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Integrating real‐world data to accelerate and guide drug development: A clinical pharmacology perspective
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Integrating real‐world data to accelerate and guide drug development: A clinical pharmacology perspective
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Journal Article
Integrating real‐world data to accelerate and guide drug development: A clinical pharmacology perspective
2022
Overview
Pharmaceutical products in the current accelerated drug development landscape can benefit from tools beyond data generated from randomized control trials. We have seen an abundance of real‐world data (RWD) and real‐world evidence, driven by the digitalization of healthcare systems and an increased awareness that has inspired a heightened interest in their potential use. Literature review suggest leveraging RWD as a promising tool to answer key questions in the areas of clinical pharmacology and translational science. RWD may increase our understanding regarding the impact of intrinsic (e.g., liver, renal impairment, or genetic polymorphisms) and extrinsic (e.g., food consumption or concomitant medications) factors on the clearance of administered drugs. Changes in clearance may lead to clinically relevant changes in drug exposure that may require clinical management strategies, such as change in dose or dosing regimen. RWD can be leveraged to potentially bridge the gaps among research, development, and clinical care. This paper highlights promising areas of how RWD have been used to complement clinical pharmacology throughout various phases of drug development; case examples will include dose/regimen extrapolation, dose adjustments for special populations (organ impairment, pediatrics, etc.), and pharmacokinetic/pharmacodynamic models to assess impact of prognostic factors on outcomes. In addition, this paper will also juxtapose limitations and promises of utilizing RWD to answer key scientific questions in drug development and articulate challenges posed by quality issues, data availability, and integration from various sources as well as the increased need for multidimensional‐omics data that can better guide the development of personalized and predictive medicine.
