In oncology area, following a phase I dose-finding trial completed in a certain population of patients, further phase I trials are often conducted to determine the recommended dose (RD) for different patient subgroups. This is due to concerns about possible differences in treatment tolerability between patient populations. Under the situation, it may be worth considering the relevant use of historical data from a previous trial to design and conduct a subsequent trial in a new population. However, when historical and current information conflict, utilizing historical data approaches can lead to inappropriate estimation results. In this research work, we propose a Bayesian approach to incorporating historical data to establish prior distributions for a dose-finding trial to develop an anti-cancer agent. Our proposed approach aims to appropriately borrow strength from a previous trial to improve the RD determination in another population of patients. We propose a historical-to-current parameter to evaluate the difference in dose-toxicity relationship between patient populations. We present a simulation study of this proposed method to explore its properties across a variety of realistic settings.