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3Structure- and ligand-based approaches in drug designing 49
3.7.1 Mean squares
This is obtaine d when the total sum of the squares is corrected for degrees of freedom. The significanc
e of regression models is evaluated using this term. Its value is used to
calculate the error of measurement in the X data.
3.7.2 Training set
This is a portion of a data set classified or used to fit a model for predicting the values that ar
e known in the tr
aining set.
3.7.3 Test set
This is a set of data used to assess the utility of a predictive relationship and its str
gth. It is prepared from the whole data set to evaluate the training set.
en
3.7.4 Advantages of QSAR
1. Quantification of the effect of structure on activity. 2
Help to predict novel analogues for synthesis.
.
3. Interaction between functional groups present in a molecule having greatest
activity with those of their target can be understood.
4. Prediction of biological activity.
3.7.5 Disadvantages of QSAR
1. False correlations may arise because of experimental error in biological data. 2
If the training data set is not large enough, the data collected may reflect the com-
.
plete property space.
3. 3D structures of ligand binding to receptor may not be available.
50 Sumitra Nain*, Garima Mathur, Girish Kumar Gupta, Sarvesh Paliwal
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
3.7.6 Application of QSAR
3.7.6.1 Chemical
1. A QSAR application was used to predict boiling points [52]. In organic chemistry, a stron
g correlation between structure and properties may exist.
2. Hammett equation, Taft equation and pKa prediction methods were developed [53].
3.7.6.2 Biological
1. The biological activity of the molecules is measured in assays to suggest the level of inhibition or acti
ation for a particular signal transduction or metabolic path-
v
ways.
2. QSAR has also been used in drug discovery to predict chemical structures having good inhibitory or agonistic effects on specific targets and low toxicity.
3. Partition coefficient log P is predicted to identify “druglikeness”.
4. QSAR also helps in the study of interactions between the structural domains of proteins [53].
References
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[2] Silverman RB. 2004. The organic chemistry of drug design and drug action, 2nd edition, San
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[8] de Azevedo WF, Dias R. 2008. Computational methods for calculation of ligand-binding affinity,
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[14] Guner, OF. 2000. Pharmacophore perception, development, and use in drug design, La Jolla,
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Compchem/feature19.html [17] Meyer H. 1899. Arch Exp Pathol Pharmakol 42, 109. [18] Overton E. 1901. Studien über die Narkose, Fischer: Jena, Germany. [19] Ferguson J. 1939. Proc R Soc London Ser B 127, 387. [20] Bell PH, Roblin RO Jr. 1942. J Am Chem Soc 64, 2905. [21] Taft RW. 1952. J Am Chem Soc 74, 3120. [22] Nelson Smith R, Hansch C, Ames MM. 1975. J Pharm Sci 64, 599. [23] Fujita T, Iwasa J, Hansch C. 1964. J Am Chem Soc 86, 5175. [24] Hansch C, Leo A. 1995. In Heller SR, ed., Exploring QSAR. Fundamentals and applications in
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[40] Fujita T, Ban T. 1971. J Med Chem 14, 148. [41] Selassie CD, Hansch C, Khwaja T. 1990. J Med Chem 33, 1914. [42] Abraham DJ. Burger’s medicinal chemistry and drug discovery, 6th edition, Volume 1: Drug
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[46] Gasteiger J, Engel T. 2003. Chemoinformatic. Weinhein: Wiley-VCH GmbH and Co. KgaA. [47] http://www.vlifesciences.com/products/vlifeMDS/descriptors.htm [48] http://www.accelrys .com /QSAR: Statistical methods. [49] Almerico AM, Tutone M, Lauria A. 2009. ARKIVOC (VIII) 85–94 ISSN1551-9012 p. 93. [50] Wold S, Eriksson L. 1995. Statistical validation of QSAR results, in Waterbeemd, Han van de, ed.
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52 Sumitra Nain*, Garima Mathur, Girish Kumar Gupta, Sarvesh Paliwal
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
[52] Fraczkiewicz R. 2007. In silico prediction of ionization. Comprehensive medicinal chemistry II.
Amsterdam: Elsevier.
[53] Freyhult EK, Andersson K, Gustafsson MG. 2003. Structural modeling extends QSAR analysis of
antibody-lysozyme interactions to 3D-QSAR, Biophysical Journal 84 (4), 2264–72.
Luciana Scotti*, Francisco J. B. Mendonça Júnior, Marcelo S. daSilva, Marcus T. Scotti
4 Drug design applied to natural products against
neglected diseases
Abstract: Natural products can be defined as those compounds isolated from plants
which provide a variety of lead structures used in the development of new drugs by the pharmaceutical industry. Interest in these substances has increased because of their beneficial effects on human health, such as antiviral, anti-allergic, antiplatelet, anti-inflammatory, antitumor, antioxidant and antiparasitic activities. The Neglected Diseases are 17 diseases transmitted by virus, protozoa, helminthes, and bacteria, they are infections principally caused by tropical parasites, affecting mainly people who live in poor countries, and, having differing symptoms, may often lead to death. The available therapeutic drugs used to treat these diseases are either obsolete, toxic, or have questionable efficacy, possibly from encountering bacterial resistance. Dis­covery of new, safe, effective, and affordable active molecules is urgently needed. Natural organisms, especially plants, provide innumerable molecules with the poten­tial to treat these diseases. This work introduces general concepts, and discusses the neglected diseases, reporting on some of the enzymatic targets studied when devel­oping new drugs to treat these ills.
4.1 Natural products
The known organic substances, in general, have all been produced by Nature, yet it is the kingdom Plantae which has contributed most significantly to the discovery of useful substances that treat human disease. The development of organic chemistry (mostly in the 19th century) occurred in parallel with the study of plants. The angio­sperms are source plants of many natural products, having an undoubtedly unique development, as evidenced by the occurrence of their distinct and complex metabo­lites [1].
Natural products often result from an optimized evolutionary process in which chemicals have been under the selective forces of coevolution, organisms producing substances in the presence of their predators. These natural compounds have been utilized by humans since ancient times to treat and cure their diseases. Traditional Chinese medicine is the best example of natural products’ efficiency, especially in the discovery of new active chemical entities. These compounds are present in fruits and vegetables and are important components of our daily diets. Of the existing plants in the world, most of which are unknown from a scientific point of view, only about 5% of the approximately 250–500thousand species have been biologically studied and
54 Luciana Scotti*, Francisco J. B. Mendonça Júnior, Marcelo S. daSilva, Marcus T. Scotti
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evaluated. Natural products are often phenolic compounds, flavonoids, alkaloids, or terpenes; secondary plant metabolites that may provide several benefits to our health. These benefits include cosmetic action, cardio-protective effects, anti-inflammatory activity, and usefulness in the treatment of cancer and the neglected diseases [2–4].
The use of natural products has been the single most successful strategy for discovering new medicines, and many medical breakthroughs are based on natural products. Half of the top 20best-selling drugs are natural chemical compounds, and their total sales amount to US$ 16billion yearly. These numbers suggest that natural chemicals may well be considered pre-optimized for bioactive potential, and there­fore possess “drug-like properties” [5–7].
New molecules are continually being reported in the literature, many with rel­evant pharmacological activity, such as taxol, forskolin, and artemisinin, etc. (see the Fig.4.1). It is important to remember that plants have contributed over the years to obtaining various widely used drugs, such as morphine, emetine, vincristine, col­chicine, and rutin, etc. (Fig.4.1). In the 1980s, consumers in the US paid more than 8billion dollars for prescriptions with active natural products, and about 80% of all people use natural compounds in the treatment of their diseases [8–11].
Taxo l
H3C
H
C
2
Forskolin
(a)
HO
HN
H3C
O
O
H
C
3
O
O
O
CH
O
OH
CH
OH
O
CH
3
3
3
O
O
O
CH
O
3
O
O
C
H
3
O
CH
CH
3
3
O
O
Artemisinin
O
CH
3
OH
O
H
H
C
3
O
O
C
3
CH
3
H3C
OH
HO
HO
OH
O
N
H
C
3
Morphine
Fig. 4.1: Some natural products.
4Drug design applied to natural products against neglected diseases 55
There are several therapeutically attractive metabolites from marine organisms that have proven to be effective modulators of biological targets such as phospholipases, adenosine receptors, and which are useful in tumor models. Examples of these can­didates (or structural models) for drugs follow: manoalide (an irreversible inhibitor of phospholipase A Prostaglandin A
CH
CH
3
O
Emetine Vincristine
3
O
N
) (Fig.4.2), lufarolide (cytotoxic in human lymphoma cells), and
2
(Fig.4.2) [11–15].
2
H3C
O
O
H3C
O
O
OH
O
N
H3C
HN
CH
CH
3
O
O CH
3
3
N
O
H
C
3
O HN
CH
3
O
N
OH
CH
3
OH
HO
O
HO
O
HO
OH
Rutin
(b)
Fig. 4.1: (continued)
O
O
O
OH
O
OH
CH
OH
3
OH
OH
H
O
N
C
H
3
O
O
O
CH
3
O
CH
3
CH
O
3
CH
3
Colchicine
56 Luciana Scotti*, Francisco J. B. Mendonça Júnior, Marcelo S. daSilva, Marcus T. Scotti
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H
C
3
C
H
C
3
H
C
3
H
C
CH
3
CH
C
H
3
OH
3
3
Prostaglandin A
2
O
OH
O
OH
O
O Manoalide
O
O
O
O
OH
O
Lupharolide
H
3
H
C
3
Fig. 4.2: Marine natural products.
New chemical bioactive entity (bioNCEs) studies done by industrial research labo­ratories have adopted techniques such as Combinatorial Chemistry to obtain more compounds. Through this technology, reactions are done in several steps, occurring in parallel or in mixtures, with few reagents. Products are reagent combinations and therefore large numbers of new compounds can be generated.
4.2 Medicinal chemistry
Medicinal chemistry, in a modern view, is dedicated to understanding the molecular mechanism, chemical relationships, and pharmacological activity involved in drug action through pharmacodynamic and pharmacokinetic factors. The introduction of new technologies has become a prime concept in medicinal chemistry, expanding its interdisciplinary character. Most drugs are small bioactive molecules that inter­act with specific macromolecules or receptors, resulting in their therapeutic effects. Modern computational methods can determine the different qualitative and quan­titative contributions of the structural subunits of different drugs. Pharmacokinetic and toxicity factors of new drug candidates can be evaluated virtually using modern computational tools. The computer has become an inseparable ally, allowing com­putational studies that model the chemistry and molecular dynamics of medicinal chemistry [16–18].
Using several computational tools, researchers can create new virtual candi­date ligands for receptor sites in three dimensions (3D). Pharmaceutical companies
4Drug design applied to natural products against neglected diseases 57
report that spending on research and development in 2004 was about 33billion US dollars, representing real growth year to year in investment, this does not correspond, however, to a proportional increase in discoveries of new active molecular candidates for innovative drugs in the market [19].
Natural products have had an important and decisive role in the development of modern medicinal chemistry and drug design. It has been observed (during the last 200years) that the complexity, chemical diversity, and biological properties of natural products have all aided in the discovery of important new drugs [20]. In the last 30years, the development of new bioassay techniques, biotech methods, bio­guided phytochemical studies, automated high-throughput screening, and high per­formance analytical methods have introduced both new concepts and possibilities for rational drug design and drug discovery. With the development of new spectroscopic techniques, organic chemists have been able to elucidate the complex molecular structures of natural constituents quickly. As examples: gossypol (Fig.4.3) obtained from the cotton seed oil (Gossypium sp.) is widely used in China as a male contracep­tive, and hypericin isolated from Saint John’s wort (Hypericum perforatum) extract, is used as an antidepressant [21].
OH
HO
H
C
3
CH
3
Gossypol Hypercin
Fig. 4.3: Gossypol and hypericin.
CH
O OH
OH
3
H
C
3
H
O
OH
OH
C
CH
3
3
HO
HO
HO
O
HO
OH
O
CH
CH
3
OH
3
Another natural substance of oriental origin is artemisinin isolated from Artemisia annua, a plant known and used in Chinese medicine, and whose structural complex-
ity has inspired new, useful drugs for the treatment of malaria. However, due its low solubility and inadequate pharmacokinetic properties for therapeutic use, various modified analogs have been synthesized, as shown in Fig. 4.4. SAR studies have shown that the endoperoxide function in the natural compound is the pharmacoph­oric subunit. Through semi-synthesis, the active compounds β-artemether, arte-ether, and sodium artesunate were obtained, and all without limitations in bioavailability (Fig.4.4) [22].
58 Luciana Scotti*, Francisco J. B. Mendonça Júnior, Marcelo S. daSilva, Marcus T. Scotti
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CH
3
H
H3C
Fig. 4.4: Artemisinin derivatives.
O
O
H
H
O
O C
3
β-Artemether R1 = CH Arte-ether R1 = CH2CH Sodium artesunate R1 = C(O)CH2CH2COONa
CH
3
O
R1
3
3
In silico methods or CADD (Computer-aided drug design studies) are increasingly being used in both industry and in universities, they involve an understanding of the molecular interactions from both qualitative and quantitative points of view. These methods generate and manipulate three-dimensional (3D) molecular structures, cal­culate descriptors and the dependent molecular properties, model constructions, and employ other tools that encompass computational drug research. Analysis of the molecular structure of a given system allows relevant information to be extracted, and to predict the potential of bioactive compounds [23].
Theoretical studies using in silico methods have aided in the process of drug discovery. Technological advances in the areas of structural characterization, com­putational science, and molecular biology have contributed to faster planning of new feasible molecules [24]. Chemoinformatic studies exist which show that a large fraction of natural products are “drug-like” or at least, “lead-like” having structural and physicochemical properties that render them as potential drugs or leads. Some authors have even suggested a “natural product-likeness score” as a means to filter large chemical databases and find new entities suitable for activity testing. The use of natural products has been the subject of increasing interest in phytochemistry, bio­chemistry and other fields of research at the chemistry-biology-ecosystems interface.
4.3 Docking
Drug discovery is a lengthy and expensive process that can take up to 15years, and cost upwards of billions of dollars. Most drug candidates fail in clinical trial. Of every 10,000 compounds tested, about one or two are marketed, the process wastes great resources and effort. Traditionally, new drugs have been discovered through studies of natural or synthetic compounds with biological activity. Today, theoretical meth­odologies have the advantages of both effectively reducing costs, and of speeding up drug discovery; which results in earlier drug marketing. As examples of this, we have: Captopril, from Bristol Myers-Squibb used for treatment of hypertension and heart failure; Dorzolamide, an anti-glaucoma agent developed by Merck; Saquina­vir, which is a protease inhibitor used for HIV therapy and produced by Hoffmann-