Introduction

Since its first discovery in 1981, AIDS has claimed more than 20 million lives. According to the latest UNAIDS report of 2006, an estimated 39.5 million people are living with HIV/AIDS (UNAIDS report, 2006). About 4.3 million people were newly infected and more than 2.9 million people died from this deadly disease in 2006 alone (UNAIDS 2006). This reflects the enormity of the AIDS epidemic in the world, especially affecting Sub-Saharan Africa and South-East Asia. Another millions of people will be infected with HIV if the current trend of infection continues and in the absence of effective preventive measures (UNAIDS, 2006). The treatment of the Acquired Immuno Deficiency Syndrome (AIDS) is the most challenging worldwide medical problem.

During the past decade, there has been steady progress in the chemotherapy of AIDS due to the availability of more effective antiretroviral drugs (De Clercq E, 2005). Most of the current strategies for treating AIDS depend on inhibiting HIV-1 reverse transcriptase enzyme. All clinically used antiretroviral drugs belong to either the protease inhibitor (PI) or reverse transcriptase inhibitor (NRTIs) classes (Rawal et al., 2007). Non nucleoside reverse transcriptase (NNRTIs) inhibitors have attracted special status as drug candidates because of their unique mode of action, non-covalent, and non-competitive binding at the allosteric site present only in the HIV-1 RT (Schafer et al, 1993). However, this scenario is changing due to the emergence of drug resistant HIV-variants.

Other class which has drawn attention is NRTI drugs. All NRTI'S (2', 3'-dideoxynucleoside analogues) undergoes 3 phosphorylation steps that convert the parent compound successively to its 5' monophosphate, di and triphosphate. The triphosphate compound acts as a competitive inhibitor /alternate substrate for the target enzyme. This dideoxynucleoside incorporates in DNA chain and causes chain termination during either first or second strand synthesis of DNA. One of the first nucleoside analogues shown to have potent anti HIV activity in-vitro is Zidovudine (AZT). The 3'-OH is replaced in most of the available drugs with fluorine, nitrogen etc. The most recent drug is tenofovir. This drug possess one aliphatic chain and phosphonate group. It is incorporated into the primer strand following translocation to the priming or ‘P' site. Afterwards an alternative conformation for the adenosine base of tenofovir flipped through 180° from the first position. Appreciable approach for combinatorial drug therapy which led the path towards the success of the HAART therapy has been demonstrated in the past (Masanori Baba et al, 1987). Computer-assisted approaches to the drug design including QSAR and docking have been used for many years in order to avoid intuitive and empirical ways for achieving an active or lead compound. Since 1991, compounds possessing anti-HIV activity have been the subject of numerous quantitative structure– activity relationship (QSAR) studies (Douali et al., 2004). In fact, QSAR, as a technique attempting to summarize chemical and biological information in order to generate relationships between structure and biological activity, hastens the drug design process.

In this paper, we report the 2D-QSAR analysis along with docking studies on a series of NRTI drugs. In addition to the QSAR studies, docking calculations were done using FLEXX and GOLD.

Materials and Methods

Data set

Dataset of 100 molecules was collected from the public domain of UNAIDS and after preliminary screening of the EC50 and IC50 values and proper literature support, a series of 30 compounds was considered in this study. All the molecules studied had the same parent skeleton (Fig 1).

IC50 is the concentration of the compound leading to 50% inhibitory effect. The logarithm of the inverse of this parameter has been used as biological end points (log IC 50) in the QSAR studies.

The selected protein HIV-1 reverse transcriptase cross-linked to tenofovir terminated template-primer (complex P) with 3.1 resolution and R value of 0.256 (work) and 0.295(free) was collected from Protein Data Bank (http://www.rcsb.org) (PDBID 1T03)(Fig 2).

Figure 1

Table 1: Dataset of molecules considered for present study

Preparation of input for Quantitative Structure and Activity Relationship (QSAR) studies

Quantitative Structure and Activity Relationship (QSAR) studies

Docking studies

Results and Discussions

Figure 2

Table 2: Predicted activities (PA), experimental activities (EA) (in pIC50) and residuals (d) of 2D-QSAR models.

Figure 3

Table 3: Statistical parameters of correlated dataset using BMLR method

Figure 4

Table 4:Co-efficient (X) and t-test values of descriptors for seventh model

Figure 5

Table 5: Dock score of NRTI drugs with HIV-1 Reverse transcriptase enzyme (in kcal/mol) using FLEXX.

Figure 6

Table 6: Dock score of NRTI drugs with HIV-1 Reverse transcriptase enzyme using GOLD

Figure 7

Figure 1: Common scaffold of the drugs.

Figure 8

Figure 2: Crystal structure of HIV -1 RT with Tenofovir

Figure 9

Figure 3: Active site of HIV reverse transcriptase protein (left).The important residues are shown in capped sticks (right)

Figure 10

Figure 4: Plot of r and q against the number of descriptors

Figure 11

Figure 5: The predicted vs. experimental activities of training and test sets are plotted in the graph.

BMLR regression analysis was performed to get the QSAR equation. This resulted in total 11 models with a maximum of 12 descriptors (Table 3).To get the optimum number of descriptors the r ² and q ² values were taken and plotted against the number of descriptors (Fig. 4). From the plot, the saturation point was checked and the one having seven descriptors was selected as a best model and analyzed further.

The descriptors obtained from this model are as follows (Table 4):

1. Maximum n-n repulsion for a C-C bond (constitutional): -276.94

2. Exchange of eng+ e-e repulsion for a H-O bond (quantum chemical): - 0.2429

3. Minimum atomic state energy for a O atom (constitutional): 0.2029

4. Maximum n-n repulsion for a C-N bond (constitutional): -0.9649

5. HA dependent HDSA-1 [quantum chemical PC]: 0.02258

6. HASA-2/TMSA (total molecular surface area) [Zefirov's PC]: 47.678

7. Maximum valency of a H atom (constitutional): 104.64

Finally, four constitutional and three quantum chemical descriptors were obtained.

The descriptors encoding significant structural information such as Maximum n-n repulsion for a C-C bond (constitutional), Exchange of eng+ e-e repulsion for a H-O bond (quantum chemical), Minimum atomic state energy for a O atom (constitutional), Maximum n-n repulsion for a C-N bond (constitutional), HA dependent HDSA-1 [quantum chemical PC], HASA-2/TMSA (total molecular surface area) [Zefirov's PC], Maximum valency of a H atom (constitutional) can be used to get a unique characteristics of compounds to build the relationship between the structure and biological activity. The t-test results provide the contribution of the descriptors in the following order Max n-n repulsion for a C-C bond> Max n-n repulsion for a C-N bond> HASA-2/TMSA [Zefirov's PC]> Exch. Eng + e-e repulsion for a H-O bond> Max valency of a H atom> Intercept> Min atomic state energy for a O atom> HA dependent HDSA-1 [Quantum chemical PC]

The docking study was carried out by taking the reverse transcriptase enzyme (PDBID- 1T03) which was crystallized with a co-crystal, Tenofovir, a reported drug. This reverse transcriptase receptor contains A, B, H, L chain and it also contains a stretch of nucleotide molecule. The drug Tenofovir is bound in the A chain. The active site of the drug is determined for the purpose of docking the set of ligands. It contains Gly231, Asp 186, Met184, Met230, and Trp229 (Fig.3). The considered drugs were docked against this particular receptor.

In the FlexX docking result, the molecule 65 (2,6–Diaminopurine-2, 3dideoxydihydroribose) gave the most negative score of -15.7 kcal/mol (Table.5), which is having the highest interaction while in GOLD result molecule 65 is coming in the best ranking list with its 7 ^th conformation with a fitness score of 32.49(Table. 6).

Conclusion

HAART (Highly Active Anti Retro Viral Therapy) is one of the most promising treatment method for AIDS where different combination of NRTI, NNRTI or protease inhibitors are prescribed to the patient to prevent the virus in different stages of its life cycle. This kind of computational approach will help us to extract the important descriptors from the existing drug series to add up a new molecule in the NRTI drug list and to combat with fast drug resistance property of the virus.

The 2D QSAR study gave a clear correlation of the drug dataset with the biological activity and extracted few constitutional and quantum chemical descriptors which may become the key factor in future drug discovery process. The result indicates an effective role of surface area of the drugs for the activity. A probable role of the Fluorine and Nitrogen in increment of electrostatic interaction is also expected. Apart from this, the docking study will help us to check out the binding capability of the drug. Just the addition of a single drug in the series often results in offshoot of the drug combinations for HAART therapy.

Thus, it can be inferred that these factors (extracted descriptors and total negative binding energy range) play a major role in the maintenance of biological activity i.e. IC50 of the drugs of NRTI series. Binding of ligands depends on several interaction factors like electrostatic bonding, hydrogen bonding, hydrophobic interaction, quantum chemical interaction; thus descriptors encoding significant structural information such as constitutional; quantum chemical environment can be used to get unique characteristics of compounds to build the relationship between the structure and biological activity.

This study will enhance our understanding on the descriptors responsible to increase the drug activity and the special features of best molecule interacting with reverse transcriptase and hence, will result in a better drug synthesis strategy.

Acknowledgement

Correspondence to

Dr. U.S.N Murty Biology Division Indian Institute of Chemical Technology, Tarnaka, Hyderabad-500007, India Email: murty_usn@yahoo.com