scholarly journals Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

2019 ◽  
Vol 9 (1) ◽  
pp. 5-20 ◽  
Author(s):  
Hugo Geerts ◽  
John Wikswo ◽  
Piet H. Graaf ◽  
Jane P.F. Bai ◽  
Chris Gaiteri ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Current Status ◽  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
Drug Discovery And Development

scholarly journals A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

2016 ◽  
Vol 21 (6) ◽  
pp. 521-534 ◽  
Author(s):  
Andrew M. Stern ◽  
Mark E. Schurdak ◽  
Ivet Bahar ◽  
Jeremy M. Berg ◽  
D. Lansing Taylor
Keyword(s):  
Drug Discovery ◽  
Personalized Medicine ◽  
Disease Progression ◽  
Development Strategy ◽  
Population Based ◽  
Resistance Mechanisms ◽  
Therapeutic Strategies ◽  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
Drug Discovery And Development

Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)–driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point.

scholarly journals Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities

2021 ◽  
Vol 23 (4) ◽  
Author(s):  
Nadia Terranova ◽  
Karthik Venkatakrishnan ◽  
Lisa J. Benincosa
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Precision Medicine ◽  
Translational Medicine ◽  
Pharmaceutical Research ◽  
Current Status ◽  
Data Sets ◽  
Patient Centered ◽  
Drug Discovery And Development ◽  
Reverse Translation

AbstractThe exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as “omics” data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.

Sign in / Sign up

Export Citation Format

Share Document