SAR and QSAR in Drug Discovery and Chemical Design—Some Examples



SAR and QSAR in Drug Discovery and Chemical Design—Some Examples


Different in silico molecular modeling techniques innovate the process of developing new chemical entities with desired properties of interest. Since there are various possible reasons for failure while pursuing costly research paradigms like drug discovery, the implementation of rational strategies becomes inevitable to enhance efficiency. In this chapter, we present examples of drugs and other chemicals that have been designed using quantitative structure–activity relationship (QSAR) and other related in silico strategies.


Keywords


Lead optimization; clinical trial; approved drug; in silico success


11.1 Introduction


The design and development of new chemicals is a challenging task. The challenge becomes tougher while dealing with molecules of biological importance (e.g., drugs and pharmaceuticals). The act of developing new drug molecules, as well as modifying the existing ones, involves a multifaceted objective covering the aspects of desired pharmacological activity, undesirable side reactions, appreciable pharmacokinetic features, and suitable form of administration. Although it may be argued that many useful drugs, like acetylsalicylic acid, quinine, and penicillin, have come through serendipitous discovery as well as classical research, it should be considered that many other drug candidates have failed or been withdrawn due to undesirable outcomes. The major objective of the pharmaceutical and other chemical industries is to develop medicines (or other chemicals) that are suitably valued by regulatory authorities, patients, healthcare professionals and providers, and others to improve the quality of life of consumers. However, considering the existing economic conditions, the industry attempts to provide the best product possible at a suitable cost and time frame in order to cope with the market. Therefore, the criteria about adjustments comprise the quality, speed, and time parameters to make a product economically viable. However, it is definitely not possible to compromise with the quality of medicinal agents that we consume; hence, the need for less time-consuming, alternative, accurate, and economic methods becomes inevitable. Considering the huge cost incurred during the discovery of a drug molecule that comprises steps like initial synthesis and preliminary screening of biological activity, preclinical development (studies on animals for pharmacological effect and short- and long-term toxicities), phase I clinical trials (studies on healthy human volunteers), phase II clinical trials (studies on limited cohort of human volunteers with the specific disease), phase III clinical trials (studies on larger populations of affected human volunteers), phase IV clinical trials (post-marketing surveillance), and post-developmental quality control and quality assurance operations in order to get final approval for release, classical and empirical techniques seem impracticable. Hence, rational drug-design approaches are highly useful in providing a theoretical basis on the features of an investigational molecule addressing almost all possible aspects of its failure. Various in silico molecular modeling studies, including the quantitative structure–activity relationship (QSAR) methodology, enable us to gather sufficient information regarding the response of a chemical by comparing it with several other similar and different molecules, thereby allowing us to choose the one with optimized features. Furthermore, the use of different in silico methodologies also provides a good option for reducing animal experimentation, which involves ethical obligations. In this chapter, we present several success stories of representative approved and investigational drugs and other chemicals that have been designed and developed employing QSAR and related in silico molecular modeling operations.


11.2 Successful Applications of QSAR and Other In Silico Methods: Representative Examples


The research and development in the realm of QSAR and related molecular modeling techniques have traversed a long journey from which several successful outcomes have been obtained. We have attempted to present here a few success stories of computational methods in deriving some drugs and other chemicals [1–7] in Tables 11.1 and 11.2, respectively.



Table 11.1


Representative list of approved and investigational drug molecules designed using QSAR and other in silico techniques



































































































































































































































































Sl. No. Name of the drug agent Chemical structure Indication Mechanism US FDA approval/clinical trial status Marketed by Product Trade name
Approved drugs
1 Saquinavir image AIDS Inhibitor of HIV-1 protease enzyme 1995 Hoffmann-La Roche Saquinavir mesylate Invirase®
2 Amprenavir image AIDS Inhibition of HIV protease enzyme 1999 GlaxoSmithKline Amprenavir Agenerase
3 Indinavir image AIDS Inhibition of HIV protease enzyme 1996 Merck Sharp Dohme Indinavir sulfate Crixivan®
4 Lopinavir image AIDS Inhibition of HIV protease enzyme 2000 Abott Lopinavir/Ritonavir Kaletra®
5 Nelfinavir image AIDS Inhibition of HIV protease enzyme 1997 Agouron Nelfinavir mesylate Viracept®
6 Raltegravir image AIDS Inhibition of HIV-1 integrase enzyme 2007 Merck Sharp Dohme Raltegravir potassium Isentress
7 Ritonavir image AIDS Inhibition of HIV protease enzyme 1996 Abott Ritonavir Norvir®
8 Dorzolamide image Open-angle glaucoma and ocular hypertension Inhibition of carbonic anhydrase II enzyme 1994 Merck Dorzolamide hydrochloride Trusopt®
9 Norfloxacin image Bacterial infection Inhibition of bacterial DNA gyrase 1986 Merck Norfloxacin Noroxin®
10 Oseltamivir image Influenza Inhibition of NA enzyme 1999 Hoffmann-La Roche Oseltamivir phosphate Tamiflu®
11 Zanamivir image Influenza Inhibition of NA enzyme 1999 GlaxoSmithKline Zanamivir Relenza®
12 Boceprevir image Hepatitis C Inhibition of NS3-NS4A serine protease of HCV 2011 Merck Sharp Dohme Boceprevir Victrelis®
13 Captopril image Hypertension Potent and reversible inhibition of ACE 1981 Bristol Myers-Squibb Captopril Capoten®
14 Aliskiren image Hypertension Inhibition of rennin 2007 Novartis Aliskiren hemifumarate Tekturna®
15 Donepezil image Alzheimer’s disease Inhibition of AChE enzyme 1996 Eisai Inc. Donepezil hydrochloride Aricept®
16 Imatinib image Cancer: Chronic myelogenous leukemia (CML), gastrointestinal stromal tumors (GISTs), etc. Inhibition of BCR-Abl tyrosine kinase enzyme 2003 Novartis Imatinib mesylate Gleevec®
17 Tirofiban image Thrombosis Inhibition of fibrinogen 2000 Medicure Tirofiban hydrochloride Aggrastat®
Investigational drugs under clinical trial
18 PRX-00023 image Depression, anxiety Agonism of 5-HT1A Phase III completed Epix Pharmaceuticals, Inc. PRX-00023 −
19 LY-517717 image Venous thromboembolism following hip or knee replacement Inhibition of factor Xa serine protease enzyme Phase II completed Eli Lilly/Protherics LY-517717 −
20 TMI-005 image Rheumatoid arthritis Inhibition of TNF-α convertase enzyme (TACE) Phase II completed Wyeth (Pfizer) TMI-005 −
21 Nolatrexed image Cancer: Unresectable Hepatocellular Carcinoma (HCC) Inhibition of thymidylate synthase enzyme Phase III completed Agouron Nolatrexed dihydrochloride Thymitaq
22 Raltitrexed image Cancer: malignant neoplasm of colon and rectum Inhibition of thymidylate synthase enzyme Phase II completed Astrazeneca Raltitrexed Tomudex
23 AUY922 (NVP-AUY922) image Cancer Inhibition of HSP90 Phase II completed Novartis AUY922 −
24 Rupintrivir (AG7088) image Rhinovirus infection Inhibition of HRV 3C protease enzyme Phase II completed Agouron Rupintrivir −


Image


Image



11.2.1 Examples of some approved drugs





Example 1



a. Name of the drug: Captopril


b. Disease indication: Used for the treatment of hypertension.


c. Mechanism: Reduction of blood pressure by antagonizing the action of angiotensin-converting enzyme (ACE) that controls the pressure of blood using the rennin–angiotensin pathway. Captopril is considered as a reversible and potent inhibitor of ACE.


d. Brief developmental history: Captopril was designed using the approach of structure-based drug design in the late 1970s [8]. The basic concept came from the inhibition of the enzyme carboxypeptidase A. Among the first developments were L-benzylsuccinic acid [9], a potent inhibitor of carboxypeptidase A and the pentapeptide BPP5a [10], an inhibitor of ACE (which is isolated from the Brazilian viper, Bothrops jararaca). The N-terminal fragment of BPP5a, including dipeptide, tripeptide, and tetrapeptide fragments, possesses the ACE inhibitory activity. Benzylsuccinic acid was considered as a model compound, assuming that succinyl amino acids act as the by-product inhibitors of ACE. Structure–activity relationship (SAR) studies on succinyl-proline moiety led to the design and development of Captopril characterized by an IC50 value of 23 nM [8]. Two essential structural modifications were incorporated in succinyl-proline. In order to establish a stronger binding interation with the zinc ion of ACE, the carboxylic acid residue of succinyl-proline was replaced with the mercapto group, and a stereospecific R-methyl group was added to the succinyl moiety emulating the methyl group that is similar to that of Ala-Pro (L-Ala residue). Figure 11.1 shows the structural developmental phases of Captopril.


e. Approval status: Approved by the US Food and Drug Administration (FDA) in 1981.





Example 2



a. Name of the drug: Dorzolamide


b. Disease indication: Used for the treatment of open angle glaucoma and ocular hypertension.


c. Mechanism: An antagonist for the carbonic anhydrase II (CA II) enzyme, leading to the blockade of local conversion of carbon dioxide to bicarbonate and thereby lowering intraocular pressure (IOP).


d. Brief developmental history: The discovery was directed by exploring the binding of compounds at the active site of the CA II enzyme using suitable tools. MK-927 was considered as the prototype chemical for reducing IOP. From chiral analysis, the S-enantiomer of MK-927 was observed to be more active than the R isomer, and X-ray crystallographic studies using the human CA II (HCA II) enzyme showed binding interaction between the zinc ion of HCA II and the deprotonated (presumably) sulfonamide nitrogen of MK-927, while the thiophene ring was reported to be placed between hydrophobic and hydrophilic walls of the active site [11,12]. Dorzolamide was developed from MK-927 through the conformational optimization of its enantiomers. At first, the pseudoequatorial conformation between the R and S enantiomers (MK-927) was observed to be preferable by employing ab initio studies at the 6–31 G* level. One difference between the two enantiomers was in the thiophenesulfonamide N-S-C-S dihedral angle, the ideal being 72°. The S-form (150°) showed a preference over the R form (170°) since the latter showed an additional twist of the thiophene ring. The second difference was in the geometry of the 4-isobutylamino substituent; an ab initio study at the 3–21 G* level showed that the trans geometry in the S-form is preferable than the gauche form in the R [12]. Two structural modifications were performed to enhance the inhibitory potency. With the aim of reducing the pseudoaxial energy penalty, a methyl group at the thienothiopyran ring was introduced; and to reduce the lipophilic effect due to the methyl group, ethylamino moiety was used in place of the 4-isobutylamino group. The final structure was termed dorzolamide, and from X-ray crystallographic analysis using HCA II, the S,S configuration was observed to be the best characterized by a favorable thiophenesulfonamide N-S-C-S dihedral angle of 140° [12]. Figure 11.2 presents the chemical structures of MK-927 and dorzolamide.


e. Approval status: Approved by the FDA in 1994.




Example 3



a. Name of the drug: Zanamivir


b. Disease indication: Used for the treatment of influenza.


c. Mechanism: Inhibition of the neuraminidase (NA) enzyme for the treatment of influenza A and B viruses.


d. Brief developmental history: The biological target to combat the influenza virus is neuraminidase enzyme (also known as sialidases) that comprises two groups: group-1 (N1, N4, N5, N8) and group-2 (N2, N3, N6, N7, N9). The influenza virus envelope comprises NA enzymes that damage the host by the hydrolytic cleavage of glycosidic bonds between the terminal sialic acid residues and adjacent sugars on hemagglutinin or surface cells [13], and the spread of the virus is actually facilitated by the cleavage of sialic acid [14]. The structure-based design and development of drugs against influenza was followed by the establishment of three-dimensional (3D) structural geometry of the group-2 neuraminidase in early 1980s. In 1993, von Itzstein and coworkers [15] used the computational tool GRID while studying the binding site of NA. The design of the NA antagonists was facilitated by the transition-state principle. The binding study of 2-deoxy-2,3-dehydro-N-acetylneuraminic acid (DANA) to NA made good progress toward the development of inhibitors, and it concluded with the possibility of further improvement by replacing the 4-hydroxy group with an amino or a larger guanidine moiety [16]. Both substitutions are found to enhance the binding affinity, of which the guanidino moiety showed more effective binding characterized by salt-bridge formation with Glu119 residue and charge–charge interaction with Glu227 residue, while the amino replacement depicted only salt-bridge interaction with Glu119 residue [15]. The guanidino compound was designated as zanamivir, which is the first-in-class NA inhibitor to get approval. The chemical structures of DANA and zanamivir are shown in Figure 11.3. The only problem with zanamivir was its poor bioavailability; hence, inhalation was the route of administration.


e. Approval status: Approved by the FDA in 1999.

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  4. Selected Statistical Methods in QSAR

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Jul 18, 2016 | Posted by in PHARMACY | Comments Off on SAR and QSAR in Drug Discovery and Chemical Design—Some Examples

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