Optimizing Combination Therapy for Acute Lymphoblastic Leukemia Using a Phenotypic Personalized Medicine Digital Health Platform: Retrospective Optimization Individualizes Patient Regimens to Maximize Efficacy and Safety

Author:

Lee Dong-Keun1,Chang Vivian Y.23,Kee Theodore4,Ho Chih-Ming345,Ho Dean13467

Affiliation:

1. Division of Oral Biology and Medicine, School of Dentistry, UCLA, Los Angeles, CA, USA

2. Division of Pediatric Hematology and Oncology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

3. Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA

4. Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, UCLA, Los Angeles, CA, USA

5. Department of Mechanical and Aerospace Engineering, Henry Samueli School of Engineering and Applied Science, UCLA, Los Angeles, CA, USA

6. Jane and Jerry Weintraub Center for Reconstructive Biotechnology, UCLA, Los Angeles, CA, USA

7. California NanoSystems Institute, UCLA, Los Angeles, CA, USA

Abstract

Acute lymphoblastic leukemia (ALL) is a blood cancer that is characterized by the overproduction of lymphoblasts in the bone marrow. Treatment for pediatric ALL typically uses combination chemotherapy in phases, including a prolonged maintenance phase with oral methotrexate and 6-mercaptopurine, which often requires dose adjustment to balance side effects and efficacy. However, a major challenge confronting combination therapy for virtually every disease indication is the inability to pinpoint drug doses that are optimized for each patient, and the ability to adaptively and continuously optimize these doses to address comorbidities and other patient-specific physiological changes. To address this challenge, we developed a powerful digital health technology platform based on phenotypic personalized medicine (PPM). PPM identifies patient-specific maps that parabolically correlate drug inputs with phenotypic outputs. In a disease mechanism–independent fashion, we individualized drug ratios/dosages for two pediatric patients with standard-risk ALL in this study via PPM-mediated retrospective optimization. PPM optimization demonstrated that dynamically adjusted dosing of combination chemotherapy could enhance treatment outcomes while also substantially reducing the amount of chemotherapy administered. This may lead to more effective maintenance therapy, with the potential for shortening duration and reducing the risk of serious side effects.

Funder

Society for Laboratory Automation and Screening

National Science Foundation

V Foundation for Cancer Research

Wallace H. Coulter Foundation

American Academy of Implant Dentistry

National Cancer Institute

Publisher

Elsevier BV

Subject

Medical Laboratory Technology,Computer Science Applications

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