AZD1775 is a highly selective, small-molecule inhibitor of WEE1 being developed to treat patients with advanced solid tumors, as monotherapy and in combination with olaparib.

Experimental Procedures Patient-derived explant (PDX) and xenografted models with a range of sensitivities to AZD1775 were tested for response to different doses and schedules of AZD1775. WEE1 inhibition was assessed by pCDK1 as a direct substrate of Wee1 and RRM2 as a surrogate of CDK2 activity. Concentration of AZD1775 in plasma was measured in the same animals. Mathematical models were developed to describe the relationship between drug exposure, biomarker modulation and resulting tumour growth inhibition.

Results The PKPD relationship of both pCDK1 and RRM2 could be described by the mathematical model with significant delay between PK and RRM2 reduction due to this being a protein degradation event. Three factors influenced anti-tumour activity in the A427 xenograft and three TNBC PDX models, namely the AZD1775 dose, the number of consecutive days of dosing and the number of days between AZD1775 doses. The minimally effective preclinical dose with anti-tumour activity was 60 mg/kg od. TGI was seen to increase with increased days of consecutive AZD1775 dosing from 3 days on, such that a 5 days on/9 days off schedule was more efficacious than 3 days on/4 days off schedule for two weeks. A mathematical model successfully described this efficacy by using the predicted dose and schedule dependent reduction in pCDK1.

Conclusions The insights from this predictive modelling informed the starting dose of 125mg BID AZD1775 on a 5 day on/9 day off schedule; with the clinical goal to optimize the days of consecutive AZD1775 dosing as well as the maximum tolerated dose.

Citation Format: James W. Yates, Elaine Cadogan, Jennifer Hare, Adina Hughes, Urszula M. Polanska, Mark O'Connor, Susan E. Critchlow. Understanding the dose and schedule dependence of efficacy for the Wee1 inhibitor AZD1775 in xenograft and patient derived explant models by mathematical modelling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4541. doi:10.1158/1538-7445.AM2017-4541