Indole-pyridine carbonitriles: multicomponent reaction synthesis and bio-evaluation as potential hits against diabetes mellitus

Background: Diabetes mellitus is a significant health disorder; therefore, researchers should focus on discovering new drug candidates. Methods: A series of indole-pyridine carbonitrile derivatives, 1-34, were synthesized through a one-pot multicomponent reaction and evaluated for antidiabetic and antioxidant potential. Results: In this library, 12 derivatives - 1, 2, 4, 5, 7, 8, 10-12, 14, 15 and 31 - exhibited potent inhibitory activities against α-glucosidase and α-amylase enzymes, in comparison to acarbose (IC50 = 14.50 ± 0.11 μM). Furthermore, kinetics, absorption, distribution, metabolism, excretion and toxicity and molecular docking studies were used to interpret the type of inhibition, binding energies and interactions of ligands with target enzymes. Conclusion: These results indicate that the compounds may be promising hits for controlling diabetes mellitus and its related complications.

In silico ADME/tox comes of age: twenty years later

In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.

Potential implications of vouacapan compounds for insecticidal activity: an in silico study

BACKGROUND

From the fruits and seeds of the species of Pterodon, it is possible to obtain two main products: the essential oil and oleoresin. In oleoresin, numerous vouacapan compounds have been demonstrated to have biological potential, including insecticidal activity.

OBJECTIVE

In silico studies were performed to identify potential candidates for natural insecticides among the vouacapans present in the genus Pterodon.

MATERIALS AND METHODS

Molecular docking and molecular dynamics studies were performed to analyze the interaction of vouacapan compounds with acetylcholinesterase of Drosophila melanogaster. Pharmacokinetic parameters regarding physicochemical properties, plasma protein binding, and activity in the central nervous system were evaluated. The toxicological properties of the selected molecules were predicted using Malathion as the reference compound.

RESULTS

6α,7β-dimethoxivouacapan-17-ene (15) showed a high number of interactions and scores in molecular docking studies. This result suggests that this compound exhibits an inhibitory activity of the enzyme acetylcholinesterase. Regarding physicochemical properties, this compound showed the best results, besides presenting low cutaneous permeability values, suggesting null absorption. Molecular dynamics studies demonstrated few conformational changes in the structure of the complex formed by compound 4 and acetylcholinesterase enzyme throughout the simulation time.

CONCLUSION

It was determined that compound 4 (vouacapan 6α,7β,17β,19-tetraol) is an excellent candidate for usage as a natural insecticide.

Chemotherapeutic potential of hesperetin for cancer treatment, with mechanistic insights: A comprehensive review

BACKGROUND

Cancer has become a significant concern in the medical sector with increasing disease complexity. Although some available conventional treatments are still a blessing for cancer patients, short-and long-term adverse effects and poor efficiency make it more difficult to treat cancer patients, demonstrating the need for new potent and selective anticancer drugs. In search of potent anticancer agents, naturally occurring compounds have always been admired due to their structural diversity, where Hesperetin (HSP) may be one of the potent candidates.

PURPOSE

We aimed to summarize all sources, pharmacological properties, anticancer activities of HSP against numerous cancers types through targeting multiple pathological processes, mechanism of HSP on sensitizing the current anti-cancer agents and other phytochemicals, overcoming resistance pattern and determining absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox).

METHODS

Information was retrieved from PubMed, Science Direct, and Google Scholar based on some key points like Hesperetin, cancer name, anticancer resistance, nanoformulation, and ADME/Tox was determined by in silico approaches.

RESULT

HSP is a phytoestrogen present in citrus fruits in a high concentration (several hundred mg/kg) and exhibited anti-cancer activities through interfering at several pathways. HSP can suppress tumor formation by targeting several cellular proteins such as cell cycle regulatory, apoptosis, metastatic, tyrosine kinase, growth factor receptor, estrogen metabolism, and antioxidant-related protein.HSP has shown remarkable synergistic properties in combination therapy and has been reported to overcome multidrug cancer resistance drugs, leading to an improved defensive mechanism. These anticancer activities of HSP may be due to proper structural chemistry.

CONCLUSION

Overall, HSP showed potential anticancer activities against all cancer and possess better pharmacokinetic properties. So this phytochemical alone or combination with other agents can be an effective alternative drug for cancer treatment.

Molecular binding studies of anthocyanins with multiple antiviral activities against SARS-CoV-2

BACKGROUND

The search for ideal drugs with absolute antiviral activity against SARS-CoV-2 is still in place, and attention has been recently drawn to natural products. Several molecular targets have been identified as points of therapeutic intervention. The targets used in this study include SARS-CoV-2 helicase, spike protein, RNA-dependent RNA polymerase, main protease, and human ACE-2. An integrative computer-aided approach, which includes molecular docking, pharmacophore modeling, and pharmacokinetic profiling, was employed to identify anthocyanins with robust multiple antiviral activities against these SARS-CoV-2 targets.

RESULT

Four anthocyanins (Delphinidin 3-O-glucosyl-glucoside, Cyanidin 3-O-glucosyl-rutinoside, Cyanidin 3-(p-coumaroyl)-diglucoside-5-glucoside), and Nasunin) with robust multiple inhibitory interactions were identified from a library of 118 anthocyanins using computer-aided techniques. These compounds exhibited very good binding affinity to the protein targets and moderate pharmacokinetic profiles. However, Cyanidin 3-O-glucosyl-rutinoside is reported to be the most suitable drug candidate with multiple antiviral effects against SARS-CoV-2 due to its good binding affinity to all five protein targets engaged in the study.

CONCLUSIONS

The anthocyanins reported in this study exhibit robust binding affinities and strong inhibitory molecular interactions with the target proteins and could be well exploited as potential drug candidates with potent multiple antiviral effects against COVID-19.

Molecular Modeling Strategies of Cancer Multidrug Resistance

Cancer remains a leading cause of morbidity and mortality worldwide. Hence, the increase in cancer cases observed in the elderly population, as well as in children and adolescents, makes human malignancies a prime target for anticancer drug development. Although highly effective chemotherapeutic agents are continuously developed and approved for clinical treatment, the major impediment towards curative cancer therapy remains multidrug resistance (MDR). In recent years, intensive studies have been carried out on the identification of new therapeutic molecules to reverse MDR efflux transporters of the ATP-binding cassette (ABC) superfamily. Although a great deal of progress has been made in the development of specific inhibitors for certain MDR efflux pumps in experimental studies, advanced computational studies can accelerate this drug development process. In the literature, there are many experimental studies on the impact of natural products and synthetic small molecules on the reversal of cancer MDR. Molecular modeling methods provide an opportunity to explain the activity of these molecules on the ABC-transporter family with non-covalent interactions as well as it is possible to carry out studies for the discovery of new anticancer drugs specific to MDR with these methods. The coordinate file of the 3-dimensional (3D) structure of the target protein is indispensable for molecular modeling studies. In some cases where a 3D structure cannot be obtained by experimental methods, the homology modeling method can be applied to obtain the file containing the target protein's information including atomic coordinates, secondary structure assignments, and atomic connectivity. Homology modeling studies are of great importance for efflux transporter proteins that still lack 3D structures due to crystallization problems with multiple hydrophobic transmembrane domains. Quantum mechanics, molecular docking and molecular dynamics simulation applications are the most frequently used molecular modeling methods in the literature to investigate non-covalent interactions between the drug-ABC transporter superfamily. The quantitative structure-activity relationship (QSAR) model provides a relationship between the chemical properties of a compound and its biological activity. Determining the pharmacophore region for a new drug molecule by superpositioning a series of molecules according to their physicochemical properties using QSAR models is another method in which molecular modeling is used in computational drug development studies with ABC transporter proteins. There are also in silico absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) studies conducted to make a prediction about the pharmacokinetic properties, and drug-likeness of new molecules. Drug repurposing studies, which have become a trending topic in recent years, involve identifying possible new targets for an already approved drug molecule. There are few studies in the literature in which drug repurposing performed by molecular modelling methods has been applied on ABC transporter proteins. The aim of the current paper is to create a complete review of drug development studies including aforementioned molecular modeling methods carried out between the years 2019-2021. Furthermore, an intensive investigation is also conducted on licensed applications and free web servers used in in silico studies. The current review is an up-to-date guide for researchers who plan to conduct computational studies with MDR transporter proteins.

Marine Brominated Tyrosine Alkaloids as Promising Inhibitors of SARS-CoV-2

There have been more than 150 million confirmed cases of SARS-CoV-2 since the beginning of the pandemic in 2019. By June 2021, the mortality from such infections approached 3.9 million people. Despite the availability of a number of vaccines which provide protection against this virus, the evolution of new viral variants, inconsistent availability of the vaccine around the world, and vaccine hesitancy, in some countries, makes it unreasonable to rely on mass vaccination alone to combat this pandemic. Consequently, much effort is directed to identifying potential antiviral treatments. Marine brominated tyrosine alkaloids are recognized to have antiviral potential. We test here the antiviral capacity of fourteen marine brominated tyrosine alkaloids against five different target proteins from SARS-CoV-2, including main protease (M^pro) (PDB ID: 6lu7), spike glycoprotein (PDB ID: 6VYB), nucleocapsid phosphoprotein (PDB ID: 6VYO), membrane glycoprotein (PDB ID: 6M17), and non-structural protein 10 (nsp10) (PDB ID: 6W4H). These marine alkaloids, particularly the hexabrominated compound, fistularin-3, shows promising docking interactions with predicted binding affinities (S-score = −7.78, −7.65, −6.39, −6.28, −8.84 Kcal/mol) for the main protease (M^pro) (PDB ID: 6lu7), spike glycoprotein (PDB ID: 6VYB), nucleocapsid phosphoprotein (PDB ID: 6VYO), membrane glycoprotein (PDB ID: 6M17), and non-structural protein 10 (nsp10) (PDB ID: 6W4H), respectively, where it forms better interactions with the protein pockets than the native interaction. It also shows promising molecular dynamics, pharmacokinetics, and toxicity profiles. As such, further exploration of the antiviral properties of fistularin-3 against SARS-CoV-2 is merited.

Designing of the N-ethyl-4-(pyridin-4-yl)benzamide based potent ROCK1 inhibitors using docking, molecular dynamics, and 3D-QSAR

Rho-associated kinase-1 (ROCK1) has been recognized for its pivotal role in heart diseases, different types of malignancy, and many neurological disorders. Hyperactivity of ROCK phosphorylates the protein kinase-C (PKC), which ultimately induces smooth muscle cell contraction in the vascular system. Inhibition of ROCK1 has been shown to be a promising therapy for patients with cardiovascular disease. In this study, we have conducted molecular modeling techniques such as docking, molecular dynamics (MD), and 3-Dimensional structure-activity relationship (3D-QSAR) on a series of N-ethyl-4-(pyridin-4-yl)benzamide-based compounds. Docking and MD showed critical interactions and binding affinities between ROCK1 and its inhibitors. To establish the structure-activity relationship (SAR) of the compounds, 3D-QSAR techniques such as Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were used. The CoMFA (q ² = 0.774, r ² = 0.965, ONC = 6, and r p r e d 2 = 0.703) and CoMSIA (q ² = 0.676, r ² = 0.949, ONC = 6, and r p r e d 2 = 0.548) both models have shown reasonable external predictive activity, and contour maps revealed favorable and unfavorable substitutions for chemical group modifications. Based on the contour maps, we have designed forty new compounds, among which, seven compounds exhibited higher predictive activity (pIC₅₀). Further, we conducted the MD study, ADME/Tox, and SA score prediction using the seven newly designed compounds. The combination of docking, MD, and 3D-QSAR studies helps to understand the coherence modification of existing molecules. Our study may provide valuable insight into the development of more potent ROCK1 inhibitors.

The relationship between solvatochromic properties and in silico ADME parameters of new chloroethylnitrosourea derivatives with potential anticancer activity and their β-Cyclodextrin complexes

In view of the anticancer effect of nitrosoureas a set of four new N-(2-chloroethyl)-N-nitrosourea (CENU) derivatives was synthesized. An in silico absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) prediction study revealed that the CENU derivatives satisfied all the required criteria for oral administration and introduced them as remarkable anticancer candidates in the central nervous system (CNS). A comparative solvatochromic study including the Kamlet-Taft, Catalán and Laurence models indicated that the solvatochromic behavior of the CENUs depended on both, unspecific and specific solvent-solute interactions. In detail, the solvatochromic effect of the solvent polarity on the absorption and emission maxima was significant for all CENUs, whereas the solvatochromic effect of the solvent's ability to donate or accept hydrogen bonds on the absorption and emission maxima was critically dependent on the electron density of the N'-aryl group. From the solvatochromic comparison method, excellent correlations (r ≥ 0.890) were obtained between the ADME parameters and the solvatochromic regression coefficients obtained by the Catalán model. As potential stabilizers, inclusion complexes of the investigated CENU derivatives with β-cyclodextrin (β-CD) were also explored. The spectrofluorimetric host-guest experiments included double-reciprocal Benesi-Hildebrand plots as well as the molar ratio and continuous variation plots (Job's plots), which established a 1:1 β-CD to CENU binding stoichiometry and relatively high affinities of β-CD for CENU derivatives.

Targeting FGL2, a molecular drug target for glioblastoma, with natural compounds through virtual screening method

Background: Fibroleukin-2 protein (FGL2) causes redevelopment of brain tumors. Inhibition of these proteins has shown to improve glioblastoma prognosis and treatment efficacy. Aim: The current study gathered recently exploited natural compounds that suppress glioblastoma proliferation in vitro, tested against FGL2 protein. Method: Twenty-five compounds were explored through a virtual screening platform. Results: Three natural compounds (betanine, hesperetin and ovatodiolide) hit the active site of FGL2. Furthermore, the influence of these compounds was also assessed using in silico gene expression, and ADMET tools showed downregulation of some genes, which caused rapid tumor development while possessing a moderate acute toxicity and pharmacokinetic profile. Conclusion: Our study presents three compounds that are good candidates for evaluation in FGL2 mutated glioblastoma animal models.

Design and identification of novel annomontine analogues against SARS-CoV-2: An in-silico approach

AIMS

COVID-19 has currently emerged as the major global pandemic affecting the lives of people across the globe. It broke out from Wuhan Province of China, first reported to WHO on 31st December 2019 as "Pneumonia of unknown cause". Over time more people were infected with this virus, and the only tactic to ensure safety was to take precautionary measures due to the lack of any effective treatment or vaccines. As a result of unavailability of desired efficacy for previously repurposed drugs, exploring novel scaffolds against the virus has become the need of the hour.

MAIN METHODS

In the present study, 23 new annomontine analogues were designed representing β-Carboline based scaffolds. A hypothesis on its role as an effective ligand was laid for target-specific binding in SARS-CoV-2. These molecules were used for molecular docking analysis against the multiple possible drug targets using the Maestro Interface. To ensure the drug safety of these molecules ADME/Tox analysis was also performed.

KEY FINDINGS

The molecular docking analysis of the 23 novel molecules indicated the efficiency of these derivates against COVID-19. The efficiency of molecules was computed by the summation of the docking score against each target defined as LigE Score and compared against Hydroxycholoquine as a standard. Based on the docking score, the majority of the annomontine derivatives were found to have increased binding affinity with targets as compared to hydroxycholoquine.

SIGNIFICANCE

Due to the lack of efficiency, effectiveness, and failure of already repurposed drugs against the COVID-19, the exploration of the novel scaffold that can act as effective treatment is much needed. The current study hence emphasizes the potential of Annomontine based - β- Carboline derivatives as a potential drug candidate against COVID-19.

Molecular interaction study of novel indoline derivatives with EGFR-kinase domain using multiple computational analysis

Epidermal growth factor receptors are constitutively overexpressed in breast cancer cells, which in turn stimulate many downstream signaling pathways that are involved in many carcinogenic processes. This makes EGFR a striking target for cancer therapy. This study focuses on the EGFR kinase domain inactivation by novel synthesized indoline derivatives. The compounds used are N-(2-hydroxy-5-nitrophenyl (4'-methyl phenyl) methyl) indoline (HNPMI), alkylaminophenols - 2-((3,4-Dihydroquinolin-1(2H)-yl) (p-tolyl) methyl) phenol (THTMP) and 2-((1, 2, 3, 4-Tetrahydroquinolin-1-yl) (4 methoxyphenyl) methyl) phenol (THMPP). To get a clear insight into the molecular interaction of EGFR and the three compounds, we have used ADME/Tox prediction, Flexible docking analysis followed by MM/GB-SA, QM/MM analysis, E-pharmacophore mapping of the ligands and Molecular dynamic simulation of protein-ligand complexes. All three compounds showed good ADME/Tox properties obeying the rules of drug-likeliness and showed high human oral absorption. Molecular docking was performed with the compounds and EGFR using Glide Flexible docking mode. This showed that the HNPMI was best among the three compounds and had interactions with key residue Lys 721. The protein-ligand complexes were stable when simulated for 100 ns using Desmond software. The interactions were further substantiated using QM/MM analysis and MM-GB/SA analysis in which HNPMI was scored as the best molecule. All the analyses were carried out with a reference molecule-Gefitinib which is a known standard inhibitor of EGFR. Thus, the study elucidates the potential role of the indoline derivatives as an anti-cancer agent against breast cancer by effectively inhibiting EGFR.Communicated by Ramaswamy H. Sarma.

Synthesis, molecular docking, and in silico ADME/Tox profiling studies of new 1-aryl-5-(3-azidopropyl)indol-4-ones: Potential inhibitors of SARS CoV-2 main protease

The virus SARS CoV-2, which causes the respiratory infection COVID-19, continues its spread across the world and to date has caused more than a million deaths. Although COVID-19 vaccine development appears to be progressing rapidly, scientists continue the search for different therapeutic options to treat this new illness. In this work, we synthesized five new 1-aryl-5-(3-azidopropyl)indol-4-ones and showed them to be potential inhibitors of the SARS CoV-2 main protease (3CLpro). The compounds were obtained in good overall yields and molecular docking indicated favorable binding with 3CLpro. In silico ADME/Tox profile of the new compounds were calculated using the SwissADME and pkCSM-pharmacokinetics web tools, and indicated adequate values of absorption, distribution and excretion, features related to bioavailability. Moreover, low values of toxicity were indicated for these compounds. And drug-likeness levels of the compounds were also predicted according to the Lipinski and Veber rules.

Cheminformatics to Characterize Pharmacologically Active Natural Products

Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.

Identification of novel αβ-tubulin modulators with antiproliferative activity directed to cancer therapy using ligand and structure-based virtual screening

Among several strategies related to cancer therapy targeting the modulation of αβ-tubulin has shown encouraging findings, more specifically when this is achieved by inhibitors located at the colchicine binding site. In this work, we aim to fish new αβ-tubulin modulators through a diverse and rational VS study, and thus, exhibiting the development of two VS pipelines. This allowed us to identify two compounds 5 and 9 that showed IC₅₀ values of 19.69 and 21.97 μM, respectively, towards possible modulation of αβ-tubulin, such as assessed by in vitro assays in C6 glioma and HEPG2 cell lines. We also evaluated possible mechanisms of action of obtained hits towards the colchicine binding site of αβ-tubulin by using docking approaches. In addition, assessment of the stability of the active (5 and 9) and inactive compounds (3 and 13) within the colchicine binding site was carried out by molecular dynamics (MD) simulations, highlighting the solvent effect and revealing the compound 5 as the most stable in the complex. At last, deep analysis of these results provided some valuable insights on the importance of using mixed ligand- and structure-based strategies in VS campaigns, in order to achieve higher chemical diversity and biological effect as well.

Integration of Acoustic Liquid Handling into Quantitative Analysis of Biological Matrix Samples

Acoustic liquid handlers deliver small volumes (nL-µL) of multiple fluid types with accuracy and dynamic viscosity profiling. They are widely used in the pharmaceutical industry with applications extending from high-throughput screening in compound management to gene expression sequencing, genomic and epigenetic assays, and cell-based assays. The capability of the Echo to transfer small volumes of multiple types of fluids could benefit bioanalysis assays by minimization of sample volume and by simplifying dilution procedures by direct dilution. In this study, we evaluated the Labcyte Echo 525 liquid handler for its ability to deliver small volumes of sample preparations in biological matrix (plasma and serum) and to assess the feasibility of integration of the Echo with three types of bioanalytical assay platforms: microplate enzyme-linked immunosorbent assay, Gyrolab immunoassay, and liquid chromatography with tandem mass spectrometry. The results demonstrated acceptable consistency of dispensed plasma samples from multiple lots and species by the Echo. Equivalent assay performance demonstrated between the Echo and manual liquid procedures indicated great potential for the integration of the Echo with the bioanalytical assay, which allows the successful implementation of microsampling strategies in drug discovery and development.

Identification of new potential HIV-1 reverse transcriptase inhibitors by QSAR modeling and structure-based virtual screening

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity and low toxicity in antiretroviral combination therapies which are used to treat HIV. To design more specific HIV-1 inhibitors, 218 diverse non-nucleoside reverse transcriptase inhibitors with their EC₅₀ values were collected. Then, different types of molecular descriptors were calculated. Also, genetic algorithm (GA) and enhanced replacement methods (ERM) were used as the variable selection approaches to choose more relevant features. Based on selected descriptors, a classification support vector machine (SVM) model was constructed to categorize compounds into two groups of active and inactive ones. The most active compound in the set was docked and was used as the input to the Pharmit server to screen the Molport and PubChem libraries by constructing a structure-based pharmacophore model. Shape filters for the protein and ligand as well as Lipinski's rule of five have been applied to filter out the output of virtual screening from pharmacophore search. Three hundred and thirty-four compounds were finally retrieved from the virtual screening and were fed to the previously constructed SVM model. Among them, the SVM model rendered seven active compounds and they were also analyzed by docking calculations and ADME/Tox parameters.

Proteomics approach to analyze protein profiling related with ADME/Tox in rat treated with Scutellariae radix and Coptidis rhizoma as well as their compatibility

ETHNOPHARMACOLOGICAL RELEVANCE

Scutellariae radix (Scutellaria baicalensis Georgi) and Coptidis rhizoma (Coptis chinensis Franch), known as traditional Chinese medicine (TCM), have been widely used with the effects of suppressing fever, dispelling dampness, purging fire and removing toxicosis. Owing to their unimaginable complexity, it is difficult to understand their pharmacokinetic properties in detail. The aim of this study was to develop an optimal proteomics approach to analyze the protein profiling related with ADME/Tox in rat liver treated with S. radix and C. rhizoma as well as their compatibility.

MATERIALS AND METHODS

Male rats were respectively administered the extracts of S. radix, C. rhizoma and their mixture for 7 days, and their liver tissue samples were prepared for the comparative proteomic analysis. The significantly differentially expressed proteins between the experimental groups and the control group were found and identified by 2-DE and MALDI-TOF-MS analyses. To validate the proteomic analysis results, glutathion peroxidase, catalase and betaine homocysteine methyl transferase were selected and confirmed by western blotting.

RESULTS

Seventy eight significantly differentially expressed proteins between the experimental groups and the control group were found and identified. By querying the relational databases, the identified differentially expressed proteins were summarized and classified into three groups, phase I drug metabolic enzymes, phase II drug metabolic enzymes and the rest proteins which mainly involve in energy metabolism, signal transduction and cytoskeleton. These proteins involved in ADME/Tox may be the targets for metabolic studies or markers for toxicity.

CONCLUSIONS

Our findings indicated S. radix and C. rhizoma as well as their compatibility can assuredly influence the expression of the proteins in rat liver. After administration, the majority of these expressions presented a downward trend, which may be closely related to the pharmacological properties of the medicine. The method in this study may open up a new road for the complementary tests for ADME/Tox properties of S. radix and C. rhizoma as well as their compatibility.

Identification of natural inhibitors against angiotensin I converting enzyme for cardiac safety using induced fit docking and MM-GBSA studies

Background:

Cleistanthins A and B are isolated compounds from the leaves of Cleistanthus collinus Roxb (Euphorbiaceae). This plant is poisonous in nature which causes cardiovascular abnormalities such as hypotension, nonspecific ST-T changes and QTc prolongation. The biological activity predictions spectra of the compounds show the presence of antihypertensive, diuretic and antitumor activities.

Objective:

Objective of the present study was to determine the in silico molecular interaction of cleistanthins A and B with Angiotensin I- Converting Enzyme (ACE-I) using Induced Fit Docking (IFD) protocols.

Materials and Methods:

All the molecular modeling calculations like IFD docking, binding free energy calculation and ADME/Tox were carried out using Glide software (Schrödinger LLC 2009, USA) in CentOS EL-5 workstation.

Results:

The IFD complexes showed favorable docking score, glide energy, glide emodel, hydrogen bond and hydrophobic interactions between the active site residues of ACE-I and the compounds. Binding free energy was calculated for the IFD complexes using Prime MM-GBSA method. The conformational changes induced by the inhibitor at the active site of ACE-I were observed based on changes of the back bone Cα atoms and side-chain chi (x) angles. The various physicochemical properties were calculated for these compounds. Both cleistanthins A and B showed better docking score, glide energy and glide emodel when compared to captopril inhibitor.

Conclusion:

These compounds have successively satisfied all the in silico parameters and seem to be potent inhibitors of ACE-I and potential candidates for hypertension.

Manipulating In-House Designed Drug Databases For The Prediction of pH-Dependent Aqueous Drug Solubility

Chemical, pharmacokinetic, and pharmacodynamics properties are available in the package inserts of every Food and Drug Administration (FDA) approved prescription drug, including all available chemotherapy drugs. These inserts follow a specific format imposed by the FDA. Whether chemotherapy drugs are administered via the parenteral route or alimentary tract, a significant factor affecting their bioavailability, elimination and consequently the drug's effectiveness and potency, is its state of aqueous solubility. Water solubility has always lent itself poorly to the different predictive and experimental measures employed in the determination of a useful quantitative assessment. In this project, we first built a chemical structure based searchable database for 85 FDA approved chemotherapy drugs and then used Bio-Rad's KnowItAll^® Informatics suite to focus on the drugs pH-dependent water solubility prediction. We compared the predicted values for water solubility to the available values reported in the drug inserts, testing the practical utility and the predictive ability of our model in reporting such a clinically relevant, underreported pharmacokinetic parameter. A relational cancer drug database (MySQL) was created to further facilitate analysis and/or prediction of a chemotherapy compound's missing pharmacokinetic properties.

Designing of Protein Kinase C β-II Inhibitors against Diabetic complications: Structure Based Drug Design, Induced Fit docking and analysis of active site conformational changes

Protein Kinase C β-II (PKC β-II) is an important enzyme in the development of diabetic complications like cardiomyopathy, retinopathy, neuropathy, nephropathy and angiopathy. PKC β-II is activated in vascular tissues during diabetic vascular abnormalities. Thus, PKC β-II is considered as a potent drug target and the crystal structure of the kinase domain of PKC β-II (PDB id: 2I0E) was used to design inhibitors using Structure-Based Drug Design (SBDD) approach. Sixty inhibitors structurally similar to Staurosporine were retrieved from PubChem Compound database and High Throughput Virtual screening (HTVs) was carried out with PKC β-II. Based on the HTVs results and the nature of active site residues of PKC β-II, Staurosporine inhibitors were designed using SBDD. Induced Fit Docking (IFD) studies were carried out between kinase domain of PKC β-II and the designed inhibitors. These IFD complexes showed favorable docking score, glide energy, glide emodel and hydrogen bond and hydrophobic interactions with the active site of PKC β-II. Binding free energy was calculated for IFD complexes using Prime MM-GBSA method. The conformational changes induced by the inhibitor at the active site of PKC β-II were observed for the back bone Cα atoms and side-chain chi angles. PASS prediction tool was used to analyze the biological activities for the designed inhibitors. The various physicochemical properties were calculated for the compounds. One of the designed inhibitors successively satisfied all the in silico parameters among the others and seems to be a potent inhibitor against PKC β-II.

Selection of an improved HDAC8 inhibitor through structure-based drug design

Histone deacetylases (HDACs) are enzymes, which catalyze the removal of acetyl moiety from acetyl-lysine within the histone proteins and promote gene repression and silencing resulting in several types of cancer. HDACs are important therapeutic targets for the treatment of cancer and related diseases. Hydroxamic acid inhibitors show promising results in clinical trials against carcinogenesis. 120 hydroxamic acid derivatives were designed as inhibitors based on hydrophobic pocket and the Zn (II) catalytic site of HDAC8 active site using Structure Based Drug Design (SBDD) approach. High Throughput Virtual screening (HTVs) was used to filter the effective inhibitors. Induced Fit Docking (IFD) studies were carried out for the screening of eight inhibitors using Glide software. Hydrogen bond, hydrophobic interactions and octahedral coordination geometry with Zn (II) were observed in the IFD complexes. Prime MM-GBSA calculation was carried out for the binding free energy, to observe the stability of docked complexes. The Lipinski's rule of five was analyzed for ADME/Tox drug likeliness using Qikprop simulation. These inhibitors have good inhibitory properties as they have favorable docking score, energy, emodel, hydrogen bond and hydrophobic interactions, binding free energy and ADME/Tox. However, one compound (Cmp22) successively satisfied all the studies among the eight compounds screened and seems to be a promising potent inhibitor against HDAC8.

A Database Developed with Information Extracted from Chemotherapy Drug Package Inserts to Enhance Future Prescriptions

Package inserts of Food and Drug Administration (FDA) approved prescription drugs, including chemotherapy drugs, must follow a specific format imposed by the FDA. These inserts are created by unrelated pharmaceutical companies and as a result tend to be very different in the way the required information is reported. Chemical and pharmacokinetic properties including absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) are crucial elements to a prescribing information packet and are often missing from the reported data. This undergraduate research project analyzes the information packets of 85 randomly chosen chemically diverse chemotherapy drugs for four parameters important to patient care; viz, volume of distribution (V_D), elimination half-life (t_1/2), bioavailability, and water solubility. The prescribing information from the package inserts of each was analyzed in detail and pertinent information was consequently tabulated into a database using a commercial informatics platform. Then using a substructure search-tool, sixty-five chemotherapy drugs containing a carbonyl group in their chemical structure were selected and as hypothesized, it was found that many of these packets were significantly lacking in the reporting of the four parameters of interest. To further enhance this cataloged data, a freely available online database was consequently developed (http://annotation.dbi.udel.edu/CancerDB/) with the intention that the chemical, biological, and clinical community will now add some of the missing parameters.

Structure Activity Relationships (SARs) Using a Structurally Diverse Drug Database: Validating Success of Predictor Tools

ADME/Tox (absorption, distribution, metabolism, elimination and toxicity) technology is traditionally associated as a tool in the drug discovery process which is often used to predict the efficiency of drug adsorption, distribution, metabolic pathways, and elimination. For the past four years we have been involved in an effort to evaluate readily available Food and Drug Administration (FDA) consumer drug profiles and pharmacological data. Portable Document Format (PDF) data from drug profiles available on the FDA Drug Information website were used to create a searchable FDA Consumer Drug Database^© using Bio-Rad's KnowItAll^® platform which includes ADME/Tox in silico predictors. 14 pertinent pharmaceutical and pharmacological properties were collected for 75 structurally diverse consumer prescription drugs, and for several drugs, not all properties were completely populated. The major objective of this investigation was to validate the platforms prediction models for plasma protein binding (PPB) and bioavailability (BIO).

Extracting Relevant Information from FDA Drug Files to Create a Structurally Diverse Drug Database Using KnowItAll®

Each Food and Drug Administration (FDA) consumer drug information file contains an inordinate amount of useful chemical, pharmaceutical, and pharmacological data. These files profile approved drugs by chemical structure, solubility, absorption, distribution, metabolism, elimination, toxicity (ADME/Tox), and possible adverse reactions. The ability to utilize this data in the classroom is a new approach to connect theory, technology, and reality. The KnowItAll^® Informatics System available through Bio-Rad Laboratories, Philadelphia, PA, offers fully integrated software and/or database desktop solutions. It holds a large collection of in silico ADME/Tox predictors and is a chemical informatics platform used to record experimental data. This project had three goals: (1) extract relevant information for 75 drugs from their freely available FDA drug files (limited to orally administrated drugs, pro-drugs, having a chemical structure), (2) build a database so this extracted FDA information is indexed for search and analysis, and when completed, (3) undergraduates involved in such a project should be capable of harvesting useful chemical, pharmaceutical, and pharmacological information; be adept in computational chemistry software tools; and should gain an enhanced vocabulary and new insights into organic chemistry, molecular biology, and physiology.