Application of a count data model to evaluate the anti-metastatic efficacy of QAP14 in 4T1 breast cancer allografts. (21st January 2023) - Guo, Yuchen; Yong, Ling; Yao, Qingyu; Han, Mengyi; Xue, Junsheng; Jian, Weizhe; Zhou, Tianyan
Highlights: QAP14 could delay the onset of lung metastasis in 4T1 breast cancer cell allografts. Generalized Poisson distribution well depicted the overdispersion of metastasis counts. The count data model was feasible to describe and predict the metastasis counts. Our model was versatile to other D1DR agonists (fenoldopam and l-stepholidine). Abstract: Dopamine D1 receptor (D1DR) is proved to be a promising target to prevent tumor metastasis, and our previous studies showed that QAP14, a potent anti-cancer agent, exerted inhibitory effect on lung metastasis via D1DR activation. Therefore, the purpose of the study was to establish count data models to quantitatively characterize the disease progression of lung metastasis and assess the anti-metastatic effect of QAP14. Data of metastatic progression were collected in 4T1 tumor-bearing mice. Generalized Poisson distribution best described the variability of metastasis counts among the individuals. An empirical PK/PD model was developed to establish mathematical relationships between steady plasma concentrations of QAP14 and metastasis growth dynamics. The latency period of metastasis was estimated to be 12 days after tumor implantation. Our model structure also fitted well to other D1DR agonists (fenoldopam and l-stepholidine) which had inhibitory impact on breast cancer lung metastasis likewise. QAP14 40 mg/kg showed the best inhibitory efficacy, for it provided the longest prolongation of metastasis-free periods compared with fenoldopam or l-stepholidine. This study provides a quantitative method to describe the lung metastasis progression of 4T1 allografts, as well as an alternative PD model structure to evaluate anti-metastatic efficacy.