Challenge of evidence in individualized medicine

The 3-I framework: a framework for developing public policies regarding pharmacogenomics (PGx) testing in Canada

The 3-I framework of analyzing the ideas, interests, and institutions around a topic has been used by political scientists to guide public policy development. In Canada, there is a lack of policy governing pharmacogenomics (PGx) testing compared to other developed nations. The goal of this study was to use the 3-I framework, a policy development tool, and apply it to PGx testing to identify and analyze areas where current policy is limited and challenges exist in bringing PGx testing into wide-spread clinical practice in Canada. A scoping review of the literature was conducted to determine the extent and challenges of PGx policy implementation at federal and provincial levels. Based on the 3-I analysis, contentious ideas related to PGx are (i) genetic discrimination, (ii) informed consent, (iii) the lack of knowledge about PGx in health care, (iv) the value of PGx testing, (v) the roles of health care workers in the coordination of PGx services, and (vi) confidentiality and privacy. The 3-I framework is a useful tool for policy makers, and applying it to PGx policy development is a new approach in Canadian genomics. Policy makers at every organizational level can use this analysis to help develop targeted PGx policies.

Defining 'elderly' in clinical practice guidelines for pharmacotherapy

OBJECTIVE

To identify how 'elderly' patients are defined and considered within Australian clinical guidelines for the use of pharmacotherapy.

METHOD

Guidelines pertaining to the use of pharmacotherapy, focusing on conditions described in National Health Priority Areas, were identified using databases (Medline, Google Scholar) and organisation websites (Department of Health and Ageing, National Heart Foundation, National Health and Medical Research Council). Guidelines were reviewed and qualitatively analysed to identify any references or definitions of 'elderly' persons.

RESULTS

Among the 20 guidelines reviewed, 3 defined 'elderly' by chronological age (i.e., years since birth) while the remaining 17 guidelines did not define 'elderly' in any way. All 20 guidelines used the term 'elderly', whilst some guidelines provided age (chronological)-based dosage recommendations suggesting an ageist or generalist approach in their representation of 'elderly', for which rationale was seldom provided. Thematic analysis of the statements revealed five key themes regarding how 'elderly' was considered within the guidelines, broadly describing 'elderly' persons as being frail and with altered pharmacology. Some guidelines also highlighted the limited evidence base to direct clinical decision-making. A continuum of perceptions of ageing also emerged out of the identified themes.

CONCLUSION

Clinical practice guidelines currently do not adequately define 'elderly' persons and provide limited guidance on how to apply treatment recommendations to older persons. The representation of 'elderly' in guidelines needs to be less based on chronological age or generic definitions focusing more on establishing a direct link between an individual patient's characteristics and the pharmacology of their prescribed medication. Clinical guidelines that do not offer any practical descriptions of the features of ageing that are specifically related to the use of pharmacotherapy, or how to assess these in individual patients, render decision-making challenging.

Opportunities and risks of diagnostic lab-on-a-chip systems in healthcare from a health system stakeholder’s perspective

Aim: The aim of personalized medicine is to respond to the needs of individuals with appropriate treatment. Lab-on-a-chip systems (LOCs) can help to individualize therapeutic algorithms at the point of care. Herein, we discuss the perspectives, demands and concerns associated with LOCs. Methods: Interviews with 30 experts in the field of personalized medicine were conducted, addressing the requirements, potentials and risks of LOCs. The interviews were transcribed and evaluated by means of qualitative content analysis. Results: The majority of experts emphasize a considerable potential for the lab-on-a-chip industry with the largest potential in the context of point-of-care diagnostics. The direct-to-costumer use is regarded as risky, in particular with respect to the reliability of the results. Conclusion: In addition to a major potential of the implementation of LOCs, their impact on delivery of healthcare have to be considered, and early communication between physicians and LOC developers and manufacturers have to be ensured.

Stochastic modeling of systems mapping in pharmacogenomics

As a basis of personalized medicine, pharmacogenetics and pharmacogenomics that aim to study the genetic architecture of drug response critically rely on dynamic modeling of how a drug is absorbed and transported to target tissues where the drug interacts with body molecules to produce drug effects. Systems mapping provides a general framework for integrating systems pharmacology and pharmacogenomics through robust ordinary differential equations. In this chapter, we extend systems mapping to more complex and more heterogeneous structure of drug response by implementing stochastic differential equations (SDE). We argue that SDE-implemented systems mapping provides a computational tool for pharmacogenetic or pharmacogenomic research towards personalized medicine.

[Clinical and health economic challenges of personalized medicine]

Healthcare systems across the globe are currently challenged by aging populations, increases in chronic diseases and the difficult task of managing a healthcare budget. In this health economic climate, personalized medicine promises not only an improvement in healthcare delivery but also the possibility of more cost-effective therapies. It is important to remember, however, that personalized medicine has the potential to both increase and decrease costs. Each targeted therapy must be evaluated individually. However, standard clinical trial design is not suitable for personalized therapies. Therefore, both scientists and regulatory authorities will need to accept innovative study designs in order to validate personalized therapies. Hence correct economic evaluations are difficult to carry out due to lack of clear clinical evidence, longitudinal accounting and experience with patient/clinician behavior in the context of personalized medicine. In terms of reimbursement, payers, pharmaceutical companies and companion diagnostic manufacturers will also need to explore creative risk-sharing concepts. Germany is no exception to the challenges that face personalized medicine and for personalized medicine to really become the future of medicine many health economic challenges first need to be overcome. The health economic implications of personalized medicine remain unclear but it is certain that the expansion of targeted therapies in current healthcare systems will create a host of challenges.