Abstract
BackgroundModern 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.MethodsTo 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.ResultsA 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.ConclusionsThe 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.
Highlights
Precision medicine offers a promising vision on the development of new targeted drugs, in particular in areas with a high medical need
The development of the classification scheme involves the following aspects: the evidence for predictive and/or prognostic biomarkers, the data type and the need for dichotomization, the scale of the effect size and the type of outcome related to the differential treatment effect
There is a need to differentiate biomarkers that are purely prognostic from those that are predictive since a prognostic marker would not necessarily justify that a part of the patients are not treated with the new treatment
Summary
Precision medicine offers a promising vision on the development of new targeted drugs, in particular in areas with a high medical need. The investigation of a predictive biomarker profile assumes a positive treatment-by-subgroup interaction, i.e. a differential treatment effect in subgroups allowing for discriminating patient groups with different expected treatment effects. 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. 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. 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
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