Classification of Companion Diagnostics: A New Framework for Biomarker-Driven Patient Selection

Abstract

Background Modern personalized medicine strategies builds on therapy companion diagnostics to stratify patients into subgroups with differential benefit/risk. In general, stratification for drug response implies a treatment-by-subgroup interaction. This interaction is usually suggested by the drug’s mechanism of action and investigated in pharmacological research or in clinical studies. In these candidate genes or pathway approaches, either biological reasons for a differential benefit/risk or statistical interaction regarding a pharmacological or clinical endpoint or both may be given. For successful drug approval, demonstration of a positive benefit/risk balance in the intended patient population is required. This also applies to situations with biomarker-selected populations. However, further regulatory considerations relate to the usefulness and plausibility of the selected patients and benefit/risk extrapolations or alternative therapy options in biomarker-negative populations. Methods To facilitate the specification of regulatory requirements and support the design of clinical development programmes, a systematic classification of biomarker-drug pairs is needed, in particular with regard to the expected underlying molecular mechanism and the clinical evidence. Results A classification of five biomarker-drug categories is proposed related to increasing evidence on the biomarker’s predictive value in relation to a specific drug. We classified biomarkers into five ascending categories with increasing evidence on the predictive nature of the biomarker in relation to a specific drug according to the comparative pharmacological and clinical evidence. Conclusions The proposed classification will facilitate regulatory decision-making and support drug development with respect to biomarker-related subgrouping, both, during clinical programme and at the time of marketing authorization application, since the grade of evidence on the differential power of the biomarker can be considered as an indicator for the usefulness of a biomarker-related subgrouping.

Identification of biomarker-defined populations in precision medicine

“…Biomarker-defined subgroups in drug development may be selected for different reasons and based on different sources of evidence. For assessing whether the presented evidence is acceptable for a biomarkerdefined subgroup related to a specific drug, I proposed a classification scheme distinguishing between biological and data-driven evidence related to subgrouping [44]. In my dissertation, I proposed different approaches for the data-driven identification of subgroups with differential treatment effects based on data from multiple clinical trials or data with a discrete time-to-event outcome.…”

“…To better assess the usefulness of biomarker-based subgrouping, a classification of the level of evidence is needed that distinguishes between empirical and biological evidence for patient selection. My work [44] proposed such a framework allowing for a classification of the underlying evidence that can support regulatory and scientific decision-making with respect to biomarker-based selections. In addition, for the proposed categories, drugs approved by the EMA with biomarker information on the label were classified into the proposed categories based on the evidence provided in their European Assessment Reports or the Summary of Product Characteristics.…”

“…In this dissertation, I address the research questions described in Chapter 1.2. The results of my research have either been published in peer-reviewed journals [42,43,44] or are currently under review [45]:…”

“…The results of my research on different types of evidence for the plausibility and usefulness of a biomarker-based patient selection are published in Huber et al [44].…”

“…Therefore, no comparative clinical evidence is available which would justify a classification into a higher category. Further examples of biomarker-drug pairs and the evidence category they belong to can be found in Huber et al [44]. In summary, I outlined a classification scheme for biomarker-drug pairs indicating increasing evidence of the usefulness of a biomarker stratifying the patient population.…”