All Optimal Dosing Roads Lead to Therapeutic Drug Monitoring-Why Take the Slow Lane

Metronomic chemotherapy in cancer treatment: new wine in an old bottle

Over the past two decades, metronomic chemotherapy has gained considerable attention and has demonstrated remarkable success in the treatment of cancer. Through chronic administration and low-dose regimens, metronomic chemotherapy is associated with fewer adverse events but still effectively induces disease control. The identification of its antiangiogenic properties, direct impact on cancer cells, immunomodulatory effects on the tumour microenvironment, and metabolic reprogramming ability has established the intrinsic multitargeted nature of this therapeutic approach. Recently, the utilization of metronomic chemotherapy has evolved from salvage treatment for metastatic disease to adjuvant maintenance therapy for high-risk cancer patients, which has been prompted by the success of several substantial phase III trials. In this review, we delve into the mechanisms underlying the antitumour effects of metronomic chemotherapy and provide insights into potential combinations with other therapies for the treatment of various malignancies. Additionally, we discuss health-economic advantages and candidates for the utilization of this treatment option.

Personalized Minimal Effective Concentration Therapy

It has been recognized for literally centuries that patients should be given only the amount of medication necessary to treat disease(s) or relieve symptoms. It is also well known that this amount can vary greatly between patients or even over time in the same patient. The ability to identify this amount, that is, to "personalize" dosing, requires a reliable measure of a patient's response to treatment. The development of analytical methods for the accurate measurement of pharmacologically meaningful drug concentrations in physiologic fluids, combined with mathematical methods for reliable prediction of how dosing changes affect these concentrations, has led to the development of therapeutic drug management (TDM) for more effective individualization of dosing. Using TDM, clinicians modify dosing to achieve concentrations or exposures (ie, AUC) found to be effective in patients with similar clinical attributes and conditions. These concentrations, called therapeutic (or target) concentrations or exposure ranges (TRs), are specific to both disease/condition and patient population. TDM is routinely used by many clinicians to adjust dosing of a wide range of medications for maximal efficacy and limited toxicity, thereby improving clinical outcomes. Failure to properly perform TDM or to appreciate the limitations of TDM have, however, contributed to the delayed acceptance of TDM by clinicians. This Commentary briefly discusses the limitations and the benefits of TR-guided TDM, and then discusses immunosuppressant drugs and anticancer medications as examples of drugs that require clinicians to change their prescribing practices from giving all patients the same or maximal tolerated doses, to instead adjusting individual doses to achieve minimal effective concentrations identified using circulating tumor- or graft-derived DNA or copy number instability rather than published TRs.

Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models

Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.