Evaluating the role of phase I expansion cohorts in oncologic drug development

Background: Over the last decades many brain imaging studies have contributed to new insights in the pathogenesis of psychiatric disease. However, in spite of these developments, progress in the development of novel therapeutic drugs for prevalent psychiatric health conditions has been limited. Objective: In this review we discuss translational, diagnostic and methodological issues that have hampered drug development in CNS disorders with a particular focus on psychiatry. The role of preclinical models is critically reviewed and opportunities for brain imaging in early stages of drug development using PET and fMRI are discussed. The role of PET and fMRI in drug development is reviewed emphasizing the need to engage in collaborations between industry, academia and phase I unitsIn this review we discuss translational, diagnostic and methodological issues that have hampered drug development in CNS disorders with a particular focus on psychiatry. The role of preclinical models is critically reviewed and opportunities for brain imaging in early stages of drug development using PET and fMRI are discussed. The role of PET and fMRI in drug development is reviewed emphasizing the need to engage in collaborations between industry, academia and phase I units. Conclusion: Brain imaging technology has revolutionized the study of psychiatric illnesses and during the last decade neuroimaging has provided valuable insights at different levels of analysis and brain organization, such as effective connectivity (anatomical), functional connectivity patterns and neurochemical information that may support both preclinical and clinical drug development. Since there is no unifying pathophysiological theory of individual psychiatric syndromes and since many symptoms cut across diagnostic boundaries, a new theoretical framework has been proposed that may help in defining new targets for treatment and thus enhance drug development in CNS diseases. In addition, it is argued that new proposals for data-mining and mathematical modelling as well as freely available databanks for neural network and neurochemical models of rodents combined with revised psychiatric classification will lead to validated new targets for drug development.

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943-P: Role of Model-Informed Drug Development in Confirming Additional Clinical Benefits of Dulaglutide

Diabetes ◽

10.2337/db20-943-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 943-P

Author(s):

LAI-SAN THAM ◽

JEANNE GEISER ◽

CHENG CAI TANG ◽

KAREN SCHNECK ◽

DAVID COX ◽

...

Keyword(s):

Drug Development ◽

Clinical Benefits

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Cathepsin L plays a key role in SARS-CoV-2 infection in humans and humanized mice and is a promising target for new drug development

Signal Transduction and Targeted Therapy ◽

10.1038/s41392-021-00558-8 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Miao-Miao Zhao ◽

Wei-Li Yang ◽

Fang-Yuan Yang ◽

Li Zhang ◽

Wei-Jin Huang ◽

...

Keyword(s):

Drug Development ◽

Cathepsin L ◽

Human Cells ◽

New Drugs ◽

Disease Course ◽

Promising Target ◽

First Time

AbstractTo discover new drugs to combat COVID-19, an understanding of the molecular basis of SARS-CoV-2 infection is urgently needed. Here, for the first time, we report the crucial role of cathepsin L (CTSL) in patients with COVID-19. The circulating level of CTSL was elevated after SARS-CoV-2 infection and was positively correlated with disease course and severity. Correspondingly, SARS-CoV-2 pseudovirus infection increased CTSL expression in human cells in vitro and human ACE2 transgenic mice in vivo, while CTSL overexpression, in turn, enhanced pseudovirus infection in human cells. CTSL functionally cleaved the SARS-CoV-2 spike protein and enhanced virus entry, as evidenced by CTSL overexpression and knockdown in vitro and application of CTSL inhibitor drugs in vivo. Furthermore, amantadine, a licensed anti-influenza drug, significantly inhibited CTSL activity after SARS-CoV-2 pseudovirus infection and prevented infection both in vitro and in vivo. Therefore, CTSL is a promising target for new anti-COVID-19 drug development.

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The Role of Network Science in Glioblastoma

Cancers ◽

10.3390/cancers13051045 ◽

2021 ◽

Vol 13 (5) ◽

pp. 1045

Author(s):

Marta B. Lopes ◽

Eduarda P. Martins ◽

Susana Vinga ◽

Bruno M. Costa

Keyword(s):

Personalized Medicine ◽

Drug Development ◽

Information Flow ◽

Clinical Studies ◽

Network Science ◽

Cancer Genomics ◽

High Dimensional ◽

Network Discovery ◽

Software Implementations

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

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The Power of Phase I Studies to Detect Clinical Relevant QTc Prolongation: A Resampling Simulation Study

BioMed Research International ◽

10.1155/2015/293564 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 11

Author(s):

Georg Ferber ◽

Ulrike Lorch ◽

Jörg Täubel

Keyword(s):

Phase I ◽

Healthy Subjects ◽

Sparse Sampling ◽

Positive Control ◽

Qtc Prolongation ◽

False Negatives ◽

Assay Sensitivity ◽

Phase I Studies ◽

Sufficient Power

Concentration-effect (CE) models applied to early clinical QT data from healthy subjects are described in the latest E14 Q&A document as promising analysis to characterise QTc prolongation. The challenges faced if one attempts to replace a TQT study by thorough ECG assessments in Phase I based on CE models are the assurance to obtain sufficient power and the establishment of a substitute for the positive control to show assay sensitivity providing protection against false negatives. To demonstrate that CE models in small studies can reliably predict the absence of an effect on QTc, we investigated the role of some key design features in the power of the analysis. Specifically, the form of the CE model, inclusion of subjects on placebo, and sparse sampling on the performance and power of this analysis were investigated. In this study, the simulations conducted by subsampling subjects from 3 different TQT studies showed that CE model with a treatment effect can be used to exclude small QTc effects. The number of placebo subjects was also shown to increase the power to detect an inactive drug preventing false positives while an effect can be underestimated if time points aroundtmaxare missed.

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