Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.
ShahMRahmanATheoretMR, et al. The drug-dosing conundrum in oncology—when less is more. N Engl J Med2021; 385(16): 1445–1447.
3.
LeeSMBackenrothDCheungYKK, et al. Case example of dose optimization using data from bortezomib dose-finding clinical trials. J Clin Oncol2016; 34(12): 1395–1401.
4.
LeeSMZhangYMinasianLM, et al. Using delayed toxicities to re-evaluate tolerability in phase 2 trials: a case example using bortezomib. Cancer Investig2017; 35(7): 484–489.
5.
Postel-VinaySGomez-RocaCMolifeLR, et al. Phase I trials of molecularly targeted agents: should we pay more attention to late toxicities?J Clin Oncol2011; 29(13): 1728–1735.
6.
Postel-VinaySAspeslaghSLanoyE, et al. Challenges of phase 1 clinical trials evaluating immune checkpoint-targeted antibodies. Ann Oncol2016; 27(2): 214–224.
7.
PuzanovIDiabAAbdallahK, et al. Managing toxicities associated with immune checkpoint inhibitors: consensus recommendations from the Society for Immunotherapy of Cancer (SITC) Toxicity Management Working Group. J Immunother Cancer2017; 5(1): 95.
FlinnIWKahlBSLeonardJP, et al. Idelalisib, a selective inhibitor of phosphatidylinositol 3-kinase-δ, as therapy for previously treated indolent non-Hodgkin lymphoma. Blood2014; 123(22): 3406–3413.
10.
KahlBSSpurgeonSEFurmanRR, et al. A phase 1 study of the pi3kδ inhibitor idelalisib in patients with relapsed/refractory mantle cell lymphoma (MCL). Blood2014; 123(22): 3398–3405.
11.
BrownJRByrdJCCoutreSE, et al. Idelalisib, an inhibitor of phosphatidylinositol 3-kinase p110δ, for relapsed/refractory chronic lymphocytic leukemia. Blood2014; 123(22): 3390–3397.
12.
Warnings and precautions, contraindications, and boxed warning sections of labeling for human prescription drug and biological products—content and format, FDA guidance document, https://www.fda.gov/media/71866/download (accessed 24 November 2023).
13.
CoutréSEBarrientosJCBrownJR, et al. Management of adverse events associated with idelalisib treatment: expert panel opinion. Leuk Lymphoma2015; 56(10): 2779–2786.
14.
HanlonABranderDM. Managing toxicities of phosphatidylinositol-3-kinase (PI3K) inhibitors. Hematology Am Soc Hematol Educ Program2020; 2020(1): 346–356.
15.
CheungYKChappellR. Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics2000; 56(4): 1177–1182.
16.
BraunTM. Generalizing the TITE-CRM to adapt for early- and late-onset toxicities. Stat Med2006; 25(12): 2071–2083.
17.
BekeleBNJiYShenY, et al. Monitoring late-onset toxicities in phase I trials using predicted risks. Biostatistics2008; 9(3): 442–457.
18.
MauguenALe DeleyMCZoharS. Dose-finding approach for dose escalation with overdose control considering incomplete observations. Stat Med2011; 30(13): 1584–1594.
19.
YuanYYinG. Robust EM continual reassessment method in oncology dose finding. J Am Stat Assoc2011; 106(495): 818–831.
20.
LiuSYinGYuanY. Bayesian data augmentation dose finding with continual reassessment method and delayed toxicity. Ann Appl Stat2013; 7(4): 1837–2457.
21.
YinGZhengSXuJ. Fractional dose-finding methods with late-onset toxicity in phase I clinical trials. J Biopharm Stat2013; 23(4): 856–870.
22.
TighiouartMLiuYRogatkoA. Escalation with overdose control using time to toxicity for cancer phase I clinical trials. PLoS ONE2014; 9(3): e93070.
23.
ZhengWZhaoYLuY, et al. A Bayesian three-parameter logistic model for early- and late-onset DLTs in oncology phase I studies. J Biopharm Stat2016; 26(2): 339–351.
24.
ChappleAGWojcikJJMcDanielLS. A regression based phase I clinical trial for late-onset toxicities without clinician elicitation. Contemp Clin Trials Commun2019; 14: 100327.
25.
AndrillonAChevretSLeeSM, et al. Dose-finding design and benchmark for a right censored endpoint. J Biopharm Stat2020; 30(6): 948–963.
26.
ZhuJSabanés BovéDLiaoZ, et al. Rolling continual reassessment method with overdose control: an efficient and safe dose escalation design. Contemp Clin Trials2021; 107: 106436.
27.
WagesNABraunTMConawayMR. Isotonic design for phase I cancer clinical trials with late-onset toxicities. J Biopharm Stat2023; 33(3): 357–370.
28.
YuanYLinRLiD, et al. Time-to-event Bayesian optimal interval design to accelerate phase I trials. Clin Cancer Res2018; 24(20): 4921–4930.
29.
LinRYuanY. Time-to-event model-assisted designs for dose-finding trials with delayed toxicity. Biostatistics2020; 21(4): 807–824.
30.
ZhouHChenCSunL, et al. A novel framework of Bayesian optimal interval design for phase I trials with late-onset toxicities. Contemp Clin Trials2021; 105: 106404.
31.
XuZLinX. Probability-of-decision interval 3+3 (POD-i3+3) design for phase I dose finding trials with late-onset toxicity. Stat Methods Med Res2022; 31(3): 534–548.
32.
JinHYinG. Time-to-event calibration-free odds design: a robust efficient design for phase I trials with late-onset outcomes. Pharm Stat2023; 22(5): 773–783.
33.
BraunTMYuanZThallPF. Determining a maximum-tolerated schedule of a cytotoxic agent. Biometrics2005; 61(2): 335–343.
34.
BraunTMThallPFNguyenH, et al. Simultaneously optimizing dose and schedule of a new cytotoxic agent. Clin Trials2007; 4(2): 113–124.
35.
LiuCABraunTM. Parametric non-mixture cure models for schedule finding of therapeutic agents. J R Stat Soc Ser C Appl Stat2009; 58(2): 225–236.
36.
ZhangJBraunTM. A phase I Bayesian adaptive design to simultaneously optimize dose and schedule assignments both between and within patients. J Am Stat Assoc2013; 108(503): 892–901.
37.
BarnettHBoixOKontosD, et al. Dose finding studies for therapies with late-onset toxicities: a comparison study of designs. Stat Med2022; 41(30): 5767–5788.
38.
O’QuigleyJPepeMFisherL. Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics1990; 46(1): 33–48.
39.
LiuSYuanY. Bayesian optimal interval designs for phase I clinical trials. J R Stat Soc C2015; 64(3): 507–523.
40.
LeeSMYing KuenCheung. Model calibration in the continual reassessment method. Clin Trials2009; 6(3): 227–238.
41.
GünhanBKWeberSSeroutouA, et al. Phase I dose-escalation oncology trials with sequential multiple schedules. BMC Med Res Methodol2021; 21(1): 69.
42.
WagesNAO’QuigleyJConawayMR. Phase I design for completely or partially ordered treatment schedules. Stat Med2014; 33(4): 569–579.
43.
FernandesLLTaylorJMMurrayS. Adaptive phase I clinical trial design using Markov models for conditional probability of toxicity. J Biopharm Stat2016; 26(3): 475–498.
44.
LyuJCurranEJiY. Bayesian adaptive design for finding the maximum tolerated sequence of doses in multicycle dose-finding clinical trials. JCO Precis Oncol2018; 2: 1–19.
45.
UrsinoMBiardLChevretS. DICE: a Bayesian model for early dose finding in phase I trials with multiple treatment courses. Biom J2022; 64(8): 1486–1497.
46.
LeeSMUrsinoMCheungYK, et al. Dose-finding designs for cumulative toxicities using multiple constraints. Biostatistics2019; 20(1): 17–29.
47.
AndrillonABiardLLeeSM. Incorporating patient-reported outcomes in dose-finding clinical trials with continuous patient enrollment. J Biopharm Stat2023; 39: 310–325.
48.
RiviereMKYuanYJourdanJH, et al. Phase I/II dose-finding design for molecularly targeted agent: plateau determination using adaptive randomization. Stat Methods Med Res2018; 27(2): 466–479.
49.
YanDTaitCWagesNA, et al. Generalization of the time-to-event continual reassessment method to bivariate outcomes. J Biopharm Stat2019; 29(4): 635–647.
50.
TakedaKMoritaSTaguriM. TITE-BOIN-ET: time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes. Pharm Stat2020; 19(3): 335–349.
51.
AltzerinakouMAPaolettiX. An adaptive design for the identification of the optimal dose using joint modeling of continuous repeated biomarker measurements and time-to-toxicity in phase I/II clinical trials in oncology. Stat Methods Med Res2020; 29(2): 508–521.
52.
KoopmeinersJSModianoJ. A Bayesian adaptive phase I-II clinical trial for evaluating efficacy and toxicity with delayed outcomes. Clin Trials2014; 11(1): 38–48.
53.
HobbsBPThallPFLinSH. Bayesian group sequential clinical trial design using total toxicity burden and progression-free survival. J R Stat Soc Ser C Appl Stat2016; 65(2): 273–297.
54.
YuanYYinG. Bayesian dose finding by jointly modelling toxicity and efficacy as time-to-event outcomes. J R Stat Soc C2009; 58(5): 719–736.
55.
BiardLLeeSMChengB. Seamless phase I/II design for novel anticancer agents with competing disease progression. Stat Med2021; 40(21): 4568–4581.
56.
AndrillonAChevretSLeeSM, et al. Surv-CRM-12: a Bayesian phase I/II survival CRM for right-censored toxicity endpoints with competing disease progression. Stat Med2022; 41(29): 5753–5766.
57.
JinIHLiuSThallPF, et al. Using data augmentation to facilitate conduct of phase I-II clinical trials with delayed outcomes. J Am Stat Assoc2014; 109(506): 525–536.
58.
LiuSJohnsonVE. A robust Bayesian dose-finding design for phase I/II clinical trials. Biostatistics2016; 17(2): 249–263.
59.
ZhangYCaoSZhangC, et al. A Bayesian adaptive phase I/II clinical trial design with late-onset competing risk outcomes. Biometrics2021; 77(3): 796–808.
60.
ZhouYLinRLeeJJ, et al. Tite-boin12: a Bayesian phase I/II trial design to find the optimal biological dose with late-onset toxicity and efficacy. Stat Med2022; 41(11): 1918–1931.
61.
PhillipsRHazellLSauzetO, et al. Analysis and reporting of adverse events in randomised controlled trials: a review. BMJ Open2019; 9(2): e024537.
62.
YapCBeddingAde BonoJ, et al. The need for reporting guidelines for early phase dose-finding trials: dose-finding consort extension. Nat Med2022; 28(1): 6–7.
63.
FrancisKEGebskiVLordSJ, et al. Reporting the trajectories of adverse events over the entire treatment course in patients with recurrent platinum-sensitive ovarian cancer treated with platinum-based combination chemotherapy regimens: a graphical approach to trial adverse event reporting. Eur J Cancer2021; 148: 251–259.
64.
StegherrRSchmoorCLübbertM, et al. Estimating and comparing adverse event probabilities in the presence of varying follow-up times and competing events. Pharm Stat2021; 20(6): 1125–1146.
65.
StegherrRSchmoorCBeyersmannJ, et al. Survival analysis for AdVerse events with VarYing follow-up times (SAVVY)—estimation of adverse event risks. Trials2021; 22(1): 420.
66.
LeeSMFanWWangA, et al. Novel approaches for dynamic visualization of adverse event data in oncology clinical trials: a case study using immunotherapy trial S1400-I (SWOG). JCO Clin Cancer Inform2023; 7: e2200165.
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