NCT06321120

Using Chronobiology to Improve Lenvatinib Efficacy

A Controlled Trial for Improving the Response to Lenvatinib in Patients With Drug-resistant Thyroid Cancer by Chronobiology

• Status: Unknown
• Phase: EARLY_Phase 1
• Study type: Interventional
• Enrollment: 10 (estimated)
• Sponsor: Hadassah Medical Organization · Academic / Other
• Sex: All
• Age: 18 Years – 80 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed 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.

Conditions

• Lenvatinib Treatment

Interventions

Type	Name	Description
DRUG	variability-based lenvatinib regimen	Dosages and administration times were tailored within individual predefined ranges to accommodate personalized therapeutic regimens. As per protocol, the daily dose was limited to match or remain below the patients' pre-enrollment dosage level. In the initial 4 weeks of the follow-up, participants followed a fixed standard regimen with the app serving as a reminder, allowing for an adaptation period. Subsequently, the algorithm-driven treatment plan was implemented for an additional 10 weeks.

Timeline

• Start date: 2023-03-01
• Primary completion: 2024-04-01
• Completion: 2024-06-01
• First posted: 2024-03-20
• Last updated: 2024-03-20

Locations

1 site across 1 country: Israel