31
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Use of Real‐World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Interest in real‐world data (RWD) and real‐world evidence (RWE) to expedite and enrich the development of new biopharmaceutical products has proliferated in recent years, spurred by the 21st Century Cures Act in the United States and similar policy efforts in other countries, willingness by regulators to consider RWE in their decisions, demands from third‐party payers, and growing concerns about the limitations of traditional clinical trials. Although much of the recent literature on RWE has focused on potential regulatory uses (e.g., product approvals in oncology or rare diseases based on single‐arm trials with external control arms), this article reviews how biopharmaceutical companies can leverage RWE to inform internal decisions made throughout the product development process. Specifically, this article will review use of RWD to guide pipeline and portfolio strategy; use of novel sources of RWD to inform product development, use of RWD to inform clinical development, use of advanced analytics to harness “big” RWD, and considerations when using RWD to inform internal decisions. Topics discussed will include the use of molecular, clinicogenomic, medical imaging, radiomic, and patient‐derived xenograft data to augment traditional sources of RWE, the use of RWD to inform clinical trial eligibility criteria, enrich trial population based on predicted response, select endpoints, estimate sample size, understand disease progression, and enhance diversity of participants, the growing use of data tokenization and advanced analytical techniques based on artificial intelligence in RWE, as well as the importance of data quality and methodological transparency in RWE.

          Related collections

          Most cited references101

          • Record: found
          • Abstract: found
          • Article: not found

          Applications of machine learning in drug discovery and development

          Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Innovation in the pharmaceutical industry: New estimates of R&D costs.

            The research and development costs of 106 randomly selected new drugs were obtained from a survey of 10 pharmaceutical firms. These data were used to estimate the average pre-tax cost of new drug and biologics development. The costs of compounds abandoned during testing were linked to the costs of compounds that obtained marketing approval. The estimated average out-of-pocket cost per approved new compound is $1395 million (2013 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a real discount rate of 10.5% yields a total pre-approval cost estimate of $2558 million (2013 dollars). When compared to the results of the previous study in this series, total capitalized costs were shown to have increased at an annual rate of 8.5% above general price inflation. Adding an estimate of post-approval R&D costs increases the cost estimate to $2870 million (2013 dollars).
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Immunostimulation with chemotherapy in the era of immune checkpoint inhibitors

                Bookmark

                Author and article information

                Contributors
                Leo.Russo@pfizer.com
                Journal
                Clin Pharmacol Ther
                Clin Pharmacol Ther
                10.1002/(ISSN)1532-6535
                CPT
                Clinical Pharmacology and Therapeutics
                John Wiley and Sons Inc. (Hoboken )
                0009-9236
                1532-6535
                28 November 2021
                January 2022
                28 November 2021
                : 111
                : 1 , Real World Evidence ( doiID: 10.1002/cpt.v111.1 )
                : 77-89
                Affiliations
                [ 1 ] Real World Evidence Pfizer Inc New York New York USA
                [ 2 ] Global Medical Epidemiology, Worldwide Medical and Safety Pfizer Inc Collegeville Pennsylvania USA
                [ 3 ] Global Medical Epidemiology, Worldwide Medical and Safety Pfizer Inc New York New York USA
                Author notes
                [*] [* ] Correspondence: Leo Russo ( Leo.Russo@ 123456pfizer.com )

                Article
                CPT2480
                10.1002/cpt.2480
                9299990
                34839524
                1c1f27af-4045-4247-88b7-1d70fcde8eeb
                © 2021 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 26 February 2021
                : 30 October 2021
                Page count
                Figures: 0, Tables: 3, Pages: 13, Words: 12091
                Categories
                State of the Art
                Reviews
                State of the Art
                Custom metadata
                2.0
                January 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:20.07.2022

                Pharmacology & Pharmaceutical medicine
                Pharmacology & Pharmaceutical medicine

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content106

                Cited by37

                Most referenced authors2,175