Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling.
Drug metabolism determines the fate of a drug when it enters the human body and is a critical factor in defining their absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Among the various drug metabolizing enzymes, cytochrome P450s (CYP450) constitute an important protein family that aside from functioning in xenobiotic metabolism, is also responsible for a diverse array of other roles encompassing steroid and cholesterol biosynthesis, fatty acid metabolism, calcium homeostasis, neuroendocrine functions and growth regulation. Although CYP450 typically converts xenobiotics into safe metabolites, there are some situations whereby the metabolite is more toxic than its parent molecule. Computational modeling has been instrumental in CYP450 research by rationalizing the nature of the binding event (i.e. inhibit or induce CYP450s) or metabolic stability of query compounds of interest. A plethora of computational approaches encompassing ligand, structure and systems based approaches have been utilized to model CYP450-ligand interactions. This review provides a brief background on the CYP450 family (i.e. its roles, advantages and disadvantages as well as its modulators) and then discusses the various computational approaches that have been used to model CYP450-ligand interaction. Particular focus was given to the use of quantitative structure-activity relationship (QSAR) and more recent proteochemometric modeling studies. Finally, a perspective on the current state of the art and future trends of the field is also provided.
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Watshara
W
Shoombuatong
National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
Philip
P
Prathipati
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Veda
V
Prachayasittikul
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Nalini
N
Schaduangrat
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Aijaz Ahmad
AA
Malik
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Reny
R
Pratiwi
Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Sompon
S
Wanwimolruk
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala 751 24, Sweden.
Jarl E S
JES
Wikberg
Department of Chemistry, Faculty of Science, Kasetsart University, Bangkok 10900. Thailand.
Matthew Paul
MP
Gleeson
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala 751 24. Sweden.
Ola
O
Spjuth
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Chanin
C
Nantasenamat
D000818:Animals; D065607:Cytochrome P-450 Enzyme Inhibitors / Q000737:chemistry / Q000494:pharmacology; D003577:Cytochrome P-450 Enzyme System / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship
D065607:Cytochrome P-450 Enzyme Inhibitors; D004364:Pharmaceutical Preparations; D003577:Cytochrome P-450 Enzyme System
ADMET; CYP450; Drug metabolism; QSAR; cytochrome P450; drug design; pharmacokinetics; proteochemometrics