Experimentally Validated QSAR Model for Surface pK _a Prediction of Heterolipids Having Potential as Delivery...

The application of lipid-based drug delivery technologies for bioavailability enhancement of drugs has led to many successful products in the market for clinical use. Recent studies on amine-containing heterolipid-based synthetic vectors for delivery of siRNA have witnessed the United States Food and Drug Administration (USFDA) approval of the first siRNA drug in the year 2018. The studies on various synthetic lipids investigated for delivery of such nucleic acid therapeutics have revealed that the surface pK _a of the constructed nanoparticles plays an important role. The nanoparticles showing pK _a values within the range of 6-7 have performed very well. The development of high-performing lipid vectors with structural diversity and falling within the desired surface pK _a is by no means trivial and requires tedious trial and error efforts; therefore, a practical solution is called for. Herein, an attempt to is made provide a solution by predicting the statistically significant pK _a through a predictive quantitative structure-activity relationship (QSAR) model. The QSAR model has been constructed using a series of 56 amine-containing heterolipids having measured pK _a values as a data set and employing a partial least-squares regression coupled with stepwise (SW-PLSR) forward algorithm technique. The model was tested using statistical parameters such as r ², q ², and pred_r ², and the model equation explains 97.2% (r ² = 0.972) of the total variance in the training set and it has an internal (q ²) and an external (pred_r ²) predictive ability of ∼83 and ∼63%, respectively. The model was validated by synthesizing a series of designed heterolipids and comparing measured surface pK _a values of their nanoparticle assembly using a 2-(p-toluidino)-6-napthalenesulfonic acid (TNS) assay. Predicted and measured surface pK _a values of the synthesized heterolipids were in good agreement with a correlation coefficient of 93.3%, demonstrating the effectiveness of this QSAR model. Therefore, we foresee that our developed model would be useful as a tool to cut short tedious trial and error processes in designing new amine-containing heterolipid vectors for delivery of nucleic acid therapeutics, especially siRNA.