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Expanded Access as a source of real-world data: An overview of FDA and EMA approvals.

ABSTRACT: AIMS:To identify, characterize and compare all Food and Drug Administration (FDA) and European Medicines Agency (EMA) approvals that included real-world data on efficacy from expanded access (EA) programmes. METHODS:Cross-sectional study of FDA (1955-2018) and EMA (1995-2018) regulatory approval documentation. We automated searching for terms related to EA in 22,506 documents using machine learning techniques. We included all approvals where EA terms appeared in the regulatory documentation. Our main outcome was the inclusion of EA data as evidence of clinical efficacy. Characterization was based on approval date, disease area, orphan designation and whether the evidence was supportive or pivotal. RESULTS:EA terms appeared in 693 out of 22,506 (3.1%) documents, which referenced 187 approvals. For 39 approvals, data from EA programmes were used to inform on clinical efficacy. The yearly number of approvals with EA data increased from 1.25 for 1993-2013 to 4.6 from 2014-2018. In 13 cases, these programmes formed the main evidence for approval. Of these, patients in EA programmes formed over half (median 71%, interquartile range: 34-100) of the total patient population available for efficacy evaluation. Almost all (12/13) approvals were granted orphan designation. In 8/13, there were differences between regulators in approval status and valuation of evidence. Strikingly, 4 treatments were granted approval based solely on efficacy from EA. CONCLUSION:Sponsors and regulators increasingly include real-world data from EA programmes in the efficacy profile of a treatment. The indications of the approved treatments are characterized by orphan designation and high unmet medical need.

SUBMITTER: Polak TB

PROVIDER: S-EPMC7444779 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Expanded Access as a source of real-world data: An overview of FDA and EMA approvals.

Polak Tobias B TB van Rosmalen Joost J Uyl-de Groot Carin A CA Uyl-de Groot Carin A CA

British journal of clinical pharmacology 20200407 9

<h4>Aims</h4>To identify, characterize and compare all Food and Drug Administration (FDA) and European Medicines Agency (EMA) approvals that included real-world data on efficacy from expanded access (EA) programmes.<h4>Methods</h4>Cross-sectional study of FDA (1955-2018) and EMA (1995-2018) regulatory approval documentation. We automated searching for terms related to EA in 22,506 documents using machine learning techniques. We included all approvals where EA terms appeared in the regulatory doc ...[more]

PMID: 32200551

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Project description:Introduction Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer. Methods Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available artificial intelligence engine, DARWEN (Pentavere, Ontario, Canada). For comparison, 100 of 333 patients that received systemic therapy were randomly selected and clinical data manually extracted by two trained abstractors using the same accepted gold standard feature definitions, including patient, disease characteristics, and treatment data. All cases were re-reviewed by an expert adjudicator. Accuracy and concordance between automated and manual methods are reported. Results Automated extraction required considerably less time (<1 day) than manual extraction (∼225 person-hr). The collection of demographic data (age, sex, diagnosis) was highly accurate and concordant with both methods (96%–100%). Accuracy (for either extraction approach) and concordance were lower for unstructured data elements in EHR, such as performance status, date of diagnosis, and smoking status (NLP accuracy: 88%–94%; Manual accuracy: 78%–94%; concordance: 71%–82%). Concurrent medications (86%–100%) and comorbid conditions (96%–100%), were reported with high accuracy and concordance. Treatment details were also accurately captured with both methods (84%–100%) and highly concordant (83%–99%). Detection of whether biomarker testing was performed was highly accurate and concordant (96%–98%), although detection of biomarker test results was more variable (accuracy 84%–100%, concordance 84%–99%). Features with syntactic or semantic variation requiring clinical interpretation were extracted with slightly lower accuracy by both NLP and manual review. For example, metastatic sites were more accurately identified through NLP extraction (NLP: 88%–99%; manual: 71%–100%; concordance: 70%–99%) with the exception of lung and lymph node metastases (NLP: 66%–71%; manual: 87%–92%; concordance: 58%) owing to analogous terms used in radiology reports not being included in the accepted gold standard definition. Conclusions Automated data abstraction from EHR is highly accurate and faster than manual abstraction. Key challenges include poorly structured EHR and the use of analogous terms beyond the accepted gold standard definition. The application of NLP can facilitate real-world evidence studies at a greater scale than could be achieved with manual data extraction

Dataset Information

Expanded Access as a source of real-world data: An overview of FDA and EMA approvals.

Publications

Expanded Access as a source of real-world data: An overview of FDA and EMA approvals.

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