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Proposal 672

Title of the Proposed Research

The Development of Toxicity Prediction Tools to Assist Oncologists in the Management of Adverse Events in Patients Receiving Treatment with Lapatinib

Lead Researcher

Dr. Mario Lacouture

Affiliation

Memorial Sloan-Kettering Cancer Center
New York, NY, US

Funding Source

Funding has been requested from GSK

Potential Conflicts of Interest

None

Data Sharing Agreement Date

16 January 2013

Lay Summary

Lapatinib (Tykerb™) was developed for the treatment of breast cancer patients who have progressed after trastuzumab-based therapy. Lapatinib is an orally administered drug. Despite being a relatively safe agent when added to other anticancer drugs, there were some side effects reported in higher frequency in patients (Geyer et al., 2007). The major toxicities that were particularly problematic were skin events (27%) and diarrhea (60%). Side effects such as rash and diarrhea can reduce patient quality of life, increase health care costs and the latter can even be life threatening in certain cases. These events can also cause treatment delays and reductions in the dose of lapatinib or even a permanent discontinuation of therapy even in a situation where the patient is responding to treatment.

Occurrences of treatment limiting toxicities (TLT) such as diarrhea and skin events are believed to be largely unpredictable. Therefore oncologists generally take action only after the event occurs (i.e. reactively). This involves trying to “rescue” the patient from a subjectively unpleasant and potentially dangerous situation, and then making adjustments to the regimen such as dose reduction and/or institution of supportive care medication to prevent another episode. Clinical care could be substantially improved if these episodes of significant toxicity could be accurately predicted, with steps taken in advance to prevent their occurrence in the first place (i.e. proactively). Such steps might include the use of appropriate supportive care medication, as well as forewarning the patient and initiating a more intensive early monitoring scheme and action plan for early intervention.

The realities of health care systems around the world preclude such arrangements being put in place, for all patients, throughout all cycles of chemotherapy. What may be possible, however, is a highly focused strategy based on the accurate prediction of patients at higher than average risk, applied “just-in-time” to pre-empt episodes of TLT. In other words, it should be possible and economical to intervene preventively if we knew who was at higher risk, and when (i.e. at what cycle) the risk would become elevated. Such a predictive model could then be made available as an ‘add-on’ to existing computer-based chemotherapy ordering systems or even delivered through an internet platform.

Therefore, the development of validated and easy to use prediction models for skin events (= grade II) and diarrhea (grade III/IV) could substantially improve the delivery of lapatinib therapy to breast cancer patients. In this proposal, the development of user-friendly prediction tools for skin events and diarrhea in breast cancer patients receiving lapatinib therapy is described.

Study Data Provided

Study EGF100151: Phase III, Randomized, Open-Label, Multicenter Study Comparing GW572016 and Capecitabine (Xeloda) versus Capecitabine in Women with Refractory Advanced or Metastatic Breast Cancer
Study EGF30008: A Randomized, Double-Blind, Placebo-Controlled, Multicenter, Phase III Study Comparing Lapatinib and Letrozole versus Letrozole in Subjects with Estrogen/Progesterone Receptor-Positive Advanced or Metastatic Breast Cancer

Statistical Analysis Plan

This will be added after the research is published.

Publication Citation