Personalised medicine, in which sophisticated diagnostics guide drug choice, dosing, and patient appropriateness, challenges older business models for both pharma and diagnostic tests. In particular, the diagnostic tests are original inventions and often require substantial original clinical research, which may or may not be intermingled with development costs for the drug.
It has become clear that pharmaceutical development in the first decades of this century will be dominated by the “personalised medicine” concept. Personalised medicine encompasses the development and marketing of molecular tests which help to target the use of pharmaceuticals and biologicals in ways that maximise their effectiveness. Of course, lab tests have always guided diagnostics: for high glucose, a physician will diagnose diabetes and prescribe insulin, and so a lab test was paired with a drug. But personalised medicine, as the term is used, almost always involves carving out new subsets of patients with a particular form of disease, such as Herceptin-sensitive breast cancer.
The interactions between the development of complex tests, drug trials, regulatory approval, and drug marketing will cause substantial shifts in the way healthcare is delivered. One obvious business issue (a smaller market for a more specific drug) has dominated thinking about how personalised medicine will develop, how it will impact the pharmaceutical industry, and indeed, whether it is “good” or “bad” for pharma. Because that topic has been discussed so frequently, the present article will focus on other ways in which a new era of interactions between lab tests and drugs will stress existing business models or require the development of new ones.
We emphasise that there is no single model for clinical trials which incorporates genetic or other complex molecular information. There are at least three basic clinical models (combinations of these models are also possible) and they are as follows:
Evidence-based medicine and diagnostic tests
Throughout the developed countries, government-based and private insurance systems are showing rising interest in the rigorous application of evidence-based medicine to control costs, by reducing unnecessary or low-yield interventions. Clinical trial experts and payer decision-makers are quite familiar with outcomes analysis of therapeutic trials. In general, control and treatment groups are matched and randomised. To the degree possible, subjects and evaluators are masked to the interventions offered. Outcomes and adverse events are statistically compared. Controversies over the choice of clinical endpoints, or the use of clinical versus surrogate endpoints, are debated but gradually move towards a consensus. For example, is a negative Prostate Serum Antigen (PSA) at two years an adequate marker of effective radiation treatment for prostate cancer, or should the therapy be considered experimental until ten-year data are in hand? Even when regulators in different countries differ in their decisions, the basic issues for therapeutic trails, such as the choice of endpoints and the weighing of risks and benefits, are familiar.
The tools for assessment of diagnostic tests are very different, and the usual terms of art like sensitivity and specificity, familiar from college statistics, fall far short when the actual decision point for regulators and payers rests on the clinical utility of a test. For example, “sensitivity” can mean the chemical threshold of a test—does it measure down to 0.1 ng/ml of PSA? Sensitivity and specificity are usually used in statistical sense, describing the performance of the test in known populations of cases and controls. The clinical accuracy of the test depends on the base rate of positive and negative cases in the population at hand, which allows prediction of true and false positives and negatives. Sometimes, other already known characteristics of the patient will be calculated together with test results to give a more sophisticated prediction for the test in the patient at hand (Bayesian statistics). All of these statistics become much more complicated for diagnostic tests that have a spectrum of results, rather than just positive or negative results. Meanwhile, specificity and sensitivity lose much of their meaning for genetic tests that answer whether the patient does or doesn’t have the gene in question; the accuracy of the test is essentially 100 per cent. But even here, because of untested mutations or interactions with other untested genes with superimposed functions, the clinical variance accounted for by the gene may be much less than 100 per cent.
When the results of the test are proposed for clinical practice, different evaluators will differ as to whether the test is well-enough established for clinical use. Often, evaluators will differ sharply on whether enough is known about the test in practice to recommend the test to be used routinely.
Paradoxes of clinical trial ethics
A distinctive problem with diagnostic tests, as opposed to therapies, occurs when retrospective data analysis suggests that a clinical use of the test is likely but not certain to be valid—say, 70, 80 or 90 per cent likely. An example occurred when retrospective studies of clinical trials with EGFR-blocking monoclonals found them to be ineffective if a downstream gene, KRAS, was constitutively activated due to a mutation. Because the results of the retrospective study are incompatible with clinical equipoise, it is impossible to conduct a clinical trial where cancer patients with KRAS-activated tumours are randomised to receive EGFR therapy, a therapy which is very likely to be ineffective for such tumours. However, the state of information at this point may be criticised due to the fact that retrospective trials are fraught with confounding variables and a critic can quickly cite many retrospective conclusions that were invalidated by prospective controlled trials. Payers may question whether the cost of the molecular test should be covered, if its clinical use is uncertain. Although the resulting debate over levels of evidence may seem arcane, this scenario is fairly common with diagnostic tests.
Development risk for complex diagnostics
At present, relatively few pharmaceutical or biotechnology firms have the in-house capabilities to develop innovative molecular diagnostics de novo and carry them through commercialisation. Therefore, when tests are used as early as clinical trials, an outsourcing contract or a more sophisticated contractual partnership exists between a test developer and the drug developer. Contractual issues can become very complex and go beyond the scope of this article. But as one example, the freestanding test developer must develop a commercialisation-ready test by the beginning of Phase II. This is because regulators will be very sensitive to technical variations between the test used in clinical trials and the test that will be commercially available after drug launch. Therefore, most of the developer’s sunk costs occur early, although the drug candidate has 90 per cent risk of failure that any drug has at the beginning of Phase II trials. The drug manufacturer will be concerned about lock-in to a contractual relationship with the test manufacturer, while the test manufacturer will worry about a competitor who could produce a rival test after the risky and costly proof-of-concept stage has been passed. Intellectual property on the diagnostic test alone may be more difficult to defend, even in the short term, than the pharma’s core patents on the molecular structure of the companion drug or biological.
Regulatory challenges for complex diagnostics
Complex diagnostics raise a number of regulatory challenges. For example, several tests at the forefront of personalised medicine are too complex to be packaged as kits. An early example is the Trofile test (Monogram Biosciences), a gateway test to the use of a new-generation HIV anti-viral, maraviroc (Selzentry). The test requires gene-splicing steps and is currently run at one centralised and standardised laboratory. In the United States, this category of test is called a “laboratory-developed test” or LDT. In other cases, the lack of a “gold standard” test or variability of testing between laboratories has raised questions about the accuracy of diagnostics even for the prototypic personalised medicine test, the Her-2-neu test (See Fitzgibbons PL et al., Arch Pathol Lab Med 2006, 130:1440-5, and references therein).
Another regulatory challenge which may have seemed like science fiction only a few years ago is the prospect of prescribing cancer drugs based on oncogene characteristics rather than gross tumour type. Today, clinical trials for cancer drugs are categorised by the type of cancer: small cell lung cancer, adenocarcinoma of the pancreas, renal cell carcinoma, and so on. However, we are already seeing targeted cancer drugs which are effective in ways that are completely unforeseen by legacy histologic classifications of tumours (Both chronic myelogenous leukemia and gastrointestinal stromal cell tumours respond to imatinib, if they express the bcr-abl oncogene translocation). Although there are some highly effective cancer / chemotherapy regimens, the impact of chemotherapy on many cancers is notoriously limited (e.g. 5-10 per cent of patients benefit, or alternatively, the number-needed-to-treat is 10 to 20 or more.) Therefore, the hurdle rate for results matching tumours to chemotherapy based on molecular expression panels (e.g. tumour X in a case that also carries the bcr-abl mutation) should be equivalent efficacy. But all regulatory conventions, and payer guidelines for chemotherapy coverage, are based on the legacy approach to classifying tumours histologically and studying them in drug-specific trials.
Economic hurdles for complex diagnostics
Laboratory tests have traditionally been treated as commodities in the medical marketplace. In most countries, costs of laboratory tests are either bundled with episodes of care or paid at fixed rates based on the chemistry of the test (e.g. nucleic acid amplification, US$ 20; serum immunoassay, US$ 25; flow cytometry, US$ 50). These fixed fee schedules appear to be adequate for the development of new tests of the same type when the main parameter of the test is accuracy. Fixed fee schedules also encourage technological change to produce the same types of tests faster and less expensively. However, laboratory test reimbursement that is administratively locked to the marginal cost of the test’s chemistry is incompatible with significant clinical trials to develop and launch a novel type of test. In economic terms, this can be very inefficient, penny wise and pound foolish. For example, a hypothetical new test which could save the healthcare system US$ 100 million over a few years costs US$ 10 million in clinical trials to develop and needs to be sold at US$ 300 to cover risk, investment, and marginal cost; but the test will never exist if the reimbursement is locked at US$ 20 and the US$ 100 million will never be saved. New regulatory schemes for payer systems will need to adapt to some form of value-based reimbursement at least where the net outcome is to encourage cost-saving forms of investment.
One bright spot in the economic logic does occur if the healthcare system bundles a spectrum of interlocking costs together, such as the costs of cancer chemotherapy and chemotherapy diagnostics. Here, the test manufacturer could command value-based market prices in its direct transactions with chemotherapy centres, if the net outcome of test usage was ultimately cost-saving for the chemotherapy centre.
Challenges in marketing
Historically, there has been relatively little advertising or detailing of laboratory tests, probably because there were undifferentiated commodity products with low margins. Therefore, there is little apparatus in place to educate physicians about new molecular diagnostics, and older physicians may find the topic of molecular diagnostics quite confusing. One solution could be integration of electronic medical records and e-prescribing programmes with pop-up information on relevant diagnostic tests, or flags in the healthcare system that hold a prescription until a relevant diagnostic test has been performed.
In addition, diagnostic tests have a rare reverse-supply chain configuration. Instead of manufacturing a drug or device in a factory, shipping to a regional warehouse, and then a retail location (pharmacy or hospital), a diagnostic test requires shipping of the test from thousands of doctors’ offices or clinics backwards to a central laboratory. The logistics become quite formidable for very complex diagnostics that are only performed at one or a very few locations, and especially if the samples must be chilled or frozen at the point of collection and during shipping.
Moving forward
Speculation on the slow growth rate for personalised medicine focusses too much on the supposed reluctance of the pharmaceutical industry to investigate diagnostics which could carve down the market size of new drugs. When discussion stops there, the analysis has fallen into a mental trap, in which one credible answer is found quickly and this halts the search for alternative and perhaps more important explanations. In fact, in the past year, several CEOs at leading pharmaceutical firms have stated that personalised medicine—the pairing of diagnostics and drugs—has to be a core competence of their development strategy. Numerous additional barriers to development and commercialisation were described in this article, but they can be dealt with by good policy and appropriate innovation in the regulatory process. Only by elevating the addition problems into view will they become part of the dialogue on personalised medicine and part of our solution kit in moving the healthcare industry forward for more effective and accurate patient care.
Clinical models
Molecular models
Bruce Quinn is US physician executive in the law firm Foley Hoag LLP. A former medical school professor and strategy consultant with Accenture, from 2004-2008 he was the regional medical director for the Medicare program in California.
