scholarly journals Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shinjo Yada
Keyword(s):  
Neural Network ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Early Phase ◽  
Patient Specific ◽  
Specific Gene ◽  
Cancer Type ◽  
Background Characteristics ◽  
Number Of Patients ◽  
Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

Abstract A185: Combining unbiased network analysis and cancer genomics to guide precision medicine-oriented early-phase clinical trials

2018 ◽  
Author(s):  
Pedro Torres-Ayuso ◽  
Sudhakar Sahoo ◽  
Christopher Chester ◽  
Cassandra Hodgkinson ◽  
Melanie Galvin ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Network Analysis ◽  
Precision Medicine ◽  
Early Phase ◽  
Cancer Genomics ◽  
Early Phase Clinical Trials

scholarly journals Signaling pathway screening platforms are an efficient approach to identify therapeutic targets in precision-medicine oriented early phase clinical trials

2018 ◽  
Author(s):  
◽  
Sudhakar Sahoo ◽  
Melanie Galvin ◽  
Hui Sun Leong ◽  
Kristopher K Frese ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Next Generation Sequencing ◽  
Precision Medicine ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
High Throughput ◽  
Early Phase ◽  
Drug Efficacy ◽  
Early Phase Clinical Trials ◽  
Generation Sequencing

AbstractPrecision medicine aims to tailor cancer therapies to target specific tumorpromoting aberrations. For tumors that lack actionable drivers, extensive molecular characterization and pre-clinical drug efficacy studies will be required to match patients with the appropriate targeted therapy. A cell line maintained at low passage and a patient-derived xenograft model (PDX) were generated using a fresh biopsy from a patient with a poorly-differentiated neuroendocrine tumor of unknown primary origin. Next-generation sequencing, high throughput signaling network analysis, and drug efficacy trials were then conducted to identify actionable targets for therapeutic intervention. No actionable mutations were identified after whole exome sequencing of the patient’s DNA; however, whole genome sequencing revealed amplification of the 3q and 5p chromosomal arms, that include the PIK3CA and RICTOR genes, respectively. Consistent with amplification of these genes, pathway analysis revealed activation of the AKT pathway. Based on this analysis, efficacy of PIK3CA and AKT inhibitors were evaluated in the tumor biopsy-derived cell culture and PDX, and response to the AKT inhibitor AZD5363 was observed both in vitro and in vivo indicating the patient would benefit from targeted therapies directed against the serine/threonine kinase AKT. In conclusion, our study demonstrates that high throughput signaling pathway analysis complements next-generation sequencing approaches for detection of actionable alterations and will aid in patient stratification into early-phase clinical trials.

Algorithmic matching of genomic profiles to precision cancer medicine clinical trials at DFCI.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6620-6620
Author(s):  
James Lindsay ◽  
Catherine Del Vecchio Fitz ◽  
Zachary Zwiesler ◽  
Priti Kumari ◽  
Khanh Tu Do ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Open Source ◽  
Patient Specific ◽  
Cancer Type ◽  
Eligibility Criteria ◽  
Enrollment Rates ◽  
And Gender ◽  
Computational Platform

6620 Background: Genomic profiling and access to precision medicine clinical trials are now standard at leading cancer institutes and many community practices. Interpreting patient-specific genomic information and tracking the complex criteria for precision medicine trials requires specialized computational tools, especially for multi-institutional basket studies such as NCI-MATCH and TAPUR. Methods: To address this challenge we have developed an open source computational platform for patient-specific clinical trial matching at Dana-Farber Cancer Institute (DFCI) called MatchMiner, which aides in both patient recruitment to precision medicine trials, as well as decision support for oncologists. Trial matches are computed based on genomic criteria, including mutations, CNAs, and SVs, as well as clinical and demographic information, including cancer type, age, and gender. A formal standard called clinical trial markup language (CTML) to encode complex clinical trial eligibility criteria has also been created. Results: MatchMiner is now available at DFCI. Currently 123 precision medicine clinical trials have been transformed into CTML and 13,000 patient records are available, with over 88% of current patients having at least 1 match (average 2.6). A total of 103 genes are specified as criteria for at least 1 trial. KRAS, TP53, PTEN, PIK3CA and BRAF are the genes driving the most number of matches. General usage statistics and trial enrollment rates are currently being monitored to determine the system effectiveness. As this is an open source initiative, the software is also now publically available at https://github.com/dfci/matchminer. Conclusions: We have developed an open source computational platform that enables patient-specific matching and recruitment to precision medicine clinical trials at DFCI. We are actively seeking collaborators and plan to make CTML a multi-institution standard for encoding complex clinical trial eligibility in a computable form.

Precision medicine in recurrent glioblastoma: A feasibility trial conducted by the Ivy Foundation Early Phase Clinical Trials Consortium.

2016 ◽  
Vol 34 (15_suppl) ◽  
pp. 2031-2031 ◽  
Author(s):  
Michael Prados ◽  
John G. Kuhn ◽  
Howard Colman ◽  
Timothy Francis Cloughesy ◽  
Susan Marina Chang ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Precision Medicine ◽  
Early Phase ◽  
Recurrent Glioblastoma ◽  
Feasibility Trial ◽  
Early Phase Clinical Trials

Monitoring event times in early phase clinical trials: some practical issues

2005 ◽  
Vol 2 (6) ◽  
pp. 467-478 ◽  
Author(s):  
Peter F Thall ◽  
Leiko H Wooten ◽  
Nizar M Tannir
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Event Times ◽  
Early Phase Clinical Trials

scholarly journals Simian adenovirus vector production for early-phase clinical trials: A simple method applicable to multiple serotypes and using entirely disposable product-contact components

Vaccine ◽  
2019 ◽  
Vol 37 (47) ◽  
pp. 6951-6961 ◽  
Author(s):  
Sofiya Fedosyuk ◽  
Thomas Merritt ◽  
Marco Polo Peralta-Alvarez ◽  
Susan J Morris ◽  
Ada Lam ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Adenovirus Vector ◽  
Simple Method ◽  
Simian Adenovirus ◽  
Early Phase Clinical Trials

scholarly journals Targeting for Success: Demonstrating Proof-of-Concept with Mechanistic Early Phase Clinical Pharmacology Studies for Disease-Modification in Neurodegenerative Disorders

2021 ◽  
Vol 22 (4) ◽  
pp. 1615
Author(s):  
Maurits F. J. M. Vissers ◽  
Jules A. A. C. Heuberger ◽  
Geert Jan Groeneveld
Keyword(s):  
Clinical Trials ◽  
Failure Rate ◽  
Neurodegenerative Disorders ◽  
Early Phase ◽  
Proof Of Concept ◽  
Mesh Terms ◽  
Disease Modifying ◽  
Early Phase Clinical Trials ◽  
Pharmacodynamic Biomarkers ◽  
Response Biomarkers

The clinical failure rate for disease-modifying treatments (DMTs) that slow or stop disease progression has been nearly 100% for the major neurodegenerative disorders (NDDs), with many compounds failing in expensive and time-consuming phase 2 and 3 trials for lack of efficacy. Here, we critically review the use of pharmacological and mechanistic biomarkers in early phase clinical trials of DMTs in NDDs, and propose a roadmap for providing early proof-of-concept to increase R&D productivity in this field of high unmet medical need. A literature search was performed on published early phase clinical trials aimed at the evaluation of NDD DMT compounds using MESH terms in PubMed. Publications were selected that reported an early phase clinical trial with NDD DMT compounds between 2010 and November 2020. Attention was given to the reported use of pharmacodynamic (mechanistic and physiological response) biomarkers. A total of 121 early phase clinical trials were identified, of which 89 trials (74%) incorporated one or multiple pharmacodynamic biomarkers. However, only 65 trials (54%) used mechanistic (target occupancy or activation) biomarkers to demonstrate target engagement in humans. The most important categories of early phase mechanistic and response biomarkers are discussed and a roadmap for incorporation of a robust biomarker strategy for early phase NDD DMT clinical trials is proposed. As our understanding of NDDs is improving, there is a rise in potentially disease-modifying treatments being brought to the clinic. Further increasing the rational use of mechanistic biomarkers in early phase trials for these (targeted) therapies can increase R&D productivity with a quick win/fast fail approach in an area that has seen a nearly 100% failure rate to date.

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