Computational analysis and predictive modeling of polymorph descriptors
1 School of Computing and Engineering, University of Missouri-Kansas City, Missouri, MO, 64110, USA
2 Division of Pharmaceutical Sciences, College of Pharmacy, University of Missouri-Kansas City, Missouri, MO 64108, USA
Chemistry Central Journal 2013, 7:23 doi:10.1186/1752-153X-7-23Published: 4 February 2013
A computation approach based on integrating high throughput binding affinity comparison and binding descriptor classifications was utilized to establish the correlation among substrate properties and their affinity to Breast Cancer Resistant Protein (BCRP). The uptake rates of Mitoxantrone in the presence of various substrates were evaluated as an in vitro screening index for comparison of their binding affinity to BCRP.
The effects of chemical properties of various chemotherapeutics, such as antiviral, antibiotic, calcium channel blockers, anticancer and antifungal agents, on their affinity to BCRP, were evaluated using HEK (human embryonic kidney) cells in which 3 polymorphs, namely 482R (wild type) and two mutants (482G and 482T) of BCRP, have been identified. The quantitative structure activity relationship (QSAR) model was developed using the sequential approaches of Austin Model 1 (AM1), CODESSA program, heuristic method (HM) and multiple linear regression (MLR) to establish the relationship between structural specificity of BCRP substrates and their uptake rates by BCRP polymorphs.
The BCRP mutations may induce conformational changes as manifested by the altered uptake rates of Mitoxantrone by BCRP in the presence of other competitive binding substrates that have a varying degree of affinities toward BCRP efflux. This study also revealed that the binding affinity of test substrates to each polymorph was affected by varying descriptors, such as constitutional, topological, geometrical, electrostatic, thermodynamic, and quantum chemical descriptors.
Descriptors involved with the net surface charge and energy level of substrates seem to be the common integral factors for defining binding specificity of selected substrates to BCRP polymorph. The reproducible outcomes and validation process further supported the accuracy of the computational model in assessing the correlation among descriptors involved with substrate affinity to BCRP polymorph. A quantitative computation approach will provide important structural insight into optimal designing of new chemotherapeutic agents with improved pharmacological efficacies.