Economic evaluations of personalized medicine: existing challenges and current developments

Abstract

Personalized medicine, with the aim of safely, effectively, and cost-effectively targeting treatment to a prespecified patient population, has always been a long-time goal within health care. It is often argued that personalizing treatment will inevitably improve clinical outcomes for patients and help achieve more effective use of health care resources. Demand is increasing for demonstrable evidence of clinical and cost-effectiveness to support the use of personalized medicine in health care. This paper begins with an overview of the existing challenges in conducting economic evaluations of genetics- and genomics-targeted technologies, as an example of personalized medicine. Our paper illustrates the complexity of the challenges faced by these technologies by highlighting the variations in the issues faced by diagnostic tests for somatic variations, generally referring to genetic variation in a tumor, and germline variations, generally referring to inherited genetic variation in enzymes involved in drug metabolic pathways. These tests are typically aimed at stratifying patient populations into subgroups on the basis of clinical effectiveness (response) or safety (avoidance of adverse events). The paper summarizes the data requirements for economic evaluations of genetics and genomics-based technologies while outlining that the main challenges relating to data requirements revolve around the availability and quality of existing data. We conclude by discussing current developments aimed to address the challenges of assessing the cost-effectiveness of genetics and genomics-based technologies, which revolve around two central issues that are interlinked: the need to adapt available evaluation methods and identifying who is responsible for generating evidence for these technologies.

Treatment-resistant schizophrenia: current insights on the pharmacogenomics of antipsychotics

“…Substantial economic barriers also remain 166. The stratification of patients with psychotic illness into more refined subsets may advance medication development, but it may also reduce the size of the potential market and deter industry investment 167.…”

The economic case for precision medicine

“…Establishing the economic case for measuring more than one biomarker is a challenge, in practice, because different permutations of tests are possible, at different points along a care pathway, resulting in different expected cost and health outcomes [22]. For example, the NICE assessment of TNFi anti-drug antibody and drug level testing evaluated concurrent testing (both biomarkers measured at the same time) and reflex testing (anti-drug antibodies only measured if drug levels were not detected) in patients that had maintained response, or lost response, to their TNFi [68].…”

The Economic Value of Genetic Testing for Tolerance of Allopurinol in Gout

“…A systematic literature review of economic evaluations of pharmacogenetic tests to reduce the incidence of adverse drug reactions found allopurinol was one of a small number of treatments with robust cost-effectiveness evidence available, albeit in a limited number of countries 19 . Stratified medicine is relatively new, and consequently there are still challenges with conducting economic evaluations in this area, such as identifying where in a patient pathway the pharmogenomic test best fits, the use of surrogate endpoints, and identifying robust clinical evidence for all comparators 20 .…”

“…It is important that studies cover a sufficient sample size to accurately estimate the burden and potential benefits in relation to adverse events that may affect only a small subgroup of the population 20 . Once this is available, cost-effectiveness studies can be conducted to determine whether such interventions could be cost-effective uses of healthcare resources.…”

“…Once this is available, cost-effectiveness studies can be conducted to determine whether such interventions could be cost-effective uses of healthcare resources. Cost-effectiveness evaluations will also need to be done carefully according to potential variations in screening uptakes and how testing fits into the patient pathway: these differences lead to structural uncertainty in economic models and without detailed investigation can limit the interpretation of results in economic evaluations 20 . It is also important to remember that healthcare systems (e.g., costs of care and resources used) vary greatly, a condition that limits the generalizability of economic evidence between countries.…”