A MAJOR APPROACH- 2-CHLOROPYRIDINE-5-TRIFLUOROMETHYL DERIVATIVE AS ANTIAMNESIC AGENTS

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Example- an imidazyl-phenyl series compounds


2.1.5. Huprine derivatives22
Huprines, Tacrine–Huperzine A hybrids have been described a few years ago as highly potent AChE inhibitors

2.1.6. Muscarinic receptor 1 agonist23
The cholinergic hypothesis of aging and of dementia suggests that the loss of central forebrain cholinergic neurons contributes to the decline in cognitive abilities associated with AD. The presynaptic cholinergic deficits in AD indicate that a cholinergic replacement therapy might be beneficial in alleviating some of the cognitive dysfunctions in this disorder.

The loss of presynaptic marker enzyme choline acetyltransferase and the muscarinic receptors of the M2 subtype are mainly responsible in causing deficits in central cholinergic transmission in Alzheimer’s patients.

M1 muscarinic receptors play a role in an apparent linkage of three major hallmarks of AD:  β-amyloid (Aβ) peptide; tau hyperphosphorylation and paired helical filaments (PHFs); and loss of cholinergic function conducive to cognitive impairments.

Muscarinic acetylcholine (mACh) receptor is a growing number of G protein-coupled receptors; each specifically regulates a different physiological and biochemical function in the body24. Four types of muscarinic receptors are known, named M1–M4 and five subtypes of muscarinic receptors have been cloned and designated m1–m5.

M1 selective muscarinic agonists, are capable of crossing the blood–brain barrier has active pharmacological application.

Most of the potent muscarinic agonists, including those which were evaluated in AD patients, show adverse central and peripheral side effects, and are either non-selective or M2> M1 selective. Thus, they may also activate inhibitory M2 autoreceptors resulting in decreased acetylcholine (ACh) release.

The following probes for mAChRs were suggested as a rational treatment strategy in AD;
(a) M1 agonists;
(b) M2 antagonists;
(c)  Mixed M1 agonist and M2 antagonist in the same compound.

In research, many structurally novel arecoline-based muscarinic agonists have been synthesized which potentially may overcome the limited oral activity, short duration of action, or lack of separation between central and peripheral effects of classical agonists.

Example-
Arecoline oximes or oxadiazoles, arecoline thiadiazoles, arecoline oxazoles, arecoline amides are the new generation muscarinic agonists, arecoline thiazolidinones, N-arylsulphonamide substituted 3-morpholino arecoline analogues and N-arylthiourea substituted 3-morpholino arecoline analogues  as muscarinic receptor 1 agonist.


2.1.7. Neuronal nitric oxide synthase inhibitors25
Neuronal nitric oxide synthase (nNOS) has been involved in various neurodegenerative diseases, including Parkinson’s disease and neuronal damage resulting from stroke.

Inhibition of nNOS could have therapeutic benefit in these and other diseases, but this inhibition must be achieved without effect on the other isoforms of NOS, endothelial (eNOS) and inducible (iNOS); inhibition of eNOS could lead to side effects such as hypertension, and inhibition of iNOS could result in a higher probability of Alzheimer’s disease.

Example-


2.1.8. Met kinase inhibitors26
Met is a receptor tyrosine kinase protein that has a high binding affinity for hepatocyte growth factor/scatter factor (HGF/SF). Upon activation with its endogenous ligand HGF/SF, Met mediates various cellular responses, such as epithelial cell dissociation (scattering), invasion, tubular morphogenesis, and angiogenesis.

While Met/ HGF signaling is essential for normal physiological events, such as placental development and liver regeneration, activation of this pathway is reported to lead to tumorigenicity and metastasis.

Due to the prevalence of Met amplification/over expression and mutations in a variety of human malignancies, inhibition of Met kinase activity by small molecules or biologics would likely have broad therapeutic utility.

Example-
Met kinase inhibitors with malonamide  and acylurea  groups substituted on the pyrrolo[2,1-f][1,2,4]triazines.

pyrrolopyridine- and aminopyridine- based Met kinase inhibitors.


3.1 SCHEME-


3.1.1 GENERAL PROCEDURE-
2-amino-3-chloro-5-trifluoromethyl pyridine was suspended in 3:1 mixture of THF/EtOH (15 ml/mmol of starting pyridine). An excess of secondary amine was then added, followed by triethylamine (1.0equiv). The mixture was heated under reflux. After cooling the solvent was removed in vacuum and resulted crude submitted to recrystllisation to give final compound.

3.2 DETERMINATION OF LIPINSKI DESCRIPTORS-
Fundamental physicochemical features of CNS drugs are related to their ability to penetrate the blood-brain barrier affinity and exhibit CNS activity. Lipinski et al looked for a generalized rule that is a “Rule of five” that govern drug like properties. The “Rule of five” is so named because all the essential physical properties are parameters of five. According to this rule, a good absorption and permeability is likely if:
1)      Molecular weight is ≤ 500.
2)      Oil/water distribution coefficient (Log P) is ≤ 5.
3)      Hydrogen bond donor ≤ 5 (expressed as the sum of OHs and NHs).
4)      Hydrogen bond acceptor ≤ 10 (expressed as the sum of Os and Ns). A fifth rule was added later:
5)      Number of rotatable bonds ≤ 10.

Lipinski also laid down rule for CNS penetration30
·         Molecular weight ≤ 400.
·         Log P ≤ 5.
·         Hydrogen bond donor ≤ 3.
·         Hydrogen bond acceptor ≤ 10.
·         Number of rotatable bonds ≤ 8.

Log P as the descriptor of lipophilicity has been observed very important for CNS penetration. For the optimal penetration of blood brain barrier log P values should be in range of 1.5 – 2.7. CNS drugs have significantly reduced molecular weight compared with other therapeutics that is less than 400. The total polar surface area (TPSA), count of hydrogen bond donors (nOHNH) and hydrogen bond acceptors (nON) are all correlated with polarity. Standard values are TPSA 60-70 ?, nOHNH ≤ 3 and nON ≤ 8.

These compounds were subjected to ‘Lipinski rules of five’. The MIPC (Mol Inspiration Property Calculator) program has been utilized (www.molinspiration.com) for calculating Lipinski descriptors30. The Lipinski descriptors for the four compounds under consideration have been provided in table 3.

Compound Code & No.

Mlog p

PSA (?2)

Mw

No. of rotatable bonds

Hydrogen bond

Acceptor(s)

Donor(s)

RST-1, (25)

2.646

16.13

224.613

2

2

0

RST-2, (26)

2.492

25.364

266.65 2

3

0

RST-3, (27)

2.583

19.368

279.693

2

3

0

RST-4, (28)

3.554

16.13

264.678

2

2

0

Table 3: Lipinski descriptors for the compounds (25-28).

On the basis of above-mentioned criterion and with a goal to provide the diversity of potent cognition enhancers, the chosen compounds were experimentally tested as nootropics.

3.3 PASS PREDICTION
In order to accelerate our search for potent New Chemical Entities (NCEs), the assistance of Computer Aided Drug Discovery Program PASS (Prediction of Biological Activity Spectra for Substances) was used to predict the cognition enhancing action for 2-chloropyridine-5-trifluoromethyl derivatives (25-28).

Contrary to many other existing methods of SAR/ QSAR/ QSPR/ molecular modeling methods focused on predicting a single type of biological activity within the same chemical series, computer-aided program PASS predicts not only for the desirable pharmacological effect but also for molecular mechanisms of action and different unwanted side effects like mutagenicity, carcinogenicity, teratogenicity and embryotoxicity. Such analysis of heterogeneous sets increases considerably the chance of discovering NCEs (e.g., cognition enhancers). The technique of PASS is based on the analysis of SARs for the training set currently including about 46,000 drugs, drug candidates and lead compounds whose biological activities are determined experimentally. The set of MNA (Multilevel Neighbourhood atoms) descriptors is generated on the basis of structural formulas presented in the MOL-file (SDF-file) form. Since MNA descriptors are generated for each compound de novo, new descriptors can be obtained upon presentation of a novel structural feature in the compound under study. Based on the statistics of MNA descriptors for active and inactive compounds from the training set, two probabilities are calculated for each activity: Pa probability of compound being active and Pi—probability of compound being inactive. Being probabilities, the Pa and Pi values vary from 0.000 to 1.000 and in general Pa + Pi < 1, since these probabilities are calculated independently31.

Recently, an Internet version of PASShas been made available at the PASS developer’s web site. The user can submit the MOL-file of the molecule under study and obtain the predicted biological activity spectrum on their computer immediately. This new internet version of PASS provides access to prediction of 783 kinds of biological activity, in contrast to an earlier version that predicted 319 activities32. MOL-files of compounds 25-28 were submitted and predicted biological activity spectra were obtained.

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