scholarly journals Analysis of Impact of Post-Treatment Biopsies in Phase I Clinical Trials

2016 ◽  
Vol 34 (4) ◽  
pp. 369-374 ◽  
Author(s):  
Randy F. Sweis ◽  
Michael W. Drazer ◽  
Mark J. Ratain
Keyword(s):  
Clinical Trials ◽  
Drug Development ◽  
Phase I ◽  
Maximum Tolerated Dose ◽  
Phase I Clinical Trials ◽  
Early Phase Clinical Trials ◽  
Pharmacodynamic Biomarkers ◽  
Tumor Biopsies ◽  
Phase Ii And Iii ◽  
The Impact

Purpose The use of biopsy-derived pharmacodynamic biomarkers is increasing in early-phase clinical trials. It remains unknown whether drug development is accelerated or enhanced by their use. We examined the impact of biopsy-derived pharmacodynamic biomarkers on subsequent drug development through a comprehensive analysis of phase I oncology studies from 2003 to 2010 and subsequent publications citing the original trials. Methods We conducted a search to identify and examine publications of phase I oncology studies including the use of biopsy-derived pharmacodynamic biomarkers between 2003 and 2010. Characteristics of those studies were extracted and analyzed, along with outcomes from the biomarker data. We then compiled and reviewed publications of subsequent phase II and III trials citing the original phase I biomarker studies to determine the impact on drug development. Results We identified 4,840 phase I oncology publications between 2003 and 2010. Seventy-two studies included a biopsy-derived pharmacodynamic biomarker. The proportion of biomarker studies including nondiagnostic biopsies increased over time (P = .002). A minimum of 1,873 tumor biopsies were documented in the 72 studies, 12 of which reported a statistically significant biomarker result. Thirty-three percent of studies (n = 24) were referenced by subsequent publications specifically with regard to the biomarkers. Only five positive biomarker studies were cited subsequently, and maximum tolerated dose was used for subsequent drug development in all cases. Conclusion Despite their increased use, the impact of biopsy-derived pharmacodynamic biomarkers in phase I oncology studies on subsequent drug development remains uncertain. No impact on subsequent dose or schedule was demonstrated. This issue requires further evaluation, given the risk and cost of such studies.

scholarly journals Prediction of Drug Approval After Phase I Clinical Trials in Oncology: RESOLVED2

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Guillaume Beinse ◽  
Virgile Tellier ◽  
Valentin Charvet ◽  
Eric Deutsch ◽  
Isabelle Borget ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Drug Development ◽  
Phase I ◽  
Attrition Rate ◽  
Phase I Clinical Trials ◽  
Test Set ◽  
Free Survival ◽  
Fda Approval ◽  
Independent Test

PURPOSE Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a machine learning algorithm (RESOLVED2) to predict drug development outcome, which could support early go/no-go decisions after P1CTs by better selection of drugs suitable for further development. METHODS PubMed abstracts of P1CTs reporting on ANAs were used together with pharmacologic data from the DrugBank5.0 database to model time to US Food and Drug Administration (FDA) approval (FDA approval-free survival) since the first P1CT publication. The RESOLVED2 model was trained with machine learning methods. Its performance was evaluated on an independent test set with weighted concordance index (IPCW). RESULTS We identified 462 ANAs from PubMed that matched with DrugBank5.0 (P1CT publication dates 1972 to 2017). Among 1,411 variables, 28 were used by RESOLVED2 to model the FDA approval-free survival, with an IPCW of 0.89 on the independent test set. RESOLVED2 outperformed a model that was based on efficacy/toxicity (IPCW, 0.69). In the test set at 6 years of follow-up, 73% (95% CI, 49% to 86%) of drugs predicted to be approved were approved, whereas 92% (95% CI, 87% to 98%) of drugs predicted to be nonapproved were still not approved (log-rank P < .001). A predicted approved drug was 16 times more likely to be approved than a predicted nonapproved drug (hazard ratio, 16.4; 95% CI, 8.40 to 32.2). CONCLUSION As soon as P1CT completion, RESOLVED2 can predict accurately the time to FDA approval. We provide the proof of concept that drug development outcome can be predicted by machine learning strategies.

A randomized controlled trial to evaluate the impact of an educational DVD on cancer patients considering participation in a phase I clinical trial

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6011-6011
Author(s):  
E. L. Strevel ◽  
C. Newman ◽  
G. R. Pond ◽  
M. Maclean ◽  
L. L. Siu
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Controlled Trial ◽  
Phase I Trials ◽  
Phase I Clinical Trials ◽  
Randomized Controlled ◽  
Clinic Appointment ◽  
The Mean ◽  
The Impact ◽  
Self Administered Questionnaire

6011 Background: Informed consent for phase I trials is controversial; gaps in patient (pt) knowledge regarding the purpose of these studies are central to this debate. This study assessed the impact of viewing an educational DVD on pt knowledge and satisfaction in cancer pts newly referred to a phase I trials clinic. Methods: Prior to physician (MD) appointment, 49 pts were randomly assigned to view either an educational DVD (n = 22) which provided information about phase I trials, or a placebo DVD (n = 27) which described research achievements by local scientists. Upon completion of DVD viewing, pts completed a self-administered questionnaire addressing their understanding of phase I trials (knowledge) and their satisfaction with the DVD (perception). The interviewing MD (n = 8), who was blinded to the intervention, also rated the pt’s understanding of phase I trials upon completion of the clinic appointment. Results: The mean pt age was 56 and 61% were male. Prior to attending the phase I clinic, most pts (86%) had previously heard of clinical trials, but only 49% were aware of phase I trials. Pts who viewed the educational DVD were less likely to believe that the goal of phase I trials is to determine the efficacy of a new drug (p = 0.019), more likely to correctly assess that drugs undergoing phase I evaluations have not been thoroughly studied in humans (p = 0.003), and less likely to believe that phase I drugs have proven activity against human cancers (p = 0.008). More pts who viewed the educational DVD than the placebo DVD agreed/strongly agreed that the DVD provided useful information (p < 0.001), believed that they had a good knowledge of phase I trials (p = 0.031), felt that the DVD helped them decide whether to enter a phase I trial (p = 0.011), and perceived that they would have more questions for their physicians as a result of watching the DVD (p = 0.017). No statistically significant differences in MD satisfaction was observed. Conclusions: Exposure to an educational DVD increased both objective measures of pt knowledge as well as pt satisfaction regarding participation in phase I clinical trials. The educational DVD did not significantly impact MD perception of pt understanding. No significant financial relationships to disclose.

Predictors of dose limiting toxicities in phase I clinical trials: The impact of age, comorbidity, and other clinical and non-clinical factors

2008 ◽  
Vol 26 (15_suppl) ◽  
pp. 9525-9525 ◽  
Author(s):  
N. K. LoConte ◽  
J. F. Cleary ◽  
J. Bozeman ◽  
G. Wilding ◽  
D. Alberti ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Clinical Factors ◽  
Phase I Clinical Trials ◽  
The Impact

scholarly journals A dynamic stopping rule for phase I clinical trials

2018 ◽  
Vol 55 (1) ◽  
pp. 17-30 ◽  
Author(s):  
M. Iftakhar Alam ◽  
Mohaimen Mansur
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Response ◽  
Parameter Estimate ◽  
Maximum Tolerated Dose ◽  
Stopping Rule ◽  
Bayesian Posterior Probability ◽  
Phase I Clinical Trials ◽  
Response Data ◽  
Number Of Patients

Summary This paper investigates a stopping rule to be utilised in phase I clinical trials. The motivation is to develop a dynamic rule so that a trial stops early if the maximum tolerated dose lies towards the beginning of a dose region. Also, it will employ many patients if the maximum tolerated dose lies towards the end of a dose region. A two-parameter logistic model is assumed for the dose-response data. A trial is stopped early before reaching the maximum number of patients when the width of the Bayesian posterior probability interval of the slope parameter meets a desired value. Instead of setting a pre-specified width to stop at, we determine it based on the parameter estimate obtained after a reasonable number of steps in a trial. Simulation studies of six plausible dose-response scenarios show that the proposed stopping rule is capable of limiting the number of patients to be recruited depending on the underlying scenario. Although the rule is applied to a D-optimum design here, it will be equally applicable to other model-based designs.

Identification of a predictive biomarker and two pharmacodynamic biomarkers to ADG106 treatment, a novel anti-CD137 agonist antibody, in phase I clinical trials.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e14505-e14505
Author(s):  
Li Zhang
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Phase Ii ◽  
Predictive Biomarker ◽  
Phase I Trials ◽  
Tumor Shrinkage ◽  
Target Engagement ◽  
Phase I Clinical Trials ◽  
Phase Ii Trials ◽  
Pharmacodynamic Biomarkers

e14505 Background: ADG106 is a fully human agonistic anti-CD137 monoclonal IgG4 antibody that mediates anti-tumor activities via unique mechanisms of action. Here we provide safety and efficacy updates from our phase I trials and report the findings of a predictive biomarker and two pharmacodynamic biomarkers which correlate with patients’ clinical responses to ADG106 treatment and demonstrate target engagement, respectively. Methods: Formalin fixed and paraffin embedded (FFPE), blood and plasma specimens were collected from 92 patients enrolled in our phase I trials. We measured expression across a panel of protein biomarkers in FFPE specimens using three highly sensitive detection technologies: multiple immunohistochemical (IHC) staining of protein expression, the BD Multitest 6-color TBNK reagent for profiling immune cell subpopulations, and the MSD-ECL electrochemiluminescence assay for detection of soluble CD137. Objective tumor responses were determined using RECIST v1.1 for solid tumor patients and Lugano classification for lymphoma patients. Results: As of November 30, 2020, ADG106 has demonstrated a favorable safety profile and efficacy in the phase I clinical trials with a disease control rate of 56%. From a retrospective analysis of 28 pretreatment FFPE specimens, we identified a predictive biomarker that correlated with tumor shrinkage upon ADG106 treatment. We identified four biomarker positive specimens from two patients with lymphoma and two with solid tumors. Three out of four biomarker positive patients achieved greater than 30% tumor shrinkage after 3mg/kg or 5mg/kg ADG106 treatment. One biomarker positive patient with stable disease received a low dose ADG106 treatment at 0.5mg/kg during dose escalation. None of the 24 biomarker negative patients showed significant clinical response. A tissue microarray study confirmed expression of this predictive biomarker in a variety of tumor types suggesting a broad indication for ADG106 therapy. Our biomarker studies also demonstrated target engagement with increased NK cell proliferation and soluble CD137 upon ADG106 treatment. Analysis of safety, efficacy, PK and PD data allowed us to select a recommended dose for the upcoming phase II study. Conclusions: We identified a biomarker predictive of response to antitumor CD137 blockade by ADG106, as well as demonstrated the involvement of NK cells in ADG106 mediated anti-tumor activities. In upcoming phase II trials, we plan to enrich for populations expressing this predictive biomarker to demonstrate a clinical benefit to ADG106 therapy further validating early biomarker-based patient stratification. We will also explore the potential of selecting patients for combination treatment with anti-PD-1 therapies. Clinical trial information: NCT03802955.

scholarly journals Adaptive Dose-Finding Studies: A Review of Model-Guided Phase I Clinical Trials

2014 ◽  
Vol 32 (23) ◽  
pp. 2505-2511 ◽  
Author(s):  
Alexia Iasonos ◽  
John O'Quigley
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Escalation ◽  
Late Onset ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Design Parameters ◽  
Phase I Trials ◽  
Phase I Clinical Trials ◽  
Trial Duration

Purpose We provide a comprehensive review of adaptive phase I clinical trials in oncology that used a statistical model to guide dose escalation to identify the maximum-tolerated dose (MTD). We describe the clinical setting, practical implications, and safety of such applications, with the aim of understanding how these designs work in practice. Methods We identified 53 phase I trials published between January 2003 and September 2013 that used the continual reassessment method (CRM), CRM using escalation with overdose control, or time-to-event CRM for late-onset toxicities. Study characteristics, design parameters, dose-limiting toxicity (DLT) definition, DLT rate, patient-dose allocation, overdose, underdose, sample size, and trial duration were abstracted from each study. In addition, we examined all studies in terms of safety, and we outlined the reasons why escalations occur and under what circumstances. Results On average, trials accrued 25 to 35 patients over a 2-year period and tested five dose levels. The average DLT rate was 18%, which is lower than in previous reports, whereas all levels above the MTD had an average DLT rate of 36%. On average, 39% of patients were treated at the MTD, and 74% were treated at either the MTD or an adjacent level (one level above or below). Conclusion This review of completed phase I studies confirms the safety and generalizability of model-guided, adaptive dose-escalation designs, and it provides an approach for using, interpreting, and understanding such designs to guide dose escalation in phase I trials.

Impact of Quality of Life on Patient Expectations Regarding Phase I Clinical Trials

2000 ◽  
Vol 18 (2) ◽  
pp. 421-421 ◽  
Author(s):  
Jonathan D. Cheng ◽  
James Hitt ◽  
Bogda Koczwara ◽  
Kevin A. Schulman ◽  
Caroline B. Burnett ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Clinical Trials ◽  
Phase I ◽  
Patient Expectations ◽  
Experimental Therapy ◽  
Phase I Clinical Trials ◽  
Potential Benefits ◽  
Research Nurses ◽  
The Impact

PURPOSE: Quality of life (QOL) is increasingly recognized as a critical cancer-treatment outcome measure, but little is known about the impact of QOL on the patient decision-making process. A pilot study was conducted in an effort to (1) measure the expectations of patients, physicians, and research nurses regarding the potential benefits and toxicities from experimental and standard therapies, and (2) determine the relationship of QOL to patient perceptions regarding treatment options. METHODS: Thirty cancer patients enrolling in phase I clinical trials, their physicians, and their research nurses were administered questionnaires that assessed demographics, QOL, and treatment expectations. RESULTS: Compared with their physicians, patients overestimated potential benefits and toxicities from experimental therapy (mean expected benefit, 59.8% v 23.8%, P < .01; mean expected toxicity, 29.8% v 16.0%, P < .01). Patients estimated a greater potential for benefit (59.8% v 36.8%, P < .01) and less potential for toxicity (29.8% v 45.6%, P = .01) for experimental therapy, compared with standard therapy. Short Form- 36 general health perception correlated with patient perception of potential benefit from experimental therapy (r = .48, P = .01). CONCLUSION: Participants in phase I clinical trial have high expectations regarding the success of experimental therapy and discount potential toxicity. Patient QOL may affect the expectation of benefit from experimental therapy and, ultimately, treatment choice. Understanding the interactions between QOL and patient expectations may guide the development of improved strategies to present appropriate information to patients considering early-phase clinical trials.

scholarly journals Number of Patients per Cohort and Sample Size Considerations Using Dose Escalation with Overdose Control

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Mourad Tighiouart ◽  
André Rogatko
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Phase I ◽  
Sample Size ◽  
Dose Escalation ◽  
Experimental Treatment ◽  
Maximum Tolerated Dose ◽  
Phase I Clinical Trials ◽  
Number Of Patients ◽  
Therapeutic Doses

The main objective of cancer phase I clinical trials is to determine a maximum tolerated dose (MTD) of a new experimental treatment. In practice, most of these trials are designed so that three patients per cohort are treated at the same dose level. In this paper, we compare the safety and efficiency of trials using the escalation with overdose control (EWOC) scheme designed with three or only one patient per cohort. We show through simulations that the number of patients per cohort does not impact the proportion of patients given therapeutic doses, safety of the trial, and efficiency of the estimate of the MTD. Additionally, we present guidelines and tabulated values on the number of patients needed to design a phase I cancer clinical trial using EWOC to achieve a given accuracy of the estimate of the MTD.

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