Biomarkers


Clinical applications for protein biomarkers: Examples of clinical utility are discussed below. Patient selection. Using appropriate biomarkers it will be increasingly possible to target the enrol- ment into clinical trials to substrata of patients within a (conventional classification of) disease. The challenge at the level of clinical development for a new medical entity is that often the validation of a presumed biomarker as a predictor of drug efficacy or safety is still at the level of plausibility, but not of clinical evidence. So more often than not both marker-positive and marker-negative patients will need to be enrolled, and the marker interro- gated as one of the parameters according to which the trial will be analysed. Still, there may be cases where the information already known about the biomarker is sufficiently compelling to use it right away for study-subject stratification, such as was the case in the clinical development of trastuzimab (Herceptin®), where only women with Her2-posi- tive malignancies were included. Treatment monitoring. Historically, 80% of the compounds in Phase II clinical trials fail for lack of efficacy. In the last five years, pharmaceutical com- panies have increased biomarker efforts early in drug discovery programmes to ensure sufficient biomarkers are in place before programmes enter Phase II.


Adverse events. Biomarkers may also allow the recognition of individuals that carry a high risk of encountering serious adverse effects, and thus avoiding exposure. Given the rare occurrence of serious adverse events, and the need for statistically reliable data, the successful detection of markers for serious adverse events may be particularly difficult. Extension of indications. Compounds that are suc- cessfully launched as new medicines addressing a particular disease based on a recognised molecular mechanism may be found effective in additional indications in which the same patho-mechanism is effective. For example, Lyrica® (Pfizer, Inc) was originally launched as an anti-seizure therapy, but has since been approved for use in neuropathic pain and fibromyalgia.


Statistical significance vs magnitude of effect: requirements for clinically applicable biomarkers


Academic biomarker research tends to be con- cerned primarily with the statistically determined reliability of a finding. Thus, if the association of a biomarker with a particular phenotype is repro- ducible, it is interpreted to convey real biological findings that may be of great interest to fundamen- tal understanding of biological mechanisms – even


Drug Discovery World Winter 2010/11


if the magnitude of effect that can be seen under a particular set of experimental constraints is small. Likewise, the Geoffrey Rose paradox explains that true, reproducible observations may be of great importance from the perspective of public health even if the overall magnitude of effect is modest. Thus, targeted modulation of variables that affect disease risk by small amounts may have important effects if applied to large populations, and are thus commonly used to guide health policy. Academic epidemiological research evaluating associations of predictors for outcomes often uses odds ratios or relative risk as its primary parameters for reporting results, particularly in the field of genetic associa- tion studies. In complex polygenic diseases, the magnitude of effects commonly found for associa- tions for individual genetic variants generally ranges between ORs of 1-2. Demonstrating statis- tical significance for such modest-sized effects usu- ally requires fairly large studies, particularly if mul- tiple comparisons are made (as is the case with genome-wide association studies).


On the other hand, when tests are to be applied to clinical decision-making in individual patients, information content with regard to the magnitude of association becomes critically important. Only reliable tests – with an acceptably low rate of false positive and/or false negative results – can respon- sibly be used in this setting. These performance parameters tend to be most directly gleaned from stating sensitivity and specificity (or positive pre- dictive value [PPV] and negative predictive value [NPV]), and these are therefore the parameters commonly referred to in clinical diagnostics. In general, tests are not considered as particularly useful if the area under the ROC does not exceed 0.8, which translates into balanced sensitivities and specificities of about 0.75. On the other hand, a balanced sensitivity and specificity of 0.75, trans- lates into an odds ratio of about 9; if an effect of this magnitude is indeed present, it will be readily recognisable even in quite modestly-sized studies.


Companion diagnostics: co-development of diagnostic test and therapeutic moiety


The definition of a companion diagnostic is a new diagnostic test developed in combination with a new therapeutic where the diagnostic test is essential to select those patients that should receive the drug. So, if the label of a new medicine is to include the oblig- atory use of a test, then the manufacturer needs to ensure that drug and test are developed in tandem, and receive regulatory approval in such a fashion that they can reach the market simultaneously.


27


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80