Molecular Modeling Strategies of Cancer Multidrug Resistance


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Yalcin-Ozkat G.

DRUG RESISTANCE UPDATES, cilt.59, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 59
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.drup.2021.100789
  • Dergi Adı: DRUG RESISTANCE UPDATES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, EMBASE, MEDLINE, Veterinary Science Database
  • Anahtar Kelimeler: Molecular modeling, Molecular docking, Molecular dynamics simulations, Homology modeling, In silico, Pharmacophore modeling, ADME, Tox, QSAR, Cancer, Multidrug resistance, BIOLOGICAL EVALUATION, PROTEIN-2 MRP2, SOFTWARE NEWS, DOCKING, DYNAMICS, VALIDATION, SIMULATION, INHIBITOR, DISCOVERY, ACCURACY
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

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