Personalized Medication Software for BCL-2 Inhibitor in AML Patients Using Machine Learning and Genomics

Acute Myeloid Leukemia Clinical Trial

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

Status Not yet recruiting

Clinical 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 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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Acute Myeloid Leukemia
• Leukemia
• Leukemia, Myeloid
• Leukemia, Myeloid, Acute

• NCT number: NCT06295029
• Study type: Observational
• Source: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
• Contact: Mengying Liu
• Phone: 025-83106666
• Email: liumengying@njglyy.com
• Status: Not yet recruiting
• Start date: March 1, 2024
• Completion date: December 31, 2027