A regression approach for assessing large molecular drug concentration in breast milk

The development of an effective method for predicting the transfer of biologics from plasma into breast milk is important to ensure the safe use of medications during lactation. The aim of this study was to develop a regression model that could predict the transfer of monoclonal antibodies (mAbs) and Fc-fusion proteins from plasma into breast milk. By searching various databases, a list of eleven mAbs and Fc-fusion proteins with available information of presence in the breast milk was generated. Physicochemical properties such as the isoelectric point (pI), molecular weight (MW), dissociation constant (K_d), and pharmacokinetic (PK) parameters such as clearance (CL), volume of distribution (V_d), and half-life (T_1/2) were collected or calculated. A two-variable non-linear regression analysis and a multivariate regression analysis were employed to establish correlation of milk-to-plasma (M/P) ratios with different combinations of two physicochemical properties. The 3D isoelectric point (pI) of the Fv region and the buried surface area (BSA) between the light and heavy chains (LC_HC) were two factors that emerged as a promising predictor of the milk-to-plasma concentration ratio (M/P). The correlation between M/P ratio, 3D pI of Fv region, and BSA_LC_HC was found to be good with R² of 0.9058. Other combinations of the physicochemical properties did not show a statistically significant correlation. The multivariate regression model was used to predict the MP ratios for 79 different mAbs. We believe that this regression model could serve as a valuable tool to estimate the M/P ratios of mAbs and Fc-fusion proteins. Further model validation is necessary when the M/P ratios of additional biologics are available. This could inform clinical decision-making and improve the safety of large molecule drug use during lactation.