Computation Prediction of Drug Response Based on Omics Data

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Breast Cancer

Intervention / Treatment
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* Other: virtual anti-cancer drug


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: 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. Exclusion Criteria: Subjects do not meet the inclusion criteria of traditional clinical trial. Subjects suffered from other cancer disease

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| the virtual cohort<br>the virtual cohort that enroll in silico clinical trial (ISCT), and will be treated by virtual anti-cancer drug. | Other: virtual anti-cancer drug<br>* the virtual anti-cancer drug was formulation generated by computer modeling and artificial intelligence technology<br>* Other names: anti-cancer drug;|
| the real cohort<br>the real cohort that enroll in real word study, and will be treated by anti-cancer drug. | |

What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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in silico clinical trial, anti-cancer drug