Barriers and opportunities for the clinical implementation of therapeutic drug monitoring in oncology

Abstract

There are few fields of medicine in which the individualisation of medicines is more important than in the area of oncology. Under‐dosing can have significant ramifications due to the potential for therapeutic failure and cancer progression; by contrast, over‐dosing may lead to severe treatment‐limiting side effects, such as agranulocytosis and neutropenia. Both circumstances lead to poor patient prognosis and contribute to the high mortality rates still seen in oncology. The concept of dose individualisation tailors dosing for each individual patient to ensure optimal drug exposure and best clinical outcomes. While the value of this strategy is well recognised, it has seen little translation to clinical application. However, it is important to recognise that the clinical setting of oncology is unlike that for which therapeutic drug monitoring (TDM) is currently the cornerstone of therapy (e.g. antimicrobials). Whilst there is much to learn from these established TDM settings, the challenges presented in the treatment of cancer must be considered to ensure the implementation of TDM in clinical practice. Recent advancements in a range of scientific disciplines have the capacity to address the current system limitations and significantly enhance the use of anticancer medicines to improve patient health. This review examines opportunities presented by these innovative scientific methodologies, specifically sampling strategies, bioanalytics and dosing decision support, to enable optimal practice and facilitate the clinical implementation of TDM in oncology.

A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

“…To extend GAN-based treatment estimations from binary to various kinds of treatment variables including categorical, and continuous, [76] applied modifications to GANITE, which they named MGANITE. Estimating continuous treatment is of high importance in applications involving dosage adjustment especially in oncology [123]. One of the main modifications was a mathematical adjustment to the loss function which takes a treatment assignment vector in both the counterfactual and ITE estimation blocks, to allow for simultaneous treatment effect estimation [76].…”

Perspectives on Model‐Informed Precision Dosing in the Digital Health Era: Challenges, Opportunities, and Recommendations

“…Implementation of model‐informed and optimal design approaches can further contribute to overcome “classical” TDM problems, such as inappropriate timing, quality, and quantity of PK or biomarker samples. Often highlighted major concerns in traditional TDM still comprise long bioanalytical turnaround times of samples (several hours to weeks), lack of standardization in workflows, and high instrumentation costs with complex sample preparation 8 . Raising awareness for benefits of optimal (often earlier and less) sampling timepoints, streamlining internal processes to shorten turnaround times, and introducing new concepts (e.g., point‐of‐care/bedside analytics, biosensors/wearables, and home‐monitoring systems 9 ), using not only plasma, but also, for example, saliva, interstitial fluid, or capillary blood, will offer practical solutions to the challenges listed above.…”

Precision medicine‐based drug treatment individualization in oncology

“…There must be better access to TDM laboratories and the provision of clinical decision support for interpreting the results of pharmacometrics that use Bayesian estimations to combine pharmacokinetics, individual patient characteristics, and drug concentrations. 23 Finally, a barrier that must be addressed to allow clinical translation of TDM is the demonstration of its economic efficacy as presented in a descriptive review by Vithanachchi et al 24 They reviewed 11 studies and noted that only a few drugs have been studied. However, all studies reviewed found TDM to be cost effective, based on established incremental cost-effectiveness ratios.…”

“…For TDM to be translated into clinical practice, the evidence base must expand, and sampling strategies need to be simplified, perhaps by micro sampling such as using dried blood spots or using body fluids other than blood. There must be better access to TDM laboratories and the provision of clinical decision support for interpreting the results of pharmacometrics that use Bayesian estimations to combine pharmacokinetics, individual patient characteristics, and drug concentrations 23 …”