Ling Jin, Zheng Luxia, Xu Mingming, Chen Gang, Wang Xiao, Mao Danzhuo, Shao Hong
NMPA Key Laboratory for Quality Control of Therapeutic Monoclonal Antibodies, Shanghai Institute for Food and Drug Control, Shanghai, China.
NMPA Key Laboratory for Quality Analysis of Chemical Drug Preparations, Shanghai Institute for Food and Drug Control, Shanghai, China.
Front Chem. 2022 Apr 13;10:887960. doi: 10.3389/fchem.2022.887960. eCollection 2022.
Therapeutic monoclonal antibodies (mAbs) are a new generation of protein-based medicines that are usually expensive and thus represent a target for counterfeiters. In the present study, a method based on Raman spectroscopy that combined extreme point sort transformation with a long short-term memory (LSTM) network algorithm was presented for the identification of therapeutic mAbs. A total of 15 therapeutic mAbs were used in this study. An in-house Raman spectrum dataset for model training was created with 1,350 spectra. The characteristic region of the Raman spectrum was reduced in dimension and then transformed through an extreme point sort transformation into a sequence array, which was fitted for the LSTM network. The characteristic array was extracted from the sequence array using a well-trained LSTM network and then compared with standard spectra for identification. To demonstrate whether the present algorithm was better, ThermoFisher OMNIC 8.3 software (Thermo Fisher Scientific Inc., U.S.) with two matching modes was selected for comparison. Finally, the present method was successfully applied to identify 30 samples, including 15 therapeutic mAbs and 15 other injections. The characteristic region was selected from 100 to 1800 cm of the full spectrum. The optimized dimensional values were set from 35 to 53, and the threshold value range was from 0.97 to 0.99 for 15 therapeutic mAbs. The results of the robustness test indicated that the present method had good robustness against spectral peak drift, random noise and fluorescence interference from the measurement. The areas under the curve (AUC) values of the present method that were analysed on the full spectrum and analysed on the characteristic region by the OMNIC 8.3 software's built-in method were 1.000, 0.678, and 0.613, respectively. The similarity scores for 15 therapeutic mAbs using OMNIC 8.3 software in all groups compared with that of the relative present algorithm group had extremely remarkable differences ( < 0.001). The results suggested that the extreme point sort transformation combined with the LSTM network algorithm enabled the characteristic extraction of the therapeutic mAb Raman spectrum. The present method is a proposed solution to rapidly identify therapeutic mAbs.
治疗性单克隆抗体（mAbs）是新一代基于蛋白质的药物，通常价格昂贵，因此成为造假者的目标。在本研究中，提出了一种基于拉曼光谱的方法，该方法将极值排序变换与长短期记忆（LSTM）网络算法相结合，用于治疗性单克隆抗体的鉴定。本研究共使用了15种治疗性单克隆抗体。利用1350个光谱创建了一个用于模型训练的内部拉曼光谱数据集。拉曼光谱的特征区域进行降维，然后通过极值排序变换转换为序列数组，用于拟合LSTM网络。使用训练有素的LSTM网络从序列数组中提取特征数组，然后与标准光谱进行比较以进行鉴定。为了证明本算法是否更好，选择了具有两种匹配模式的ThermoFisher OMNIC 8.3软件（美国赛默飞世尔科技公司）进行比较。最后，本方法成功应用于30个样品的鉴定，包括15种治疗性单克隆抗体和15种其他注射剂。特征区域从全光谱的100至1800 cm处选取。15种治疗性单克隆抗体的优化维数值设定为35至53，阈值范围为0.97至0.99。稳健性测试结果表明，本方法对测量中的光谱峰漂移、随机噪声和荧光干扰具有良好的稳健性。本方法在全光谱上分析以及通过OMNIC 8.3软件的内置方法在特征区域上分析的曲线下面积（AUC）值分别为1.000、0.678和0.613。与相对本算法组相比，所有组中使用OMNIC 8.3软件对15种治疗性单克隆抗体的相似性得分存在极其显著差异（<0.001）。结果表明，极值排序变换与LSTM网络算法相结合能够实现治疗性单克隆抗体拉曼光谱的特征提取。本方法是一种用于快速鉴定治疗性单克隆抗体的解决方案。
