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| Name | Class |
|---|---|
| Beijing Phil Rivers Technology | UNKNOWN |
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The goal of this observational study is to assess the performance of computational medicine technology in predicting patients response to anticancer drugs based on omics data.The main question it aims to answer is test consistency between the computing drug response and the response of real-world clinical trials. Participants will take part in silico.
A companion trial in silico was planned to compare head-to-head with a real clinical study of anti-tumor registered new drugs to verify the consistency between the efficacy prediction results of virtual clinical studies and the efficacy results of traditional clinical trials.
Subjects simultaneously entered real world clinical trials and virtual clinical trials built by computer modeling and artificial intelligence technology. The results of traditional clinical trials were compared with those of virtual clinical trials to calculate the consistency of virtual clinical trials.
By predicting the population with consistent efficacy, locking the response population to new drugs, using the innovative technology of computational medicine, grasping the omics characteristics of the response population, and using this as a starting point to determine the target population of clinical trials, so as to determine new screening conditions, design new clinical trials, accurately match the effective population, and revolutionary change the efficiency of clinical trials, thereby shortening the process and cost of clinical trial development.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| the virtual cohort | the virtual cohort that enroll in silico clinical trial (ISCT), and will be treated by virtual anti-cancer drug. |
| |
| the real cohort | the real cohort that enroll in real word study, and will be treated by anti-cancer drug. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| virtual anti-cancer drug | Other | the virtual anti-cancer drug was formulation generated by computer modeling and artificial intelligence technology |
|
| Measure | Description | Time Frame |
|---|---|---|
| consistency | To compare the consistency of the tumor response between two cohorts. Tumor response for Patients in traditional clinical trial cohort will be assessed by New response evaluation criteria in solid tumours v1.1. Tumor response for virtual patients in virtual study will be predicted by the trained model.The efficacy prediction model will be trained using 4-5 patients evaluated for tumor response according to New response evaluation criteria in solid tumours v1.1, including at least 2 patients with Complete Response or Partial Response . The training of this model is based on the Damage Assessment of Genomic Mutations algorithm（EBioMedicine. 2021 Jul;69:103446）with the input of patients' genomic data. | 8 weeks after the first administration of the drug for subjects |
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Inclusion Criteria:
Exclusion Criteria:
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the patients with triple-negative breast cancer will participate in the traditional clinical trials and be treated by anti-cancer drug.
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| Name | Affiliation | Role |
|---|---|---|
| Min Jiang | Peking University Cancer Hospital & Institute | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Shuhua Zhao | Beijing | Beijing Municipality | 100142 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31561483 | Background | Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci. 2019 Sep 26;20(19):4781. doi: 10.3390/ijms20194781. | |
| 34157485 | Background | Yang M, Fan Y, Wu ZY, Gu J, Feng Z, Zhang Q, Han S, Zhang Z, Li X, Hsueh YC, Ni Y, Li X, Li J, Hu M, Li W, Gao H, Yang C, Zhang C, Zhang L, Zhu T, Cheng M, Ji F, Xu J, Cui H, Tan G, Zhang MQ, Liang C, Liu Z, Song YQ, Niu G, Wang K. DAGM: A novel modelling framework to assess the risk of HER2-negative breast cancer based on germline rare coding mutations. EBioMedicine. 2021 Jul;69:103446. doi: 10.1016/j.ebiom.2021.103446. Epub 2021 Jun 19. |
| Label | URL |
|---|---|
| The latest global cancer burden data for 2020 | View source |
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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| ID | Term |
|---|---|
| D004354 | Drug Screening Assays, Antitumor |
| ID | Term |
|---|---|
| D003584 | Cytological Techniques |
| D019411 | Clinical Laboratory Techniques |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
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peripheral blood
|
| 26928437 | Background | DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: New estimates of R&D costs. J Health Econ. 2016 May;47:20-33. doi: 10.1016/j.jhealeco.2016.01.012. Epub 2016 Feb 12. |
| Annual progress report on clinical trials of new drug registration in China ( 2021 ) | View source |
| At the end of 2021, Center for Drug Evaluation of National Medical Products Administration issued the Guideline: Guiding principles for clinical research and development of anti-tumor drugs ' | View source |
| D017437 |
| Skin and Connective Tissue Diseases |
| D008919 | Investigative Techniques |
| D004353 | Drug Evaluation, Preclinical |
| D005069 | Evaluation Studies as Topic |