The efficacy of hematological tumor drugs has shown inter-patient variability. Pharmacogenomics focuses on gene polymorphisms of drug metabolizing enzymes, drug transporters and therapeutic targets and its impact on pharmacokinetics (PK)/pharmacodynamics (PD). Initial dose and adverse reaction can be predicted based on inherited gene polymorphisms, and therapeutic drug monitoring (TDM) also helps to adjust drug dosage in the course of treatment. Thus can achieve rational drug use and personalized medicine, and improve the efficacy and reduce the incidence of adverse drug reactions. This article will introduce the relevant research progress in recent years, and the concept of personalized medication fingerprints is proposed.

Objective To develop a HPLC-MS/MS method for determination of Imatinib and Dasatinib in CML patient,and make it used in clinic trial.Methods The separation was performed on a Ultimate XB-C18 column with a mobile phase of water(containing 2 mmol/L ammonium acetate and 0.1 ml/dl formic acid)and methanol(containing 0.1 ml/dl formic acid). The way of eluting was gradient.Mass spectrum detection method was ESI positive ion mode and monitoring Imatinib m/z 494.5>394.3 and Dasatinib m/z 488.3>401.3.Results The standard curve of Imatinib was linear over the range of 0.05~7.5 μg/ml,Y =5.6×105 X+5× 103 (R =0.999 8).Thestandard curve of Dasatinib was linear over the range of 5~250 ng/ml,Y =211X+66.6(R=0.999 6).The relative recovery was among the range of 90%~107%.RSDs of intra-and inter-day validation were less than 10%.Conlusion This method is convenient,accurate and rapid,and can be used for the deter-mination of Imatinib and Dasatinib in clinic test.

Objective:To investigate the clinical characteristics and related risk factors of thyroid gland injury (TGI) in patients with a malignant tumor treated with a programmed death-1 (PD-1) inhibitor.Methods:A Retrospective case-control study. Data from 198 patients with a malignant tumor who received treatment with a PD-1 inhibitor in Chinese PLA General Hospital from October 2019 to October 2021 were collected and analyzed retrospectively. According to the TGI incurred after receiving treatment with a PD-1 inhibitor, patients were divided into a thyroid gland normal (TGN) group and TGI group. The prevalence, type, time of occurrence, and outcome of TGI were analyzed. The risk factors that may contribute to TGI were analyzed further by logistic regression.Results:TGI prevalence was 29.8% (59/198 cases) after treatment with a PD-1 inhibitor. There were significant differences with respect to previous radiotherapy and targeted therapy between the TGN group and TGI group ( P<0.01 for both), but there were no significant differences with regard to sex, age, tumor type, previous surgery, previous chemotherapy, tumor metastasis, or type of PD-1 inhibitor ( P>0.05 for all). Patients in the TGI group included those with subclinical hypothyroidism (32.2%, n=19), hypothyroidism (27.1%, n=16), thyrotoxicosis (23.7%, n=14), subclinical thyrotoxicosis (10.2%, n=6), and thyroiditis with normal thyroid function (6.8%, n=4), and the median time of occurrence (months) was 3.00, 3.00, 1.50, 1.50, and 0.80 after treatment with a PD-1 inhibitor, respectively. Among 20 patients who presented initially with thyrotoxicosis or subclinical thyrotoxicosis, 12 cases developed hypothyroidism or subclinical hypothyroidism subsequently. Logistic regression analysis suggested that previous radiotherapy ( OR=3.737, 95% CI 1.390-10.046), targeted therapy ( OR=3.763, 95% CI 1.553-9.117), thyroglobulin antibodies at baseline ( OR=12.082, 95% CI 1.199-121.775), and thyroid-peroxidase antibodies at baseline ( OR=10.874, 95% CI 1.010-117.047) were risk factors associated with the TGI caused by treatment with a PD-1 inhibitor. Conclusions:After treatment with a PD-1 inhibitor, TGI prevalence was high, especially in those with hypothyroidism or subclinical hypothyroidism. Some patients had a transition from thyrotoxicosis to hypothyroidism. Patients who underwent radiotherapy previously, had targeted therapy, or were thyroid autoantibody-positive at baseline may carry an increased risk of TGI following treatment with a PD-1 inhibitor.

OBJECTIVE To establish fingerprint， chemical pattern recognition and multi-component quantification analysis of Sanzi powder， and evaluate its quality. METHODS HPLC method was adopted. The fingerprints of 15 batches of Sanzi powder were established by using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （2012 edition）. Cluster analysis， principal component analysis and orthogonal partial least squares-discriminant analysis were also conducted. The variable importance in projection （VIP） value greater than 1 was used as the index to screen the differential markers， and the contents of the differential markers were determined by the same HPLC method. RESULTS A total of 21 common peaks in the HPLC fingerprints of 15 batches of Sanzi powder were calibrated， and the similarities of them were 0.994- 0.999； 6 common peaks were identified， including gallic acid （peak 3）， garminoside （peak 10）， corilagin （peak 11）， chebulinic acid （peak 16）， ellagic acid （peak 18）， crocin Ⅰ （peak 19）. According to the results of cluster analysis， YKD2024LH005，No.YKD2023LH062） principal component analysis and orthogonal partial least squares-discriminant analysis， 15 batches of samples could be clustered into two categories： S1， S5， S7， S9， S14 were clustered into one category； S2-S4， S6， S8， S10-S13， S15 were clustered into one category. VIP values of 11 differential components such as corilagin， chebulinic acid and ellagic acid were higher than 1. Among 15 batches of samples， the contents of corilagin， chebulinic acid and ellagic acid ranged 2.667-5.152， 9.506- 13.522， 0.891-1.811 mg/g. CONCLUSIONS Established HPLC fingerprint and multi-component quantification analysis of Sanzi powder are rapid and simple， and can be used for quality evaluation of Sanzi powder by combining with chemical pattern recognition. Eleven components such as corilagin， chebulinic acid and ellagic acid are differential markers affecting the quality of Sanzi powder.