scholarly journals Status and Recommendations for Incorporating Biomarkers for Cutaneous Neurofibromas into Clinical Research

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012426
Author(s):  
Deeann Wallis ◽  
Anat Stemmer-Rachamimov ◽  
Sarah Adsit ◽  
Bruce Korf ◽  
Dominique Pichard ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Working Group ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Predictive Biomarkers ◽  
Neurofibromatosis Type ◽  
Proteomic Data ◽  
Group A ◽  
Existing Data

Objective:To summarize existing biomarker data for cutaneous neurofibroma (cNF) and inform the incorporation of biomarkers into clinical trial design for cNFs.Methods:The cNF working group, a subgroup of the Response Evaluation in Neurofibromatosis and Schwannomatosis (REiNS) consortium, was formed to review and inform clinical trial design for cNFs. Between June 2018 and February 2020, the cNF working group performed a review of existing data on genetic biomarkers for cNFs in the setting of Neurofibromatosis Type 1 (NF1). We also reviewed criteria for successful biomarker application in the clinic. The group then met during a series of meetings to develop a consensus report.Results:Our systematic literature review of existing data revealed a lack of validated biomarkers for cNFs. In our report, we summarize the existing signaling, genomic, transcriptomic, histopathologic and proteomic data relevant to cNF. Finally, we make recommendations for incorporating exploratory aims for predictive biomarkers in clinical trials through biobanking samples.Conclusion:These recommendations are intended to provide both researchers and clinicians with best practices for clinical trial design to aid in the identification of clinically validated biomarkers for cNF.

A note on the draft International Council for Harmonisation guidance on estimands and sensitivity analysis

2019 ◽  
Vol 16 (4) ◽  
pp. 339-344 ◽  
Author(s):  
Devan V Mehrotra
Keyword(s):  
Clinical Trial ◽  
Sensitivity Analysis ◽  
Clinical Trials ◽  
Working Group ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Steering Committee ◽  
Therapeutic Area ◽  
International Council ◽  
Expert Working Group

In the second half of 2014, the Steering Committee of the International Council for Harmonisation endorsed the formation of an expert working group to develop an addendum to the International Council for Harmonisation E9 guideline ( Statistical Principles for Clinical Trials). The addendum was to focus on two clinical trial topics: estimands and sensitivity analysis. A draft of the addendum, referred to as E9/R1, was developed by the expert working group and made available for public comments across the International Council for Harmonisation regions in the second half of 2017. A structured framework for clinical trial design and analysis proposed in the draft addendum are briefly described, including four key inputs for developing objective-driven estimands and strategies for tackling one of the inputs (‘intercurrent events’). The proposed framework aligns each clinical trial objective with the corresponding statistical target of estimation (estimand), trial design and data to be collected, main method of estimation/inference, and sensitivity analysis to pressure test key analytic assumption(s) in the main analysis. A case study from the diabetes therapeutic area illustrates how the framework can be implemented in practice. International Council for Harmonisation E9/R1 is expected to enable better planning, conduct, analysis, and interpretation of randomised clinical trials. This will facilitate improvements in new drug applications and strengthen understanding of decision making by regulatory authorities and advisory committees.

Using qualitative methods to guide clinical trial design: Parent recommendations for intervention modification in Type 1 diabetes.

2011 ◽  
Vol 25 (6) ◽  
pp. 868-872 ◽  
Author(s):  
Maureen Monaghan ◽  
Risa E. Sanders ◽  
Katherine Patterson Kelly ◽  
Fran R. Cogen ◽  
Randi Streisand
Keyword(s):  
Clinical Trial ◽  
Type 1 Diabetes ◽  
Qualitative Methods ◽  
Trial Design ◽  
Clinical Trial Design

scholarly journals Hierarchical Bayesian modelling of disease progression to inform clinical trial design in centronuclear myopathy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Eve Fouarge ◽  
◽  
Arnaud Monseur ◽  
Bruno Boulanger ◽  
Mélanie Annoussamy ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Natural History ◽  
Bayesian Statistics ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Threshold Probability ◽  
Congenital Diseases ◽  
Type 1 Error ◽  
Rate Of Response

Abstract Background Centronuclear myopathies are severe rare congenital diseases. The clinical variability and genetic heterogeneity of these myopathies result in major challenges in clinical trial design. Alternative strategies to large placebo-controlled trials that have been used in other rare diseases (e.g., the use of surrogate markers or of historical controls) have limitations that Bayesian statistics may address. Here we present a Bayesian model that uses each patient’s own natural history study data to predict progression in the absence of treatment. This prospective multicentre natural history evaluated 4-year follow-up data from 59 patients carrying mutations in the MTM1 or DNM2 genes. Methods Our approach focused on evaluation of forced expiratory volume in 1 s (FEV1) in 6- to 18-year-old children. A patient was defined as a responder if an improvement was observed after treatment and the predictive probability of such improvement in absence of intervention was less than 0.01. An FEV1 response was considered clinically relevant if it corresponded to an increase of more than 8%. Results The key endpoint of a clinical trial using this model is the rate of response. The power of the study is based on the posterior probability that the rate of response observed is greater than the rate of response that would be observed in the absence of treatment predicted based on the individual patient’s previous natural history. In order to appropriately control for Type 1 error, the threshold probability by which the difference in response rates exceeds zero was adapted to 91%, ensuring a 5% overall Type 1 error rate for the trial. Conclusions Bayesian statistical analysis of natural history data allowed us to reliably simulate the evolution of symptoms for individual patients over time and to probabilistically compare these simulated trajectories to actual observed post-treatment outcomes. The proposed model adequately predicted the natural evolution of patients over the duration of the study and will facilitate a sufficiently powerful trial design that can cope with the disease’s rarity. Further research and ongoing dialog with regulatory authorities are needed to allow for more applications of Bayesian statistics in orphan disease research.

Clinical trial design for local therapies for brain metastases: a guideline by the Response Assessment in Neuro-Oncology Brain Metastases working group

2018 ◽  
Vol 19 (1) ◽  
pp. e33-e42 ◽  
Author(s):  
Brian M Alexander ◽  
Paul D Brown ◽  
Manmeet S Ahluwalia ◽  
Hidefumi Aoyama ◽  
Brigitta G Baumert ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Brain Metastases ◽  
Working Group ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Response Assessment

A transatlantic perspective on the integration of immuno-oncology prognostic and predictive biomarkers in innovative clinical trial design

2018 ◽  
Vol 52 ◽  
pp. 158-165 ◽  
Author(s):  
M. Morfouace ◽  
S.M. Hewitt ◽  
R. Salgado ◽  
K. Hartmann ◽  
S. Litiere ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Predictive Biomarkers

scholarly journals DDRE-03. INTERNATIONAL PRECLINICAL DRUG DISCOVERY AND BIOMARKER PROGRAM INFORMING AN ADOPTIVE COMBINATORIAL TRIAL FOR DIFFUSE MIDLINE GLIOMAS

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii61-ii62
Author(s):  
Justyna Przystal ◽  
Sridevi Yadavilli ◽  
Christina Coleman Abadi ◽  
Viveka Nand Yadav ◽  
Sandra Laternser ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Trial Design ◽  
Meta Analysis ◽  
Clinical Trial Design ◽  
Predictive Biomarkers ◽  
Pharmacokinetic Analysis ◽  
Molecular Testing ◽  
Zebrafish Model

Abstract INTRODUCTION DMG-ACT (DMG- multi-arm Adaptive and Combinatorial Trial) aims to implement a highly innovative clinical trial design of combinatorial arms for patients with diffuse midline gliomas (DMGs) at all disease stages that is adaptive to pre-clinical data generated in ten collaborating institutions. Novel drug and drug combination were tested, predictive biomarkers were identified and incorporated in clinical trial design. METHODS In vitro (n=15) and in vivo (n=8) models of DMGs across ten institutions were used to assess single and combination treatments with ONC201, ONC206, marizomib, panobinostat, 5-Azacytidine, Val-083, GDC0084 and TAK228. In vivo drug toxicity screenings were conducted using larval zebrafish model and murine PDX models. Predictive biomarkers for ONC201 and ONC206 were identified using meta-analysis, and extensive molecular assays including CRISPR, RNAseq, FACS, and IHC. RESULTS Inhibitory concentrations (IC50) were established and validated multiple preclinical models. ONC201 and ONC206, ONC201 and TAK228, ONC201 and GDC0084 showed synergism. In vivo survival assays showed increased survival for: ONC201 (p=0.01), ONC206 (p=0.01), ONC201+ONC206 (p=0.02), and ONC201+panobinostat (p=0.01). Marizomib showed toxicity in murine/zebrafish PDXs models. Murine pharmacokinetic analysis showed peak brain levels of ONC201 and ONC206 above pre-clinical IC50. Molecular testing and analyses of existing drug screen across 537 cancer cell lines validated mitochondrial protease ClpP and ATF4 as ONC201/6 targets. Predictive biomarkers of response to drug were identified. CONCLUSION Thorough preclinical testing in a multi-site laboratory setting is feasible and identified ONC201 in combination with ONC206, TAK228 and GDC0084 as promising therapeutics for DMGs.

Integrating radiomics into clinical trial design

2019 ◽  
Vol 63 (4) ◽  
Author(s):  
Jessica J. Waninger ◽  
Michael D. Green ◽  
Catherine Cheze Le Rest ◽  
Benjamin Rosen ◽  
Issam El Naqa
Keyword(s):  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design

Avenues to Precision Oncology

2020 ◽  
pp. 36-45
Author(s):  
Alexander Meisel
Keyword(s):  
Precision Medicine ◽  
Trial Design ◽  
Ad Hoc ◽  
Molecular Targets ◽  
Clinical Trial Design ◽  
Predictive Biomarkers ◽  
Precision Oncology ◽  
Companion Diagnostics ◽  
Bona Fide ◽  
The One

Until recently, the clinical management of cancer heavily relied on anatomical and histopathological criteria, with ad hoc guidelines directing the therapeutic choices in specific indications. In the last years, the development and therapeutic implementation of novel anticancer therapies significantly improved the clinical outcome of cancer patients. Nonetheless, such cutting-edge approaches revealed the limitation of the one-size-fits-all paradigm. The newly discovered molecular targets can be exploited either as bona fide targets for subsequent drug development, or as tools to precision medicine, in the form of prognostic and/or predictive biomarkers. This article provides an overview of some of the most recent advances in precision medicine in oncology, with a focus on novel tissue-agnostic anticancer therapies. The definition and implementation of biomarkers and companion diagnostics in clinical trials and clinical practice are also discussed, as well as the changing landscape in clinical trial design.

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