Computation Prediction of Drug Response Based on Omics Data

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: Breast Cancer
• Intervention / Treatment: Other: virtual anti-cancer drug

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

Study Locations

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

Participation Criteria

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

How is the study designed?

Number of groups / cohorts

2

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	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

• 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

General Publications

• 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.
• 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.
• 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.

Helpful 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 '

Study record dates

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: in silico clinical trial; anti-cancer drug
• Additional Relevant MeSH Terms: Antineoplastic Agents
• 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