Breast CancerNCT05833802
Computation Prediction of Drug Response Based on Omics Data
An observational study of Breast Cancer, run by Peking University Cancer Hospital & Institute at 1 site in China.
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. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Breast Cancer
- What is being tested?
- virtual anti-cancer drug
- How many people
- 25 participants
- Where
- 1 siteChina
- Study dates
- 15 Feb 2023to 15 Sep 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: the patients with triple-negative breast cancer will participate in the traditional clinical trials and be treated by anti-cancer drug.
Who may be able to join
- 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.
Who cannot join
- Subjects do not meet the inclusion criteria of traditional clinical trial.
- Subjects suffered from other cancer disease
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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.
- Conditions
- Breast Cancer
- Being tested
- virtual anti-cancer drug
- Keywords
- in silico clinical trial, anti-cancer drug
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
virtual anti-cancer drugOther
Also called anti-cancer drug
the virtual anti-cancer drug was formulation generated by computer modeling and artificial intelligence technology
| Study group | Role | What they receive |
|---|---|---|
| the virtual cohort | the virtual cohort that enroll in silico clinical trial (ISCT), and will be treated by virtual anti-cancer drug. Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 8 weeks after the first administration of the drug for subjects
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Apr 2023
- Study start15 Feb 2023
- Primary completion15 May 2024 (estimated)
- Study completion15 Sep 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- More trials run by Peking University Cancer Hospital & InstituteThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 25 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University Cancer Hospital & Institute
- Sponsor type
- University, hospital or other
- Collaborators
- Beijing Phil Rivers Technology
- Responsible party
- Sponsor
- Investigators
- Min Jiang, Principal investigator, Peking University Cancer Hospital & Institute
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05833802
- Sponsor's protocol ID
- 2022YJZ109
- Status last verified
- Mar 2023
- First submitted
- 24 Mar 2023
- Last updated
- 27 Apr 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 31561483Yang 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.
PubMed 34157485DiMasi 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.
PubMed 26928437
Study at a glance
From the registry record, last updated by the sponsor on 27 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Companion Trial in Silico: Computing Drug Response for Cancer Patients in Clinical Trials（PRincipal-001)
- ClinicalTrials.gov ID
- NCT05833802
- Acronym / sponsor ID
- 2022YJZ109
- Conditions
- Breast Cancer
- Keywords
- in silico clinical trial, anti-cancer drug
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 25 participants (estimated)
- Lead sponsor
- Peking University Cancer Hospital & InstituteUniversity, hospital or other
- Collaborators
- Beijing Phil Rivers Technology
- Study start
- 15 Feb 2023 (actual)
- Primary completion
- 15 May 2024 (estimated)
- Study completion
- 15 Sep 2024 (estimated)
- First posted
- 27 Apr 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| the virtual cohort | – | Other: virtual anti-cancer drugthe virtual cohort that enroll in silico clinical trial (ISCT), and will be treated by virtual anti-cancer drug. |
| the real cohort | – | –the real cohort that enroll in real word study, and will be treated by anti-cancer drug. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- the patients with triple-negative breast cancer will participate in the traditional clinical trials and be treated by anti-cancer drug.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- Subjects do not meet the inclusion criteria of traditional clinical trial.
- Subjects suffered from other cancer disease
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| virtual anti-cancer drugAlso: anti-cancer drugthe virtual anti-cancer drug was formulation generated by computer modeling and artificial intelligence technology | Other | the virtual cohort |
Primary outcome
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Apr 2023.
- Background: PubMed 31561483Olivier 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.
- Background: PubMed 34157485Yang 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.
- Background: PubMed 26928437DiMasi 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.
- The latest global cancer burden data for 2020Link provided by the sponsor.
- Annual progress report on clinical trials of new drug registration in China ( 2021 )Link provided by the sponsor.
- 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 'Link provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Min Jiang principal investigator
Peking University Cancer Hospital & Institute
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by Peking University Cancer Hospital & Institute at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 27 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
consistency (time frame: 8 weeks after the first administration of the drug for subjects). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05833802, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Apr 2023.