目的 基于質(zhì)量源于設(shè)計（QbD）理念和序貫設(shè)計法優(yōu)化丹參川芎嗪注射液前處理過程中的水沉工藝。方法 研究聯(lián)用了魚刺圖法及基于全局式序貫設(shè)計的中心復(fù)合實驗法作為優(yōu)化方法，在基礎(chǔ)實驗設(shè)計中關(guān)鍵參數(shù)維度的軸向方向增加實驗點，再進行新的多元線性回歸擬合，最終得到了水沉工藝的設(shè)計空間并對其進行了驗證，同時給出了較為穩(wěn)健且全面的操作空間。結(jié)果 根據(jù)設(shè)計空間，推薦操作空間為水沉體系pH值為3.1～3.4時，加水倍量3.25～5.00，靜置時間為7～17 h，靜置溫度為7℃。結(jié)論 首次在中藥制藥過程工藝優(yōu)化中引入序貫設(shè)計法。相較于樣本量巨大的均勻空間網(wǎng)格設(shè)計、正交設(shè)計等實驗設(shè)計方法而言，序貫設(shè)計在實現(xiàn)與前者同樣的樣本對可行域覆蓋程度的基礎(chǔ)上，能夠有效降低工藝優(yōu)化實驗的工作量，避免樣本與人力物力的浪費，同時保證較高的模型預(yù)測性能。

Objective To optimize the water-precipitation in the pretreatment manufacture process of Salviae Miltiorrhizae (Danshen) and Ligustrazine Hydrochloride Injection (SMLHI) based on the quality by design (QbD) concept and the sequential design. Methods A design space was obtained by the global-type sequential design methodology combined with the fishbone-diagram risk analysis and central composite experiment based on global type sequential design method. The preliminary optimized zone of critical process parameters was extended in axial direction on the basis of the statistical result gained from the first central composite design. Then an updated multivariate linear regression model was fitted and a design space was delineated and verified. Results According to the design space, the recommended operation space of the water-precipitation was as follows:when the pH value of the material was between 3.1 and 3.4, 3.25-5.00 times of water adding into the solution was recommened, the standing time was 7-17 h and the standing temperature was 7℃. Conclusion This study introduces the sequential design into the process optimization of traditional Chinese medicine pharmaceuticals. Compared with the experimental design methods such as uniform space grid design and orthogonal design with a large number of samples, while samples cover the same feasible zone in a continuous operation form, sequential design methodology can effectively reduce the workload of the optimization experiment, avoid the waste of manpower and resources as well as guarantee the prediction ability of the fitting model.

國家“重大新藥創(chuàng)制”科技重大專項（2018ZX09201011-002）