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Roy H, Nandi S. In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery. Curr Pharm Des 2020; 25:3292-3305. [PMID: 31481001 DOI: 10.2174/1381612825666190903155935] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 09/01/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly. METHODS To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status. RESULTS The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound. CONCLUSION It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.
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Affiliation(s)
- Harekrishna Roy
- Nirmala College of Pharmacy, Mangalagiri, Guntur, Affiliated to Acharya Nagarjuna University, Andhra Pradesh-522503, India
| | - Sisir Nandi
- Department of Pharmaceutical Chemistry, Global Institute of Pharmaceutical Education and Research, Affiliated to Uttarakhand Technical University, Kashipur-244713, India
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Carvalho AM, Fernandes E, Gonçalves H, Giner-Casares JJ, Bernstorff S, Nieder JB, Real Oliveira MECD, Lúcio M. Prediction of paclitaxel pharmacokinetic based on in vitro studies: Interaction with membrane models and human serum albumin. Int J Pharm 2020; 580:119222. [PMID: 32194209 DOI: 10.1016/j.ijpharm.2020.119222] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 03/07/2020] [Accepted: 03/09/2020] [Indexed: 01/10/2023]
Abstract
Interactions of paclitaxel (PTX) with models mimicking biological interfaces (lipid membranes and serum albumin, HSA) were investigated to test the hypothesis that the set of in vitro assays proposed can be used to predict some aspects of drug pharmacokinetics (PK). PTX membrane partitioning was studied by derivative spectrophotometry; PTX effect on membrane biophysics was evaluated by dynamic light scattering, fluorescence anisotropy, atomic force microscopy and synchrotron small/wide-angle X-ray scattering; PTX distribution/molecular orientation in membranes was assessed by steady-state/time-resolved fluorescence and computer simulations. PTX binding to HSA was studied by fluorescence quenching, derivative spectrophotometry and dynamic/electrophoretic light scattering. PTX high membrane partitioning is consistent with its efficacy crossing cellular membranes and its off-target distribution. PTX is closely located in the membrane phospholipids headgroups, also interacting with the hydrophobic chains, and causes a major distortion of the alignment of the membrane phospholipids, which, together with its fluidizing effect, justifies some of its cellular toxic effects. PTX binds strongly to HSA, which is consistent with its reduced distribution in target tissues and toxicity by bioaccumulation. In conclusion, the described set of biomimetic models and techniques has the potential for early prediction of PK issues, alerting for the required drug optimizations, potentially minimizing the number of animal tests used in the drug development process.
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Affiliation(s)
- Ana M Carvalho
- CF-UM-UP, Centro de Física das Universidades do Minho e Porto, Departamento de Física da Universidade do Minho, Campus of Gualtar, 4710-057 Braga, Portugal; Nanophotonics Department, Ultrafast Bio- and Nanophotonics Group, INL - International Iberian Nanotechnology Laboratory, Braga, Portugal
| | - Eduarda Fernandes
- CF-UM-UP, Centro de Física das Universidades do Minho e Porto, Departamento de Física da Universidade do Minho, Campus of Gualtar, 4710-057 Braga, Portugal
| | | | - Juan J Giner-Casares
- Department of Physical Chemistry and Applied Thermodynamics, University of Córdoba, Campus de Rabanales, Edificio Marie Curie, Córdoba E-14014, Spain.
| | - Sigrid Bernstorff
- Elettra-Sincrotrone Trieste S.C.p.A., Strada Statale 14, km 163.5, in Area Science Park, I-34149 Basovizza, Trieste, Italy.
| | - Jana B Nieder
- Nanophotonics Department, Ultrafast Bio- and Nanophotonics Group, INL - International Iberian Nanotechnology Laboratory, Braga, Portugal.
| | - M Elisabete C D Real Oliveira
- CF-UM-UP, Centro de Física das Universidades do Minho e Porto, Departamento de Física da Universidade do Minho, Campus of Gualtar, 4710-057 Braga, Portugal.
| | - Marlene Lúcio
- CF-UM-UP, Centro de Física das Universidades do Minho e Porto, Departamento de Física da Universidade do Minho, Campus of Gualtar, 4710-057 Braga, Portugal; CBMA, Centro de Biologia Molecular e Ambiental, Departamento de Biologia, Universidade do Minho, 4710-057 Braga, Portugal.
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Dong J, Wang NN, Yao ZJ, Zhang L, Cheng Y, Ouyang D, Lu AP, Cao DS. ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminform 2018; 10:29. [PMID: 29943074 PMCID: PMC6020094 DOI: 10.1186/s13321-018-0283-x] [Citation(s) in RCA: 333] [Impact Index Per Article: 55.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 06/16/2018] [Indexed: 01/24/2023] Open
Abstract
Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at http://admet.scbdd.com/ .
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Affiliation(s)
- Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
- Hunan Key Laboratory of Grain-oil Deep Process and Quality Control, College of Food Science and Engineering, National Engineering Laboratory for Deep Processing of Rice and Byproducts, Central South University of Forestry and Technology, Changsha, People's Republic of China
- Hunan Key Laboratory of Processed Food for Special Medical Purpose, Central South University of Forestry and Technology, Changsha, People's Republic of China
| | - Ning-Ning Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Zhi-Jiang Yao
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Lin Zhang
- Hunan Key Laboratory of Processed Food for Special Medical Purpose, Central South University of Forestry and Technology, Changsha, People's Republic of China
| | - Yan Cheng
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, People's Republic of China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, People's Republic of China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, People's Republic of China.
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Legehar A, Xhaard H, Ghemtio L. IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data. J Cheminform 2016; 8:33. [PMID: 27303447 PMCID: PMC4906584 DOI: 10.1186/s13321-016-0141-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 05/20/2016] [Indexed: 02/05/2023] Open
Abstract
Background The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process. Description We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and biologics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data. Conclusion IDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database.FDA approved drug data integration for predictive modeling ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0141-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ashenafi Legehar
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790 Helsinki, Finland
| | - Henri Xhaard
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790 Helsinki, Finland ; Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, P.O. Box 56, 00014 Helsinki, Finland
| | - Leo Ghemtio
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790 Helsinki, Finland
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