Computation Prediction of Drug Response Based on Omics Data

Breast Cancer Clinical Trial

Official title:

A Companion Trial in Silico: Computing Drug Response for Cancer Patients in Clinical Trials（PRincipal-001)

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT05833802
• Other study ID #: 2022YJZ109
• Status: Enrolling by invitation
• Start date: February 15, 2023
• Est. completion date: September 15, 2024

Study information

• Verified date: March 2023
• Source: Peking University Cancer Hospital & Institute
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 25
• Est. completion date: September 15, 2024
• Est. primary completion date: May 15, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

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

Related Conditions & MeSH terms

• Breast Cancer

Intervention

Other:
• virtual anti-cancer drug
the virtual anti-cancer drug was formulation generated by computer modeling and artificial intelligence technology

Locations

Country	Name	City	State
China	Shuhua Zhao	Beijing	Beijing

Sponsors (2)

Lead Sponsor	Collaborator
Peking University Cancer Hospital & Institute	Beijing Phil Rivers Technology

Country where clinical trial is conducted

China, 

References & Publications (3)

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. — View 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. — View 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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

External Links

•   Annual progress report on clinical trials of new drug registration in China ( 2021 )
•   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 '
•   The latest global cancer burden data for 2020