scholarly journals Dose–Response Relationship in Phase I Clinical Trials: A European Drug Development Network (EDDN) Collaboration Study

2014 ◽  
Vol 20 (22) ◽  
pp. 5663-5671 ◽  
Author(s):  
Victor Moreno García ◽  
David Olmos ◽  
Carlos Gomez-Roca ◽  
Philippe A. Cassier ◽  
Rafael Morales-Barrera ◽  
...  
2011 ◽  
Vol 29 (15_suppl) ◽  
pp. 3084-3084 ◽  
Author(s):  
P. A. Cassier ◽  
V. Moreno Garcia ◽  
C. Gomez-Roca ◽  
D. Olmos ◽  
R. Morales ◽  
...  

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Guillaume Beinse ◽  
Virgile Tellier ◽  
Valentin Charvet ◽  
Eric Deutsch ◽  
Isabelle Borget ◽  
...  

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.


Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 1877-1877 ◽  
Author(s):  
Pierre Squifflet ◽  
Stefan Michiels ◽  
David S. Siegel ◽  
Ravi Vij ◽  
Sunhee Ro ◽  
...  

Abstract Abstract 1877 Introduction: Carfilzomib (CFZ) is a next-generation proteasome inhibitor that selectively and irreversibly binds to its target. Phase (ph) 1 and 2 studies with CFZ have demonstrated durable single-agent activity and acceptable safety in patients (pts) with relapsed and/or refractory multiple myeloma (MM). Preliminary side-by-side comparison of efficacy results from PX-171-003-A0 and PX-171-003-A1, 2 studies performed with nearly identical entry criteria and schedules but using different doses of CFZ (20 mg/m2 vs 20/27 mg/m2), suggested a dose–response relationship. The rates of pts achieving either a partial response (PR) or better or a minimal response or better appeared to be greater in 003-A1 than in 003-A0. An analogous comparison of efficacy results from the 2 bortezomib (BTZ)-naïve dosing cohorts (20/27 mg/m2 vs 20 mg/m2) in PX-171-004 revealed similar trends. The present analysis was undertaken to rigorously evaluate the evidence of a potential dose–response relationship for CFZ by employing dose– response modelling. Methods: A pooled multivariate dose–response model was derived using data from the following ph 2 studies of CFZ: 003-A1, 004 (BTZ-naïve), and 004 (BTZ-treated). A multivariate logistic regression analysis for the primary outcome of overall response rate (ORR) was fitted that adjusted for study, CFZ dose, and a broad range of prognostic covariates (eg, cytogenetic status and International Staging System (ISS) stage). In addition, a repeated measures model used generalized estimating equations (GEE) to analyze the association between cycle-specific response status and cycle-specific CFZ dose. Multivariate Cox regression models with the same covariates, stratified by study, were fitted for the time-to-event endpoints (duration of response [DOR], progression-free survival [PFS], and overall survival [OS]). Time-dependent Cox regression models were also fitted for these endpoints using cycle-specific cumulative dose. Results: Evaluation of the effect of actual CFZ dose on the primary efficacy outcome of overall response rate (ORR) demonstrated that the odds of achieving a PR or better for a given pt treated with 27 mg/m2 were 4.08-fold higher (95% CI: 2.30–7.24, P&lt;0.001) than for a pt receiving 20 mg/m2. When using the average dose as a continuous variable and adjusting for study effect, the odds of a response increased by 1.28-fold (95% CI: 1.17–1.40; P&lt;0.001) for each 1 mg/m2 increase in average CFZ dose, equivalent to a 5.52-fold increase in the odds of a response if the average dose increased from 20 mg/m2 to 27 mg/m2. Figure 1 presents the dose–response curve for each of the 3 study populations derived by applying the inverse logit transformation to the estimated odds ratio. Results were similar after adjusting for various baseline prognostic covariates across studies (eg, female gender, higher Hgb level). Multivariate Cox regression models with same prognostic factors were fitted for the secondary efficacy endpoints of DOR, PFS, and OS, and highly significant effects of CFZ dose were again observed. A repeated measures model (using cycle-specific average dose as a dose effect variable) using GEE was applied to remove a potential bias of differential pt characteristics in those receiving higher doses of carfilzomib. In this analysis, the per-cycle odds of an overall response was estimated to increase by 1.12-fold (95% CI: 1.06, 1.18; p&lt;0.001) for each 1 mg/m2 increase in CFZ dose. Additionally, time-dependent Cox regression models were fitted to account for the potential bias due to confounding factors. Results of this analysis confirmed the initial dose–response relationship observed. Conclusions: Preliminary observations of a dose–response relationship for CFZ from ph 2 studies have been confirmed and extended using a statistically rigorous, multivariate analysis. This dose–response relationship is apparent both in terms of the proportion of responding pts across all response endpoints evaluated (ORR, DOR, PFS, and OS) and in the depth of individual responses. While a corresponding dose–toxicity analysis has not been performed to date, carfilzomib has been shown to have a similar tolerability profile when comparing 20 mg/m2 (003-A0) vs 20/27 mg/m2 (003-A1). These findings are being further assessed in exploratory clinical trials (eg, PX-171-007) evaluating higher dosing regimens. Disclosures: Squifflet: International Drug Development Institute: Employment. Michiels:International Drug Development Institute: Consultancy. Siegel:Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Vij:Onyx Pharmaceuticals: Consultancy, Research Funding; Celgene: Research Funding, Speakers Bureau; Millennium: Speakers Bureau. Ro:Onyx Pharmaceuticals: Employment, Equity Ownership. Buyse:International Drug Development Institute: Employment, Equity Ownership.


2018 ◽  
Vol 55 (1) ◽  
pp. 17-30 ◽  
Author(s):  
M. Iftakhar Alam ◽  
Mohaimen Mansur

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.


2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 10513-10513 ◽  
Author(s):  
S. P. Chawla ◽  
V. S. Chua ◽  
L. Fernandez ◽  
D. Quon ◽  
A. Saralou ◽  
...  

10513 Background: (1) To evaluate the safety/anti-tumor potential of intravenous (i.v.) Rexin-G in chemotherapy-resistant sarcoma (Phase I/II), and (2) to confirm the efficacy/safety of i.v. Rexin-G in chemotherapy-resistant osteosarcoma (Phase II). Methods: Twenty patients in the Phase I/II study and 22 patients in the Phase II study received 1–2 × 10e11 cfu Rexin-G i.v., 2–3 times a week for 4 weeks. Treatment was continued if the patient had < Grade 1 toxicity. Results: Treatment-related adverse events included chills (n=1), presyncope (n=1), photophobia (n=1) of Grade 1 severity, and fatigue (n=4) of Grade 1–2 severity. In the Phase I/II sarcoma study, 3/6 patients had stable disease at Dose Level 0, median progression free survival (PFS) was 5 weeks, and overall survival (OS) was 14 weeks, while 10/14 patients had stable disease at Dose Level I-II, median PFS was 16 weeks and median OS was 34 weeks. Cox regression analysis showed a dose-response relationship between PFS/OS and Rexin-G dosage (p = 0.02 and 0.005, respectively). The Table below shows the results for evaluable patients (n=17) in the Phase II study for osteosarcoma. Kaplan-Meier analysis shows a dose-response relationship between overall survival and Rexin-G dosage in the combined Phase I/II sarcoma and Phase II osteosarcoma studies (p = 0.02; n=42). Two patients achieved surgical remissions which are sustained for >26 weeks. Conclusions: These studies suggest that (i) intravenous Rexin-G is safe and well-tolerated, and (ii) Rexin-G controls tumor growth and prolongs progression-free survival and overall survival in a dose-dependent manner in chemotherapy-resistant osteosarcoma and sarcoma. [Table: see text] [Table: see text]


Author(s):  
Adrien Ollier ◽  
Sarah Zohar ◽  
Satoshi Morita ◽  
Moreno Ursino

Bridging studies are designed to fill the gap between two populations in terms of clinical trial data, such as toxicity, efficacy, comorbidities and doses. According to ICH-E5 guidelines, clinical data can be extrapolated from one region to another if dose–reponse curves are similar between two populations. For instance, in Japan, Phase I clinical trials are often repeated due to this physiological/metabolic paradigm: the maximum tolerated dose (MTD) for Japanese patients is assumed to be lower than that for Caucasian patients, but not necessarily for all molecules. Therefore, proposing a statistical tool evaluating the similarity between two populations dose–response curves is of most interest. The aim of our work is to propose several indicators to evaluate the distance and the similarity of dose–toxicity curves and MTD distributions at the end of some of the Phase I trials, conducted on two populations or regions. For this purpose, we extended and adapted the commensurability criterion, initially proposed by Ollier et al. (2019), in the setting of completed phase I clinical trials. We evaluated their performance using three synthetic sets, built as examples, and six case studies found in the literature. Visualization plots and guidelines on the way to interpret the results are proposed.


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