Pharmaceutical Biotechnology:  Drug Discovery and Clinical Applications Edited by O. Kayser and R. R. Müller (Free University of Berlin). Wiley-VCH, Weinheim. 2004. xxv + 311 pp. 7 × 10.5 in. $190.00. ISBN 3-527-30554-8.

2004 ◽  
Vol 67 (10) ◽  
pp. 1771-1771
Author(s):  
Bradley S. Moore
Keyword(s):  
Drug Discovery ◽  
Clinical Applications ◽  
University Of Berlin ◽  
Pharmaceutical Biotechnology

Clinical Trials and Machine Learning: Regulatory Approach Review

2021 ◽  
Vol 16 ◽  
Author(s):  
Diego Alejandro Dri ◽  
Maurizio Massella ◽  
Donatella Gramaglia ◽  
Carlotta Marianecci ◽  
Sandra Petraglia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Trials ◽  
Patient Safety ◽  
Drug Discovery ◽  
Clinical Applications ◽  
Clinical Development ◽  
Regulatory Approach ◽  
National Competent Authorities ◽  
Review Current

: Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has significantly contributed to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has constantly been growing. Moreover, it is now also impacting National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed, or generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient safety, health protection, and fostering immediate access to effective treatments.

scholarly journals Feature Issue Introduction: Bio-Optics in Clinical Applications, Nanotechnology, and Drug Discovery

2010 ◽  
Vol 1 (3) ◽  
pp. 746 ◽  
Author(s):  
Robert J. Nordstrom ◽  
Adah Almutairi ◽  
Elizabeth M.C. Hillman
Keyword(s):  
Drug Discovery ◽  
Clinical Applications

scholarly journals Genomic drug discovery and clinical applications of DDS.

2002 ◽  
Vol 17 (5) ◽  
pp. 408-418
Author(s):  
Kazuhiro Tsukamoto
Keyword(s):  
Drug Discovery ◽  
Clinical Applications

scholarly journals Marine Spirotetronates: Biosynthetic Edifices That Inspire Drug Discovery

Marine Drugs ◽  
2019 ◽  
Vol 17 (4) ◽  
pp. 232 ◽  
Author(s):  
Alexander A. Braddock ◽  
Emmanuel A. Theodorakis
Keyword(s):  
Drug Discovery ◽  
Solid Tumors ◽  
Antimicrobial Properties ◽  
Structure Optimization ◽  
Class Ii ◽  
Clinical Applications ◽  
Complex Structures ◽  
Antibiotic Resistant ◽  
Synthetic Routes

Spirotetronates are actinomyces-derived polyketides that possess complex structures and exhibit potent and unexplored bioactivities. Due to their anticancer and antimicrobial properties, they have potential as drug hits and deserve further study. In particular, abyssomicin C and tetrocarcin A have shown significant promise against antibiotic-resistant S. aureus and tuberculosis, as well as for the treatment of various lymphomas and solid tumors. Improved synthetic routes to these compounds, particularly the class II spirotetronates, are needed to access sufficient quantities for structure optimization and clinical applications.

scholarly journals Pharmacogenomics of antidepressant treatment effects

2011 ◽  
Vol 13 (1) ◽  
pp. 63-71 ◽  
Keyword(s):  
Drug Discovery ◽  
Major Depression ◽  
Critical Appraisal ◽  
State Of The Art ◽  
Antidepressant Treatment ◽  
Clinical Applications ◽  
The Past ◽  
Body Of Knowledge ◽  
Tremendous Progress ◽  
Novel Targets

There has been considerable promise and hope that pharmacogenomics will optimize existing treatments for major depression, as well as identify novel targets for drug discovery. Immediately after the sequencing of the human genome, there was much hope that tremendous progress in pharmacogenomics would rapidly be achieved. In the past 10 years this initial enthusiasm has been replaced by a more sober optimism, as we have gone a long way towards the goal of guiding therapeutics based on genomics. While the effort to translate discovery to clinical applications is ongoing, we now have a vast body of knowledge as well as a clear direction forward. This article will provide a critical appraisal of the state of the art in the pharmacogenomics of depression, both in terms of pharmacodynamics and pharmacokinetics.

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