Development of a predictive algorithm for the efficacy of half-life extension strategies

Abstract

A challenge in development of peptide and protein therapeutics is rapid elimination from the body, necessitating frequent dosing that may lead to toxicities and/or poor patient compliance. To solve this issue, there has been great investment into half-life extension of rapidly eliminated drugs using approaches such as albumin binding, fusion to albumin or Fc, or conjugation to polyethylene glycol. Despite clinical successes of half-life extension products, no clear relationship has been drawn between properties of drugs and the pharmacokinetic parameters of their half-life extended analogues. In this study, non-compartmentally derived pharmacokinetic parameters (half-life, clearance, volume of distribution) were collected for 186 half-life extended drugs and their unmodified parent molecules. Statistical testing and regression analysis was performed to evaluate relationships between pharmacokinetic parameters and a matrix of variables. Multivariate linear regression models were developed for each of the three pharmacokinetic parameters and model predictions were in good agreement with observed data with r2 values for each parameter being: half-life: 0.879, clearance: 0.820, volume of distribution: 0.937. Significant predictors for each parameter included the corresponding pharmacokinetic parameter of the parent drug and descriptors related to molecular weight. This model represents a useful tool for prediction of the potential benefits of half-life extension.