Overexpressing cell systems are a competitive option to primary adipocytes when predicting in vivo potency of dual GPR81/GPR109A agonists

Publication date: 1 March 2018
Source:European Journal of Pharmaceutical Sciences, Volume 114
Author(s): Joachim Almquist, Daniel Hovdal, Christine Ahlström, Ola Fjellström, Peter Gennemark, Monika Sundqvist
Mathematical models predicting in vivo pharmacodynamic effects from in vitro data can accelerate drug discovery, and reduce costs and animal use. However, data integration and modeling is non-trivial when more than one drug-target receptor is involved in the biological response. We modeled the inhibition of non-esterified fatty acid release by dual G-protein-coupled receptor 81/109A (GPR81/GPR109A) agonists in vivo in the rat, to estimate the in vivo EC50 values for 12 different compounds. We subsequently predicted those potency estimates using EC50 values obtained from concentration-response data in isolated primary adipocytes and cell systems overexpressing GPR81 or GPR109A in vitro. A simple linear regression model based on data from primary adipocytes predicted the in vivo EC50 better than simple linear regression models based on in vitro data from either of the cell systems. Three models combining the data from the overexpressing cell systems were also evaluated: two piecewise linear models defining logical OR- and AND-circuits, and a multivariate linear regression model. All three models performed better than the simple linear regression model based on data from primary adipocytes. The OR-model was favored since it is likely that activation of either GPR81 or GPR109A is sufficient to deactivate the cAMP pathway, and thereby inhibit non-esterified fatty acid release. The OR-model was also able to predict the in vivo selectivity between the two receptors. Finally, the OR-model was used to predict the in vivo potency of 1651 new compounds. This work suggests that data from the overexpressing cell systems are sufficient to predict in vivo potency of GPR81/GPR109A agonists, an approach contributing to faster and leaner drug discovery.

Graphical abstract

http://ift.tt/2B84zQ4