Cytotoxic Evaluation and Molecular Docking Studies of Aminopyridine Derivatives as Potential Anticancer Agents
Umair IlyasLina Tariq AlkuryShagufta NaazSyed Aun MuhammadHumaira NadeemReem AltafShahiq uz ZamanMuhammad FaheemImran SajidMohsin Tassawar CheemaAbdul MannanFawad Ali ShahShupeng Li
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The development of resistance to available anticancer drugs is increasingly becoming a major challenge and new chemical entities could be unveiled to compensate for this therapeutic failure.The current study demonstrated whether N-protected and deprotected amino acid derivatives of 2- aminopyridine could attenuate tumor development using colorectal cancer cell lines.Biological assays were performed to investigate the anticancer potential of synthesized compounds. The in silico ADME profiling and docking studies were also performed by docking the designed compounds against the active binding site of beta-catenin (CTNNB1) to analyze the binding mode of these compounds. Four derivatives 4a, 4b, 4c, and 4d were selected for investigation of in vitro anticancer potential using colorectal cancer cell line HCT 116. The anti-tumor activities of synthesized compounds were further validated by evaluating the inhibitory effects of these compounds on the target protein beta-catenin through in vitro enzyme inhibitory assay.The docking analysis revealed favorable binding energies and interactions with the target proteins. The in vitro MTT assay on colorectal cancer cell line HCT 116 and HT29 revealed potential anti-tumor activities with an IC50 range of 3.7-8.1μM and 3.27-7.7 μM, respectively. The inhibitory properties of these compounds on the concentration of beta-catenin by ELISA revealed significant percent inhibition of target protein at 100 μg/ml.In conclusion, the synthesized compounds showed significant anti-tumor activities both in silico and in vitro, having potential for further investigating its role in colorectal cancer.Keywords:
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Docking (animal)
IC50
The strategy to screen compounds solely for pharmacological potency and selectivity in the early stages of drug discovery brought the pharmaceutical industry to face the stark reality of disproportionate attrition later in the development stage due to poor drug disposition characteristics. This attrition contributed to the exorbitant costs of discovering and developing drugs. Considering ADME (Absorption, Distribution, Metabolism, and Excretion) characteristics of compounds early in the discovery process can wisely direct resources to compounds that have greater potential to survive the clinical trial stages of drug development. However, experimental determination of ADME characteristics is not practical for large numbers of compounds. Therefore, focus is being centered on bringing in silico approaches earlier in the discovery process to assess ADME properties solely from molecular structure. Given that metabolism is one of the most important of the ADME properties, in this paper we review a number of metabolism in silico tools and models that have potential applications in drug discovery. We then describe a step-by-step process, as practiced in our laboratories, to construct and deploy reliable in silico metabolic stability and other ADME screens. Additionally, we give examples of the application of our metabolic stability in silico screens in scaffold selection, ADME space enrichment, and rationalizing synthesis and testing of compounds in the drug discovery process. Agreements between the experimental and in silico metabolic stability values ranging from 84% to 100% have convinced many discovery project teams to routinely use these in silico models. Finally, we present our ideas on the successful implementation of in silico models and tools for significant impact on drug discovery and development Keywords: In silico, ADME, metabolism, metabolic stability, drug discovery, QSAR models, ADME software
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The application of combinatorial chemistry and high-throughput screening technique enables the large number of chemicals to be generated and tested simultaneously, which will facilitate the drug development and discovery. At the same time, it brings about a challenge of how to efficiently identify the potential drug candidates from thousands of compounds. A way used to deal with the challenge is to consider the drug pharmacokinetic properties, such as absorption, distribution, metabolism and excretion (ADME), in the early stage of drug development. Among ADME properties, metabolism is of importance due to the strong association with efficacy and safety of drug. The review will focus on in silico approaches for prediction of Cytochrome P450-mediated drug metabolism. We will describe these predictive methods from two aspects, structure-based and data-based. Moreover, the applications and limitations of various methods will be discussed. Finally, we provide further direction toward improving the predictive accuracy of these in silico methods.
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Nowadays, in silico tools are widely used to provide the potential structure of the metabolites formed depending on the site of metabolism. These methods can also highlight the molecular moieties that help to direct the molecule into the cytochrome cavity so that the site of metabolism is in proximity to the catalytic center. In this minireview, we summarized three aspects of the in silico methods in the application of prediction of ADME (absorption, distribution, metabolism and excretion) properties of compounds: structure-based approaches for predicting molecular modeling of drug metabolizing enzymes; in silico metabolite prediction; and pharmacophore models for analysis substrate specificity. Moreover, we also extended the in silico studies in Chinese herbal medicines (CHM) research.
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Profiling of different pharmacokinetic parameters like the absorption, distribution, metabolism, and elimination known as ADME properties of drug molecules during initial phase of drug development might be beneficial in selection of molecules with less adverse ADME characteristics. ADME screening by in vivo testing is very time consuming, costly, and includes the animals. On the other hand, in silico ADME investigation is cheaper, better and offers correct results rapidly. In the current research study, the in-silico methods, namely SwissADME and admetSAR were used for brief and complete ADME profiling of previously selected (SR7, SR9, SR11, SR29, SR41 and SR43) tryptamine derivatives. The webservers utilized in this research are available for free. In-silico analyses have revealed that all the derivatives under study have high gastrointestinal absorption which make them a good oral drug candidate. However, results showed that SR41 & SR43 were able to pass blood- brain barrier as compared to other synthetic compounds of this series.
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Abstract Summary: The study of pharmacokinetic properties (PK) is of great importance in drug discovery and development. In the present work, PK/DB (a new freely available database for PK) was designed with the aim of creating robust databases for pharmacokinetic studies and in silico absorption, distribution, metabolism and excretion (ADME) prediction. Comprehensive, web-based and easy to access, PK/DB manages 1203 compounds which represent 2973 pharmacokinetic measurements, including five models for in silico ADME prediction (human intestinal absorption, human oral bioavailability, plasma protein binding, blood–brain barrier and water solubility). Availability: http://www.pkdb.ifsc.usp.br Contact: aandrico@if.sc.usp.br
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The high attrition rate of drug candidates during clinical trials for poor pharmacokinetic and metabolic properties has created a need to do these studies as early as it is possible during the drug discovery process. In addition the most successful drug is often not the most potent one but the one that has the suitable level of potency, safety, and pharmacokinetics. Science and technology development during the last few years and the generation of last databases and information has created the basis for doing early experimental PK and ADME studies in addition to eADME. Similarly, testing safety features as early as possible is key to affordable drug discovery and development. Throughput and cost are crucial for early application. In silico methods have by far the highest throughput, followed by the in vitro and in vivo approaches. On the other hand, with regard to relevance and reliability of data the ranking is the opposite. The great challenge for in silico methods is generation of models that correlate more closely with in vivo systems. For the in vitro assays increasing the throughput is an absolute must. Ex silico methods that combine in silico predictions with experimental methods are new additions to the scientific repertoire (e.g. Chromatographic Hydrophobicity Index that is deduced from the reverse phase HPLC data can be used for calculation of lipophilicity). The emerging new approaches have clear impact on the design of early stage screening and combinatorial libraries. In addition to the Lipinskis rules descriptors such as number of rotatable bonds, number of aromatic rings, branching behavior and polar surface area (PSA) are commonly used is the drug design process. Keywords: Silico, Chromatographic, Hydrophobicity Index, Metabolic Databases, Theoretical Models
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The high-throughput screening (HTS) of large proprietary compound collections and combinatorial libraries has increased the pressure on gathering pharmacokinetic and drug metabolism data as early as possible. Properties related to absorption, distribution, metabolism and excretion (ADME) can be estimated by a range of in vivo and in vitro methods, most of which are now available or under development in high(er)-throughput modus. In addition, progress has been made in in silico methods using various quantitaTive structure-activity relationship (QSAR) and molecular modeling techniques that employ a range of recently introduced descriptors tailored to e-ADME. These in silico approaches are promising filters for virtual libraries to aid synthesis as well as the selection of compounds for acquisition and screening in the early stages of drug discovery.
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High-throughput screening technologies in biological sciences of large libraries of compounds obtained via combinatorial or parallel chemistry approaches, as well as the application of design rules for drug-likeness, have resulted in more hits to be evaluated with respect to their ADME or drug metabolism and pharmacokinetic properties. The traditional in vivo methods using preclinical species, such as rat, dog or monkey, are no longer sufficient to cope with this demand. This editorial discusses the changes towards medium- to high-throughput in vitro and in silico ADME screening. In addition, much more attention is now put on early safety and risk assessment of promising lead series and potential clinical candidates.
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