Emerging HER2-directed therapeutic agents for gastric cancer in 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.

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

Vaccine ◽

10.1016/j.vaccine.2019.04.056 ◽

2019 ◽

Vol 37 (47) ◽

pp. 6951-6961 ◽

Cited By ~ 2

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

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

International Journal of Molecular Sciences ◽

10.3390/ijms22041615 ◽

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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Unified exact design with early stopping rules for single arm clinical trials with multiple endpoints

Statistical Methods in Medical Research ◽

10.1177/09622802211013062 ◽

2021 ◽

pp. 096228022110130

Author(s):

Wei Wei ◽

Denise Esserman ◽

Michael Kane ◽

Daniel Zelterman

Keyword(s):

Clinical Trials ◽

Early Phase ◽

Decision Rules ◽

Stopping Rules ◽

Multiple Endpoints ◽

Shiny App ◽

R Shiny ◽

Early Phase Clinical Trials ◽

User Friendly ◽

Exact Design

Adaptive designs are gaining popularity in early phase clinical trials because they enable investigators to change the course of a study in response to accumulating data. We propose a novel design to simultaneously monitor several endpoints. These include efficacy, futility, toxicity and other outcomes in early phase, single-arm studies. We construct a recursive relationship to compute the exact probabilities of stopping for any combination of endpoints without the need for simulation, given pre-specified decision rules. The proposed design is flexible in the number and timing of interim analyses. A R Shiny app with user-friendly web interface has been created to facilitate the implementation of the proposed design.

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Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials

Cancers ◽

10.3390/cancers13102443 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2443

Author(s):

Bethany Geary ◽

Erin Peat ◽

Sarah Dransfield ◽

Natalie Cook ◽

Fiona Thistlethwaite ◽

...

Keyword(s):

Overall Survival ◽

Clinical Trials ◽

Early Phase ◽

Characteristic Curve ◽

Performance Status ◽

Area Under The Curve ◽

Protein Biomarkers ◽

Late Stage Cancer ◽

Early Phase Clinical Trials

TARGET (tumour characterisation to guide experimental targeted therapy) is a cancer precision medicine programme focused on molecular characterisation of patients entering early phase clinical trials. Performance status (PS) measures a patient’s ability to perform a variety of activities. However, the quality of present algorithms to assess PS is limited and based on qualitative clinician assessment. Plasma samples from patients enrolled into TARGET were analysed using the mass spectrometry (MS) technique: sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. SWATH-MS was used on a discovery cohort of 55 patients to differentiate patients into either a good or poor prognosis by creation of a Wellness Score (WS) that showed stronger prediction of overall survival (p = 0.000551) compared to PS (p = 0.001). WS was then tested against a validation cohort of 77 patients showing significant (p = 0.000451) prediction of overall survival. WS in both sets had receiver operating characteristic curve area under the curve (AUC) values of 0.76 (p = 0.002) and 0.67 (p = 0.011): AUC of PS was 0.70 (p = 0.117) and 0.55 (p = 0.548). These signatures can now be evaluated further in larger patient populations to assess their utility in a clinical setting.

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