Generally speaking, the challenge is to find the balance between the patient’s benefit, economic value and clinical merit for biomarker-based diagnostics. Pharmaceutical companies are beginning to focus more on such biomarker-based diagnostics that come along with companion diagnostic tests that identify a patient’s likelihood of responding to a drug or experiencing side effects (toxicities) and are intended to assist physicians in making treatment decisions for their patients. The two main groups of companion diagnostics include (i) tests that have been developed after a drug has come to market and (ii) tests that are being developed in conjunction, as a companion, to the drug. Today, a majority of drugs in developmental pipeline come along with associated biomarker programs, with the number likely to be increasing. Such companion diagnostic tests can improve research productivity by decreasing trial sizes, increasing the speed to market and supporting higher drug prices. Companion diagnostics have the potential to significantly influence drug development and the commercialization of lead candidates through safer drugs with enhanced therapeutic efficacy. Today, knowledge about the molecular mechanisms and pathways a drug interferes with gained through next-generation genomic technologies is crucial for drug developers before clinical symptoms are studied in clinical trials. Such genomic technologies identify biomarkers that are qualified to be used as diagnostic and prognostic markers in certain diseases. In oncology, genome-based diagnostics are rapidly evolving as many pharmaceutical companies focus on the development of targeted therapies and consider the benefits for a diagnostic test to pair with a specific treatment. Such tests are showing potential in reducing tremendously the costs of clinical trials (close to 2/3rds of the clinical trial costs in some cases). A recent report estimates over 130 million in savings for pharma companies per approved compound. Unfortunately, scientific and clinical factors place limits on the pace of such developments. For many pathophysiological conditions, the underlying principles are far from being understood or the current scientific knowledge is insufficient to select for specific biomarkers at early stages of a disease. In other areas, there is no immediate clinical need for companion diagnostics. It seems that, in general, the potential to generate a greater value after market launch, through increasing market shares, is much more important for the economics of pharmaceutical and biomedical companies than making development more productive, and companion diagnostics may not contribute a lot to improve development productivity. Actually, they might even increase overall costs and delay drug developments since clinical trials must frequently be larger when companion diagnostics are employed and candidate biomarkers must be tested if it is unclear, which markers will be predictive. Further, regulatory authorities require marker-negative patients to be included in clinical trials. Moreover, decision analysis and an increased understanding of how humans make decisions (see generally Kahneman, Thinking Fast and Slow (Kahneman 2011) and the identification of the “cognitive biases” (unconscious distortions in reasoning that affect the rationality of decision making) it seems increasingly understood that the neutralization, quantification of data and examination through neutral mechanisms will greatly increase the correct decisions in any system, but especially in those that are extremely data laden and complex, like health and patient care.
Nonetheless, companion diagnostics and software, data analytics and other information technology advancements all have the potential to create a significant commercial benefit in markets with pricing flexibility in terms of market share, but are likely to be of higher value for later-to-market entrants. This is due to the fact that companion diagnostics and corresponding theranostic assays divide the market of treatable patients into groups and clusters, thus reducing market share. A companion drug, however, that is capable of identifying a group of patients that responds to a specific therapy very well, enables higher pricing and thus generates value to stakeholders. In this regard, the key is the payer’s price sensitivity that varies a lot by disease area; therefore, drug classes can be segmented according to their scientific and commercial potential.
Pharmaceutical companies may be more likely to invest in diagnostics and technologies that impact larger groups, generally such as those in areas like infectious diseases, immunology and oncology, with the latter being the most advanced field for personalized medicine. The segmentation also reveals disease areas where incentives are not aligned to drive investment, despite technical feasibility and clinical need; among them are antipsychotics or anticoagulants. Firms focusing on diagnostic tests provide a huge variety of different systems, among them recurrence and monitoring tests in cancer medicine, early-stage diagnostics, susceptibility tests for adverse or toxic side-effects of drugs, genomic tests for risk assessments of a particular inherited disease as well as tests that include companion diagnostics. Amid the excitement and attention paid to personalized cancer treatment, it is crucial to remember that other conditions like psychiatric disorders carry as great a societal burden, yet remain too poorly understood to benefit.
From an economic view, revenue generation with diagnostic tests remains challenging; albeit diagnostic tests influence most clinical decision-making, they only account for a small percentage of today’s health insurance expenditures.
However, P4 medicine indeed has a great potential to catalyze changes in the increasing costs of medical care and will ultimately result in reduced costs to the point where P4 medicine will be exported to the developing world; as such, P4 medicine will be the foundation of global health care in the future. The reduction of costs will be achieved through a variety of factors, among them is the digitalization of health care and advances in health care IT, novel translational technologies such as next-generation sequencing technologies entering clinical laboratories, and the emerging field of single-cell omics that allow the analysis of thousands of cells in a high throughput manner (Hood and Galas 2003). The central challenge of modern medicine is the stratification of patients, e.g. based on novel biomarkers, and the classification of patients into subgroups with different combinations of disease-perturbed networks (Ivshina 2006). Crucially, the adoption of the P4 concept will enable the focus of medicine to shift from disease to wellness, with enormous attendant cost savings to society resulting in a lower requirement for sick leave and a concurrent increase in productivity. Furthermore, many factors will converge to bring the costs of health care down in a striking manner so that the benefits of P4 medicine can be shared by rich and poor nations alike.