Virtual screening in drug-likeness and structure/activity relationship of pyridazine derivatives as Anti-Alzheimer drugs

3.1 CNS MPO desirability as a measure of CNS drug-likeness

The diseases of the central nervous system (CNS) represent an unsatisfied medical zone of enormous needs. The discovery of drugs in this therapeutic zone faces particular challenges. For designing a CNS drug, it must have optimal pharmacokinetic and safety properties and have a great chance to penetrate the blood-brain barrier (BBB) (Ghose et al., 2011).

A new tool for optimization of CNS parameters (CNS MPO Desirability score) has been developed to optimize the design of molecules with increased penetration of the brain and drug-like properties (Wager et al., 2010b, a).

This new CNS MPO algorithm was based on a set of six fundamental physicochemical parameters: ((1) lipophilicity, partition coefficient logP; (2) distribution coefficient at pH = 7.4 logD; (3) molecular weight MW; (4) topological polar surface area TPSA; (5) number of hydrogen bond donors HBD; (6) most basic center pKa). The desirability score for these physicochemical properties is 0.0 to 1.0 for each property. The most desirable (T0 = 1.0) and least desirable (T0 = 0.0) inflection points are marked with green and red arrows, respectively are given in Fig. 1, the total CNS MPO desirability score ranging from 0.0 to 6.0. Compounds having a CNS MPO ≥ 4 show better drug like (Wager et al., 2010b).

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Fig. 1

Each ploy represents one of the six physicochemical property desirability function used to generate the CNS MPO (Wager et al., 2010b). Copyright 2010 ACS.

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In our study, CNS MPO algorithm was applied to a set for a class of cholinesterase inhibitors (AChE, 17 molecules) being pyridazine derivatives, which are synthesized characterized by Contreras et al. (2001, 1999) (Fig. 2).

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Fig. 2

3D structures of pyridazines derivatives.

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Transformed values (T0) of the six properties were determined for each compound, and overall CNS MPO desirability scores for pyridazines derivatives drugs are shown in Table 1. The CNS MPO algorithm shows that 12 compounds (1,5,8,9,10,11,12,13,14,15,16,17) displayed a high CNS MPO score ≥ 4. The CNS MPO score of these compounds may allow for the prospective design of compounds that occupy diverse property space while maintaining the desirable drug-like attributes including CNS penetration.

The likelihood of a compound with desirable ADME in vitro attributes increases with increased CNS desirability score. The compounds 10,12,13,14 and 16 have the highest CNS MPO desirability score ≥ 5. The 77% of the drugs with CNS MPO desirability scores of ≥ 5 showed full alignment of all three ADME attributes in one molecule (high passive permeability (P_app), low P-gp liability, low metabolic clearance (Cl_int,u)) and safety attributes (high cell viability (THLE Cv) and low risk of interference with the cardiac ion channel hERG) (Wager et al., 2010b,a). So, the five compounds10, 12, 13, 14 and 16 with the highest CNS MPO ≥ 5 leads to desirable ADME and safety attributes with an ability to penetrate the BBB.

3.2 Structure activity/property relationship for pyridazine derivative

In most cases, it is more advantageous to try to improve the pharmacological activity and properties of drug, such as solubility, stability and permeability during drug discovery. This is best achieved by modifying the chemical structure. Medicinal chemists determine the relationship between structure and activity /properties by developing qualitative approach of structure activity/Property relationships (SAR/SPA) (Belaidi et al., 2015; Melkemi and Belaidi, 2014).

The SAR /SPA study is applied on our anti-cholinesterase series (Fig. 2). The physico-chemical parameters involved are: Surface area grid (SAG), Molar volume (MV), Hydration energy (HE), Molecular weight (MW), partition coefficient octanol/water (logP), polar surface area (TPSA) and hydrogen bond donors/ acceptors (HBDs, HBAs) (Tables 2, 3).

Molecular weight (MW) is among the factors that determines drug permeability of drug candidates. CNS drugs have significantly reduced molecular weights compared with other therapeutics. An analysis of small drug-like molecules (MW < 450) suggested that for better brain permeation and to have a good oral absorption (Atkinson et al., 2002; Van de Waterbeemd et al., 1998).

The golden triangle and Warring rules (Johnson et al., 2009; Waring, 2009) show that the distribution coefficient at pH 7.4 (log D_7.4) and the molecular weight (MW) have an important impact on ADME and drug-likeness properties.

Waring suggests that logD_7.4 and molecular weight are the important factors in determining the permeability of drug candidates. The influences of molecular weight and logD_7.4 are not independent and the logD_7.4 threshold for high permeability is a lower value for compounds of lower molecular weight (Martin,2005). According to the Warring rule, there is a 74% chance of achieving high permeability for compounds with MW < 414 and logD_7.4 > 1.3. most of our compounds are within the limits of Lipinski rules. These results should help design compounds with improved permeability.

The Golden Triangle is a visualization tool developed from in vitro permeability, in vitro clearance and computational data designed to help medicinal chemists obtain metabolically stable, permeable and potent drug candidates. The probability of success in maximizing potency, stability and permeability is realized by moving the design properties into an area with a baseline of log D_7.4 = −2.0 to log D_7.4 = 5.0 at MW = 200 and a peak at log D_7.4 = 1, 0–2,0 and MW = 450, these boundaries give a triangular shape, called golden triangle. For our series of molecules, it is apparent that the compounds 1,9,10,11,12,14 and 16 have good permeability and low clearance because are concentrated within golden triangle area (Fig. 3).

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Fig. 3

In vitro permeability and clearance trends across MW and logD.

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According to the results obtained by these two rules, these compounds (1,9,10,11,12,14,16) have highest probability of success in maximizing potency, stability and permeability.

Pajouhesh et al. (Pajouhesh and Lenz, 2005), showed that two to five nitrogen atoms and zero to two oxygen atoms are the best range for CNS drugs(5 heteroatoms or less). These are applied to our compounds.

The calculation of the hydration energy is based on the exposed surface which depends on the type of atom of the molecular groups which can be donors or acceptor of the hydrogen bonds. The highest HE in absolute value (11.320) was observed for compound 14. The compound 6 have lowest hydration energy value (6.310). The HE increases in the presence of hydrophilic groups in the molecule. The compound 14 (Fig. 4)possesses one hydrogen bond donor HBD (NH) and four hydrogen bond acceptors HBA (three cyclic nitrogen, NH), result the increase in the hydration energy. The HBA are a great number that leads to poor permeability across a membrane bi-layer. The smaller number leads to better permeability (Lipinski et al., 1997).

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Fig. 4

Donor and acceptor sites of compound 14.

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Lipophilicity (logP) is an important factor in the processes of solubility, ADME properties, as well as pharmacological activity. A general guide for good oral bioavailability (good permeability and solubility) is to have a moderate logP (0 < log P < 3). Indeed, for a log P that is too high, the drug has a low aqueous solubility. In the case of a very low log P, the drug has a difficulty penetrating into the lipid bilayers of cell membranes (Kerns and Li, 2008). For several classes of CNS active substances, Hansch and Leo found that blood-brain barrier penetration is optimal when the logP values are in the range of 1.5–2.7 (Hansch and Leo, 1979).

The compounds 10,12,14 and 16 have logP values (1.68, 2.75, 2.42 and 1.3, respectively). These compounds are considered CNS oral drug, because of their better brain permeation (Bazooyar et al., 2013) and their good intestinal permeability (Fichert et al., 2003).

The polar surface area (TPSA) of a molecule is currently defined as the surface sum over all polar atoms, primarily oxygen and nitrogen. The increasing TPSA is associated with increasing percentages of compound that are not permeable or not bioavailable (Palm et al.,1997). The upper limit of PSA for a molecule to enter the brain is about 90. (Pajouhesh and Lenz, 2005). TPSA was used to calculate the percentage of absorption (%ABS) according to the equation (Zhao et al., 2002): %ABS = 109 ± 0.345 × TPSA.

TPSA values were found in the range of 58.12–38.25, these compounds have a chance to cross the BBB and have better bioavailability. We can observe obviously that all exhibited a great%ABS ranging from 88.949 to 94.838%, indicating that these compounds should have good cellular plasmatic membrane permeability.

The number of rotatable bonds (NRB) expresses the flexibility of a molecule. NRB is the number of bonds which allow free rotation around themselves (Veber et al., 2002). Molecular flexibility relates to the ease by which the molecule transverses the membrane. Number of rotatable bonds of a successful CNS drug is < 8 (Pajouhesh and Lenz, 2005).

All the compounds of our anti-cholinesterase inhibitors series (AChE) have NBR < 8, except the compounds 9 and 11 their NBR is 10, 9 respectively.

3.3 Drug-likeness properties and lipophilicity indices

The main objective of this study is to evaluate oral bioavailability of seventeen pyridazine derivatives (Fig. 2). High oral bioavailability is frequently an important consideration for the development of bioactive molecules as therapeutic agents. (Salah et al., 2015; BenBrahim et al., 2016; Kerassa et al., 2016)

In vivo pharmacokinetic parameters are strongly influenced by the physicochemical properties of a drug. The earliest thorough analysis of ADME properties was performed by Lipinski (Lipinski et al., 1997), and resulted in the famous “rule of 5”, which argues that good absorption or permeation are more likely when: The molecular weight (MW) <500, The number of hydrogen bond donors (HBDs) <5 (counting the sum of all NH and OH groups) partition coefficient octanol/water Log P < 5, The number of hydrogen bond acceptors (HBAs) <10 (counting all N and O atoms). The total number of violation in this ROF-score, which lies between 0 and 4. The results obtained are shown in Table 3.

There are two other descriptors identified by Veber et al. (2002): Number of Rotatable bonds (NBR) <10 and Polar surface area (PSA) <140 Å2.

All compounds meet the Lipinski and Veber rules of the five, suggesting that these compounds theoretically have ideal oral bioavailability. These physicochemical parameters are associated with acceptable aqueous solubility and intestinal permeability that are the first steps in oral bioavailability.

The control of lipophilic efficiency indices such as ligand lipophilic efficiency LipEand ligand-efficiency dependent lipophilicity LELP, which may contribute significantly to the overall quality of drugs at different stages of discovery.

If the lipophilicity is too high, there is an increased likelihood of binding to other targets than those desired and, therefore, there is more potential for toxicity (Leeson and Springthorpe, 2007). To facilitate the optimization of the affinity with respect to lipophilia. Leeson and Springthorpe (2007) defined the efficacy of ligand lipophilicity efficiency LipE (LLE); this parameter tries to improve potency while maintaining low lipophilicity, which makes the interaction with the receptor more specific. $$\text{LipE}={\text{pIC}}_{50}-\text{LogP}$$

The 90% of the drugs have LipE value > 3.3 (Wager et al., 2010a). That compound 1, and 14 have value and were deemed to be the optimal compounds.

To obtain optimal ADMET properties, molecular size and lipophilicity are important factors to consider. Keseru and Makara (2009) propose ligand efficiency-dependent lipophilicity index (LELP) to combine molecular size and lipophilia into a single measure of efficacy. The optimal LELP scores as −10 < LELP < 10. $$\text{LELP}=\text{LogP}/\text{LE}$$

Where LE is ligand efficiency (LE = 1.4pIC₅₀/N_H) (Hopkins et al., 2004).The compounds 10,12,14 and 16 reach an LELP of 8.129, 9.939, 4.772 and 6.026, which are situated in the suggested range –10 < LELP < 10. On the other side, all the compounds have LELP less than 16.5, which mean that these compounds are in the Lipinski zone (ROF-score = 4). Except the compounds 4 and 6 their LELP is 19.706 and 24.028respectively are in agreement with their weak ROF-score < 4.