Time-dependent treatment effects of metronomic chemotherapy in unfit AML patients: a secondary analysis of a randomised controlled trial

Metronomic chemotherapy in hematology: Lessons from preclinical and clinical studies to build a solid rationale for future schedules

Metronomic chemotherapy (mCHEMO), based on frequent, regular administration of low, but pharmacologically active drug doses, optimizes antitumor efficacy by targeting multiple targets and reducing toxicity of antineoplastic drugs. This minireview will summarize preclinical and clinical studies on cytotoxic drugs given at weekly, daily, or at continuous metronomic schedules alone or in combination with novel targeted agents for hematological malignancies, including lymphoma, multiple myeloma, and leukemia. Most of the preclinical in vitro and in vivo studies have reported a significant benefit of both mCHEMO monotherapy and combinatorial regimens compared with chemotherapy at the maximum tolerated dose. However, the combination of mCHEMO with targeted drugs is still little explored in the hematologic clinical setting. Data obtained from preclinical studies on low dose metronomic chemotherapy in hematological malignancies clearly suggested the possibility to clinically investigate more tolerable and effective strategies for the treatment of patients with advanced hematological malignancies, or at least for those frail and elderly patients, who are not eligible or resistant to standard treatments.

Metronomic Chemotherapy in Elderly Patients

PURPOSE OF REVIEW

This review describes the most relevant studies found in the scientific literature regarding metronomic chemotherapy (MCT) in the geriatric oncology population to support its use as a feasible treatment of care in the frail elderly patients.

RECENT FINDINGS

Recent years have seen a reevaluation of cancer chemotherapeutic drugs and MCT is an emerging schedule in phase II and III clinical trials. Ageing is one of the risk factors for the development of cancer, the incidence of whom increases dramatically in people who live longer. To date, standard oncological protocols involve chemotherapeutic drugs in short cycles of therapy at the maximum tolerated dose (MTD). Although these therapeutic regimens may be successful, they can cause important adverse drug reactions, especially in elderly or frail patients. MCT is a different modality of delivery of chemotherapeutic drugs (frequent low dose for prolonged time) and it looks at the overcoming of the limitations and disadvantages of MTD, in particular the toxicity aspect. We reviewed the experience of clinicians who have used MCT in clinical trials enrolling elderly patients with different cancer types.

Nonproportional hazards model with a frailty term for modeling subgroups with evidence of long-term survivors: Application to a lung cancer dataset

With advancements in medical treatments for cancer, an increase in the life expectancy of patients undergoing new treatments is expected. Consequently, the field of statistics has evolved to present increasingly flexible models to explain such results better. In this paper, we present a lung cancer dataset with some covariates that exhibit nonproportional hazards (NPHs). Besides, the presence of long-term survivors is observed in subgroups. The proposed modeling is based on the generalized time-dependent logistic model with each subgroup's effect time and a random term effect (frailty). In practice, essential covariates are not observed for several reasons. In this context, frailty models are useful in modeling to quantify the amount of unobservable heterogeneity. The frailty distribution adopted was the weighted Lindley distribution, which has several interesting properties, such as the Laplace transform function on closed form, flexibility in the probability density function, among others. The proposed model allows for NPHs and long-term survivors in subgroups. Parameter estimation was performed using the maximum likelihood method, and Monte Carlo simulation studies were conducted to evaluate the estimators' performance. We exemplify this model's use by applying data of patients diagnosed with lung cancer in the state of São Paulo, Brazil.