Using Chronobiology to Improve Lenvatinib Efficacy

NCT ID: NCT06321120

Last Updated: 2024-03-20

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: EARLY_PHASE1
• Total Enrollment: 10 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-03-01
• Study Completion Date: 2024-06-30

Brief 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

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

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.

Group Type: EXPERIMENTAL

variability-based lenvatinib regimen

Intervention Type: DRUG

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.

Interventions

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.

Intervention Type: DRUG

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Hadassah Medical Organization

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Hadassah Medical Organization

Jerusalem, , Israel

Site Status: RECRUITING

Countries

Israel

Central Contacts

Aharon Popovtzer, MD

Role: CONTACT

ARON@HADASSAH.ORG.IL

972509010225

Tal Sigawi, MD

Role: CONTACT

SIGAW@HADASSAH.ORG.IL

09725115691

Facility Contacts

Hadas Lemberg, PhD

Role: primary

lhadas@hadassah.org.il

+97226777572

References

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.

Reference Type: BACKGROUND

PMID: 25671254 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 36905809 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 32671136 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 33246316 (View on PubMed)

Other Identifiers

0749-21-HMO-CTIL

Identifier Type: -

Identifier Source: org_study_id