Computation Prediction of Drug Response Based on Omics Data

April 25, 2023 updated by: Peking University Cancer Hospital & Institute

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

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.

Study Overview

Status

Enrolling by invitation

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Anticipated)

25

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Beijing
      • Beijing, Beijing, China, 100142
        • Shuhua Zhao

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

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.

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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 virtual anti-cancer drug was formulation generated by computer modeling and artificial intelligence technology
Other Names:
  • anti-cancer drug
the real cohort
the real cohort that enroll in real word study, and will be treated by anti-cancer drug.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
consistency
Time Frame: 8 weeks after the first administration of the drug for subjects
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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Peking University Cancer Hospital & Institute

Collaborators

Beijing Phil Rivers Technology

Investigators

  • Principal Investigator: Min Jiang, Peking University Cancer Hospital & Institute

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 15, 2023

Primary Completion (Anticipated)

May 15, 2024

Study Completion (Anticipated)

September 15, 2024

Study Registration Dates

First Submitted

March 24, 2023

First Submitted That Met QC Criteria

April 25, 2023

First Posted (Actual)

April 27, 2023

Study Record Updates

Last Update Posted (Actual)

April 27, 2023

Last Update Submitted That Met QC Criteria

April 25, 2023

Last Verified

March 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2022YJZ109

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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