The prediction of bioavailability is one of the major barriers in the clinical translation of subcutaneously (SC) administered therapeutic monoclonal antibodies (mAbs) due to the lack of reliable in vitro and preclinical in vivo predictive models. Recently, multiple linear regression (MLR) models were developed to predict human SC bioavailability of mAbs using human linear clearance (CL) and isoelectric point (pI) of the whole antibody or Fv regions as independent variables. Unfortunately, these models cannot be applied to mAbs at the preclinical development stage because human CLs of these mAbs are unknown. In this study, we predicted human SC bioavailability of mAbs using preclinical data only by two approaches. In the first approach, allometric scaling was used to predict human linear CL from non-human primate (NHP) linear CL. The predicted human CL and the pI of the whole antibody or Fv regions were then incorporated into two previously published MLR models to predict the human bioavailability of 61 mAbs. In the second approach, two MLR models were developed using NHP linear CL and the pI of whole antibody or Fv regions of 41 mAbs in a training set. The two models were validated using an independent test dataset containing 20 mAbs. The four MLR models generated 77-85% of predictions within 0.8- to 1.2-fold deviations from observed human bioavailability. Overall, this study demonstrated that human SC bioavailability of mAbs at the preclinical stage could be predicted using NHP CL and pI of mAbs.