Using Chronobiology to Improve Lenvatinib Efficacy

Hadassah Medical Center

Status and phase: Enrolling; Early Phase 1

Conditions: Lenvatinib Treatment

Treatments: Drug: variability-based lenvatinib regimen

Study type: Interventional

Funder types: Other

Identifiers

NCT06321120

0749-21-HMO-CTIL

Details and patient eligibility

About

The goal of this proof-of-concept clinical trial is to assess the efficacy and safety of chronobiology implementation into lenvatinib treatment regimens of thyroid cancer patients, via a mobile application.

Participants will use a mobile application to follow variability-based physician approved drug administration schedules.

Full description

Systemic treatments for thyroid cancer have emerged in the past decade, accompanied by a deeper understanding of its underlying molecular mechanisms. Among these, lenvatinib, a multi-targeted tyrosine kinase inhibitor, was approved as a monotherapy for treating locally advanced or metastatic radioactive iodine refractory differentiated thyroid cancer. Despite its efficacy, lenvatinib is associated with a spectrum of adverse events (AEs), including hypertension, fatigue, proteinuria, and gastrointestinal disturbances, which often necessitate dose reduction, interruption, or permanent discontinuation. To overcome these challenges, the investigators address to the Constrained Disorder Principle (CDP), an innovative approach that emphasizes the exploration of constrained variability in treatment regimens to optimize drug effectiveness and minimize AEs. In other disease contexts, such as congestive heart failure, multiple sclerosis, and chronic pain, the integration of CDP-based second-generation artificial intelligence (AI) systems into treatment regimens has shown promising results in enhancing therapeutic outcomes by dynamically adjusting treatment parameters. The investigators hypothesize that a personalized dynamic adjustment of lenvatinib dosages and administration timing, guided by an AI-driven approach via a mobile application, may reduce AEs, improve adherence, and enhance overall treatment efficacy. In this proof-of-concept study, the investigators aim to evaluate the feasibility and efficacy of utilizing a CDP-based second-generation AI system to optimize the therapeutic regimen of lenvatinib in patients with cancer.

Enrollment: 10 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Age 18-80 years
2. Lenvatinib treated cancer patients, who suffer from loss of response of dose-limiting adverse effects.

Exclusion criteria

1. Current or history of drug abuse
2. Pregnancy/lactation/planned pregnancy
3. The subject is currently enrolled in or has not yet completed at least 60 days since ending another investigational device or drug trial.
4. Unable to comply with study requirements.

Trial design

Primary purpose: Treatment

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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10 participants in 1 patient group

Variability-based lenvatinib treatment

Experimental group

Description:

Dosages and administration times were tailored within individual predefined ranges to accommodate personalized therapeutic regimens. The first level of the algorithm, employed in the present study, utilizes a pseudo-random number generator to select dosages and administration times from the ranges stipulated by the physician.

Treatment:

Drug: variability-based lenvatinib regimen