N Sharma, S Kushwaha, P Chauhan, M Shakya
binding energy, drug analogs, dutasteride, molecular dynamics, side-effect
N Sharma, S Kushwaha, P Chauhan, M Shakya. Development of Efficient Drug Analogs for Dutasteride through Insilico Modeling. The Internet Journal of Medical Informatics. 2008 Volume 5 Number 1.
Action of drug depends on the quantity of drug that reaches to the receptor and the degree of affinity between drug and receptor. Once drug bound to its receptor, it shows its desired effect (intrinsic activity) along with major and minor side-effects. More than 500 drugs with significant side-effects are available in market. In the present study the efforts have been made to identify new candidate compounds for dutasteride through modeled structure of two existing drug targets to improve efficacy by optimizing various parameters. Three dimensional structures of both drug targets of dutasteride were generated by MODELLER, validated by PROCHECK and ERRAT programs (90.04% of model-I and 84.647% of model-II). Energy minimization and molecular dynamics calculations were done through GROMACS using OPLS force field. RMSD calculated for both simulated model attain a constant deviation within 10,000 cycles. For analog generation, mono-substitution is preferred instead of multi-substitution. Objective and subjective both approaches were used for identification and calculation of chemical descriptors. A comparison of the calculated binding affinities for structurally similar inhibitors of dutasteride gave suitable analogs. Total 169 analogs were generated and eight are selected for comparative study. Our finding reveals that three dutasteride drug analogs (-CH2NH2, -CH2OH, -CF2OH) were most suitable analogs, showing theoretically more superior results. These findings need to be further evaluations in laboratory.
Soft-computing techniques are concerned with approximate calculation, imprecision, analysis where human capability of making decisions is in dilemma. The recent developments in soft-computing techniques are expanding its scope in pharmaceutical industry. At present thousands of drugs are in market but many of them needed to improve efficacy (minimization of biological toxicity effects and side effects) [1, 2]. In the present study dutasteride (AVODART) drug for hair loss treatment is studied . Dutasteride inhibits both isoforms of 5-alpha reductase, which are considered as drug targets. The most common adverse reactions of Dutasteride are impotence, decreased libido, breast disorders, and ejaculation disorders. Dutasteride belongs to: Drug type - small molecule, Drug category - Approved, Investigational, Anti-baldness agent and Antihyperplasia agents (http://www.drugbank.ca/). The PDB structures of both the drug targets are not available and additionally the molecular modeling of targets was a tougher task due to very low sequence similarity with known PDB templates.
In this paper an attempt has been made to model the tertiary structure of drug targets through multiple templates with incorporation of fold assignment and secondary structure information [4, 5]. After molecular dynamics calculations and model assessment, active site characterization, analogs generation, analog selection and descriptors calculations of analogs has been done through various offline and online softwares and tools. Furthermore to explore the efficacy of dutasteride, a comparative analysis of generated analogs has been performed with dutasteride.
Material & Methods
Dutasteride and its drug target information were obtained from drug bank. Dutasteride drug targets are existing in two isoform i.e. 3-oxo-5-alpha-steroid4-dehydrogenase 1(Drug target-I) and 3-oxo -5-alpha-steroid 4-dehydrogenase 2(Drug target-II) as reported in drug bank database. For these targets 290 and 292 SNPs are reported  at location chromosome-5 locus 5p15 and chromosome-2, locus-2p23 respectively.
Modeling of drug targets
The PDB structures of both drug targets are not available. So, modeling of both the target proteins were performed using MODELLER. A template search has been performed through BLAST and PSI-BLAST programs . Global alignment method was used for comparison between the target-template sequences . Gaps with variable gap penalty function are included for structural loops and core regions, in order to get maximum correspondence between the sequences. Alignment file for MODELLER was prepared by CLUSTALW . Fold recognization was done through mGenThreader and LOMETS server for fold assignment . Energy minimization of generated 3D-model was done through GROMACS (OPLS force field) by using Steepest Descent and Conjugate Gradient Algorithms . Parameters like covalent bond distances and angles, stereochemical validation, atom nomenclature were validated using PROCHECK and overall quality factor of non-bonded interactions between different atoms types were measured by ERRAT program . RMSD (root-mean-square deviation) and RMSF (Root Mean Square Fluctuation) was calculated for modeled structures. Functionally important residues (Active-site) were identified through comparative result of POCKETFINDER and SURFACE RACER 4.0.
Analog generation Characterization and Docking
Drug analogs were created through mono- substitution in the hydrophilic region on the target molecule with other functional groups (-I Group to +I Group) through CHEMSKETCH 10.0. Generated analogs were further analyzed by comparative study of molecular descriptors, various energies and QSAR studies [12, 13, 14, 15, and 16]. Drug-binding specificity of target to analogs was also performed with docking through AUTODOCK 4.0 [17, 18]. Mono-substitution studies have more relevant results and valuable in the effective analog development. Initially 169 analogs were generated from the reference compound (Dutasteride, Drug bank accession DB01126). Prediction and identification of potential drugs and non-drugs from generated analogs is most important task . Objectives (Independent variables) as well as subjective (Dependent variables) approaches were used in identification and selections of chemical descriptors .
Toxicity Analysis of Selected analogs
Prediction and identification of potential drugs and non-drugs from generated analogs is most important task. Toxicity behavior of selected analogs was studied through PALLAS and different types of toxicity (i.e. Oncogen, Mutagen, Teratogen, Irritation, Immunotox and Neurotox) were reported in selected analogs on the basis of toxic fragments.
Drug likeness (Lipinski Rules)
The Lipinski “Rule of Five” states that compounds are likely to have good absorption and permeation in biological systems. Pharmaceutical industry is following this index for evaluation of drug health. We performed the lipinski evaluation for all the selected analogs.
Results & Discussions
Target protein modeling, model verification, active site characterization, analogs generation, docking and QSAR studies resulted through various offline and online tools are as follows.
For modelling of both drug targets, template search were performed with the BLAST and PSI-BLAST and did not find single suitable PDB template for target protein sequence. Thermus Aquaticus Core Rna Polymerase-Rifampicin Complex (1I6V) and methanococcus Jannaschii phosphosulfolactate synthase (1QWG) are identified as templates for drug target-I. 1UTH and 2UYE were identified as templates for drug target-II along with other PDBs. Modelling of dutasteride drug targets was a tedious task due to very low sequence similarity and coverage. Three dimensional models of drug targets were generated through identified templates along with fold fitting. Fold recognization was done through mGenThreader and LOMETS server for fold assignment. Helices have dominance over other secondary structure i.e. sheet, coil in both generated model. Both modeled structure of drug target-I (Model-I) and drug target II (Model-II) is shown in figure-1(a&b).
Energy minimization of generated 3D-model was done through GROMACS (OPLS force field by using steepest descent and conjugate gradient algorithms).The generated 3D model (Figure 1a and 1b) of target proteins was checked by Ramachandran plot (Figure-2a & 2b) through PROCHECK program.
For model-I, 91.03% residues was lies in most favoured region whereas 88.09% residues were lies in most favoured region in model-II in Ramachandran plot .Besides very low sequence similarity and sequence coverage to PDB templates, the overall quality factor for drug target model-I (90.04%) and model-II (84.647) were reported through structure validation server ERRAT (Figure-3a & 3b).
Dark black regions were showing high error due to high structural variability (high transition of E, C and H) in generated models. Like –in model-I two dark places (100-120 and 245-259).
RMSD and RMSF - Reliable Indicators to Check Variability.
The molecular dynamics simulations have provided significant new information on the nature of proteins. RMSD measures the accuracy whereas dynamic fluctuations (RMSF) of proteins around their average conformations play an important indicator of many biological processes such as enzyme activity, macromolecular recognition, and complex formations .
The changes in structural conformation were monitored in terms of RMSD and RMSF . Figure -4(a, b, c) shows, for model-I RMSD after 10 ps time when calculated for Backbone-Backbone was 0.19, for Protein-Protein was 0.23. Further when the simulation cycles were increased to 50ps time RMSD for Backbone-Backbone was 0.22 and Protein-Protein was 0.26. Results clearly indicated that energy minimization should be done for few steps only. More energy minimization steps can cause more structural hindrance .
Figure -5(a, b, c) shows, model-II RMSD after 10 ps time (Backbone-Backbone (0.225), Protein-Protein (0.265) whereas RMSD after 50ps (Backbone-Backbone (0.3), Protein-Protein (0.265).
Model-I has sequence length of 259 residues whereas model-II contains 254 residues. During the course of MD trajectory, coils are more prone to fluctuations than the alpha helices (figure-6). After modeling of target proteins, characterization of structure is another very important task for rational drug designing. Prediction of active site of target protein was done by comparative result of POCKET FINDER and SURFACE RACER-4.0 which is shown in table-1.
Analog generation and optimization results
Drug analogs were created through mono- substitution in the hydrophilic region on the drug molecule with other functional groups (-I Group to +I Group) through CHEMSKETCH 10.0 and compatibility of analogs was checked through AUTODOCK 4.0 against modeled structures of target proteins(Target-I + Target-II) with binding energies.
Comparative results of the molecular descriptors of selected analogs are given table-3. It was observed that changes in molecular descriptors of selected analogs are under considerable range.
Toxicity prediction is most important criteria for new analog selection. Toxicity of a molecule is highly dependent upon its structural elements. Certain molecule segments are characteristic of hazardeous compounds and therefore called toxic segments. Toxicity prediction is therefore based on Segment body of chemical, Positive condition, Negative condition that can be used to predict certain health hazards of organic chemicals combining toxicological knowledge and judgment with the use of leading-edge QSAR artificial intelligence techniques. Chemical segment body of analog generated from dutasteride is alpha-beta unsaturated (thio) amide, Aliphatic Polyholide-1-2, Benzyl halide/sulphate, T_Har_Hat_hat08. Segment body of analogs shown in Table .
These analogs are showing different type of biological toxicity effects (Oncogenicity, Mutagenicity, Teratogenicity, Membrane Irritation, Sensitization, Immunotoxicity, and Neurotoxicity). Level of toxic effects is high in three analogs (-CCL3, -CH3, -CCL2OH) which is shown in Graph-1
The Lipinski “Rule of Five” states that compounds are likely to have good absorption and permeation in biological systems. Pharmaceutical industry is following this index for evaluation of drug health.
Results clearly indicate that before synthesis and biochemical testing of new analogs, one can use molecular mechanics based methods for qualitative assessment of relative binding affinities, toxicity and Lipinski evaluation to speed up drug discovery process by eliminating less potent compounds from synthesis
The present model can be further extended with some modifications, if necessary for analysis of other drug compounds which have more side effects. All these analysis and predictions are made on the basis of bioinformatics tools & techniques by statistical analysis. Theoretical calculation may varie from software to software but the protocol which is developed is up to mark for the new drug discovery. This knowledge will contribute positively to develop new drug analogs with more efficacies as well as less side effects.
We are grateful to Department of Bioinformatics, MANIT, Bhopal, India for support and cooperation.