Clinical trial NCT06321120: Using Chronobiology to Improve Lenvatinib Efficacy
Official title: A Controlled Trial for Improving the Response to Lenvatinib in Patients With Drug-resistant Thyroid Cancer by Chronobiology
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: EARLY_PHASE1
Conditions: Lenvatinib Treatment
Keywords: cancer
lenvatinib
constrained disorder principle
digital pill
Lead sponsor: Hadassah Medical Organization
Sponsor class: OTHER
First posted: 2024-03-20
Enrollment: 10
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: An open-labeled, prospective, single-center proof-of-concept clinical trial lasting 14 weeks was conducted to investigate the impact of an algorithm-based regimen on enhancing lenvatinib effectiveness.
Primary purpose: TREATMENT

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.

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.

Primary outcomes:
measure: disease progression/ tumor response
timeFrame: at enrollment and at study completion (14 weeks later)
Secondary outcomes:
measure: Adverse effects occurrence
timeFrame: 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.
Other outcomes:
Locations:
city: Jerusalem
country: Israel
facility: Hadassah Medical Organization
Study officials:
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.