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.

OBJECTIVE To evaluate the quality of Mongolian medicine Sendeng-4 based on qualitative and quantitative analysis combined with chemical pattern recognition， in order to provide the reference for its quality control. METHODS The chemical components in Sendeng-4 were analyzed qualitatively by HPLC-Q-Exactive-MS. The contents of 16 components （methyl gallate， ethyl gallate， epicatechin， dihydromyricetin， genipin-1-O-β-D-gentiobioside， caffeic acid， catechin， corilagin， deacetylasperulosidic acid methyl ester， rutin， geniposide， luteolin， myricetin， quercetin， ferulic acid， and toosendanin） in 15 batches of Sendeng-4 （sample S1-S15） were quantitatively analyzed by HPLC-MS/MS. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares discriminant analysis were conducted and variable importance projection （VIP） value greater than 1 was used as the index to screen the differential components. RESULTS A total of 73 chemical components were identified in Sendeng-4， including 20 flavonoids， 16 tannins， 14 organic acids， etc. According to the quantitative analysis， the results exhibited that the average contentsthe of above 16 components in 15 batches of Sendeng-4 were 3.683-7.730， 2.391-6.952， 2 275.538-4 377.491， 2 699.188-3 537.924， 858.266-1 377.393， 3.366-11.003， 140.624-315.683，414.629-978.334， 285.501-1 510.457， 27.799-48.325， 3 625.415-6 309.563， 0.506-0.656， 442.337-649.283， 47.093-59.736， 12.942-15.822， 127.738-326.649 μg/g， respectively. According to the results of CA and PCA， 15 batches of samples could be clustered into two categories: S1-S3， S5-S6， S9-S10 and S13 were clustered into one category； S4， S7-S8， S11-S12， S14-S15 were clustered into one category. VIP values of geniposide， epicatechin， deacetylasperulosidic acid methyl ester and genipin-1-O- β-D-gentiobioside were all greater than 1. CONCLUSIONS HPLC-Q-Exactive-MS and HPLC-MS/MS techniques are employed for the qualitative and quantitative analysis of Sendeng-4. Through chemical pattern recognition analysis， four differential components are identified: geniposide， epicatechin， deacetylasperulosidic acid methyl ester， and genipin-1-O-β-D-gentiobioside.

OBJECTIVE To comprehensively evaluate the quality of Sanzi powder from different batches based on 12 components quantitative analysis combined with chemical pattern recognition and entropy weight-TOPSIS method. METHODS The contents of 12 components in 15 batches of Sanzi powder （No. S1-S15） were determined by HPLC-MS/MS， such as ethyl gallate， gallic acid， ferulic acid， corilagin， genipin-1-O-β-D-gentiobioside， toosendanin， geniposide， caffeic acid， methyl deacetylated coumarinate， tannic acid， rutin， quercetin. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were conducted on the assay results. Using variable importance projection （VIP） value＞1 and P＜0.05 as the evaluation criteria， the quality differential markers in Sanzi powder were screened. The entropy weight method was used to calculate the weight value， and TOPSIS method was used to rank the quality of 15 batches of Sanzi powder from superior to inferior. RESULTS The contents of the 12 components were 13.494-24.292， 2 069.608-3 188.100， 1.410-3.616， 1 065.030-2 630.584， 1 404.704-1 838.078， 101.640-354.268， 9 193.720-14 777.854， 1.240-5.060， 148.028-5 541.990， 4 261.422-5 607.438， 107.560- 195.512， 2.226-4.192 μg/g， respectively. The results of CA， PCA and OPLS-DA indicated that 15 batches of Sanzi powder could be clustered into two groups. Specifically， batches S3， S7， S10 and S15 were grouped into one category， and remaining batches were grouped into one category. VIP values of geniposide， quercetin， caffeic acid， and methyl deacetylated coumarinate were all greater than 1， with corresponding P-values less than 0.05. The results of the entropy weight-TOPSIS analysis revealed that methyl deacetylate exhibited the smallest information entropy and the highest weight. The relative closeness degrees of samples S3， S7， S10 and S15 ranged from 0.789 to 0.973， while the remaining samples ranged from 0.054 to 0.172. CONCLUSIONS The contents of 12 components in Sanzi powder could be determined accurately by using HPLC-MS/MS technology. Methyl deacetylated coumarinate， geniposide， quercetin and caffeic acid were identified as the quality differential markers. It was found that the overall quality of samples S3， S7， S10 and S15 were superior to that of other batches. Notably， the quality of Gardeniae Fructus decoction pieces emerges as a critical factor in ensuring the consistency of the preparation’s quality.