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Trial Title:
Computation Prediction of Drug Response Based on Omics Data
NCT ID:
NCT05833802
Condition:
Breast Cancer
Conditions: Official terms:
Antineoplastic Agents
Conditions: Keywords:
in silico clinical trial
anti-cancer drug
Study type:
Observational
Overall status:
Enrolling by invitation
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Other
Intervention name:
virtual anti-cancer drug
Description:
the virtual anti-cancer drug was formulation generated by computer modeling and
artificial intelligence technology
Arm group label:
the virtual cohort
Other name:
anti-cancer drug
Summary:
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.
Detailed description:
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.
Criteria for eligibility:
Study pop:
the patients with triple-negative breast cancer will participate in the traditional
clinical trials and be treated by anti-cancer drug.
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
1. clinical diagnosis of triple-negative breast cancer
2. The subjects agreed to participate in the traditional clinical trial and signed
informed consent.
3. The subjects agreed to participate in the virtual study and signed informed consent.
Exclusion Criteria:
1. Subjects do not meet the inclusion criteria of traditional clinical trial.
2. Subjects suffered from other cancer disease
Gender:
All
Minimum age:
18 Years
Maximum age:
75 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
Shuhua Zhao
Address:
City:
Beijing
Zip:
100142
Country:
China
Start date:
February 15, 2023
Completion date:
September 15, 2024
Lead sponsor:
Agency:
Peking University Cancer Hospital & Institute
Agency class:
Other
Collaborator:
Agency:
Beijing Phil Rivers Technology
Agency class:
Other
Source:
Peking University Cancer Hospital & Institute
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05833802
https://gco.iarc.fr/
https://www.cde.org.cn/main/news/viewInfoCommon/1839a2c931e1ed43eb4cc7049e189cb0
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