Modeling the pharmacokinetics of a locked nucleic acid oligonucleotide targeting miR-221 on rats and monkey data using non linear mixed effects

Cancer is a large group of diseases that can affect almost any organ or tissue of the body. It represents the second leading cause of death globally, due to the lack of an efficient tool for early detection and the absence of quality treatments in many countries worldwide. Although an improvement in the survival rates has been registered in the past years in countries with strong health systems; the cancer burden is still one of the major issues in today’s society. MicroRNA miR-221 was proven to play a key role in the progression of various types of cancer, indeed, an abnormal expression of miR-221 is often associated with the presence of a malignant tumor. In addition to that, higher levels of miR-221 are a signal of a progressed cancer. Therefore, miR-221 represents a promising detection tool for the diagnosis of many cancer types. Furthermore, potential inhibitors of miR-221, as the locked nucleic acid LNA-i-miR-221, constitute novel therapeutic tool. Many preclinical animal studies, consisting of intravenous (IV) injections of LNA-i-miR-221, have been performed in the past years to study the kinetics and toxicity of LNA-i-miR-221; however a population analysis to assess the pharmacokinetics of this oligonucleotide is still missing. In this project, two databases will be used: the first one containing data collected in rats after a repeated IV injection of the analyte, the second one consisting of samples taken from a monkey administered with a single dose of the test item. In this project, starting from data collected in rats and monkeys, a Non-Linear Mixed Effect (NLME) model able to describe the drug’s kinetic was developed for each species. Such models were built using Monolix, an advanced software containing useful tools to build population pharmacokinetics/pharmacodynamics models for drug development. Several models of increasing complexity were developed and tested before selecting the most suitable one for each species. The final models were validated using standard methodologies, and they both offer a good description of the pharmacokinetic data, with physiologically meaningful and well-estimated parameters. The developed models were then used to predict the pharmacokinetics parameters of LNA-imiR-221 in humans. To achieve this goal, several single and multiple species allometric scaling methods (SSS and MSS respectively), such as simple allometry, integrated with correction factors such as the brain weight (BrW) and the maximum life span potential (MLP), were applied to extrapolate the humans’ response to LNA-i-miR-221. Finally, all the predicted values obtained using the different techniques were compared to each other. Future works include the validation of the predicted humans pharmacokinetic parameters, obtained with allometric scaling methods, against phase I clinical data. Through this comparison, it will be possible to assess the performance of all the different approaches. The best allometric scaling method for each pharmacokinetic parameter will be selected.