Translational pharmacokinetic/pharmacodynamic modeling to inform target concentration and dose projection of a novel selective estrogen receptor degrader (SIM0270) for early clinical development.

SIM0270 is a novel oral selective estrogen receptor degrader with high brain penetration and potent antitumor effects in preclinical studies. This study aimed to estimate target concentrations and project potentially clinically effective doses for early clinical development using an in silico translational pharmacokinetic (PK)/pharmacodynamic (PD) modeling approach. A translational PK/PD model was developed by integrating preclinical PK and PD data. The model incorporated a 1-compartment PK model with time-varying clearance for SIM0270, a turnover model with a capacity-limited inhibition effect of SIM0270-induced progesterone receptor (PR) downregulation, and tumor growth inhibition driven by PR reduction. SIM0270 concentrations in the tumor were 27.2 times higher than the paired plasma concentrations. The concentration for half of the maximum inhibitory effect was 17.4 ng/mL. The suppression of PR slows cancer progression, with half of the maximum tumor growth effect seen when normalized PR dropped to 0.576. Model-based simulations predicted that tumor stasis would occur at 17.4 ng/mL, whereas maximal tumor suppression would be approached at 22.7 ng/mL. Combination with palbociclib further enhanced tumor reduction by 42.7%. Accounting for penetration, simulations suggested that daily doses of 45 and 60 mg may correspond to tumor growth inhibition and near-maximal tumor suppression, respectively. These findings inform dose selection for early clinical development and highlight the value of translational PK/PD modeling in generating model-informed hypotheses for initial dose selection. SIGNIFICANCE STATEMENT: This study develops a translational pharmacokinetic/pharmacodynamic modeling framework for a novel oral selective estrogen receptor degrader that quantitatively links exposure to antitumor efficacy. Model-based simulations identify target concentrations for tumor stasis and maximal suppression, informing rational clinical dose selection. The iterative framework integrates nonclinical priors and adapts to emerging clinical data, improving decision-making across drug development.