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 Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

2020 ◽  
Vol 10 (7) ◽  
pp. 2376 ◽  
Author(s):  
Rob C. van Wijk ◽  
Rami Ayoun Alsoud ◽  
Hans Lennernäs ◽  
Ulrika S. H. Simonsson
Keyword(s):  
Drug Discovery ◽  
Drug Interactions ◽  
Development Strategy ◽  
Drug Exposure ◽  
Rapid Development ◽  
Drug Combinations ◽  
Pharmacodynamic Modeling ◽  
Drug Discovery And Development

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Accelerating safe drug development: an ideal approach to approval

Hematology ◽  
2013 ◽  
Vol 2013 (1) ◽  
pp. 24-29
Author(s):  
Michael R. Grever
Keyword(s):  
Drug Discovery ◽  
Drug Development ◽  
Hematologic Malignancies ◽  
Therapeutic Strategies ◽  
Cancer Drug ◽  
Task Completion ◽  
Close Monitoring ◽  
Design Strategies ◽  
Drug Discovery And Development ◽  
Therapeutic Research

Abstract Although enormous progress in therapeutic research has improved the lives of patients with hematologic malignancies, these earlier achievements resulted from strategic combinations of agents with unique mechanisms of action and nonoverlapping toxicities. Continued investment in the modern era of drug discovery and development will focus on targeted therapies. Targeting of specific molecular pathways is expected to achieve effective tumor cell reduction with less overall toxicity. The translational processes involved in moving novel therapeutic strategies from the laboratory toward the clinic require close monitoring. The efforts in both cancer drug discovery and development will require extensive collaboration among basic scientists, clinical investigators, and regulatory scientists. The transition from older methods of therapeutic research will require laboratory support to define eligible patients based upon their pretreatment profile. The principles of preclinical drug development based upon decades of experience in predicting toxicity and designing therapeutic strategies are still needed to insure that safety is a high priority. The opportunities for developing novel targeted combination therapies in uniquely profiled patients will hopefully enable successful breakthroughs. Several concrete examples of exciting new agents are discussed here. Defining the predicted mechanism of resistance to these new targeted agents will enable investigators to subsequently design strategies to circumvent resistance with effective combinations. Drug discovery and development are complex and expensive, so efficiency and cooperation in task completion must be tracked.

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