Computational modeling and in-vitro/in-silico correlation of phospholipid-based prodrugs for targeted drug delivery in inflammatory bowel disease.
Arik DahanMilica MarkovicShahar KeinanIgor KurnikovAaron AponickEllen M ZimmermannShimon Ben-ShabatPublished in: Journal of computer-aided molecular design (2017)
Targeting drugs to the inflamed intestinal tissue(s) represents a major advancement in the treatment of inflammatory bowel disease (IBD). In this work we present a powerful in-silico modeling approach to guide the molecular design of novel prodrugs targeting the enzyme PLA2, which is overexpressed in the inflamed tissues of IBD patients. The prodrug consists of the drug moiety bound to the sn-2 position of phospholipid (PL) through a carbonic linker, aiming to allow PLA2 to release the free drug. The linker length dictates the affinity of the PL-drug conjugate to PLA2, and the optimal linker will enable maximal PLA2-mediated activation. Thermodynamic integration and Weighted Histogram Analysis Method (WHAM)/Umbrella Sampling method were used to compute the changes in PLA2 transition state binding free energy of the prodrug molecule (∆∆Gtr) associated with decreasing/increasing linker length. The simulations revealed that 6-carbons linker is the optimal one, whereas shorter or longer linkers resulted in decreased PLA2-mediated activation. These in-silico results were shown to be in excellent correlation with experimental in-vitro data. Overall, this modern computational approach enables optimization of the molecular design of novel prodrugs, which may allow targeting the free drug specifically to the diseased intestinal tissue of IBD patients.
Keyphrases
- cancer therapy
- drug delivery
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- molecular docking
- prognostic factors
- randomized controlled trial
- magnetic resonance imaging
- electronic health record
- drug induced
- blood pressure
- single molecule
- computed tomography
- mass spectrometry
- machine learning
- artificial intelligence
- big data
- data analysis
- systematic review
- resistance training
- deep learning
- binding protein
- network analysis