Computation Prediction of Drug Response Based on Omics Data

NCT05833802 | Unknown

• 1 other identifier: 2022YJZ109
• Study Type: observational
• Target: 25
• Locations: 1 country
• Sites: 1

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.

Conditions

Breast Cancer

Keywords

in silico clinical trial; anti-cancer drug

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (2)

the virtual cohort

the virtual cohort that enroll in silico clinical trial (ISCT), and will be treated by virtual anti-cancer drug.

Other: virtual anti-cancer drug

the real cohort

the real cohort that enroll in real word study, and will be treated by anti-cancer drug.

Interventions

virtual anti-cancer drug OTHER

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

Also known as: anti-cancer drug

the virtual cohort

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• clinical diagnosis of triple-negative breast cancer
• The subjects agreed to participate in the traditional clinical trial and signed informed consent.
• The subjects agreed to participate in the virtual study and signed informed consent.

You may not qualify if:

• Subjects suffered from other cancer disease

Sponsors & Collaborators

• Peking University Cancer Hospital & Institute: lead
• Beijing Phil Rivers Technology: collaborator

Study Sites (1)

Shuhua Zhao

Beijing, Beijing Municipality, 100142, China

Location

Related Publications (3)

• 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.

  PMID: 31561483 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.

  PMID: 34157485 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.

  PMID: 26928437 BACKGROUND

Related Links

• The latest global cancer burden data for 2020
• 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 '

Biospecimen

Retention: SAMPLES WITH DNA

peripheral blood

MeSH Terms

Conditions

Breast Neoplasms

Interventions

Drug Screening Assays, Antitumor

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Intervention Hierarchy (Ancestors)

Cytological Techniques; Clinical Laboratory Techniques; Diagnostic Techniques and Procedures; Diagnosis; Investigative Techniques; Drug Evaluation, Preclinical; Evaluation Studies as Topic

Study Officials

• Min Jiang

  Peking University Cancer Hospital & Institute

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 24, 2023
• First Posted: April 27, 2023
• Study Start: February 15, 2023
• Primary Completion: May 15, 2024
• Study Completion: September 15, 2024
• Last Updated: April 27, 2023

Record last verified: 2023-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)