The FDA Advisory Panel has rejected Pfizer’s proposal to expand the use of Talzenna, a PARP inhibitor, for the treatment of metastatic castration-resistant prostate cancer (mCRPC). The panel’s decision was based on the lack of sufficient evidence to support the efficacy of Talzenna in this patient population. Talzenna is currently approved for the treatment of breast cancer in patients with a BRCA1 or BRCA2 mutation.
The rejection is a significant setback for Pfizer, which had hoped to expand the use of Talzenna into a larger market. The company had presented data from the TALAPRO-2 trial, which showed that Talzenna combined with enzalutamide improved progression-free survival (PFS) compared to enzalutamide alone. However, the panel was not convinced that the data was sufficient to support approval.
The rejection of Talzenna’s expanded use comes as the use of artificial intelligence (AI) is gaining traction in clinical trials. AI can help to improve the diversity of clinical trials by identifying patients who are more likely to respond to a particular treatment. This is particularly important in prostate cancer, where African American men are more likely to develop aggressive disease and have poorer outcomes.
The use of AI in clinical trials can also help to reduce costs and improve efficiency. By using machine learning algorithms to analyze large datasets, researchers can identify patterns and trends that may not be apparent through traditional analysis methods. This can help to identify new potential treatments and improve the design of clinical trials.
The rejection of Talzenna’s expanded use highlights the need for more diverse and representative clinical trials. The TALAPRO-2 trial was criticized for its lack of diversity, with only 3% of patients being African American. This lack of diversity makes it difficult to determine whether the results of the trial will apply to all patient populations.
In contrast, some clinical trials are now using AI to recruit more diverse patient populations. For example, the Prostate Cancer Clinical Trials Consortium is using AI to identify patients who are eligible for clinical trials and to match them with trials that are recruiting patients with similar characteristics. This approach has the potential to improve the diversity of clinical trials and to ensure that new treatments are effective in all patient populations.
Overall, the rejection of Talzenna’s expanded use highlights the need for more diverse and representative clinical trials. The use of AI in clinical trials has the potential to improve the diversity of trials and to ensure that new treatments are effective in all patient populations.