ReviewClinical and translational science2022
Integrating real-world data to accelerate and guide drug development: A clinical pharmacology perspective.
Review in Clinical and translational science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- Integrating Real-World Data and Pharmacometrics to Bridge Evidence Gaps in Special Populations: A State-of-the-Art Review.Pharmaceutics · 2026Review
- Cancer Therapy-Induced Cardiotoxicity: Results of the Analysis of the UK DEFINE Database.Cancers · 2025Article
- Integrating real-world data and machine learning: A framework to assess covariate importance in real-world use of alternative intravenous dosing regimens for atezolizumab.Clinical and translational science · 2024Article
- Real-World Evidence Application in Translational Medicine: Making Use of Prescription Claims to Inform Drug-Drug Interactions of a New Psoriasis Treatment.Journal of clinical pharmacology · 2024Article
- Alzheimer's Disease: Combination Therapies and Clinical Trials for Combination Therapy Development.CNS drugs · 2024Review
- Machine-Learning Assisted Screening of Correlated Covariates: Application to Clinical Data of Desipramine.The AAPS journal · 2024Article
- Article
- The role of early-phase trials and real-world evidence in drug development.Nature cardiovascular research · 2024Review
- Model-based approach to identify predictors of paclitaxel-induced myelosuppression in "real-world" administration.CPT: pharmacometrics & systems pharmacology · 2023Article
- Real-world comparison between weekly versus biweekly dosing of cetuximab for metastatic colorectal cancer.Journal of comparative effectiveness research · 2023Article
- Integrating real-world data to accelerate and guide drug development: A clinical pharmacology perspective.Clinical and translational science · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.