ABSTRACT
BACKGROUND: Precision oncology medicines represent a paradigm shift compared to non-precision oncology medicines in cancer therapy, in some situations delivering more clinical benefit, and potentially lowering healthcare costs. We determined whether employing a companion diagnostic (CDx) approach during oncology medicines development delivers effective therapies that are within the cost constraints of current health systems. R&D costs of developing a medicine are subject to debate, with average estimates ranging from $765 million (m) to $4.6 billion (b). Our aim was to determine whether precision oncology medicines are cheaper to bring from R&D to market; a secondary goal was to determine whether precision oncology medicines have a greater return on investment (ROI). METHOD: Data on oncology medicines approved between 1997 and 2020 by the US Food and Drug Administration (FDA) were analysed from the Securities and Exchange Commission (SEC) filings. Data were compiled from 10-K, 10-Q, and 20-F financial performance filings on medicines' development costs through their R&D lifetime. Clinical trial data were split into clinical trial phases 1-3 and probability of success (POS) of trials was calculated, along with preclinical costs. Cost-of-capital (CoC) approach was applied and, if appropriate, a tax rebate was subtracted from the total. RESULTS: Data on 42 precision and 29 non-precision oncology medicines from 56 companies listed by the National Cancer Institute which had complete data available were analysed. Estimated mean cost to deliver a new oncology medicine was $4.4b (95% CI, $3.6-5.2b). Costs to bring a precision oncology medicine to market were $1.1b less ($3.5b; 95% CI, $2.7-4.5b) compared to non-precision oncology medicines ($4.6b; 95% CI, $3.5-6.1b). The key driver of costs was POS of clinical trials, accounting for a difference of $591.3 m. Additional data analysis illustrated that there was a 27% increase in return on investment (ROI) of precision oncology medicines over non-precision oncology medicines. CONCLUSION: Our results provide an accurate estimate of the R&D spend required to bring an oncology medicine to market. Deployment of a CDx at the earliest stage substantially lowers the cost associated with oncology medicines development, potentially making them available to more patients, while staying within the cost constraints of cancer health systems.
ABSTRACT
BACKGROUND: In 2014, the COIN-B clinical trial demonstrated that intermittent cetuximab (IC) was a safe alternative to continuous cetuximab (CC), with less cytotoxic chemotherapy, in first-line treatment for KRAS wild-type metastatic colorectal cancer (mCRC). Cetuximab has been available for this indication in England since 2015, but treatment breaks beyond 6 weeks were prohibited, despite real-world evidence that therapy de-escalation maintains equivalent disease control, but with superior Quality-of-Life (QoL). We performed health economic analyses of IC versus CC and used this evidence to help underpin policy change and guide clinical practice through reduction in unnecessary treatment for mCRC patients. METHODS: Employing cost-minimization analysis, we conducted partitioned survival modelling (PSM) and Markov Chain Monte-Carlo (MCMC) simulation to determine costs and quality-adjusted-life-years for IC versus CC. RESULTS: IC reduced costs by £â¯35,763 (PSM; pâ¯<â¯0.001) or £â¯30,189 (MCMC) per patient annually, while preserving treatment efficacy and enhancing QoL. Extrapolating to all mCRC patients eligible for cetuximab therapy would have generated cost savings of ~£â¯1.2 billion over this cohort's lifetime. These data helped underpin a request to NHS England to remove treatment break restrictions in first-line mCRC therapy, which has been adopted as an interim treatment option policy in colorectal cancer during the Covid-19 pandemic. CONCLUSIONS: Our results highlight substantial cost savings achievable by treatment de-escalation, while also reinforcing the importance of therapy breaks to potentially increase tumour responsiveness and reduce treatment toxicity. Our study also highlights how health economic evidence can influence health policy, championing reduced treatment intensity approaches without compromising patient outcomes, which is of particular relevance when addressing the reduced capacity and treatment backlogs experienced during the pandemic.
