort. Addresses of the authors indicate the following 11 countries (in alphabetical order): Argentina, Cuba, Denmark, Egypt, India, Italy, Japan, Portugal, Romania, Spain, and USA.

Quantitative structure-activity relationships (QSARs) allow medicinal chemists to explore the infinite “chemical and stereochemical space”, and to select structures that offer hope in finding new “lead compounds”, which will be the basis for developing medicinal drugs after further investigations concerning ADME-Tox properties (absorption, distribution, metabolism, excretion and toxicity of drugs). At present, a patent offers the inventors a protection for 20 years during which the pharmaceutical company is expected to recover the investment that approaches one billion US$ for a new drug on the market. However, the cell culture tests followed by animal and clinical tests last for at least 10 years, reducing by half the real protection time for brand medicines before generic drugs appear. Because of these huge costs, all main pharmaceutical companies have large teams of computer-aided specialists for screening possible structures and in order to abandon early the unproductive avenues. The present thesaurus of about 50 million chemical compounds contained in the Chem. Abstr. database is enriched with about one million new structures through the combined effort of researchers from universities and from chemical, materials sciences, or especially pharmaceutical companies. Many more structures per year are currently obtained by combinatorial chemical syntheses, but the Chem. Abstr. database records only the substances that are selected, isolated and characterized.

The development of quantitative structure-activity or structure-property relationships (QSARs and QSPRs, respectively) has to be based on molecular descriptors in order to provide a metric to chemical structures. This can be done in several ways, for instance by the fragment-based approach, by associating biological activities with other (measurable or computable) properties such as the 1-octanol ─ water partition coefficient or the lipophilicity (logP), number of hydrogen-bond donors or acceptors, molecular weight, etc.

In drug development, there are necessary sequences of events: hit identification → lead generation → lead optimization → candidate drug nomination. At present, the biochemical tests gained a new, much more economical, first step: in silicoin vitroin vivo tests. For details, one may consult a recently published book edited by Oprea, T., Chemoinformatics in Drug Discovery, Wiley-VCH, Weinheim, 2005.

The close correspondence between chemical constitution and chemical graphs has led to one class of molecular descriptors that are simple to compute and offer a large field of applications: topological indices. Several books on such descriptors have been published during the last 15 years: (i) Kier, L. B.; Hall, L. H., Molecular Structure Description: The Electrotopological State, Academic Press, 1999; (ii) Devillers, J.; Balaban, A. T. (Editors), Topological Indices and Related Descriptors in QSAR and QSPR, Gordon and Breach, The Netherlands, 1999; (iii) Karelson, M.,Molecular Descriptors in QSAR/QSPR, Wiley-Interscience, New York, 2000; (iv) Todeschini, R.; Consonni, V., Molecular Descriptors for Chemoinformatics, Wiley-VCH, New York, 2009; (v) Gonzalez-Diaz, H. and Munteanu, C. R. (Editors), Topological Indices for Medicinal Chemistry, Biology, Parasitology, Neurological and Social Networks, Transworld Research Network, Kerala, 2010

A real-world example can serve to illustrate the usefulness of combining several types of molecular descriptors. For optimizing a lead decapeptide with immunosuppressive properties, a virtual library with 64 billion structures resulted by assigning 35 natural and non-natural amino acids to seven of the ten positions in the decapeptide. Thirteen molecular descriptors belonging to four classes (one which consisted of four topological Kier-Hall and Balaban indices) provided “windows” of favorable or unfavorable numerical values for gradually filtering the different decapeptides using computed values. As a final result, five decapeptides were predicted to have the desired immunosuppressive properties; they were synthesized, and one of them was indeed found to improve 100-fold the activity of the lead decapeptide (Lahana, R. and coworkers,Nature Biotechnol. 1998, 16, 748-752).

Many QSAR studies benefit at present from the availability of computer programs that calculate hundreds of molecular descriptors from constitutional formulas (tridimensional features are seldom taken into account). Two such computer programs are Katritzky’s CODESSA and Todeschini’s DRAGON. Then a statistically-driven selection of a few descriptors follows with validation of the results that yields confidence in predictions within the range of the tested structures.

The present developments in understanding the mechanism of action for many natural antibiotics have often been based on observing directly the complex interactions at molecular level from X-ray diffraction studies. Of course, natural evolution works under strict limitations imposed by the availability of starting materials and thermodynamically-allowed processes using ATP-based energy transfer, and receptors manufactured from the 20 natural amino acids. Drug design is free from such constraints and is limited only by the imagination of medicinal chemists. In this context, one may recall that chemistry, like mathematics, is a science that combines rigor with imagination and intuition, adding art-like qualities. Accordingly, drug design was designated by German scientists as the “art of the four G’s”: Geduld (patience), Glück (luck), Geschick (skill), and Geld (money).

Alexandru T. Balaban
Texas A&M University at Galveston,
Texas,
USA

Foreword By Roberto Todeschini

QSARs are based on the assumption that the structure of a molecule must contain the features responsible for its physical, chemical and biological properties and on the ability to capture these features into one or several numerical descriptors. With QSAR models, the biological activity (or property, reactivity, etc.) of a newly designed or untested chemical can be inferred from the molecular structure of similar compounds, whose activities (properties, reactivities, etc.) have already been assessed.

For QSAR as well as all the research related human activities, knowledge should not be considered as something given once and for all, based on some final basic theories, but as a network of models in progress. This network primarily consists of knots, i.e. objects, facts, theories, statements, and models, and the links between the knots are relationships, comparisons, differences, and analogies: such a network is something more than a collection of facts, resulting in a powerful engine for analogical reasoning. This analogical reasoning, which can now be based on an experience of around 50 years, should further strengthen the field of QSAR and broaden its applications.

Indeed, it has been nearly 5 decades since the QSAR modeling was practiced in agrochemistry, drug design, toxicology, industrial and environmental chemistry. In the coming years, growing use of QSARs can be mainly attributed to the rapid and extensive development in methodologies and computational techniques, that have allowed to delineate and refine the many variables and approaches used to model the molecular properties. Furthermore, the popularity of QSARs is growing day by day since their applications are no longer just confined to academic research, but are widespread in several public and private sectors within medicine, pharmacology and toxicology and, in general, for all the issues where the human health is involved. For instance, the usefulness of QSAR to generate data on chemicals in the interest of time and cost effectiveness or their contribution in modern science towards drug discovery are amongst several advancements that have considerably broaden the perspectives of QSAR.

However, it should be recognized that, often QSAR analysis on its own, cannot give useful answers to several complex problems. In such cases, the analysis has to be accompanied by one or several tools that can bridge this gap, making it feasible for QSAR to deal with highly complex problems. The increasing complexity of QSAR can be easily reflected by its highly ambitious objectives: from the classical simple models evaluated on few congeneric compounds, the interest was concentrated on modeling several thousands of diverse compounds provided by huge databases. This step was accompanied by the major developments in chemoinformatics which, together with several other tools, allowed easy handling of huge and complex data sets.

However, the rising complexity of the studied systems has not always reflected a corresponding increase in the quality of the modeling tools. Indeed, to deal with this increased complexity, we need to catch not only the linear relationships but also the nonlinear relationships need to be taken into account. This point is particularly important because the loss of all the nonlinear aspects of the problem often leads to incomplete analysis and practically unuseful models.

The problem complexity is closely related to the current availability of several thousands of molecular descriptors. Indeed, molecular descriptors are of crucial importance in the research field of QSAR, where they are the independent chemical information used to predict the biological activities of interest. However, the use of irrelevant descriptors, not only increases unnecessarily the model complexity, but usually also lowers the predictive capability of the model. This leads to the need of variable subset selection approaches, which further adds to this increasing complexity.

As an additional term of complexity, it should be also remembered that, from a theoretical point of view and unlike other systems, molecules constitute an intrinsic discrete space, i.e. the space between two molecules does not exist in principle.

Considering all these aspects, the perspective to derive a unique generalized model valid for all chemical categories and able to predict some of their responses, not just appears very ambitious but it almost seems like a dream. An obvious feasible alternative is to explore the possibilities to build local models, which are able to make predictions based on the information available for the most similar compounds to the target compound, such as suggested, for example, by the read-across strategy.

But it can’t be forgotten that, dreams sometimes become true…!

In this book, another prominent step towards the improvement of QSAR methodologies, their effective solutions and applications using several interesting case studies are proposed, with an aim to highlight new significant contributions to the field of QSAR.

Roberto Todeschini
Department of Environmental Sciences
University of Milano-Bicocca
Milano
Italy


Preface

The volume Recent Trends on QSAR in the Pharmaceutical Perceptions provides the readers an overview of recent discoveries and trends in the field of Quantitative Structure-Activity Relationship (QSAR). The QSAR is a very expansive field of research from biomedical science to drug discovery to target discovery to material sciences. This volume encloses fourteen equally high quality chapters from distinguished scientists around the globe with their extensive years of research in the field.

The volume aiming researchers who are centering their attentions to develop new approaches or tools and besides the scientists who are trying to appoint in deciphering their very explicit predicaments via QSAR methods.

In Chapter 1, Munteanu and co-workers introduced blood serum mass spectra based Proteome-Early Drug Induced Cardiac Toxicity Relationships which authors called Pro-EDICToRs. This approach opens a new door to the application of SP-MS graph parameters to toxico-proteomics in the near future. The Chapter 2, authored by Duda-Seiman et al., deals with the calcium channel blockers, their past and future.

Chapter 3 covenants about antimicrobial peptides (AMPs). Here in this chapter authors Avram and co-workers presented sets of pharmaceutical descriptors to perform QSAR analyses and eventually which helped to develop number of AMPs also utilizing computational mutagenesis.

Gavernet et al. in the chapter 4, overviews recent report on the developments of new anticonvulsants utilizing different in silico approaches, like Comparative Molecular Field Analysis (CoMFA), Chemical Graph Theory, Molecular Docking, Similarity Measures, Pharmacophore postulation and Virtual Screening.

In the chapter 5, Cordeiro and co-workers explained how to bridge the gaps between the chemical and biological spaces utilizing QSAR and three-dimensional (3D) molecular descriptors.

Luan and Cordeiro in their chapter (#6) wrote a comprehensive overview of QSAR modeling in the rational drug design with real life exmaple and explaining the each steps involved in

Fujisawa and Kadoma in their chapter 7, introduced method to predict cytotoxicity through QSAR modeling using quantum chemical descriptors, like the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), etc.

Sivakumar and Doble in chapter 8 demonstrated the example of ANN-based QSAR method for developing prostaglandin H2 synthase or cyclooxygenase (COX)-2 inhibtors.

In chapter 9, Talevi and co-workers briefly reviewed recent studies related to similarity coefficients and different fingerprinting systems and feature weighting schemes used in similarity assessment. Applications of these advances in chemoinformatics are also briefly discussed, including similarity-based virtual screening, assessment of chemical libraries diversity and applicability domain estimation based on similarity measures.

In chapter 10, Casañola-Martin and co-workers surveys the results achieved in the elucidation of new tyrosinase inhibitors by using QSAR and TOpological MOlecular COMputational Design-Computer-Aided Rational Drug Design (TOMOCOMD-CARDD) approach. In the chapter authors described the approach in detailed with several examples from their published works.

Taboureau and co-workers in their chapter (11) introduced the QSAR approaches for rational peptide design which has been successfully used for the prediction of the major histocompatibility complex (MHC)-peptide binding and for the optimization of AMPs.

Saleh and co-workers in chapter 12 presented some basic considerations concerning QSAR including history and some important descriptors, like electronic parameters; hydrophobic (Lipophilic) parameters and steric parameters. Finally they presented some applications of using these parameters.

Most of the authors in this volume agreed that the major challenges of this emerging field is to evaluate the models in real life in a reasonable manner and then to put them together into practice successfully. Nevertheless it may be tough, but the prospects evidently promising! I anticipate readers will take pleasure going through the volume.

Mahmud Tareq Hassan Khan,PhD
Tromsø, Norway
January, 2011

List of Contributors

Editor(s):
Mahmud T. Hassan Khan
University of Illinois at Chicago
USA




Contributor(s):
Concepción Abad
Departament de BioquímicaiBiologia Molecular
Universitat de València
Burjassot, E-46100
Spain


Speranta AVRAM
Anatomy, Animal Physiology and Biophysics Dept
University Bucharest, Faculty of Biology
91-95th Spl. Independentei
Bucharest, 076201
Romania


Florin BORCAN
Biochemistry Dept
University of Medicine and Pharmacy “Victor Babes” Timisoara
2ndEftimieMurguSq
Timisoara, RO-300041
Romania


Fernanda Borges
Department of Organic Chemistry
Physico-Chemical Molecular Research Unit
Faculty of Pharmacy
Porto, 4050-047
Portugal


Luis E Bruno - Blanch
Medicinal Chemistry, Department of Biological Sciences
Faculty of Exact Sciences
Nacional University of La Plata (UNLP)
B1900AVV, La Plata
Buenos Aires
Argentina


Catalin BUIU
Anatomy, Animal Physiology and Biophysics Dept
University Bucharest, Faculty of Biology
91-95th Spl. Independentei
Bucharest, 076201
Romania


Bogdan BUMBACILA
University of Medicine and Pharmacy “Victor Babes” Timisoara
Faculty of Pharmacy
2ndEftimieMurguSq
Timisoara, RO-300041
Romania


Gerardo M. Casañola-Martin
Departament de BioquímicaiBiologia Molecular
Universitat de València
Burjassot, E-46100
Spain


Eduardo A. Castro
Department of Chemistry, Faculty of ExactSciences
Instituto de Fisicoquímica Teórica y Aplicada (INIFTA)
Nacional University of La Plata (UNLP) Suc.4
C.C. 16, B1900AVV, La Plata
Buenos Aires
Argentina


Rodica CINCA
Pharmacology Dept
University of Medicine and Pharmacy “Victor Babes” Timisoara
2ndEftimieMurguSq
Timisoara, RO-300041
Romania


M. Natália D. S. Cordeiro
Chemistry Department
REQUIMTE/Science Faculty, University of Porto
Porto, 4169-007
Portugal


Maykel Cruz-Monteagudo
Chemistry Department
REQUIMTE/Science Faculty, University of Porto
Porto, 4169-007
Portugal


Mukesh Doble
Department of Biotechnology
Indian Institute of Technology
Chennai
Madras, 600036
India


Corina DUDA-SEIMAN
Chemistry Department
West University of Timisoara
16th Pestalozzi Str.
Timisoara, RO-300115
Romania


Daniel DUDA-SEIMAN
Medical ambulatory Dept.
University of Medicine and Pharmacy “Victor Babes” Timisoara
Cardiovascular prevention, 49th C.D. LogaBvd.
Romania


Wael M. Elshemey
Biophysics Department
Faculty of Science
University of Cairo
Giza
Egypt


Seiichiro Fujisawa
Meikai University School of Dentistry
1-1 Keyakidai, Sakado
Saitama, 350-0283
Japan


Luciana Gavernet
Medicinal Chemistry, Department of Biological Sciences
University of La Plata (UNLP, CCT La Plata - CONICET)
47 and 115, La Plata (B1900AJI)
Buenos Aires
Argentina


Humberto González-Díaz
Unit of Bioinformatics & Connectivity Analysis
Institute of Industrial Pharmacy
and Department of Organic Chemistry
Faculty of Pharmacy, USC
Santiago de Compostela, 15782
Spain


Aliuska Morales Helguera
REQUIMTE, Department of Chemistry and Biochemistry
Faculty of Sciences, University of Porto
Porto
4169-007
Portugal


Medhat Ibrahim
Spectroscopy Department
National Research Centre, Dokki
Cairo
Egypt


Yoshinori Kadoma
Departments of Applied Function Molecules
Institute of Biomaterials and Bioengineering
Tokyo Medical and Dental University
2-3-10, Kanda-Surugadai
Chiyoda-Ku, Tokyo, 101-0062
Japan


Mahmud Tareq Hassan Khan
Present address:Center for Pharmaceutical Biotechnology (MC 870)
College of Pharmacy
University of Illinois at Chicago
Molecular Biology Research Building
Chicago
IL, 60607
USA


Huong Le-Thi-Thu
Present address:Center for Pharmaceutical Biotechnology (MC 870)
College of Pharmacy
University of Illinois at Chicago
Molecular Biology Research Building
Chicago
IL, 60607
USA


Feng Luan
REQUIMTE/Science Faculty, Chemistry Department
University of Porto
Porto, 4169-007
Portugal


Ole Lund
REQUIMTE/Science Faculty, Chemistry Department
Porto, 4169-007
Portugal


Claus Lundegaard
Center for Biological Sequences Analysis, Department of Systems Biology
Technical University of Denmark
Building 208
Lyngby, DK-2800
Denmark


Yovani Marrero-Ponce
Institut Universitari de Ciència Molecular
Universitat de València
Edificid'Instituts de Paterna, P. O. Box 22085
València, E-46071
Spain


Dan MIHAILESCU
Anatomy, Animal Physiology and Biophysics Dept
91-95th Spl. Independentei
Bucharest, 076201
Romania


Cristian Robert Munteanu
Computer Science Faculty
University of A Coruña
Campus de Elviña
s/n A
Coruña, 15071
Spain


Morten Nielsen
Center for Biological Sequences Analysis, Department of Systems Biology
Technical University of Denmark
Building 208
Lyngby, DK-2800
Denmark


Antonio Rescigno
Sezione Di ChimicaBiologica
Dip. Scienze E TecnologieBiomediche
Università Di Cagliari
CittadellaUniversitaria
Monserrato (Ca), 09042
Italy


Noha A. Saleh
Biophysics Department
University of Cairo
Giza
Egypt


Lourdes Santana
Unit of Bioinformatics & Connectivity Analysis
Institute of Industrial Pharmacy
and Department of Organic Chemistry
Faculty of Pharmacy, USC
Santiago de Compostela, 15782
Spain


P. M. Sivakumar
Department of Biotechnology
Indian Institute of Technology
Chennai
Madras, 600036
India


Olivier Taboureau
Center for Biological Sequences Analysis, Department of Systems Biology
Technical University of Denmark
Building 208
Lyngby, DK-2800
Denmark


Alan Talevi
Medicinal Chemistry, Department of Biological Sciences
University of La Plata (UNLP, CCT La Plata - CONICET). 47 and 115
Buenos Aires
Argentina


Francisco Torrens
Institut universitari de ciència molecular
universitat de valència
edificid'instituts de paterna, p. O. Box 22085
(valència), e-46071
Spain




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