A Likelihood-Based Approach for Computing the Operating Characteristics of the Standard Phase I Clinical Trial Design

In phase I clinical trials, the standard ‘3+3’ design has passed the test of time and survived various sample size adjustments, or other dose-escalation dynamics. The objective of this study is to provide a probabilistic support for analyzing the heuristic performance of the ‘3+3’ design. Our likelihood method is based on the evidential paradigm that uses the likelihood ratio to measure the strength of statistical evidence for one simple hypothesis over the other. We compute the operating characteristics and compare the behavior of the standard algorithm under different hypotheses, levels of evidence, and true (or best guessed) toxicity rates. Given observed toxicities per dose level, the likelihood-ratio is evaluated according to a certain k threshold (level of evidence). Under an assumed true toxicity scenario the following statistical characteristics are computed and compared: i) probability of weak evidence, ii) probability of favoring H1 under H1(analogous to 1-α), iii) probability of favoring H2 under H2 (analogous to 1-β). This likelihood method allows consistent inferences to be made and evidence to be quantified regardless of cohort size. Moreover, this approach can be extended and used in phase I designs for identifying the highest acceptably safe dose and is akin to the sequential probability ratio test

Dose-finding designs for trials of molecularly targeted agents and immunotherapies

Recently, there has been a surge of early phase trials of molecularly targeted agents (MTAs) and immunotherapies. These new therapies have different toxicity profiles compared to cytotoxic therapies. MTAs can benefit from new trial designs that allow inclusion of low-grade toxicities, late-onset toxicities, addition of an efficacy endpoint, and flexibility in the specification of a target toxicity probability. To study the degree of adoption of these methods, we conducted a Web of Science search of articles published between 2008 and 2014 that describe phase 1 oncology trials. Trials were categorized based on the dose-finding design used and the type of drug studied. Out of 1,712 dose-finding trials that met our criteria, 1,591 (92.9%) utilized a rule-based design, and 92 (5.4%; range 2.3% in 2009 to 9.7% in 2014) utilized a model-based or novel design. Over half of the trials tested an MTA or immunotherapy. Among the MTA and immunotherapy trials, 5.8% used model-based methods, compared to 3.9% and 8.3% of the chemotherapy or radiotherapy trials, respectively. While the percentage of trials using novel dose-finding designs has tripled since 2007, only 7.1% of trials use novel designs

G-CSF/anti-G-CSF antibody complexes drive the potent recovery and expansion of CD11b+Gr-1+ myeloid cells without compromising CD8+ T cell immune responses

BACKGROUND: Administration of recombinant G-CSF following cytoreductive therapy enhances the recovery of myeloid cells, minimizing the risk of opportunistic infection. Free G-CSF, however, is expensive, exhibits a short half-life, and has poor biological activity in vivo. METHODS: We evaluated whether the biological activity of G-CSF could be improved by pre-association with anti-G-CSF mAb prior to injection into mice. RESULTS: We find that the efficacy of G-CSF therapy can be enhanced more than 100-fold by pre-association of G-CSF with an anti-G-CSF monoclonal antibody (mAb). Compared with G-CSF alone, administration of G-CSF/anti-G-CSF mAb complexes induced the potent expansion of CD11b(+)Gr-1(+) myeloid cells in mice with or without concomitant cytoreductive treatment including radiation or chemotherapy. Despite driving the dramatic expansion of myeloid cells, in vivo antigen-specific CD8(+) T cell immune responses were not compromised. Furthermore, injection of G-CSF/anti-G-CSF mAb complexes heightened protective immunity to bacterial infection. As a measure of clinical value, we also found that antibody complexes improved G-CSF biological activity much more significantly than pegylation. CONCLUSIONS: Our findings provide the first evidence that antibody cytokine complexes can effectively expand myeloid cells, and furthermore, that G-CSF/anti-G-CSF mAb complexes may provide an improved method for the administration of recombinant G-CSF

sj-pdf-1-ctj-10.1177_17407745241240401 – Supplemental material for The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies

Supplemental material, sj-pdf-1-ctj-10.1177_17407745241240401 for The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies by Cody Chiuzan and Hakim-Moulay Dehbi in Clinical Trials</p

An Adaptive Dose-Finding Design Based on Both Safety and Immunologic Responses in Cancer Clinical Trials

<p>Dose-finding in cancer clinical trials has been dominated by algorithmic designs on the principle that the highest tolerable dose is also the most effective dose. This assumption no longer applies to the biologic treatments that are characterized by different toxicity and/or efficacy profiles to the extent that the best therapeutic dose might be well below any dose that produces serious toxicity. As such, we propose a two-stage design with focus on immunotherapy trials, incorporating both safety and efficacy information. The first stage establishes the safety profile of each dose, with escalation decisions based on likelihood principles. Continuous immunologic outcomes are used to evaluate the relative efficacy of the doses. The second stage employs an adaptive randomization to assign patients to doses showing higher efficacy. Safety is being continuously monitored throughout Stage 2, where some doses may be ‘closed’ due to unacceptable toxicity. The proposed design is compared to the modified toxicity probability interval (mTPI) design using percent dose allocation and estimation of outcomes under different scenarios. We show that by using an efficacy-driven adaptive randomization with safety constraints, the allocation distribution is skewed towards more efficacious doses, and thus limit the number of patients exposed to toxic or non-therapeutic doses. Supplementary materials for this article are available online.</p

Is more better?:An analysis of toxicity and response outcomes from dose-finding clinical trials in cancer

BACKGROUND: The overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like CRM and EWOC. These methods assume that the incidences of efficacy and toxicity always increase as dose is increased. This assumption is widely accepted with cytotoxic therapies. In recent decades, however, the search for novel cancer treatments has broadened, increasingly focusing on inhibitors and antibodies. The rationale that higher doses are always associated with superior efficacy is less clear for these types of therapies. METHODS: We extracted dose-level efficacy and toxicity outcomes from 115 manuscripts reporting dose-finding clinical trials in cancer between 2008 and 2014. We analysed the outcomes from each manuscript using flexible non-linear regression models to investigate the evidence supporting the monotonic efficacy and toxicity assumptions. RESULTS: We found that the monotonic toxicity assumption was well-supported across most treatment classes and disease areas. In contrast, we found very little evidence supporting the monotonic efficacy assumption. CONCLUSIONS: Our conclusion is that dose-escalation trials routinely use methods whose assumptions are violated by the outcomes observed. As a consequence, dose-finding trials risk recommending unjustifiably high doses that may be harmful to patients. We recommend that trialists consider experimental designs that allow toxicity and efficacy outcomes to jointly determine the doses given to patients and recommended for further study. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12885-021-08440-0)

Dose-level toxicity and efficacy outcomes from dose-finding clinical trials in oncology

This dataset contains information on the outcomes observed at each dose-level in a large number of dose-finding clinical trials in oncology. These trials typically seek to identify a maximum tolerable dose or a recommended phase 2 dose. A common rule-based method for conducting such trials is the 3+3 algorithm. A common model-based alternative is the continual reassessment method (CRM). Each of these methods assumes that as dose is increased, the probabilities of toxicity and efficacy will each increase monotonically. If this assumption is violated, both methods may recommend inappropriate doses. For instance, if the probability of efficacy does not increase in dose but the probability of toxicity does, the 3+3 and CRM designs may recommend a dose that is unjustifiably high. The same is true of any design that assumes that "more is better". We collated this dataset to investigate the appropriateness of the monotonicity assumptions in dose-finding clinical trials. See README or https://github.com/brockk/dosefindingdata for more information. v1.1 is a minor update correcting typos