PMID- 32951518
OWN - NLM
STAT- MEDLINE
DCOM- 20211125
LR  - 20211125
IS  - 1520-5711 (Electronic)
IS  - 1054-3406 (Linking)
VI  - 30
IP  - 6
DP  - 2020 Nov 1
TI  - TEPI-2 and UBI: designs for optimal immuno-oncology and cell therapy dose finding 
      with toxicity and efficacy.
PG  - 979-992
LID - 10.1080/10543406.2020.1814802 [doi]
AB  - Conventional dose finding designs in oncology drug development target on the 
      identification of the maximum tolerated dose (MTD), with the assumption that the 
      MTD has the most potential of clinical activity among those identified tolerable 
      dose levels. However, immuno-oncology (I-O) and cell therapy area, may lack 
      dose-efficacy monotonicity, posing significant challenges in the statistical 
      designs for dose finding trials. A desirable design should empower the trial to 
      identify the right dose level with tolerable toxicity and acceptable efficacy. 
      Such dose is called as optimal biological dose (OBD), which is more appropriate 
      to be considered as the primary objective of the first-in-human trial in I-O and 
      cell therapy than MTD. We propose two model-assisted designs in this setting: the 
      toxicity and efficacy probability interval-2 (TEPI-2) design and the 
      utility-based interval (UBI) design that incorporate the toxicity and efficacy 
      outcomes simultaneously and identify a dose that has high probability of 
      acceptable efficacy with manageable toxicity. The proposed designs can generate 
      decision tables before trial starts to facilitate practical and easy-to-implement 
      applications. Through simulation studies, our proposed novel designs demonstrate 
      superior performance in accuracy, efficiency, and safety. Additionally, they can 
      reduce the number of patients and shorten clinical development timeline. We also 
      illustrate the advantages of proposed methods by redesigning a CAR T-cell therapy 
      phase I clinical trial for multiple myeloma and summarize our recommendations in 
      the discussion section.
FAU - Li, Pin
AU  - Li P
AUID- ORCID: 0000-0003-4508-3762
AD  - Department of Public Health Sciences, Henry Ford Hospital Systems, Detroit, 
      Michigan, USA.
FAU - Liu, Rachael
AU  - Liu R
AUID- ORCID: 0000-0001-6033-4510
AD  - Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, 
      Massachusetts, USA.
FAU - Lin, Jianchang
AU  - Lin J
AUID- ORCID: 0000-0002-9123-0690
AD  - Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, 
      Massachusetts, USA.
FAU - Ji, Yuan
AU  - Ji Y
AD  - Department of Public Health Sciences, University of Chicago, Chicago, Illinois, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20200920
PL  - England
TA  - J Biopharm Stat
JT  - Journal of biopharmaceutical statistics
JID - 9200436
SB  - IM
MH  - Bayes Theorem
MH  - Cell- and Tissue-Based Therapy
MH  - Computer Simulation
MH  - Dose-Response Relationship, Drug
MH  - Humans
MH  - Maximum Tolerated Dose
MH  - *Multiple Myeloma
MH  - *Research Design
OTO - NOTNLM
OT  - Bayesian optimal interval
OT  - Immuno-oncology
OT  - cell therapy
OT  - optimal biological dose
OT  - toxicity efficacy probability interval
OT  - utility-based interval
EDAT- 2020/09/22 06:00
MHDA- 2021/11/26 06:00
CRDT- 2020/09/21 05:31
PHST- 2020/09/22 06:00 [pubmed]
PHST- 2021/11/26 06:00 [medline]
PHST- 2020/09/21 05:31 [entrez]
AID - 10.1080/10543406.2020.1814802 [doi]
PST - ppublish
SO  - J Biopharm Stat. 2020 Nov 1;30(6):979-992. doi: 10.1080/10543406.2020.1814802. 
      Epub 2020 Sep 20.