A biomarker has been described as “… a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.”[35
] Biomarkers can play many different roles in drug development, including a predictor of response or resistance to specific therapies, being a correlative end-point and used in longitudinal models referred (see
“Recent developments in group sequential designs”), being a surrogate end point in modeling and simulation (see
“Modeling and simulation for dose and dose regimen selection”), or being used as a means for patient-enrichment designs.
Factors used to limit the study population to patients believed more likely to benefit from the experimental therapy are termed enrichment factors.[36
] Such factors may be predictive biomarkers, or they may be biomarkers, clinical-pathologic, demographic characteristics associated with a predictive biomarker or with the target of a therapeutic agent. The smaller the proportion of truly benefiting patients in the population, the more advantageous it is to consider studying an enriched population.
Biomarkers that could be useful as enrichment factors during the drug development process might still need further refinement before they are ready for clinical use as predictive factors. This is because many enrichment biomarkers used in drug development either do not have sufficiently high positive or negative predictive value to justify clinical use or the assay used to measure the biomarker during the drug development process might not be sufficiently robust and reproducible for routine clinical use. The main purpose of using an enrichment biomarker in drug development is to improve the chances that the drug will show benefit in the tested subgroup of patients to more quickly establish that the drug is worth pursuing further. If information is available to suggest subgroups of patients who are more likely to benefit from a therapy, it may be reasonable to conduct a confirmatory trial only in those patients.
These kinds of observations are growing in medicine, where increasing use of molecular signatures reveals that the traditional tools used for diagnosis are lumping diverse phenotypes together. A recent report calls for precision medicine by which is meant the use of genomic, epigenomic, exposure and other data to define individual patterns of disease and phenotypes with more granularity, potentially leading to better individual treatment.[38
] Precision medicine couples established clinical-pathologic indexes with state-of-the-art molecular profiling to create diagnostic, prognostic and therapeutic strategies tailored for specific groups of patients.
The aspect of “one size fits all” surrounding the conventional design of clinical trials has been challenged, particularly when the diseases are heterogeneous due to observable clinical characteristics and/or unobservable underlying genomic and epigenomic characteristics and/or the experimental therapy is tailored to specific mechanism of action. An extension from the traditional single population design objective to one in which several possible patient subpopulations are studied will allow more informative evaluation in the patients having different degrees of responsiveness to the therapy. Building into traditional clinical trials a prospectively planned selection of subpopulations with higher response to the therapy is appealing from the patient's perspective as it addresses personalized medicine in adequate and well-controlled clinical trials. These new adaptive designs, called adaptive patient-enrichment or population-enrichment designs, allow modification to study hypothesis, the reallocation of patients and re-estimation of the sample size midstream to achieve the pre-planned objective.
It has been shown recently that such adaptive enrichment designs can be constructed to study a clinical hypothesis of treatment effect in the full population as well as several hypotheses of treatment effect in prespecified subsets more efficiently than the conventional nonadaptive approach.[39
] The statistical methodology is very similar to the statistical methodology of seamless Phase II/III designs referred to above.
While in a seamless Phase II/III design the adaptation relates to the selection of treatment arms, in the enrichment design the primary selection concerns the population. Such a study progresses seamlessly either in the subpopulation(s) of patients or in the whole population on the basis of data obtained in the first stage. At the end of the trial, the data from both stages are combined in the final analysis to assess the efficacy of the selected subpopulation(s), preserving its validity by strong control of the family-wise type-I error rate. As in the seamless Phase II/III design, enrichment designs may be more efficient than separate Phase II and Phase III programs in that fewer patients are required to achieve a given program-level power. Again, the benefit arises from the inclusion of the final stage data on the selected subpopulations, suitably adjusted for multiplicity, in the final analysis at the end of the trial.
An example of a complex population-enrichment design is the ongoing I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis) trial involving a randomized Phase II stage in which a number of experimental agents are tested in combination with standard neoadjuvant chemotherapy for patients with high-risk primary breast cancer.[42
] The primary end-point is pathologic complete response (pCR) at the time of surgery, with the objective being to identify biomarker signatures that predict pCR for drugs or combinations of drugs. The study is to be used to evaluate many drugs and drug/biomarker combinations, with successful combinations being “graduated” to a Phase III study and failures being dropped for futility.
One particular aspect of the I-SPY 2 TRIAL trial is the co-operative nature of the study in that multiple sponsors provided the experimental agents that are being used. The advantage to individual sponsors is to spread the costs by the use of a single control group. But the absence of a definitive noninvasive biomarker would hamper this approach in PAH.
Whether these types of designs will be used in PAH remains to be seen. Given the orphan nature of the disease and the difficulty of recruitment, it may be optimistic to expect that sufficient patients will be available to conduct such subgroup searches. Certainly we may need to restrict the number of subgroups considered.