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Predicting success in regulatory approval from Phase I results.


ABSTRACT: Drug development in oncology is resource intensive and has a high failure rate. In this exploratory analysis, we aimed to identify the characteristics and outcomes of published Phase I studies associated with future Food and Drug Administration (FDA) approval.Phase I studies of approved and non-approved anticancer agents between 2000 and 2013 were retrospectively examined. Fisher's exact and chi-squared tests were used to compare the potential predictive measures.Phase I studies of 88 anticancer agents (54 approved and 34 non-approved by the FDA), treating a total of 4,423 subjects, were examined. The median number of patients in Phase I trials of approved and non-approved agents was 44.5 and 32, respectively. A total of 423 subjects (86 reporting studies) had a complete responses, and 342 subjects (80 reporting studies) had a partial responses (PR). A higher number of PR (P < 0.001), PR rate (P = 0.003) and longer PR duration (P = 0.001) were predictive of regulatory success.These preliminary findings indicate that objective responses in Phase I trials may have predictive value for later regulatory approval.

SUBMITTER: Malik L 

PROVIDER: S-EPMC4489154 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Predicting success in regulatory approval from Phase I results.

Malik Laeeq L   Mejia Alex A   Parsons Helen H   Ehler Benjamin B   Mahalingam Devalingam D   Brenner Andrew A   Sarantopoulos John J   Weitman Steven S  

Cancer chemotherapy and pharmacology 20140923 5


<h4>Purpose</h4>Drug development in oncology is resource intensive and has a high failure rate. In this exploratory analysis, we aimed to identify the characteristics and outcomes of published Phase I studies associated with future Food and Drug Administration (FDA) approval.<h4>Methods</h4>Phase I studies of approved and non-approved anticancer agents between 2000 and 2013 were retrospectively examined. Fisher's exact and chi-squared tests were used to compare the potential predictive measures.  ...[more]

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