The next generation of evidence-based medicine
Ramsey, B. W., Nepom, G. T. & Lonial, S. Academic, foundation, and industry collaboration in finding new therapies. N. Engl. J. Med. 376, 1762–1769 (2017).
Google Scholar
Butler, D. Translational research: crossing the valley of death. Nature 453, 840–842 (2008).
Google Scholar
DiMasi, J. A., Grabowski, H. G. & Hansen, R. W. Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ. 47, 20–33 (2016).
Google Scholar
Wouters, O. J., McKee, M. & Luyten, J. Estimated research and development investment needed to bring a new medicine to market, 2009–2018. JAMA 323, 844–853 (2020).
Google Scholar
Subbiah, V. A global effort to understand the riddles of COVID-19 and cancer. Nat. Cancer 1, 943–945 (2020).
Google Scholar
Flaherty, K. T. et al. Rethinking cancer clinical trial conduct induced by COVID-19: an academic center, industry, government, and regulatory agency perspective. Cancer Discov. 11, 1881–1885 (2021).
Google Scholar
Samimi, G. et al. Lessons learned from the impact of COVID-19 on NCI-sponsored cancer prevention clinical trials: moving toward participant-centric study designs. Cancer Prev. Res. 15, 279–284 (2022).
Google Scholar
National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP). Health and Economic Costs of Chronic Diseases (2022).
Menta, A. K., Subbiah, I. M. & Subbiah, V. Bringing wearable devices into oncology practice: fitting smart technology in the clinic. Discov. Med. 26, 261–270 (2018).
Krittanawong, C., Johnson, K. W. & Tang, W. W. How artificial intelligence could redefine clinical trials in cardiovascular medicine: lessons learned from oncology. Per. Med. 16, 83–88 (2019).
Google Scholar
Subbiah, V. & Kurzrock, R. Challenging standard-of-care paradigms in the precision oncology era. Trends Cancer 4, 101–109 (2018).
Google Scholar
Woodcock, J. & LaVange, L. M. Master protocols to study multiple therapies, multiple diseases, or both. N. Engl. J. Med. 377, 62–70 (2017).
Google Scholar
Park, J. J. H. et al. Systematic review of basket trials, umbrella trials, and platform trials: a landscape analysis of master protocols. Trials 20, 572 (2019).
Google Scholar
Dickson, D. et al. The master observational trial: a new class of master protocol to advance precision medicine. Cell 180, 9–14 (2020).
Google Scholar
Das, S. & Lo, A. W. Re-inventing drug development: a case study of the I-SPY 2 breast cancer clinical trials program. Contemp. Clin. Trials 62, 168–174 (2017).
Google Scholar
Redman, M. W. et al. Biomarker-driven therapies for previously treated squamous non-small-cell lung cancer (Lung-MAP SWOG S1400): a biomarker-driven master protocol. Lancet Oncol. 21, 1589–1601 (2020).
Google Scholar
Subbiah, V. et al. Pan-cancer efficacy of vemurafenib in BRAFV600-mutant non-melanoma cancers. Cancer Discov. 10, 657–663 (2020).
Google Scholar
Subbiah, V. et al. Dabrafenib and trametinib treatment in patients with locally advanced or metastatic BRAFV600-mutant anaplastic thyroid cancer. J. Clin. Oncol. 36, 7–13 (2018).
Google Scholar
Subbiah, V. et al. Pan-cancer efficacy of pralsetinib in patients with RET fusion-positive solid tumors from the phase 1/2 ARROW trial. Nat. Med. 28, 1640–1645 (2022).
Google Scholar
Drilon, A. et al. Efficacy of selpercatinib in RET fusion–positive non–small-cell lung cancer. N. Engl. J. Med. 383, 813–824 (2020).
Google Scholar
Subbiah, V. et al. Tumour-agnostic efficacy and safety of selpercatinib in patients with RET fusion-positive solid tumours other than lung or thyroid tumours (LIBRETTO-001): a phase 1/2, open-label, basket trial. Lancet Oncol. 23, 1261–1273 (2022).
Google Scholar
Normand, S.-L. T. The RECOVERY platform. N. Engl. J. Med. 384, 757–758 (2020).
Google Scholar
Turner, N. C. et al. Circulating tumour DNA analysis to direct therapy in advanced breast cancer (plasmaMATCH): a multicentre, multicohort, phase 2a, platform trial. Lancet Oncol. 21, 1296–1308 (2020).
Google Scholar
Gold, S. M. et al. Platform trials and the future of evaluating therapeutic behavioural interventions. Nat. Rev. Psychol. 1, 7–8 (2022).
Google Scholar
Dickson, D. et al. Snapshot: trial types in precision medicine. Cell 181, 208 (2020).
Google Scholar
Ochoa, D. et al. Human genetics evidence supports two-thirds of the 2021 FDA-approved drugs. Nat. Rev. Drug Discov. 21, 551 (2022).
Google Scholar
Wickström, K. & Moseley, J. Biomarkers and surrogate endpoints in drug development: a european regulatory view. Invest. Ophthalmol. Vis. Sci. 58, BIO27–BIO33 (2017).
Google Scholar
Robb, M. A., McInnes, P. M. & Califf, R. M. Biomarkers and surrogate endpoints: developing common terminology and definitions. JAMA 315, 1107–1108 (2016).
Google Scholar
Landers, M., Dorsey, R. & Saria, S. Digital endpoints: definition, benefits, and current barriers in accelerating development and adoption. Digit. Biomark. 5, 216–223 (2021).
Google Scholar
Yang, Y. et al. Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals. Nat. Med. 28, 2207–2215 (2022).
Perez, M. V. et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N. Engl. J. Med. 381, 1909–1917 (2019).
Google Scholar
Guo, Y. et al. Mobile photoplethysmographic technology to detect atrial fibrillation. J. Am. Coll. Cardiol. 74, 2365–2375 (2019).
Google Scholar
Lubitz, S. A. et al. Rationale and design of a large population study to validate software for the assessment of atrial fibrillation from data acquired by a consumer tracker or smartwatch: the Fitbit Heart Study. Am. Heart J. 238, 16–26 (2021).
Google Scholar
Rizas, K. D. et al. Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial. Nat. Med. 28, 1823–1830 (2022).
FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource (2016).
Desai, A. & Subbiah, V. COVID-19 pandemic and cancer clinical trial pandemonium: finding the silver lining. J. Immunother. Precis. Oncol. 4, 64–66 (2020).
Google Scholar
Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Multimodal biomedical AI. Nat. Med. 28, 1773–1784 (2022).
Food and Drug Administration. Digital health technologies for remote data acquisition in clinical investigations, draft guidance for industry, investigators, and other stakeholders. (2022).
Thomas, K. A. & Kidziński, Ł. Artificial intelligence can improve patients’ experience in decentralized clinical trials. Nat. Med. (2022).
Wong, D. R., Bhattacharya, S. & Butte, A. J. Prototype of running clinical trials in an untrustworthy environment using blockchain. Nat. Commun. 10, 917 (2019).
Google Scholar
Brown, D. G., Wobst, H. J., Kapoor, A., Kenna, L. A. & Southall, N. Clinical development times for innovative drugs. Nat. Rev. Drug Discov. 21, 793–794 (2021).
Google Scholar
Anderson, A. S. A lightspeed approach to pandemic drug development. Nat. Med. 28, 1538 (2022).
Google Scholar
Subbiah, V. et al. Accelerated approvals hit the target in precision oncology. Nat. Med. 28, 1976–1979 (2022).
Google Scholar
Kepplinger, E. E. FDA’s expedited approval mechanisms for new drug products. Biotechnol. Law Rep. 34, 15–37 (2015).
Google Scholar
Ncube, B. M., Dube, A. & Ward, K. Establishment of the African Medicines Agency: progress, challenges and regulatory readiness. J. Pharm. Policy Pract. 14, 29 (2021).
Google Scholar
Moyers, J. T. & Subbiah, V. Think globally, act locally: globalizing precision oncology. Cancer Discov. 12, 886–888 (2022).
Google Scholar
Degtiar, I. & Rose, S. A review of generalizability and transportability. Annu. Rev. Stat. Appl. 10, 1 (2023).
Ramagopalan, S. V. et al. Transportability of overall survival estimates from US to Canadian patients with advanced non–small cell lung cancer with implications for regulatory and health technology assessment. JAMA Netw. Open 5, e2239874 (2022).
Google Scholar
Nguengang Wakap, S. et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur. J. Hum. Genet. 28, 165–173 (2020).
Google Scholar
Pizzamiglio, C., Vernon, H. J., Hanna, M. G. & Pitceathly, R. D. S. Designing clinical trials for rare diseases: unique challenges and opportunities. Nat. Rev. Methods Primers 2, 13 (2022).
Google Scholar
Food and Drug Administration. FDA approves alpelisib for PIK3CA-related overgrowth spectrum. (2022).
Food and Drug Administration. Real-time oncology review. (2022).
Food and Drug Administration. Assessment aid. (2022).
Lillie, E. O. et al. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per. Med. 8, 161–173 (2011).
Google Scholar
Kim, J. et al. Patient-customized oligonucleotide therapy for a rare genetic disease. N. Engl. J. Med. 381, 1644–1652 (2019).
Google Scholar
Fajgenbaum, D. C. et al. Identifying and targeting pathogenic PI3K/AKT/mTOR signaling in IL-6 blockade–refractory idiopathic multicentric Castleman disease. J. Clin. Investig. 129, 4451–4463 (2019).
Google Scholar
Subbiah, V. et al. Selective RET kinase inhibition for patients with RET-altered cancers. Ann. Oncol. 29, 1869–1876 (2018).
Google Scholar
Woodcock, J. & Marks, P. Drug regulation in the era of individualized therapies. N. Engl. J. Med. 381, 1678–1680 (2019).
Google Scholar
Björnsson, B. et al. Digital twins to personalize medicine. Genome Med. 12, 4 (2019).
Google Scholar
Laubenbacher, R., Sluka, J. P. & Glazier, J. A. Using digital twins in viral infection. Science 371, 1105–1106 (2021).
Google Scholar
Sherman, R. E., Davies, K. M., Robb, M. A., Hunter, N. L. & Califf, R. M. Accelerating development of scientific evidence for medical products within the existing US regulatory framework. Nat. Rev. Drug Discov. 16, 297–298 (2017).
Google Scholar
Concato, J. & Corrigan-Curay, J. Real-world evidence—where are we now? N. Engl. J. Med. 386, 1680–1682 (2022).
Google Scholar
Concato, J., Stein, P., Dal Pan, G. J., Ball, R. & Corrigan-Curay, J. Randomized, observational, interventional, and real-world—what’s in a name? Pharmacoepidemiol. Drug Saf. 29, 1514–1517 (2020).
Google Scholar
Food and Drug Administration. Real-world evidence. (2022).
Mishra-Kalyani, P. S. et al. External control arms in oncology: current use and future directions. Ann. Oncol. 33, 376–383 (2022).
Google Scholar
Popat, S. et al. Addressing challenges with real-world synthetic control arms to demonstrate the comparative effectiveness of pralsetinib in non-small cell lung cancer. Nat. Commun. 13, 3500 (2022).
Google Scholar
Gainor, J. F. et al. Pralsetinib for RET fusion-positive non-small-cell lung cancer (ARROW): a multi-cohort, open-label, phase 1/2 study. Lancet Oncol. 22, 959–969 (2021).
Google Scholar
Subbiah, V. et al. Pralsetinib for patients with advanced or metastatic RET-altered thyroid cancer (ARROW): a multi-cohort, open-label, registrational, phase 1/2 study. Lancet Diabetes Endocrinol. 9, 491–501 (2021).
Center for Drug Evaluation and Research, Food and Drug Administration. NDA multi-disciplinary review and evaluation: NDA 213756 Koselugo (selumetinib). (2020).
Casey, D. et al. FDA approval summary: selumetinib for plexiform neurofibroma. Clin. Cancer Res. 27, 4142–4146 (2021).
Google Scholar
Wilkinson, S. et al. Assessment of alectinib vs ceritinib in ALK-positive non–small cell lung cancer in phase 2 trials and in real-world data. JAMA Netw. Open 4, e2126306 (2021).
Google Scholar
Bartlett, R. H. et al. Extracorporeal membrane oxygenation (ECMO) in neonatal respiratory failure. 100 cases. Ann. Surg. 204, 236–245 (1986).
Google Scholar
McCune, S. & Portman, R. J. Innovation and opportunities in pediatric therapeutic development. Ther. Innov. Regul. Sci. 53, 564–566 (2019).
Google Scholar
Subbiah, V. Fast-tracking novel drugs in pediatric oncology. Cell Cycle 14, 1127–1128 (2015).
Google Scholar
Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).
Google Scholar
Weissler, E. H. et al. The role of machine learning in clinical research: transforming the future of evidence generation. Trials 22, 537 (2021).
Google Scholar
Adashek, J. J., Subbiah, I. M. & Subbiah, V. Artificial intelligence systems assisting oncologists? Resist and desist or enlist and coexist. Oncologist 24, 1291–1293 (2019).
Google Scholar
Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).
Google Scholar
Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat. Med. 26, 1364–1374 (2020).
Google Scholar
Cruz Rivera, S. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat. Med. 26, 1351–1363 (2020).
Google Scholar
Kickingereder, P. et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 20, 728–740 (2019).
Google Scholar
Jarow, J. P., LaVange, L. & Woodcock, J. Multidimensional evidence generation and FDA regulatory decision making: defining and using “real-world” data. JAMA 318, 703–704 (2017).
Google Scholar
Dean, B. Social network usage & growth statistics: how many people use social media in 2022? Backlinko (2021).
Wicks, P., Vaughan, T. E., Massagli, M. P. & Heywood, J. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nat. Biotechnol. 29, 411–414 (2011).
Google Scholar
Morgan, G. et al. The (r)evolution of social media in oncology: engage, enlighten, and encourage. Cancer Discov. 12, 1620–1624 (2022).
Google Scholar
Pessoa-Amorim, G. et al. Making trials part of good clinical care: lessons from the RECOVERY trial. Future Healthc. J. 8, e243–e250 (2021).
Google Scholar
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