scholarly journals Lost in Translation – Crossing the Valley of Death across preclinical and clinical divide

2019 ◽  
Author(s):  
Attila A Seyhan
Keyword(s):  
Drug Development ◽  
Clinical Research ◽  
Basic Research ◽  
Clinical Impact ◽  
Laboratory Setting ◽  
Attrition Rates ◽  
New Treatments ◽  
Lost In Translation ◽  
Valley Of Death

A rift that has opened up between basic research (bench) and clinical research and patients (bed) who need their new treatments, diagnostics and prevention, and this rift is widening and getting deeper. The crisis involving the “translation” of basic scientific findings in a laboratory setting to human applications and potential treatments or biomarkers for disease is widely recognized both in academia and industry. Despite the attempts that have been made to mitigate this problem, the high attrition rates of drug development and the problem with reproducibility and translatability of preclinical findings to human applications remain a fact and the return on the investment has been limited in terms of clinical impact.

scholarly journals Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Attila A. Seyhan
Keyword(s):  
Drug Development ◽  
Basic Research ◽  
Organizational Structures ◽  
Attrition Rates ◽  
Funding Mechanisms ◽  
The Many ◽  
New Treatments ◽  
Governmental Funding ◽  
Lost In Translation ◽  
New Strategies

AbstractA rift that has opened up between basic research (bench) and clinical research and patients (bed) who need their new treatments, diagnostics and prevention, and this rift is widening and getting deeper. The crisis involving the “translation” of basic scientific findings in a laboratory setting into human applications and potential treatments or biomarkers for a disease is widely recognized both in academia and industry. Despite the attempts that have been made both in academic and industry settings to mitigate this problem, the high attrition rates of drug development and the problem with reproducibility and translatability of preclinical findings to human applications remain a fact and the return on the investment has been limited in terms of clinical impact.Here I provide an overview of the challenges facing the drug development, and translational discordance with specific focus on a number of “culprits” in translational research including poor hypothesis, irreproducible data, ambiguous preclinical models, statistical errors, the influence of organizational structures, lack of incentives in the academic setting, governmental funding mechanisms, the clinical relevance of basic research, insufficient transparency, and lack of data sharing in research. I further provide some suggestions and new strategies that include some new aspects on open innovation models, entrepreneurship, transparency, and decision making to overcome each of the many problems during the drug discovery and development process and to more dynamically adjust for innovation challenges with broader scientific feedback.

pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset

2019 ◽  
Vol 15 (5) ◽  
pp. 472-485 ◽  
Author(s):  
Kuo-Chen Chou ◽  
Xiang Cheng ◽  
Xuan Xiao
Keyword(s):  
Drug Development ◽  
Subcellular Localization ◽  
Basic Research ◽  
The Other ◽  
Training Dataset ◽  
Sequence Information ◽  
Eukaryotic Proteins ◽  
Validation Tests ◽  
User Friendly ◽  
Better Than

<P>Background/Objective: Information of protein subcellular localization is crucially important for both basic research and drug development. With the explosive growth of protein sequences discovered in the post-genomic age, it is highly demanded to develop powerful bioinformatics tools for timely and effectively identifying their subcellular localization purely based on the sequence information alone. Recently, a predictor called “pLoc-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems where many proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mEuk was trained by an extremely skewed dataset where some subset was about 200 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. </P><P> Methods: To alleviate such bias, we have developed a new predictor called pLoc_bal-mEuk by quasi-balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLocmEuk, the existing state-of-the-art predictor in identifying the subcellular localization of eukaryotic proteins. It has not escaped our notice that the quasi-balancing treatment can also be used to deal with many other biological systems. </P><P> Results: To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mEuk/. </P><P> Conclusion: It is anticipated that the pLoc_bal-Euk predictor holds very high potential to become a useful high throughput tool in identifying the subcellular localization of eukaryotic proteins, particularly for finding multi-target drugs that is currently a very hot trend trend in drug development.</P>

Intelligent Drug Development

2014 ◽  
Author(s):  
Michael Tansey
Keyword(s):  
Clinical Trials ◽  
Cost Effectiveness ◽  
Drug Development ◽  
Clinical Research ◽  
Development Process ◽  
Drug Development Process ◽  
Individual Trial ◽  
Specific Technology

Clinical research is heavily regulated and involves coordination of numerous pharmaceutical-related disciplines. Each individual trial involves contractual, regulatory, and ethics approval at each site and in each country. Clinical trials have become so complex and government requirements so stringent that researchers often approach trials too cautiously, convinced that the process is bound to be insurmountably complicated and riddled with roadblocks. A step back is needed, an objective examination of the drug development process as a whole, and recommendations made for streamlining the process at all stages. With Intelligent Drug Development, Michael Tansey systematically addresses the key elements that affect the quality, timeliness, and cost-effectiveness of the drug-development process, and identifies steps that can be adjusted and made more efficient. Tansey uses his own experiences conducting clinical trials to create a guide that provides flexible, adaptable ways of implementing the necessary processes of development. Moreover, the processes described in the book are not dependent either on a particular company structure or on any specific technology; thus, Tansey's approach can be implemented at any company, regardless of size. The book includes specific examples that illustrate some of the ways in which the principles can be applied, as well as suggestions for providing a better context in which the changes can be implemented. The protocols for drug development and clinical research have grown increasingly complex in recent years, making Intelligent Drug Development a needed examination of the pharmaceutical process.

Drug-induced cardiac toxicity: emphasizing the role of electrocardiography in clinical research and drug development

2004 ◽  
Vol 37 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Ihor Gussak ◽  
Jeffrey Litwin ◽  
Robert Kleiman ◽  
Scott Grisanti ◽  
Joel Morganroth
Keyword(s):  
Drug Development ◽  
Clinical Research ◽  
Cardiac Toxicity ◽  
Drug Induced
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