Acute Myeloid LeukemiaNCT06295029
Personalized Medication Software for BCL-2 Inhibitor in AML Patients Using Machine Learning and Genomics
An observational study of Acute Myeloid Leukemia, run by The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School.
Trial summary
Severe neutropenia caused by venetoclax,a B-cell lymphoma-2(BCL-2) inhibitor, is the main cause of venetoclax tapering, drug discontinuation, and treatment delay. This study combines machine learning and genomics, hoping to develop models to predict venetoclax dose in Acute myeloid leukemia(AML) patients and compare… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Acute Myeloid Leukemia
- How many people
- 200 participants
- Where
- No sites listed yet
- Study dates
- 1 Mar 2024to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study aims to include 200 patients with AML who were treated with venetoclax. A database of venetoclax medication for AML patients will be constructed based on demographic information, medication information, laboratory test information, blood drug concentration, SNPs, and drug-related adverse reactions. Randomly divide the data into training and testing sets in a 7:3 ratio, and construct the model through machine learning.
Who may be able to join
- Age ≥ 18 years old, regardless of gender;
- Diagnosed as an AML patient according to the Diagnosis and Treatment Guidelines for Adult Acute Myeloid Leukemia (Non Acute Promyelocytic Leukemia) in China (2021 Edition) and receiving treatment with venetoclax;
- Before receiving venetoclax treatment, absolute neutrophil count (ANC) ≥ 1.0 ×10 ^9/L, white blood cell count (WBC) ≥ 2.0 ×10 ^9/L, platelet count (PLT) ≥ 50 ×10 ^9/L, and hemoglobin (HB) ≥ 90g /L;
- Before receiving venetoclax treatment, liver and kidney function were normal (aspartate aminotransferase ≤ 3 times the upper limit of normal (ULN), alanine aminotransferase ≤ 3.0 x ULN, bilirubin ≤ 1.5 x ULN, urea nitrogen:3.2-7.1 mmol/L, glomerular filtration rate (eGFR) ≥ 60ml/min;
- Sign an informed consent form.
Who cannot join
- Age<18 years old;
- Non AML patients;
- Patients who plan to use a treatment regimen without venetoclax;
- Patients with poor medication adherence;
- Liver and kidney function damage before medication;
- Before medication, ANC<1.0 x 10 ^9/L or WBC<2.0 x 10 ^9/L or PLT<50 x 10 ^9/L or HB<90g /L;
- Pregnant and lactating women;
- Cases deemed unsuitable for inclusion by researchers
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Dose Optimization and Personalized Medication Software Research of BCL-2 Inhibitor Based on Machine Learning Combined With Genomics in Patients With Acute Myeloid Leukemia
Severe neutropenia caused by venetoclax,a B-cell lymphoma-2(BCL-2) inhibitor, is the main cause of venetoclax tapering, drug discontinuation, and treatment delay. This study combines machine learning and genomics, hoping to develop models to predict venetoclax dose in Acute myeloid leukemia(AML) patients and compare the efficacy and safety differences of model-guided individualized medication regimen with current conventional regimen. According to the demographic information, the drug information, the drug concentration of the target patients, the laboratory examination, the single nucleotide polymorphism(SNP) information and the adverse reactions of the AML patients, and the model was constructed through machine learning.
Read the detailed description
Introduction:The successful development of venetoclax offers new hope for AML patients not eligible for strong induction chemotherapy. However, there are some clinical problems, such as severe neutropenia is the main reason for treatment delay and discontinuation of patients. The Asian population has higher drug exposure than the non-Asian population, and the blood concentration of venetoclax varies greatly individually, and the blood drug concentration is associated with efficacy and adverse effects. We urgently need an individualized study of venetoclax for Chinese AML patients to reduce the incidence of adverse events while ensuring efficacy.
Objective：Construction of a venetoclax dose prediction model for AML patients based on machine learning combined genomics;
Methods：1.Venetoclax plasma concentration determination;determination of SNPs of related genes in patient blood cells; 2.venetoclax dose prediction model for AML patients based on machine learning techniques combined with genomics Collect the clinical data and establish a database Mining variables to explore the factors affecting the dosage of venetoclax Building a predictive model based on a machine-learning algorithm Model performance was evaluated, and the optimal model was selected Interpretation and optimization of the model
The AML patients were conditionally screened by the study physician involved in the project department to assess their enrollment. Communicate fully with the patients and their family members who meet the enrollment criteria, obtain the patient's informed consent, and sign the informed consent form. After enrollment, patient clinical data were recorded. Evaluation according to the efficacy and safety evaluation criteria.
- Conditions
- Acute Myeloid Leukemia
- Keywords
- Acute Myeloid Leukemia, Venetoclax, Machine Learning, genomics, Dose Prediction, Personalized medicine
- Medical subject headings
- Leukemia, Myeloid, Acute
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall survival (OS)
the time from the start of the trial until the patient died from all causes
Measured: From date of randomization until the date of first documented date of death from anyh cause, whichever came first, assessed up to 100 months
Progression-free survival (PFS)
From the time of trial initiation to the time of objective tumor progression or death.
Measured: From date of randomization until the date of first documented progression, whichever came first, assessed up to 100 months
Overall adverse event rate
According to the association evaluation of adverse drug reactions adopted by the National Adverse Drug Reaction Monitoring Center, the adverse drug reactions occurred in this study were classified into five levels: sure, probable, probable, suspicious and impossible.Adverse reactions with reference to the U.S. department of health and human services release of the common adverse reaction term evaluation criteria (CommonTerminologyCriteriaforAdverseEvents CTCAE) version 5.0
Measured: up to 24 weeks
Incidence of grade III and above adverse events
According to the association evaluation of adverse drug reactions adopted by the National Adverse Drug Reaction Monitoring Center, the adverse drug reactions occurred in this study were classified into five levels: sure, probable, probable, suspicious and impossible.Adverse reactions with reference to the U.S. department of health and human services release of the common adverse reaction term evaluation criteria (CommonTerminologyCriteriaforAdverseEvents CTCAE) version 5.0
Measured: up to 24 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Mar 2024
- Study start1 Mar 2024 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Acute Myeloid LeukemiaStudies of the same condition, open and under way.
- More trials run by The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical SchoolThe 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
- 200 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yudong Qiu, Study director, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06295029
- Sponsor's protocol ID
- CPA-Z05-ZC-2023-002
- Status last verified
- Mar 2024
- First submitted
- 26 Feb 2024
- Last updated
- 6 Mar 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Dose Optimization and Personalized Medication Software Research of BCL-2 Inhibitor Based on Machine Learning Combined With Genomics in Patients With Acute Myeloid Leukemia
- ClinicalTrials.gov ID
- NCT06295029
- Acronym / sponsor ID
- CPA-Z05-ZC-2023-002
- Conditions
- Acute Myeloid Leukemia
- Keywords
- Acute Myeloid Leukemia, Venetoclax, Machine Learning, genomics, Dose Prediction, Personalized medicine
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical SchoolUniversity, hospital or other
- Study start
- 1 Mar 2024 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 6 Mar 2024 (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.
Severe neutropenia caused by venetoclax,a B-cell lymphoma-2(BCL-2) inhibitor, is the main cause of venetoclax tapering, drug discontinuation, and treatment delay. This study combines machine learning and genomics, hoping to develop models to predict venetoclax dose in Acute myeloid leukemia(AML) patients and compare the efficacy and safety differences of model-guided individualized medication regimen with current conventional regimen. According to the demographic information, the drug information, the drug concentration of the target patients, the laboratory examination, the single nucleotide polymorphism(SNP) information and the adverse reactions of the AML patients, and the model was constructed through machine learning.
Detailed description
Introduction:The successful development of venetoclax offers new hope for AML patients not eligible for strong induction chemotherapy. However, there are some clinical problems, such as severe neutropenia is the main reason for treatment delay and discontinuation of patients. The Asian population has higher drug exposure than the non-Asian population, and the blood concentration of venetoclax varies greatly individually, and the blood drug concentration is associated with efficacy and adverse effects. We urgently need an individualized study of venetoclax for Chinese AML patients to reduce the incidence of adverse events while ensuring efficacy.
Objective：Construction of a venetoclax dose prediction model for AML patients based on machine learning combined genomics;
Methods：1.Venetoclax plasma concentration determination;determination of SNPs of related genes in patient blood cells; 2.venetoclax dose prediction model for AML patients based on machine learning techniques combined with genomics Collect the clinical data and establish a database Mining variables to explore the factors affecting the dosage of venetoclax Building a predictive model based on a machine-learning algorithm Model performance was evaluated, and the optimal model was selected Interpretation and optimization of the model
The AML patients were conditionally screened by the study physician involved in the project department to assess their enrollment. Communicate fully with the patients and their family members who meet the enrollment criteria, obtain the patient's informed consent, and sign the informed consent form. After enrollment, patient clinical data were recorded. Evaluation according to the efficacy and safety evaluation criteria.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study aims to include 200 patients with AML who were treated with venetoclax. A database of venetoclax medication for AML patients will be constructed based on demographic information, medication information, laboratory test information, blood drug concentration, SNPs, and drug-related adverse reactions. Randomly divide the data into training and testing sets in a 7:3 ratio, and construct the model through machine learning.
Key inclusion criteria (5 listed)
- Age ≥ 18 years old, regardless of gender;
- Diagnosed as an AML patient according to the Diagnosis and Treatment Guidelines for Adult Acute Myeloid Leukemia (Non Acute Promyelocytic Leukemia) in China (2021 Edition) and receiving treatment with venetoclax;
- Before receiving venetoclax treatment, absolute neutrophil count (ANC) ≥ 1.0 ×10 ^9/L, white blood cell count (WBC) ≥ 2.0 ×10 ^9/L, platelet count (PLT) ≥ 50 ×10 ^9/L, and hemoglobin (HB) ≥ 90g /L;
- Before receiving venetoclax treatment, liver and kidney function were normal (aspartate aminotransferase ≤ 3 times the upper limit of normal (ULN), alanine aminotransferase ≤ 3.0 x ULN, bilirubin ≤ 1.5 x ULN, urea nitrogen:3.2-7.1 mmol/L, glomerular filtration rate (eGFR) ≥ 60ml/min;
- Sign an informed consent form.
Key exclusion criteria (8 listed)
- Age<18 years old;
- Non AML patients;
- Patients who plan to use a treatment regimen without venetoclax;
- Patients with poor medication adherence;
- Liver and kidney function damage before medication;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Overall survival (OS)
Time frame: From date of randomization until the date of first documented date of death from anyh cause, whichever came first, assessed up to 100 months
the time from the start of the trial until the patient died from all causes
Progression-free survival (PFS)
Time frame: From date of randomization until the date of first documented progression, whichever came first, assessed up to 100 months
From the time of trial initiation to the time of objective tumor progression or death.
Overall adverse event rate
Time frame: up to 24 weeks
According to the association evaluation of adverse drug reactions adopted by the National Adverse Drug Reaction Monitoring Center, the adverse drug reactions occurred in this study were classified into five levels: sure, probable, probable, suspicious and impossible.Adverse reactions with reference to the U.S. department of health and human services release of the common adverse reaction term evaluation criteria (CommonTerminologyCriteriaforAdverseEvents CTCAE) version 5.0
Incidence of grade III and above adverse events
Time frame: up to 24 weeks
According to the association evaluation of adverse drug reactions adopted by the National Adverse Drug Reaction Monitoring Center, the adverse drug reactions occurred in this study were classified into five levels: sure, probable, probable, suspicious and impossible.Adverse reactions with reference to the U.S. department of health and human services release of the common adverse reaction term evaluation criteria (CommonTerminologyCriteriaforAdverseEvents CTCAE) version 5.0
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Mar 2024.
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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.
Yudong Qiu study director
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Questions about this trial
What is this clinical trial studying?
An observational study of Acute Myeloid Leukemia, run by The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School.
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 5 inclusion and 8 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: opening soon, as of the sponsor's last update on 6 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Mengying Liu (025-83106666, liumengying@njglyy.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall survival (OS) (time frame: From date of randomization until the date of first documented date of death from anyh cause, whichever came first, assessed up to 100 months). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 6 Mar 2024.