Abstracts

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Graphic Rule for Drug Metabolism Systems
Kuo-Chen Chou

Using graphic rules to deal with kinetic systems is an elegant approach by combining the graph representation (schematic representation) and rigorous mathematical derivation. It bears the following advantages: (1) providing an intuitive picture or illuminative insights; (2) helping grasp the key points from complicated details; (3) greatly simplifying many tedious, laborious, and error-prone calculations; and (4) able to double-check the final results. In this mini review, the non-steady state graphic rule in enzyme-catalyzed kinetics and protein-folding kinetics was extended to cover drugmetabolic systems. As a demonstration, a step-by-step illustration is presented showing how to use the graphic rule to derive the concentrations of the parent drug and its metabolites vs. time for the seliciclib, vildagliptin, and cyclin-dependent kinase inhibitor (AG-024322) metabolic systems, respectively. It can be seen from these paradigms that the graphic rule is particularly useful to analyze complicated drug metabolic systems and ensure the correctness of the derived results. Meanwhile, the intuitive feature of graphic representation may facilitate analyzing and classifying drug metabolic systems; e.g., according to their directed graphs, the metabolism of seliciclib and the metabolism of vildagliptin can be categorized as 0→5 mechanism while that of AG-024322 as 0→4→3mechanism.

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Review of MARCH-INSIDE & Complex Networks Prediction of Drugs: ADMET, Anti-parasite Activity, Metabolizing Enzymes and Cardiotoxicity Proteome Biomarkers
Humberto Gonzalez-Diaz, Aliuska Duardo-Sanchez, Florencio M. Ubeira, Francisco Prado-Prado, Lazaro G. Perez-Montoto, R. Concu, Gianni Podda and Bairong Shen

In this communication we carry out an in-depth review of a very versatile QSPR-like method. The method name is MARCH-INSIDE (MARkov CHains Ivariants for Network Selection and DEsign) and is a simple but efficient computational approach to the study of QSPR-like problems in biomedical sciences. The method uses the theory of Markov Chains to generate parameters that numerically describe the structure of a system. This approach generates two principal types of parameters Stochastic Topological Indices (sto-TIs). The use of these parameters allows the rapid collection, annotation, retrieval, comparison and mining structures of molecular, macromolecular, supramolecular, and non-molecular systems within large databases. Here, we review and comment by the first time on the several applications of MARCH-INSIDE to predict drugs ADMET, Activity, Metabolizing Enzymes, and Toxico-Proteomics biomarkers discovery. The MARCH-INSIDE models reviewed are: a) drug-tissue distribution profiles, b) assembling drug-tissue complex networks, c) multi-target models for anti-parasite/anti-microbial activity, c) assembling drug-target networks, d) drug toxicity and side effects, e) web-server for drug metabolizing enzymes, f) models in drugs toxico-proteomics. We close the review with some legal remarks related to the use of this class of QSPR-like models.

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Molecular Modeling of Cytochrome P450 and Drug Metabolism
Jing-Fang Wang and Kuo-Chen Chou

The cytochrome P450 family is a large and diverse group of hemoproteins that are located in virtually all types of organism, such as bacteria, eukaryotes and even Archaea. These proteins are found throughout the body, however the highest concentrations are associated with liver. As the Human Genome Project completed, there are 57 genes and more than 59 pseudogenes divided among 18 families of CYP genes and 43 subfamilies have been detected. In humans, CYPs are the major enzymes involved in drug metabolism and bioactivation, accounting for almost 75% of the total drug metabolism. The variability in drug metabolisms that are mainly induced by the CYP polymorphisms is reflected on the differences of the maximal plasma concentrations, half lives of some drugs and their clearance. Besides, it can also lead to adverse drug reactions that are considered as a major factor in drug toxicity. So, the genotype-activity relationships of the CYP proteins have become a hot topic in recent years. It is important to further understand why a certain genotype influences enzyme activity and how to predict more structure-activity relationships.

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Predictions of the ADMET Properties of Candidate Drug Molecules Utilizing Different QSAR/QSPR Modelling Approaches
Mahmud Tareq Hassan Khan

The integration of early ADMET (absorption, distribution, metabolism, excretion and toxicity) profiling, or simply prediction, of 'lead' molecules to speed-up the 'lead' selection further for phase-I trial without losing large amount of revenue. The ADMET profiling and prediction is mostly dependent of a number of molecular descriptors, for example, Lipinski's 'Rule of 5' (Ro5). Recently a large number of articles have been reporting that it possible to do some prediction of the ADMET properties using the structural features of the molecules, utilizing several and multiple approaches. One of the most important approaches is the QSAR/QSPR modelling of the data derived from their activity profiles and their different structural features (i.e., quantitative molecular descriptors).

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Nanoparticles for Tumor Targeted Therapies and Their Pharmacokinetics
Jianqiu Wang, Meihua Sui and Weimin Fan

Various types of nanoparticles, such as liposomes, polymeric micelles, dendrimers, superparamagnetic iron oxide crystals, and colloidal gold, have been employed in targeted therapies for cancer. Both passive and active targeting strategies can be utilized for nanodrug delivery. Passive targeting is based on the enhanced permeability and retention (EPR) effect of the vasculature surrounding tumors. Active targeting relies on ligand-directed binding of nanoparticles to receptors expressed by tumor cells. Release of loaded drugs from nanoparticles may be controlled in response to changes in environmental condition such as temperature and pH. Biodistribution profiles and anticancer efficacy of nano-drugs in vivo would be different depending upon their size, surface charge, PEGylation and other biophysical properties. This review focuses on the recent development of nanoparticles for tumor targeted therapies, including physicochemical properties, tumor targeting, control of drug release, pharmacokinetics, anticancer efficacy and safety. Future perspectives are discussed as well.

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Artificial Intelligence Techniques for Colorectal Cancer Drug Metabolism: Ontologies and Complex Networks
Marcos Martinez-Romero, Jose M. Vazquez-Naya, Juan R. Rabunal, Salvador Pita-Fernandez, Ramiro Macenlle, Javier Castro-Alvarino, Leopoldo Lopez-Roses, Jose L. Ulla, Antonio V. Martinez-Calvo, Santiago Vazquez, Javier Pereira, Ana B. Porto-Pazos, Julian Dorado, Alejandro Pazos and Cristian R. Munteanu

Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process subontology from the Gene Ontology.


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QSAR & Complex Network Study of the HMGR Inhibitors Structural Diversity
Isela Garcia, Yagamare Fall and Generosa Gomez

Efficient drugs such as statins or mevinic acids are inhibitors of the rate-limiting enzyme of cholesterol biosynthesis, 3- hydroxy-3-methyl-glutaryl coenzyme A reductase (HMGR), an enzyme responsible for the double reduction of 3-hydroxy-3-methyl-glutaryl coenzyme A. These compounds promoted the synthesis and evaluation of new inhibitors for HMGR, named HMGRIs. The high number of possible candidates creates the necessity of Quantitative Structure-Activity Relationship models in order to guide the HMGRI (3-hydroxy-3-methyl-glutaryl coenzyme A inhibitor) synthesis. In this work, we revised different computational studies for a very large and heterogeneous series of HMGRIs. First, we revised QSAR studies with conceptual parameters such as flexibility of rotation, probability of availability, etc; we then used the method of regression analysis; and QSAR studies in order to understand the essential structural requirement for binding with receptor. Next, we reviewed 3D QSAR, CoMFA and CoMSIA with different compounds to find out the structural requirements for 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitory activity.


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Mathematical Methods to Analysis of Topology, Functional Variability and Evolution of Metabolic Systems Based on Different Decomposition Concepts
Yassine Mrabet and Nabil Semmar

Complexity of metabolic systems can be undertaken at different scales (metabolites, metabolic pathways, metabolic network map, biological population) and under different aspects (structural, functional, evolutive). To analyse such a complexity, metabolic systems need to be decomposed into different components according to different concepts. Four concepts are presented here consisting in considering metabolic systems as sets of metabolites, chemical reactions, metabolic pathways or successive processes. From a metabolomic dataset, such decompositions are performed using different mathematical methods including correlation, stoichiometric, ordination, classification, combinatorial and kinetic analyses. Correlation analysis detects and quantifies affinities/oppositions between metabolites. Stoichiometric analysis aims to identify the organisation of a metabolic network into different metabolic pathways on the hand, and to quantify/optimize metabolic flux distributions through the different chemical reactions of the system. Ordination and classification analyses help to identify different metabolic trends and their associated metabolites leading to highlight chemical polymorphism representing different variability poles of the metabolic system. Then, metabolic processes/correlations responsible for such a polymorphism can be extracted in silico by combining metabolic profiles representative of different metabolic trends according to a weighting bootstrap approach. Finally, evolution of metabolic processes in time can be analysed by different kinetic/dynamic modelling approaches.


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Metabolism of Designer Drugs of Abuse: An Updated Review
Markus R. Meyer and Hans H. Maurer

This paper reviews the metabolism of new designer drugs of abuse that have emerged on the black market during the last years and is an update of a review published in 2005. The presented review contains data concerning the so-called 2C compounds (phenethylamine type) such as 4-bromo-2,5-dimethoxy-beta-phenethylamine (2C-B), 4-iodo-2,5-dimethoxy-beta-phenethylamine (2C-I), 2,5- dimethoxy-4-methyl-beta-phenethylamine (2C-D), 4-ethyl-2,5-dimethoxy-beta-phenethylamine (2C-E), 4-ethylthio-2,5-dimethoxy-beta-phenethylamine (2C-T-2), and 2,5-dimethoxy-4-propylthio-beta-phenethylamine (2C-T-7), beta-keto designer drugs such as 2- methylamino-1-(3,4-methylenedioxyphenyl)butan-1-one (butylone, bk-MBDB), 2-ethylamino-1-(3,4-methylenedioxyphenyl)propan-1- one (ethylone, bk-MDEA), 2-methylamino-1-(3,4-methylenedioxyphenyl)propan-1-one (methylone, bk-MDMA), and 2-methylamino-1- p-tolylpropane-1-one (mephedrone, 4-methyl-methcathinone), pyrrolidinophenones such as 4-methyl-pyrrolidinobutyrophenone (MPBP) and alpha-pyrrolidinovalerophenone (PVP), phencyclidine-derived drugs such as N-(1-phenylcyclohexyl)-propanamine (PCPr), N-(1- phenyl-cyclohexyl)-2-ethoxyethanamine (PCEEA), N-(1-phenylcyclohexyl)-3-methoxypropanamine (PCMPA), and N-(1-phenylcyclohexyl)- 2-methoxyethanamine (PCMEA), tryptamines such as 5-methoxy-N,N-diisopropyltryptamine (5-MeO-DIPT), and finally alpha- methylfentanyl (alpha-MF) and 3-methylfentanyl (3-MF). Papers have been considered and reviewed on the identification of in vivo or in vitro human or animal metabolites and the cytochrome P450 or monoamineoxidase isoenzyme-dependent metabolism.


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Gender Specific Drug Metabolism of PF-02341066 in Rats — Role of Sulfoconjugation
Wei-Zhu Zhong, Jenny Zhan, Ping Kang and Shinji Yamazaki

PF-02341066 is a selective c-Met/Alk tyrosine kinase inhibitor currently in clinical development as an anticancer agent. Non-clinical toxicokinetic evaluation in rats revealed gender-related differences in pharmacokinetics with at least 2-fold higher PF-02341066 plasma concentrations in males than females when administered the same dose. In general, lower systemic exposure of drugs that undergoes oxidative metabolism in male than female rats has been well known to be attributed to gender-specific expression of CYP genes in rats. It is of interest to understand why the gender-related pharmacokinetics in rats for PF-02341066 was opposite to the general observations and if the gender-related pharmacokinetics would be seen in humans that may impact the drug efficacy and toxicity profiles. The potential gender-related differences in PF-02341066 metabolism were investigated both in vitro and in vivo using [3H]PF-02341066. Oxidation was found to be the major metabolic pathway in male rat liver S9 incubations whereas sulfoconjugation was the predominant metabolic pathway in females. There was no qualitative difference in metabolite profiles of PF-02341066 between man and woman liver S9 incubations. Following a single oral administration of [3H]PF-02341066 to rats at 150 mg/kg, the primary route of excretion of the radioactivity was via feces, in which, the most abundant radio-component in male rat was the parent drug (29% of dose) and in female rat was the parent sulfate (44% of dose). The more extensive formation of the parent solfoconjugate in female rats most likely explains why the female rat had lower drug exposure compared to male rat, as gender-related changes of sulfotransferase expression were widely reported in rats. The human liver S9 study suggests that gender-related pharmacokinetics of PF-02341066 are unlikely to occur in humans.