CN115881311A - Method for screening antibody coupling drug indications by using tumor biopsy simulation clinical test - Google Patents
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Abstract
The invention relates to the technical field of pharmacy and biomedicine, and particularly discloses a method for screening antibody-conjugated drug indications by using a tumor living tissue simulation clinical test. The method comprises the following steps: screening the tumor biopsy sample by a tumor biopsy biological sample library and a database; constructing a mouse PDTX model; a mouse PDTX model is utilized to simulate clinical tests to evaluate the drug effect of the antibody coupled drug, and then the genomics indication characteristics of the antibody coupled drug are confirmed. The invention aims at antibody coupling drugs to establish a pre-clinical human tumor living tissue functional pharmacodynamic detection queue (D A C T M mode) for the first time, simulates a patient to carry out a PDTX clinical test to carry out in-vivo pharmacodynamic detection, and then preliminarily confirms the drug-sensitive tumor biomarker and the indication characteristics before the clinical test according to the comprehensive evaluation pharmacodynamic result and referring to the tumor living tissue database information.
Description
Technical Field
The invention relates to the technical field of pharmacy and biomedicine, in particular to a method for screening antibody-conjugated drug indications by using a tumor living tissue simulation clinical test.
Background
Cytotoxic chemotherapeutic drugs are still currently the commonly used drugs in the clinical treatment of most tumors as a classical anti-tumor treatment option. Chemotherapy drugs can strongly inhibit rapidly proliferating tumor cells, but can also accidentally injure normal naturally proliferating human normal cells (such as bone marrow cells, digestive tract mucosa, hair follicles and the like), and related adverse reactions often limit the clinical application of the chemotherapy drugs. With the continuous and deep study of genomics and tumorigenesis and development mechanisms, the concept of precise medicine and individualized treatment is deeply focused and successfully verified in clinical practice. Under the background, personalized treatment strategies of traditional antitumor drugs need to be further researched and optimized.
The Antibody-Drug Conjugates (ADC) are formed by coupling an Antibody Drug targeting a specific antigen and a payload (such as a small-molecule cytotoxic Drug) through a linker, and have the powerful killing effect of the traditional small-molecule cytotoxic Drug and the tumor targeting property of the Antibody Drug. The main goal in developing ADC drugs is to achieve relatively low systemic exposure of the payload, effectively increasing the benefit-to-risk ratio of anti-tumor therapy, by targeted delivery of the payload to a specific site.
Since ADC drugs are different in the aspects of target antigen selection, effective load selection, linker selection, coupling mode and the like, the structure of the ADC drugs has diversity and complexity; ADC drugs are not simply equivalent to the combination of antibody drugs and payloads, presenting many challenges in clinical development, including pharmacokinetic complexity, tumor targeting and insufficient payload release, and drug resistance; meanwhile, the ADC drug has a complex structure, and needs to perform a drug effect comparison study on the naked antibody, the unconjugated small molecule compound, and the complete ADC drug to confirm that the antibody and the small molecule compound have the effect of enhancing the drug effect after conjugation.
The human-derived tumor xenograft (PDTX) model is a tumor model constructed by transplanting tumor tissues of patients to immunodeficient mice. PDTX can maintain the original molecular, genetic, tissue and other characteristics of tumor patients and the heterogeneity in tumors and among tumors. The tumor biopsy biological sample bank can be used for establishing a PDTX model of primary tumor tissues of patients, the biological characteristics of the newborn tumor biopsy are highly consistent with the primary tumor tissues, and stable passage and accumulation can be carried out.
At present, clinical trial research of tumor drugs enters a brand new period, and establishment of a suitable pre-clinical trial research model is the key for research transformation of antitumor innovative drugs. At present, research on antitumor innovative drugs is rapidly developed, but due to the lack of a suitable preclinical research model, the clinical trial passing rate of the current antitumor drugs is only 3.4%. Modern new drug development increasingly depends on biological sample sources and preclinical drug tests, and improvement of the passing rate of subsequent clinical test stages through perfection of preclinical test research is urgently needed. But is limited by the time and cost of collection of preclinical test samples, excessive interference factors during clinical trials (including subject willingness, past medical history and medication, individual differences, etc.), incomplete omics information for group samples (including biomarker expression associated with drug sensitivity or rare mutation information, etc.), development cycles that may take years, and cost investments that may require billions of dollars. If a biological sample library provides a proper sample source and provides related information data support, the method is helpful for improving the research and development efficiency of new drugs.
At present, sufficient biological sample sources meeting the requirements of a drug action mechanism are lacked, in-vivo pharmacodynamic detection aiming at the antibody conjugate drug is lacked, and tumor biomarkers and indication characteristics related to antibody conjugate drug sensitivity cannot be confirmed in advance.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for screening antibody-conjugated drug indications by using a tumor biopsy simulation clinical test. The invention utilizes tumor living tissue to simulate clinical test, and simultaneously utilizes PDTX model technology to evaluate the antitumor effect and screen ADC drug indications.
In order to achieve the purpose, the invention adopts the technical scheme that:
the invention provides a method for screening antibody coupling drug indications by using a tumor biopsy simulation clinical test, which comprises the following steps:
1) Screening a sufficient amount of suitable tumor biopsy samples from the tumor biopsy biological sample library and the database;
2) Constructing a mouse PDTX model;
3) Simulating a clinical test by using a mouse PDTX model to evaluate the drug effect of the antibody coupled drug, and then confirming the genomics indication characteristic of the antibody coupled drug;
wherein the content of the evaluation comprises: selecting a drug effect evaluation mode, controlling data quality, counting autologous tumor change difference data of a test group, counting tumor volume change difference data between the test group and a blank group, and analyzing a comprehensive result;
according to the efficacy evaluation mode, setting the number of the drug schemes, the number of the cancer species and the number of the same cancer living tissues to be grouped, and calculating the number N of PDTX models required by all experimental groups, wherein the calculation formula is as follows:
N＝D*C*T*M+A*C*T*M+S*C*T*M；
calculating the number X of PDTX models required by all blank groups according to the number of cancer species and the number of live tissues of the same cancer in the experimental group, wherein the calculation formula is as follows:
X＝C*T*M；
calculating the number Y of the tumor living tissues in the required tumor living tissue biological sample library according to the number of the cancer species and the number of the same cancer living tissues in the test group, wherein the calculation formula is as follows:
Y＝C*T；
wherein, D: the number of the medicine schemes is that D is an integer and is more than or equal to 1; a: the antibody type, A is an integer, and A is more than or equal to 1; s: small molecular compound species, S is an integer, and S is more than or equal to 1; c: the number of cancer species, C is an integer, and C is more than or equal to 1; t: the number of the same cancer living tissues is equal, T is an integer and is more than or equal to 1; m: the modeling quantity of each part of living tissue, wherein M is an integer and is more than or equal to 3.
In the technical scheme of the invention, a tumor living tissue biological sample library and a database are utilized to provide sufficient tumor living tissue samples which meet the requirement of a drug action mechanism; the invention firstly establishes a queue (D A S C T M mode) of the functional pharmacodynamic test of the pre-Clinical human tumor living tissue aiming at the antibody coupling drug, simulates a patient to carry out a PDTX Clinical test (PDTX Mouse Clinical Trial) to carry out in-vivo pharmacodynamic test, and then preliminarily confirms the tumor biomarker sensitive to the drug and the indication characteristic before the Clinical test according to the comprehensive evaluation pharmacodynamic result and referring to the database information of the tumor living tissue.
The invention provides sufficient and appropriate biological sample sources for the clinical trial of the antitumor drugs by utilizing the humanized tumor living tissue biological sample library and the database, can confirm the grouping scheme of the indication patients of the clinical trial in the real world in advance through the mouse clinical trial based on the sample library and the database, establishes a set of indication screening method which has wide application range, is objective and accurate and is suitable for the antibody coupling drugs based on the tumor living tissue biological sample library, the database and various PDTX technologies, provides an appropriate clinical trial research model for the clinical trial of the antitumor drugs, can greatly shorten the period of the clinical trial, reduces the overall cost and simultaneously improves the success rate of the clinical trial.
Preferably, all the tumor biopsy samples in the tumor biopsy biological samples are biopsy which can be stably amplified after passage by PDTX technology and are stored in a gas-phase liquid nitrogen storage tank, the proportion of tumor cell components in the tumor biopsy and the modeling success rate after recovery can be improved, and the characteristics of most primary tumors are retained in the new tumor biopsy on the histopathology and molecular biology levels. All tissue sample information data are stored in a tumor biopsy database, and sufficient and appropriate tumor biopsy samples are screened out for PDTX modeling and drug effect evaluation according to the expected indication characteristics of the detected drug.
Preferably, tumor biopsyOf 2 x 2mm 3 。
In a preferred embodiment of the method of the present invention, in step 3), after the efficacy of the antibody conjugate drug is evaluated by a mouse PDTX model simulation clinical test, the antibody conjugate drug with a drug efficacy and the cancer type with primary drug sensitivity adaptability are screened, a tumor biopsy with a known mutation target of a target cancer species is screened from a tumor biopsy biological sample library for PDTX resuscitation modeling, the antibody conjugate drug with a drug efficacy is administered, and the efficacy is evaluated again.
As a preferred embodiment of the method of the present invention, the evaluation of drug efficacy comprises the steps of:
and (3) setting the number of the same cancer living tissues in the group according to the efficacy evaluation mode, and calculating the number N of PDTX models required by all test groups according to the following calculation formula:
N＝T*M；
calculating the number X of PDTX models required by all blank groups according to the number of the tested group-entering same cancer living tissues, wherein the calculation formula is as follows:
X＝T*M；
wherein, T: the number of the living tissues of the same cancer is equal, T is an integer and is more than or equal to 1; m: the modeling quantity of each part of living tissue, wherein M is an integer and is more than or equal to 3.
As a preferred embodiment of the method of the present invention, in the step 3), the statistics of the autologous tumor variation difference data of the test group specifically include:
when the test group reaches the expected administration period, recording the terminal time tumor volume of the test group mouse PDTX model and the initial time volume of the corresponding individual for difference comparison, and calculating the tumor growth inhibition rate TGI of the test group mouse PDTX model 1 ；
Tumor growth inhibition by itself TGI 1 Is calculated as TGI 1 (％)＝(V0-Vt)/V 0 *100% of, wherein V 0 : measurement of tumor volume, V, obtained at initial dose in PDTX model in mice t : tumor volume at time t measured for each mouse PDTX model;
evaluation of PDTX model in each mouse according to evaluation criteria of curative effect of solid tumorThe result of tumor drug effect is when TGI 1 If the concentration is more than 95%, judging the concentration to be mCR; when the content of the TGI is more than or equal to 95 percent 1 If the concentration is more than 30%, judging the concentration to be mPR; when the content of 30% is more than or equal to TGI 1 When > -20%, the mSD is judged; when TGI 1 When the content is less than or equal to-20%, the mPD is judged.
As a preferred embodiment of the method of the present invention, in step 3), the statistics of the difference data of tumor volume changes between the test group and the blank group specifically include: when the test group reaches the expected administration period, recording the terminal time tumor volume of the test group mouse PDTX model and the terminal time tumor volume of the blank group mouse PDTX model for difference comparison, and calculating the tumor growth inhibition rate TGI between the groups 2 Tumor growth inhibition rate TGI between groups 2 Is calculated as TGI 2 (％)＝(1-RTV T /RTV C )*100％，RTV T ＝(T t -T 0 )/T 0 ；RTV C ＝(C t -C 0 )/C 0 (ii) a Wherein, T 0 : measuring the volume of the obtained tumor when the PDTX model of the mice in the test group is initially administrated; t is t : tumor volume at time t measured by the test group mouse PDTX model; c 0 : measuring the resulting tumor volume at the initial dosing of the PDTX model in the blank group of mice; c t : tumor volume at time t measured for the blank mouse PDTX model;
tumor growth inhibition rate TGI between the blank and test groups if the recording time period is different 2 The calculation formula is as follows:
TGI 2 (％)＝(1-RTV T /RTV C )*100％；
RTV T ＝T t1 /T 0 /t 1 ；
RTV C ＝C t2 /C 0 /t 2 ；
wherein, t 1 Recording the time period for the PDTX model of the mice of the actual experimental group; t is t1 PDTX model for experimental group mice at time t 1 Tumor volume at time of measurement; t is t 2 Recording the time period for the actual blank group mouse PDTX model; c t2 Mice PDTX model as blank group at time t 2 Tumor volume at time of measurement;
according to the evaluation standard of PDTX (phytochemical delivery) efficacy detection of tumor drugs, when TGI is used 2 When the concentration is more than or equal to 60 percent, the drug effect is judged to be positive; when TGI 2 If the concentration is less than 60%, the drug effect is judged to be negative.
As a preferred embodiment of the method of the present invention, in the step 3), the step of confirming the genomics indication characteristics of the antibody-conjugated drug comprises the following steps:
a) Counting whether the number of the tumor biopsy samples meets the counting requirement;
b) If yes, inquiring the gene mutation or wild condition of the tumor biopsy sample, and screening out the somatic mutation gene of the tumor biopsy sample; if not, re-screening the tumor biopsy sample from the tumor biopsy biological sample library, re-establishing a mouse PDTX model and carrying out pharmacodynamic detection;
c) And (3) chi-square test analysis, and preliminarily determining the genomics indication characteristics of the antibody coupled drug.
In a preferred embodiment of the method of the present invention, in step b), after the antibody conjugate drug efficacy is completed, somatic mutation genes of the tumor biopsy sample corresponding to the drug-effective antibody conjugate drug are selected, and genes with mutation frequencies of more than 10% are selected according to the order of the gene mutation frequencies from high to low, and the mutation ratio of the genes in the tumor biopsy sample corresponding to the drug-effective antibody conjugate drug needs to be higher than the ratio in the tumor biopsy sample corresponding to the drug-ineffective antibody conjugate drug.
As a preferred embodiment of the method of the present invention, the step b) further comprises: and (3) confirming that no significant correlation exists between tumor biopsy samples by Pearson correlation test and adopting 0.5 as a rejection threshold, then eliminating gene mutation in significant linkage disequilibrium by Plink software, and finally screening out an alternative gene queue.
As a preferred embodiment of the method of the present invention, in the step b), the somatic mutation gene comprises a tumor-driving mutation gene and a non-tumor-driving mutation gene; mutations occurring in intron regions, untranslated regions upstream and downstream of the transcript, and synonyms in exon regions are excluded from somatic mutations, and it is desirable to retain mutations including those occurring in splice sites, nonsynonyms of exon regions, and types of initiation, termination, and premature termination.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a method for screening antibody coupling drug indications by using tumor biopsy simulation clinical tests, which provides sufficient tumor biopsy samples meeting the requirements of drug action mechanisms by using a tumor biopsy biological sample library and a database; aiming at the antibody coupling drug, the invention firstly establishes a queue (D A S C T M mode) for detecting the functional drug effect of the living tissue of the human-derived tumor before Clinical treatment, simulates a patient to carry out a PDTX Clinical test (PDTX Mouse Clinical Trial) to carry out in-vivo pharmacodynamic detection, and then screens out the antibody coupling drug with the best effect according to the comprehensive evaluation drug effect result; the invention aims at the detection of the drug effect of the antibody conjugate drug and refers to the characterization information of the corresponding sample in the tumor living tissue database, preliminarily confirms the drug-sensitive tumor biomarker and the indication characteristics before the development of clinical tests, provides reference for the inclusion and exclusion standards of the antibody conjugate drug clinical tests, and improves the passing rate of the antibody conjugate drug clinical tests.
Drawings
FIG. 1 is a flow chart of PDTX modeling;
FIG. 2 is a flow chart of test 1;
FIG. 3 is a flow chart of run 2;
FIG. 4 is a flow chart of comprehensive efficacy assessment;
FIG. 5 is an interface of a comparative assay system for detecting drug effect;
FIG. 6 is a graph showing the analysis of the results of drug action;
FIG. 7 is a flow chart for identifying characteristics of a genomics indication for a test drug;
FIG. 8 is a graph of differential analysis of autologous tumor changes in different test groups;
FIG. 9 is a graph showing the differential analysis of the change in tumor volume between different experimental groups and a blank group.
Detailed Description
To better illustrate the objects, aspects and advantages of the present invention, the present invention will be further described with reference to the accompanying drawings and specific embodiments.
In the following examples, the experimental methods used were all conventional methods unless otherwise specified, and the materials, reagents and the like used were commercially available unless otherwise specified.
Example 1 method for screening antibody-conjugated drug indications using tumor biopsy simulation clinical test
1. Establishing a tumor biopsy biological sample library and a tumor biopsy database:
the tumor tissues stored in the tumor living tissue biological sample library are all living tissues which can be stably amplified after passage by a PDTX technology and are stored in a gas-phase liquid nitrogen storage tank, the proportion of tumor cell components in the tumor living tissues and the modeling success rate after recovery can be improved, and the characteristics of most primary tumors are retained in the new tumor living tissues on the histopathology and molecular biology levels.
All the tissue sample information data are stored in a tumor biopsy database, and sufficient and appropriate tumor biopsy samples are screened out for PDTX modeling and drug effect evaluation according to the expected indication characteristics of the detected drugs.
2. And (3) data quality control:
take 2 x 2mm 3 The tumor biopsy of (a) was inoculated into an immunodeficient mouse for PDTX modeling, and then the change in tumor tissue volume and the change in mouse body weight in a general immunodeficient mouse model were observed 2 times per week.
According to the ethical requirements of animal experiments, animals are euthanized if any one or more of the following conditions occur:
1) Tumor volume is too large: tumor volume growth in model mice to over 2000mm 3 When the current is in the normal state;
2) Weight loss: weight loss of more than 20%, or cachexia or wasting disease in the animal;
3) Loss of appetite: mice lost appetite completely for 24 hours or anorexia (less than 50% of normal) for 3 days;
4) Frailty (inability to eat or drink): the animals can not stand or can stand only extremely reluctantly for 24 hours under the state of no anesthesia or sedation;
5) Moribund/moribund: when the animals show dysthymia with hypothermia (below 37 ℃) without anesthesia or sedation;
6) Infection: whether apparently or judged to be due to an increase in white blood cell numbers due to an increase in body temperature, and when antibiotic treatment is ineffective with concomitant symptoms of systemic discomfort in the animal;
7) Loss of organ function: clinical symptoms of severe organ loss and ineffective treatment occur, or the prognosis is judged to be poor by animal center veterinarians;
tumor volume in the presence of model mice exceeding 2000mm 3 For the corresponding samples, the tumor volume data and mouse weight data at the time point of the data and the time points after the time point are removed; tumor volume over 2000mm in the absence of tumor 3 The tumor volume data does not need to be processed.
The tumor volume calculation formula is as follows:
V(mm 3 )＝(a*b 2 )/2
wherein, a: tumor major diameter, b: tumor minor diameter.
If data that the weight of the model mouse is reduced by more than 20% exists, removing the data of the tumor volume of the corresponding time point when the weight of the mouse relatively changes by more than 20% and the time point for the corresponding sample; if there is no data on relative changes in mouse body weight of more than 20%, the tumor volume data need not be processed.
The formula for calculating the weight change of the mice is as follows:
Pw(％)＝(Wt-W0)/W0*100
wherein, wt: mouse body weight at time t, W0: body weight of mice at the start of dosing.
3. PDTX modeling:
according to the actual research requirements, selecting appropriate tumor from the tumor biopsy biological sample bankResuscitating the tissue, taking 8mm from immunodeficient mouse as resuscitative mouse 3 Inoculating the tumor tissue block to the subcutaneous part of a resuscitated mouse for PDTX modeling, and observing the change of the tumor tissue volume and the change of the mouse weight in the resuscitated mouse model 2 times per week until the tumor volume reaches 100mm 3 When the recovery of the tumor tissue is successful, the recovery of the tumor tissue is indicated.
The tumor tissue is passaged after being successfully recovered, the immunodeficient mouse is continuously used as a modeling mouse, and the successfully recovered tumor tissue is divided into 8mm 3 Several parts (the amount is according to design requirement) are inoculated into subcutaneous tissue of the modeling mouse, and then the change of the tumor tissue volume and the change of the mouse weight in the modeling mouse model are observed for 2 times every week when the tumor volume reaches 100mm 3 When it is time, it means that PDTX modeling was successful and experimental grouping was performed.
Randomly dividing the test result into (n + 1) groups according to the number (n) of the detected drug types, wherein the (n + 1) groups comprise 1 group of blank control and n groups of drug test groups, the number of PDTX model mice in each group is more than or equal to 3, and the specific process refers to the figure 1.
4. PDTX efficacy detection and evaluation:
the establishment content of the drug effect evaluation system comprises selection of a drug effect evaluation mode, data quality control, statistics of autologous tumor change difference data of a test group, statistics of tumor volume change difference data between the test group and a blank group, statistics of survival quality data of the test group and comprehensive result analysis, and the drug effect evaluation system is suitable for drug effect evaluation of antitumor innovative drugs under different conditions.
4.1 detection of drug effect of antibody coupling drug:
1) According to the known target point information of the ADC medicine, screening tumor biopsy with known mutation target points from a tumor biopsy biological sample library to carry out PDTX resuscitation modeling; the transplantation mode can be selected to carry out in-situ transplantation, subcutaneous transplantation and the like; when the tumor volume is increased to 100mm 3 At that time, dosing is initiated.
2) According to the actual clinical use condition of the medicine, the administration mode can be selected from intraperitoneal injection, tail vein injection, subcutaneous injection, oral intragastric administration and the like; the administration period and dosage can be converted according to the administration method of the experimental animal model.
3) Test 1: and performing test grouping design according to the types of the antibodies and the types of the small molecular compounds in the ADC drug structure to be tested. According to the efficacy evaluation mode DASCTM, the number of the drug schemes, the number of cancer species and the number of the same-cancer living tissues are set in a group, the number N of PDTX models needed by all test groups is calculated, and the calculation formula is as follows:
N＝D*C*T*M+A*C*T*M+S*C*T*M；
calculating the number X of PDTX models required by all blank groups according to the number of cancer species and the number of live tissues of the same cancer in the experimental group, wherein the calculation formula is as follows:
X＝C*T*M；
calculating the tumor living tissue number Y in the required tumor living tissue biological sample library according to the number of the cancer species to be tested in the group and the number of the living tissues with the same cancer, wherein the calculation formula is as follows:
Y＝C*T；
wherein, D: the number of the medicine schemes is that D is an integer and is more than or equal to 1; a: the antibody type, A is an integer, and A is more than or equal to 1; s: small molecular compound species, S is an integer, and S is more than or equal to 1; c: the number of cancer species, C is an integer, and C is more than or equal to 1; t: the number of the same cancer living tissues is equal, T is an integer and is more than or equal to 1; m: the modeling quantity of each part of living tissue, wherein M is an integer and is more than or equal to 3; note: each biopsy corresponds to an individual with independent phenotypic information.
The number of PDTX models required for the efficacy evaluation system was N + X, where the number of models per blank and drug test group was T × M. The flow of the above test 1 is shown in FIG. 2.
4) Test 2: screening an effective antibody coupling drug type (namely, the drug effect of the antibody coupling drug group is better than that of a naked antibody group and that of a non-coupling small molecule compound group, p is less than 0.05 and is used as a target drug of a test 2) and a cancer type with preliminary drug sensitivity adaptability (namely, the TGI value of a PDTX model of the cancer is more than or equal to 60 percent and is used as a target cancer of the test 2) according to the data of the test 1, and screening tumor biopsies (n is more than or equal to 30) with known mutation targets of the target cancer species from a tumor biopsy biological sample library to carry out PDTX resuscitation modeling; the transplantation mode can be selected to carry out in-situ transplantation, subcutaneous transplantation and the like; when the tumor volume is increased to 100mm 3 At first, openThe target drug is administered.
According to the efficacy evaluation mode DASCTM, the number of the same cancer living tissues in the group is set, and the number N of PDTX models required by all test groups is calculated according to the following formula:
N＝T*M；
calculating the number X of PDTX models required by all blank groups according to the number of the experimental in-group same cancer living tissues, wherein the calculation formula is as follows:
X＝T*M；
wherein, T: the number of the living tissues of the same cancer is equal, T is an integer and is more than or equal to 1; m: the modeling quantity of each part of living tissue, wherein M is an integer and is more than or equal to 3; note: each living tissue corresponds to an individual with independent phenotypic information
Therefore, the number of PDTX models required for the efficacy evaluation system was N + X, where the number of models per blank and drug test group was T × M. The flow of the above test 1 is shown in FIG. 3.
4.2 experimental group autologous tumor variation difference statistics (autologous comparison TGI):
when the test group reaches the expected administration period, the Tumor volume at the end time of the mouse model of the test group is recorded and compared with the initial time volume of the corresponding individual differentially, and the Tumor Growth Inhibition rate (GI) of the test group itself is calculated.
The self TGI calculation formula is as follows:
TGI 1 (％)＝(V 0 -V t )/V 0 *100％
wherein, V 0 : measurement of tumor volume, V, obtained at the time of initial administration of each mouse t : tumor volume at time t was measured for each mouse.
The tumor efficacy results (mPD, mPR, mSD, mCR) were evaluated for each mouse according to the Solid tumor efficacy Evaluation Criteria (Response Evaluation Criteria in Solid Tumors, RECIST). When the TGI is more than 95 percent, judging the product to be mCR; when 95% or more of TGI is more than 30%, judging as mPR; when 30% is more than or equal to TGI > -20%, determining as mSD; when the TGI is less than or equal to-20 percent, the product is judged to be mPD.
4.3 difference in tumor volume change between test and blank (TGI between test and blank groups):
when the test group reaches the expected administration period, the tumor volume at the end point time of the test group mouse PDTX model is recorded and is differentially compared with the tumor volume at the end point time of the blank group mouse PDTX model, and the tumor growth inhibition rate TGI between the groups is calculated 2 Tumor growth inhibition rate TGI between groups 2 Is calculated as TGI 2 (％)＝(1-RTV T /RTV C )*100％，RTV T ＝(T t -T 0 )/T 0 ；RTV C ＝(C t -C 0 )/C 0 (ii) a Wherein, T 0 : measuring the volume of the obtained tumor when the PDTX model of the mice in the test group is initially administrated; t is t : tumor volume at time t measured by the test group mouse PDTX model; c 0 : measuring the resulting tumor volume at the initial dosing of the PDTX model in the blank group of mice; c t : tumor volume at time t measured for the blank mouse PDTX model;
tumor growth inhibition rate TGI between groups if the recording time period of blank group and test group is different 2 The calculation formula is as follows:
TGI 2 (％)＝(1-RTV T /RTV C )*100％；
RTV T ＝T t1 /T 0 /t 1 ；
RTV C ＝C t2 /C 0 /t 2 ；
wherein, t 1 Recording the time period for the PDTX model of the mice in the actual experimental group; t is t1 PDTX model for test group mice at time t 1 Tumor volume at time of measurement; t is t 2 Recording the time period for the actual blank group mouse PDTX model; c t2 Mice PDTX model as blank group at time t 2 Tumor volume at time of measurement;
according to the evaluation standard of the tumor drug PDTX efficacy detection, when TGI 2 When the concentration is more than or equal to 60 percent, the drug effect is judged to be positive; when TGI 2 If the concentration is less than 60%, the drug effect is judged to be negative. The flow chart of steps 4.2-4.3 is shown in fig. 4, and the interface of the comparative assay system for detecting drug effect is shown in fig. 5.
4.4 analysis of the pharmacodynamic results:
and comprehensively evaluating the drug effect of the detection drugs by combining the self comparison TGI1 value of the test group and the comparison TGI2 value between the test group and the blank group, and simultaneously carrying out comparative analysis on the detection drugs corresponding to a plurality of test groups. The analysis mode is mainly to classify the detection data of the drug effect of the test group into a drug effective group and a drug ineffective group according to whether the calculated value of TGI1 and/or TGI2 is more than 60 percent (refer to the Cut-off value of the antitumor effect tested in a model body from the technical guideline for non-clinical research of cytotoxic antitumor drugs). The data of the drug effect detection of each test group will be described statistically by using the mean ± standard deviation or median (minimum, maximum). A flowchart of the pharmacodynamic result analysis is shown in fig. 6.
5. Confirmation of genomics indications characteristics of the tested drugs:
after the evaluation of the drug effect is completed, whether the number of the tumor living tissue samples (drug effective groups) corresponding to the antibody coupling drugs with drug effect and the number of the tumor living tissue samples (drug ineffective groups) corresponding to the antibody coupling drugs without drug effect both meet the statistical requirement (n is more than or equal to 10) or not is judged, and when the number of the samples in one group cannot meet the statistical requirement, the samples are selected again from a tumor living tissue biological sample library to establish a PDTX model and carry out pharmacodynamic detection.
When the number of the grouped samples meets the statistical requirement, the wild/mutation conditions of the gene loci corresponding to the grouped samples are retrospectively inquired, and the genomic information of all the samples is collected and stored in a tumor biopsy database through whole exon sequencing before PDTX modeling.
(1) The somatic mutation gene in each sample needs to be screened, the specific process is to detect all somatic and germ-line mutations by using general biological information software such as Mutect 2.0, and then obtain the somatic mutation by filtration;
(2) Referring to the mutation frequency distribution data in international universal public databases such as Chinese population in thousand people genome project or sub-population in GenomaD database, when the population frequency of the mutation detected in the sample in the database is less than 1/10000, the mutation is judged as somatic mutation, and the mutation comprises tumor-driven mutation and non-tumor-driven mutation; among these somatic mutations, mutations occurring in intron regions, untranslated regions upstream and downstream of the transcript, and synonymous in exon regions are excluded, and mutations that occur in splice sites, nonsynonymous in exon regions, and types of initiation deletion, termination deletion, and premature termination need to be retained.
(3) And (3) confirming that no significant correlation exists between samples by Pearson correlation test and adopting 0.5 as an exclusion threshold, eliminating gene mutation in significant linkage disequilibrium by PlinaK software, and finally screening out an alternative gene queue.
(4) After the screening is completed, genes whose mutation frequency in the drug effective group exceeds 10% are selected in the drug effective group in the order of the frequency of gene mutation from high to low, and the mutation ratio of the genes in the drug effective group needs to exceed the ratio in the drug ineffective group. All statistical tests used the mean t-test for both sets of samples, and differences tested were considered statistically significant when P < 0.05.
(5) Analyzing and summarizing the characteristic information of the samples related to the effective group of the drugs, and preliminarily confirming the characteristics of genomics indications sensitive to the drugs. A flowchart for identifying characteristics of a genomics indication for a test drug is shown in figure 7.
Example 2 comparative analysis of the therapeutic Effect of antibody-conjugated drugs on gastric cancer individuals by PDTX clinical trial
This example uses two antibody-conjugated drugs: a and B (with the same conjugated antibody and different small molecular compounds) are taken as research objects, and the target cancer species is gastric cancer; aiming at comparing and analyzing the treatment effect of the antibody coupling medicament A and the antibody coupling medicament B on gastric cancer individuals through PDTX clinical tests and primarily screening the antibody coupling medicament with better drug sensitivity reaction on gastric cancer.
1 part of gastric cancer tumor biopsy is selected from a tumor biopsy biological sample library according to the type of cancer to establish a model for biopsy resuscitation, and the selected DASCTM mode specifically comprises the following steps: d =2, a =1, S =2, C =1, T =1, M =3; the number of the tumor living tissues required by the pharmacodynamic evaluation test design is 1, and the number of PDTX models established by the tumor living tissues after resuscitation is 18; and (3) distributing the recovered and established PDTX model to a test group and a blank group, starting administration of the test group model and the blank group model, simultaneously recording the body weight and the tumor volume of all model animals, calculating and summarizing drug effect detection data indexes of the test group model, including TGI1 and TGI2, and performing the method according to the embodiment 1.
The data and analysis results entered in the pharmacodynamic comparative analysis system are as follows:
1. test group autologous tumor changes (TGI) 1 ，％)
(1) Antibody-conjugated drug a:
test group 1 (conjugated antibody): a. -245.44; b. 173.83; c. -368.05;
test group 2 (small molecule compound a): a. 189.91; b. -233.26; c. -204.85;
test group 3 (antibody conjugate drug a): a.40.26; b.62.99; c.45.37;
(2) Antibody-conjugated drug B:
test group 1 (conjugated antibody): a. -245.44; b. 173.83; c. -368.05;
test group 4 (small molecule compound B): a. -250.17; b. 491.87; c. -330.43;
test group 5 (antibody conjugated drug B): a. -24.03; b. -41.40; c. -63.59;
and (3) differential analysis:
and judging the result, as shown in fig. 8, the comprehensive comparative analysis result is: group 3 > group 5 > group 2 > group 1 > group 4.
2. Tumor volume change (TGI) between test and blank groups 2 ，％)
(1) Antibody-conjugated drug a:
test group 1 (conjugated antibody): a.56.84; b.69.43; c.35.28;
test group 2 (small molecule compound a): a.28.65; b.58.98; c.63.98;
test group 3 (antibody conjugate drug a): a.119.19; b.111.08; c.107.98;
(2) Antibody-conjugated drug B:
test group 1 (conjugated antibody): a.56.84; b.69.43; c.35.28;
test group 4 (small molecule compound B): a.56.01; b.13.50; c.41.89;
test group 5 (antibody conjugated drug B): a.95.77; b.92.72; c.88.52;
and (3) differential analysis: as shown in fig. 9, the comprehensive comparative analysis results are: group 3 > group 5 > group 1 > group 2 > group 4.
3. And (4) comprehensive result analysis:
(1) The evaluation of the drug effect of the test group 3 (antibody conjugated drug A) is superior to that of the test group 1 (conjugated antibody) and the test group 2 (small molecule compound A), and the antibody conjugated drug A has potential value in clinical application.
(2) The pharmacodynamic evaluation of the test group 5 (antibody conjugated drug B) is superior to that of the test group 1 (conjugated antibody) and the test group 4 (small molecule compound B), and the antibody conjugated drug B is suggested to have potential value in clinical application.
(3) The evaluation of the drug effect of the test group 3 (antibody conjugated drug A) is better than that of the test group 5 (antibody conjugated drug B), which suggests that the effect of the antibody conjugated drug A on the gastric cancer individual is better.
Example 3 preliminary confirmation of characteristics of genomics indications for sensitivity to antibody-conjugated drugs
In the embodiment, the drug sensitivity of the antibody coupled drug A is taken as a research object, and the target cancer species is gastric cancer; the method aims to combine the genomics information of the group samples through PDTX clinical tests, analyze and summarize the characteristic information of the samples related to the effective group of the drugs and preliminarily confirm the characteristics of genomics indications sensitive to the drugs.
Selecting 30 parts of gastric cancer tumor biopsy from a tumor biopsy biological sample library according to the types of cancers to establish a model for biopsy resuscitation, wherein the selected DASCTM mode specifically comprises the following steps: d =1, a =1, S =1, C =30, T =1, M =3; the number of the tumor living tissues required by the pharmacodynamic evaluation test design is 30, and the number of PDTX models established by the tumor living tissues after resuscitation is 180; distributing the recovered PDTX model to a test group and a blank group, starting administration of the test group model and the blank group model, simultaneously recording the body weight and the tumor volume of all model animals, and calculating and summarizing drug effect detection data indexes of the test group model, including TGI 1 And TGI 2 。
TABLE 1 test group autologous tumor changes (TGI) 1 ，％)
TABLE 2 tumor volume change (TGI) between test and blank groups 2 ，％)
1. And (3) analyzing a pharmacodynamic result:
1) Test group autologous tumor changes (TGI) 1 %) tumor efficacy results (mPD, mPR, mSD, mCR) were evaluated for each mouse according to Solid tumor efficacy Evaluation Criteria (Response Evaluation Criteria in Solid Tumors, RECIST). When TGI 1 If the concentration is more than 95%, judging the concentration to be mCR; when 95% is more than or equal to TGI 1 If the concentration is more than 30%, judging the concentration to be mPR; when the content of 30% is more than or equal to TGI 1 When > -20%, the mSD is judged; when TGI 1 Judging the mPD when the concentration is less than or equal to-20 percent; wherein mCR, mPR and mSD are positive evaluation results of drug effect, and mPD is negative evaluation result of drug effect.
2) Tumor volume change (TGI) between test and blank groups 2 And%) according to the evaluation standard of the PDTX (PDTX) efficacy of the tumor drug, when TGI is used 2 When the concentration is more than or equal to 60 percent, the drug effect is judged to be positive; when TGI 2 If the concentration is less than 60%, the drug effect is judged to be negative.
3) When TGI 1 And TGI 2 When the evaluation results show that the drug effect is positive, classifying the drug into a drug effective group; when TGI 1 And TGI 2 When the evaluation results are all negative in efficacy, classifying the test result into a drug-ineffective group; when TGI 1 And TGI 2 If the evaluation results are inconsistent, the samples are classified as the nondeterministic group and are not used as the genomics indications to be screened into the group samples.
Specific grouping conditions:
a. drug effective group (sample number): 2. 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 17, 18, 28, 30;
b. drug null group (sample number): 1. 6, 7, 16, 19, 23, 24, 25, 27, 29.
2. Genomics indication feature validation:
and retrospectively inquiring the wild/mutation condition of the gene locus corresponding to the grouped samples through a tumor biopsy database, and screening the somatic mutation gene in each sample.
1) The somatic mutation genes in each sample need to be screened out, the specific process is that the general bioinformatics software such as Mutect 2.0 is used for detecting all somatic and germ line mutations, and then the somatic mutation is obtained by filtration;
2) Referring to the mutation frequency distribution data in international general public databases such as Chinese population in thousand people genome project or sub-population in GenomaD database, when the population frequency of the mutation detected in the sample in the database is less than 1/10000, the mutation is judged as somatic mutation, and the mutation comprises tumor-driven mutation and non-tumor-driven mutation; among these somatic mutations, mutations occurring in intron regions, untranslated regions upstream and downstream of the transcript, and synonymous in exon regions are excluded, and mutations including those occurring in splice sites, nonsynonymous in exon regions, and types of initiation deletion, termination deletion, and premature termination need to be retained;
3) Through Pearson correlation test, adopting 0.5 as an exclusion threshold value, confirming that no significant correlation exists between samples, then eliminating gene mutation in significant linkage disequilibrium through PlinK software, and finally screening out an alternative gene queue;
4) After the screening is completed, genes whose mutation frequency in the drug effective group exceeds 10% are selected in the drug effective group in order of the frequency of gene mutation from high to low, and the mutation ratio of the genes in the drug effective group needs to exceed that in the drug ineffective group. The distribution of gene mutations in the drug effective and ineffective groups was screened by the chi-square test and differences tested were considered statistically significant when P < 0.05.
5) The specific gene locus wild/mutation distribution is as follows:
6) Analyzing and summarizing the characteristic information of the relevant samples of the effective group of the medicaments, and preliminarily confirming that the characteristics of genomics indications sensitive to the medicaments are as follows: a. the drug sensitivity related gene is alternative gene 2, 8, 9, 11; drug resistance related genes are alternative genes 7 and 10; provides reference basis for the formulation of the clinical test sample inclusion standard of the antibody conjugated drug A.
In summary, the invention firstly establishes a pre-Clinical human tumor biopsy functional pharmacodynamic test queue (D × A × S × C × T × M mode) for the antibody conjugated drug, simulates the patient to carry out a PDTX Clinical test (PDTX Mouse Clinical Trial) to carry out in vivo pharmacodynamic test, and then preliminarily confirms the drug-sensitive tumor biomarker and indication characteristics before Clinical test according to the comprehensive evaluation pharmacodynamic result and referring to the tumor biopsy database information.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the protection scope of the present invention, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. A method for screening for an antibody-conjugated drug indication using a tumor biopsy simulation clinical trial, the method comprising the steps of:
1) Screening a sufficient amount of suitable tumor biopsy samples from the tumor biopsy biological sample library and the database;
2) Constructing a mouse PDTX model;
3) Simulating a clinical test by using a mouse PDTX model to evaluate the drug effect of the antibody coupled drug, and then confirming the genomics indication characteristic of the antibody coupled drug;
wherein the content of the evaluation comprises: selecting a drug effect evaluation mode, controlling data quality, counting autologous tumor change difference data of a test group, counting tumor volume change difference data between the test group and a blank group, counting survival quality data of the test group and analyzing a comprehensive result;
according to the efficacy evaluation mode, setting the number of the drug schemes, the number of the cancer species and the number of the same cancer living tissues to be grouped, and calculating the number N of PDTX models required by all experimental groups, wherein the calculation formula is as follows:
N＝D*C*T*M+A*C*T*M+S*C*T*M；
calculating the number X of PDTX models required by all blank groups according to the number of cancer species and the number of living tissues of the same cancer in the test group, wherein the calculation formula is as follows:
X＝C*T*M；
calculating the number Y of the tumor living tissues in the required tumor living tissue biological sample library according to the number of the cancer species and the number of the same cancer living tissues in the test group, wherein the calculation formula is as follows:
Y＝C*T；
wherein, D: the number of the medicine schemes is that D is an integer and is more than or equal to 1; a: the antibody type, A is an integer, and A is more than or equal to 1; s: small molecular compound species, S is an integer, and S is more than or equal to 1; c: the number of cancer species, C is an integer, and C is more than or equal to 1; t: the number of the living tissues of the same cancer is equal, T is an integer and is more than or equal to 1; m: the modeling quantity of each part of living tissue, wherein M is an integer and is more than or equal to 3.
2. The method of claim 1, wherein in step 3), after evaluating the drug effect of the antibody conjugate drug by using a mouse PDTX model simulation clinical test, the antibody conjugate drug with a drug effect and the cancer type with primary drug sensitivity are screened, a tumor biopsy with a known mutation target of a target cancer species is screened from a tumor biopsy biological sample bank for PDTX resuscitation modeling, and the antibody conjugate drug with a drug effect is administered and the drug effect evaluation is performed again.
3. The method of claim 2, wherein the pharmacodynamic assessment comprises the steps of:
and (3) setting the number of the same cancer living tissues in the group according to the efficacy evaluation mode, and calculating the number N of PDTX models required by all test groups according to the following calculation formula:
N＝T*M；
calculating the number X of PDTX models required by all blank groups according to the number of the experimental in-group same cancer living tissues, wherein the calculation formula is as follows:
X＝T*M；
wherein, T: the number of the living tissues of the same cancer is equal, T is an integer and is more than or equal to 1; m: the modeling quantity of each part of living tissue, wherein M is an integer and is more than or equal to 3.
4. The method of claim 1, wherein in step 3), the statistical details of the test group autologous tumor change difference data are:
when the test group reaches the expected administration period, recording the terminal time tumor volume of the test group mouse PDTX model and the initial time volume of the corresponding individual for difference comparison, and calculating the tumor growth inhibition rate TGI of the test group mouse PDTX model 1 ；
Tumor growth inhibition by itself TGI 1 Is TGI 1 (％)＝(V0-Vt)/V 0 *100% of, wherein V 0 : the resulting tumor volume, V, was measured at the initial dose for each mouse PDTX model t : tumor volume at time t measured for each mouse PDTX model;
evaluating the tumor efficacy of the PDTX model of each mouse according to the evaluation standard of the solid tumor efficacy when the TGI 1 If the concentration is more than 95%, judging the concentration to be mCR; when the content of the TGI is more than or equal to 95 percent 1 If the concentration is more than 30%, judging the concentration to be mPR; when the content of 30% is more than or equal to TGI 1 When > -20%, the mSD is judged; when TGI 1 When the content is less than or equal to-20%, the mPD is judged.
5. The method of claim 1, wherein in step 3), the trial is performedThe specific content of the tumor volume change difference data statistics between the test group and the blank group comprises the following specific contents: when the test group reaches the expected administration period, recording the terminal time tumor volume of the test group mouse PDTX model and the terminal time tumor volume of the blank group mouse PDTX model for difference comparison, and calculating the tumor growth inhibition rate TGI between the groups 2 Tumor growth inhibition rate TGI between groups 2 Is calculated as TGI 2 (％)＝(1-RTV T /RTV C )*100％，RTV T ＝(T t -T 0 )/T 0 ；RTV C ＝(C t -C 0 )/C 0 (ii) a Wherein, T 0 : measuring the volume of the obtained tumor when the PDTX model of the mice in the test group is initially administrated; t is a unit of t : tumor volume at time t measured by the test group mouse PDTX model; c 0 : measuring the resulting tumor volume at the initial dosing of the PDTX model in the blank group of mice; c t : tumor volume at time t measured for the blank mouse PDTX model;
tumor growth inhibition rate TGI between the blank and test groups if the recording time period is different 2 The calculation formula is as follows:
TGI 2 (％)＝(1-RTV T /RTV C )*100％；
RTV T ＝T t1 /T 0 /t 1 ；
RTV C ＝C t2 /C 0 /t 2 ；
wherein, t 1 Recording the time period for the PDTX model of the mice of the actual experimental group; t is a unit of t1 PDTX model for experimental group mice at time t 1 Tumor volume at time of measurement; t is t 2 Recording the time period for the actual blank group mouse PDTX model; c t2 Mice PDTX model as blank group at time t 2 Tumor volume at time of measurement;
according to the evaluation standard of PDTX (phytochemical delivery) efficacy detection of tumor drugs, when TGI is used 2 When the concentration is more than or equal to 60 percent, the drug effect is judged to be positive; when TGI 2 If the concentration is less than 60%, the drug effect is judged to be negative.
6. The method of claim 1, wherein said step 3) of identifying the genomics indication profile of the antibody-conjugated drug comprises the steps of:
a) Counting whether the number of the tumor biopsy samples meets the counting requirement;
b) If yes, inquiring the gene mutation or wild condition of the tumor biopsy sample, and screening the somatic mutation gene of the tumor biopsy sample; if not, re-screening the tumor biopsy sample from the tumor biopsy biological sample library, re-establishing a mouse PDTX model and carrying out pharmacodynamic detection;
c) And (3) chi-square test analysis, and preliminarily determining the genomics indication characteristics of the antibody coupled drug.
7. The method of claim 6, wherein in step b), after the antibody conjugate drug effect is completed, the somatic mutation genes of the tumor biopsy samples corresponding to the drug effective antibody conjugate drugs are screened, and the genes with mutation frequencies of more than 10% are selected according to the sequence from high to low of the gene mutation frequencies, and the mutation ratio of the genes in the tumor biopsy samples corresponding to the drug effective antibody conjugate drugs needs to be higher than that in the tumor biopsy samples corresponding to the drug ineffective antibody conjugate drugs.
8. The method of claim 6, wherein step b) further comprises: and (3) confirming that no significant correlation exists between the tumor biopsy samples by a Pearson correlation test and adopting 0.5 as an exclusion threshold, eliminating gene mutation in significant linkage disequilibrium by Plinak software, and finally screening an alternative gene array.
9. The method of claim 1, wherein in step b), the somatic mutation gene comprises a tumor-driven mutation gene and a non-tumor-driven mutation gene; mutations occurring in intron regions, untranslated regions upstream and downstream of the transcript, and synonyms in exon regions are excluded from somatic mutations, and it is desirable to retain mutations including those occurring in splice sites, nonsynonyms of exon regions, and types of initiation, termination, and premature termination.
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