Inflammation Immune modulation, rheumatoid arthritis Lipid metabolism Nucleotide metabolism
Oxidative stress (diabetes, heart disease, arthritis, obesity, and cancer) Type 1 and 2 diabetes mellitus Satiety and satiation
Tissue remodeling weight of evidence that demonstrates that changes in the biomarker correlate strongly with the desired clinical outcome. The best known physiologic surrogate marker is blood pressure reduction as an indicator of reduced incidence of stroke and myocardial infarction. This surrogate marker is now used as a clinical endpoint, because a strong relationship between reduction in blood pressure and a reduction in the incidence of myocardial infarctions and strokes has previously been established.
For innovative products, limited information may be available on the relationship between the PK of a drug, the effect on a candidate biomarker, and the ultimate clinical response. Although this may limit the use of predetermined biomarkers in the early development phases of a drug, the incorporation of recent advances in genomics, pharmacogenetics, and proteomics may help to qualify various candidate biomarkers. Pilot exploratory studies should therefore be conducted to qualify and validate candidate biomarkers for predictive clinical assessment of disease progression and the effect of drug intervention. Mechanistic approaches using new technological advances in target validation, functional biology, proteomics, genomics, and the monitoring of gene expression using DNA arrays, imaging, and the correlation of these effects to the disease state may hold promise for an earlier understanding of disease and toxicological processes, improve the predictiveness of preclinical pharmacology and toxicology data to humans, and find suitable biomarkers for use in the early stage of drug development of innovative products [2,10].
The appropriate combination of biomarker identification and selection, bioanalytical methods development and validation for drugs and biomarkers, and mechanism- based PK/PD models for fitting data and predicting future clinical endpoints and outcomes may provide powerful insights and guidance for effective drug development, toward safe and efficacious medicine for individual patients [6-8,11,12] . The U.S. Food and Drug Administration (FDA) is developing these concepts as part of its critical path initiative. where the agency recognizes that the major contributor to inefficiency in development was the absence of innovative new methods for preclinical and clinical testing of drugs. The FDA recommended new tools and various opportunities to improve drug development, including the use of validated biomarkers and the application of model-based drug development (MBDD). In the following sections we focus on the PK/PD correlation assessment of a drug and a bio-marker, and the use of PK/PD modeling and simulation to optimize drug development and decision making.
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