It might be argued that the ability to generate data in the life sciences is presently somewhat ahead of innovation in ways to make that data useful. There is the sense, looking at just how much can be measured and the power of modern computing and analysis methodologies, that there must be more ways to extract useful predictions of the life-extending potential of various treatments.
The direction of much of modern medicine is to focus on the development of treatments that work by altering gene expression or otherwise manipulating levels of specific proteins. The process of finding targets is made more effective via analysis of the vast amounts of gene expression data that can be cheaply obtained from patients and healthy individuals nowadays. Relationships between proteins can be established and differences between healthy and unhealthy biology identified.
My objection to this approach taken as a whole is that it is essentially only a more efficient continuation of the same old take on applied medicine: to patch over the problem rather than address the underlying cause. It is adjusting the engine's fuel feed to force your way past the fact that critical components are worn and faulty. Some forms of adjustment in biology can produce overall benefits on the level of damage in the system: think of approaches that boost the operation of cellular housekeeping, for example. But that isn't the case for most of what comes out of this school of research and development.
Here is an example of applying this approach to aging - the paper is open access, but not yet available in plain text format, so note the provisional PDF link on the page if you want to dive in. I'd say that this is a great path ahead if you favor programmed aging theories, as in that worldview aging is caused by evolved changes in gene expression over time: reverse the changes and you reverse aging. If you hold of the majority view of aging as accumulated cellular and molecular damage, however, then all of what I said above applies.
The major challenges of aging research include absence of the comprehensive set of aging biomarkers, the time it takes to evaluate the effects of various interventions on longevity in humans and the difficulty extrapolating the results from model organisms to humans. To address these challenges we propose the in silico method for screening and ranking the possible geroprotectors followed by the high-throughput in vivo and in vitro validation.
The proposed method evaluates the changes in the signaling pathway cloud constructed using the gene expression data and epigenetic profiles of young and old patients' tissues. The possible interventions are selected and rated according to their ability to regulate age-related changes and minimize differences in the signaling pathway cloud. This flexible and scalable approach may be used to predict the efficacy of the many drugs that may extend human longevity before conducting pre-clinical work and expensive clinical trials.