Evolving Landscape of US Food and Drug Administration Drug Approval in the Era of Precision Oncology: Finding the Right Balance Between Access and Safety

Drug development for major chronic health conditions-aligning with growing public health needs: Proceedings from a multistakeholder think tank

The global pharmaceutical industry portfolio is skewed towards cancer and rare diseases due to more predictable development pathways and financial incentives. In contrast, drug development for major chronic health conditions that are responsible for a large part of mortality and disability worldwide is stalled. To examine the processes of novel drug development for common chronic health conditions, a multistakeholder Think Tank meeting, including thought leaders from academia, clinical practice, non-profit healthcare organizations, the pharmaceutical industry, the Food and Drug Administration (FDA), payors as well as investors, was convened in July 2022. Herein, we summarize the proceedings of this meeting, including an overview of the current state of drug development for chronic health conditions and key barriers that were identified. Six major action items were formulated to accelerate drug development for chronic diseases, with a focus on improving the efficiency of clinical trials and rapid implementation of evidence into clinical practice.

Update to Drugs, Devices, and the FDA: How Recent Legislative Changes Have Impacted Approval of New Therapies

Two major legislative actions since 2015, the 21st Century Cures Act of 2016 and the U.S. Food and Drug Administration (FDA) Reauthorization Act of 2017, contain significant provisions that potentially streamline drug development times, and by extension, may reduce costs. Evidence suggests, however, that development times have already been significantly affected by previous legislation and FDA programs, through accelerated approval pathways and adoption of more flexible definitions of clinical evidence of efficacy. The COVID-19 pandemic is pushing researchers and commercial entities to further test the limits of drug and vaccine development times and approvals, at an as yet unknown level of risk to patients. COVID-19 drug and vaccine trials are even now making use of accelerated drug approval programs, blended trials, and adaptive trial design to accelerate approval of therapeutics in the pandemic.

Prediction of Drug Approval After Phase I Clinical Trials in Oncology: RESOLVED2

PURPOSE

Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a machine learning algorithm (RESOLVED2) to predict drug development outcome, which could support early go/no-go decisions after P1CTs by better selection of drugs suitable for further development.

METHODS

PubMed abstracts of P1CTs reporting on ANAs were used together with pharmacologic data from the DrugBank5.0 database to model time to US Food and Drug Administration (FDA) approval (FDA approval-free survival) since the first P1CT publication. The RESOLVED2 model was trained with machine learning methods. Its performance was evaluated on an independent test set with weighted concordance index (IPCW).

RESULTS

We identified 462 ANAs from PubMed that matched with DrugBank5.0 (P1CT publication dates 1972 to 2017). Among 1,411 variables, 28 were used by RESOLVED2 to model the FDA approval-free survival, with an IPCW of 0.89 on the independent test set. RESOLVED2 outperformed a model that was based on efficacy/toxicity (IPCW, 0.69). In the test set at 6 years of follow-up, 73% (95% CI, 49% to 86%) of drugs predicted to be approved were approved, whereas 92% (95% CI, 87% to 98%) of drugs predicted to be nonapproved were still not approved (log-rank P < .001). A predicted approved drug was 16 times more likely to be approved than a predicted nonapproved drug (hazard ratio, 16.4; 95% CI, 8.40 to 32.2).

CONCLUSION

As soon as P1CT completion, RESOLVED2 can predict accurately the time to FDA approval. We provide the proof of concept that drug development outcome can be predicted by machine learning strategies.