Ignite Creation Date:
2025-12-25 @ 4:01 AM
Ignite Modification Date:
2025-12-26 @ 2:55 AM
Study NCT ID:
NCT05833802
Status:
UNKNOWN
Last Update Posted:
2023-04-27
First Post:
2023-03-24
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Computation Prediction of Drug Response Based on Omics Data
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001943', 'term': 'Breast Neoplasms'}], 'ancestors': [{'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D001941', 'term': 'Breast Diseases'}, {'id': 'D012871', 'term': 'Skin Diseases'}, {'id': 'D017437', 'term': 'Skin and Connective Tissue Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D004354', 'term': 'Drug Screening Assays, Antitumor'}], 'ancestors': [{'id': 'D003584', 'term': 'Cytological Techniques'}, {'id': 'D019411', 'term': 'Clinical Laboratory Techniques'}, {'id': 'D019937', 'term': 'Diagnostic Techniques and Procedures'}, {'id': 'D003933', 'term': 'Diagnosis'}, {'id': 'D008919', 'term': 'Investigative Techniques'}, {'id': 'D004353', 'term': 'Drug Evaluation, Preclinical'}, {'id': 'D005069', 'term': 'Evaluation Studies as Topic'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'peripheral blood'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 25}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'ENROLLING_BY_INVITATION', 'startDateStruct': {'date': '2023-02-15', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-03', 'completionDateStruct': {'date': '2024-09-15', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-04-25', 'studyFirstSubmitDate': '2023-03-24', 'studyFirstSubmitQcDate': '2023-04-25', 'lastUpdatePostDateStruct': {'date': '2023-04-27', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-04-27', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-05-15', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'consistency', 'timeFrame': '8 weeks after the first administration of the drug for subjects', 'description': "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."}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['in silico clinical trial', 'anti-cancer drug'], 'conditions': ['Breast Cancer']}, 'referencesModule': {'references': [{'pmid': '31561483', 'type': 'BACKGROUND', 'citation': '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': '34157485', 'type': 'BACKGROUND', 'citation': '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': '26928437', 'type': 'BACKGROUND', 'citation': '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.'}], 'seeAlsoLinks': [{'url': 'https://gco.iarc.fr/', 'label': 'The latest global cancer burden data for 2020'}, {'url': 'https://www.cde.org.cn/main/news/viewInfoCommon/1839a2c931e1ed43eb4cc7049e189cb0', 'label': 'Annual progress report on clinical trials of new drug registration in China ( 2021 )'}, {'url': 'https://www.cde.org.cn/zdyz/domesticinfopage?zdyzIdCODE=67c30813bd94792b5b2a9f9bd7121763', 'label': "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 '"}]}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': '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.\n\nSubjects 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.\n\nBy 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'the patients with triple-negative breast cancer will participate in the traditional clinical trials and be treated by anti-cancer drug.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. clinical diagnosis of triple-negative breast cancer\n2. The subjects agreed to participate in the traditional clinical trial and signed informed consent.\n3. The subjects agreed to participate in the virtual study and signed informed consent.\n\nExclusion Criteria:\n\n1. Subjects do not meet the inclusion criteria of traditional clinical trial.\n2. Subjects suffered from other cancer disease'}, 'identificationModule': {'nctId': 'NCT05833802', 'briefTitle': 'Computation Prediction of Drug Response Based on Omics Data', 'organization': {'class': 'OTHER', 'fullName': 'Peking University Cancer Hospital & Institute'}, 'officialTitle': 'A Companion Trial in Silico: Computing Drug Response for Cancer Patients in Clinical Trials（PRincipal-001)', 'orgStudyIdInfo': {'id': '2022YJZ109'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'the virtual cohort', 'description': 'the virtual cohort that enroll in silico clinical trial (ISCT), and will be treated by virtual anti-cancer drug.', 'interventionNames': ['Other: virtual anti-cancer drug']}, {'label': 'the real cohort', 'description': 'the real cohort that enroll in real word study, and will be treated by anti-cancer drug.'}], 'interventions': [{'name': 'virtual anti-cancer drug', 'type': 'OTHER', 'otherNames': ['anti-cancer drug'], 'description': 'the virtual anti-cancer drug was formulation generated by computer modeling and artificial intelligence technology', 'armGroupLabels': ['the virtual cohort']}]}, 'contactsLocationsModule': {'locations': [{'zip': '100142', 'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Shuhua Zhao', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}], 'overallOfficials': [{'name': 'Min Jiang', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Peking University Cancer Hospital & Institute'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Peking University Cancer Hospital & Institute', 'class': 'OTHER'}, 'collaborators': [{'name': 'Beijing Phil Rivers Technology', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}