Clinical trial NCT05833802: Computation Prediction of Drug Response Based on Omics Data
Official title: A Companion Trial in Silico: Computing Drug Response for Cancer Patients in Clinical Trials（PRincipal-001)
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Breast Cancer
Keywords: in silico clinical trial
anti-cancer drug
Lead sponsor: Peking University Cancer Hospital & Institute
Sponsor class: OTHER
Collaborators: name: Beijing Phil Rivers Technology
First posted: 2023-04-27
Enrollment: 25
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: the patients with triple-negative breast cancer will participate in the traditional clinical trials and be treated by anti-cancer drug.

Brief 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.

Eligibility 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

Primary outcomes:
measure: consistency
timeFrame: 8 weeks after the first administration of the drug for subjects
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Shuhua Zhao
state: Beijing
Study officials:
affiliation: Peking University Cancer Hospital & Institute
name: Min Jiang
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
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