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Selecting Pharmacological Interventions through Rapid Screening Motifs and Proper Cell Models
There is no doubt that the magnitude and complexity of the challenges to pharmacological intervention in disease processes are increasing exponentially. These challenges span all facets of intervention from countering antimicrobial resistance to expanding the pharmacogenomic capacity towards individualizing therapy and prevention. While the drug development landscape is rapidly evolving, current pipelines to approval and applied medicine often rely on a well established compartmentalized research models. In this rubric, targets are first identified in laboratories, frequently on the basis of the now discredited philosophy of “disease-causing” targets. More appropriately, target-specific therapeutics are capable of being designed with the goal of increasing efficacy while better managing undesirable toxicities. However, it has recently been proposed that target-based approaches often will fail under clinical trials due in part to the robust nature of the networks which control biological processes. The issue is one of having the needed target specificity as screenable with high throughput cell-based assessment models where those models accurately reflect the complexity of the in vivo cell-cell interactions and associated gene expression patterns largely now in evidence in traditional 2D monoculture conditions.
The goal is then to connect diseases with underlying pharmacologically tractable targets and with drugs that can be translated for their clinical management. To this aim, I will present the case for increasing research into novel technological approaches based on the hypothesis that the most effective therapies for a given disease would target a complex set of interactions between different components of the system rather than the activity of a single component. I will expand upon how the discovery of effective disease therapeutic modulators requires the development of a novel kinds of in vitro assays, which recapitulate the complexity of disease-specific tissue microenvironments.
Keywords: pharmacogenomics, disease, intervention