Using Chronobiology to Improve Lenvatinib Efficacy

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

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.

Study Overview

• Status: Recruiting
• Conditions: Lenvatinib Treatment
• Intervention / Treatment: Drug: variability-based lenvatinib regimen

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.

• Study Type: Interventional
• Enrollment (Estimated): 10
• Phase: Early Phase 1

Contacts and Locations

• Study Contact: Name: Aharon Popovtzer, MD; Phone Number: 972509010225; Email: ARON@HADASSAH.ORG.IL
• Study Contact Backup: Name: Tal Sigawi, MD; Phone Number: 09725115691; Email: SIGAW@HADASSAH.ORG.IL

Study Locations

• Israel
  • Jerusalem, Israel, 91120
    • Recruiting
    • Hadassah Medical Organization
    • Contact: Hadas Lemberg, PhD; Phone Number: +97226777572; Email: lhadas@hadassah.org.il

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: Variability-based lenvatinib treatment; 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.

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
disease progression/ tumor response	tumor response according to positron emission tomography-computed tomography (PET-CT) and tumor markers (thyroglobulin)	at enrollment and at study completion (14 weeks later)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Adverse effects occurrence	Safety assessments are performed throughout the study and include the recording of symptoms and emergency room visits or hospitalizations through a regular monthly telephone check-up and a hospital and ambulatory medical records review. Additionally, patients can report AEs online via the application. Hematological and biochemical laboratory testing, urinalysis, and self-conducted home blood pressure monitoring are also executed.	Blood tests will be drawn at enrollment and at study completion (14 weeks later). Telephone check-ups will be conducted monthly during the follow-up.

Collaborators and Investigators

• Sponsor: Hadassah Medical Organization

Publications and helpful links

General Publications

• Schlumberger M, Tahara M, Wirth LJ, Robinson B, Brose MS, Elisei R, Habra MA, Newbold K, Shah MH, Hoff AO, Gianoukakis AG, Kiyota N, Taylor MH, Kim SB, Krzyzanowska MK, Dutcus CE, de las Heras B, Zhu J, Sherman SI. Lenvatinib versus placebo in radioiodine-refractory thyroid cancer. N Engl J Med. 2015 Feb 12;372(7):621-30. doi: 10.1056/NEJMoa1406470.
• Gelman R, Hurvitz N, Nesserat R, Kolben Y, Nachman D, Jamil K, Agus S, Asleh R, Amir O, Berg M, Ilan Y. A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: Results of a feasibility open-labeled clinical trial. Biomed Pharmacother. 2023 May;161:114334. doi: 10.1016/j.biopha.2023.114334. Epub 2023 Mar 9.
• Ilan Y. Overcoming Compensatory Mechanisms toward Chronic Drug Administration to Ensure Long-Term, Sustainable Beneficial Effects. Mol Ther Methods Clin Dev. 2020 Jun 10;18:335-344. doi: 10.1016/j.omtm.2020.06.006. eCollection 2020 Sep 11.
• Ilan Y, Spigelman Z. Establishing patient-tailored variability-based paradigms for anti-cancer therapy: Using the inherent trajectories which underlie cancer for overcoming drug resistance. Cancer Treat Res Commun. 2020;25:100240. doi: 10.1016/j.ctarc.2020.100240. Epub 2020 Nov 19.

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2023
• Primary Completion (Estimated): April 1, 2024
• Study Completion (Estimated): June 1, 2024

Study Registration Dates

• First Submitted: March 11, 2024
• First Submitted That Met QC Criteria: March 17, 2024
• First Posted (Actual): March 20, 2024

Study Record Updates

• Last Update Posted (Actual): March 20, 2024
• Last Update Submitted That Met QC Criteria: March 17, 2024
• Last Verified: March 1, 2024

More Information

Terms related to this study

• Keywords: cancer; lenvatinib; constrained disorder principle; digital pill
• Additional Relevant MeSH Terms: Molecular Mechanisms of Pharmacological Action; Enzyme Inhibitors; Antineoplastic Agents; Protein Kinase Inhibitors; Lenvatinib
• Other Study ID Numbers: 0749-21-HMO-CTIL

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: irrelevant

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No