scholarly journals A Model‐Informed Drug Discovery and Development Strategy for the Novel Glucose‐Responsive Insulin MK‐2640 Enabled Rapid Decision Making

2020 ◽  
Vol 107 (6) ◽  
pp. 1296-1311
Author(s):  
Sandra A.G. Visser ◽  
Bhargava Kandala ◽  
Craig Fancourt ◽  
Alexander W. Krug ◽  
Carolyn R. Cho
Keyword(s):  
Decision Making ◽  
Drug Discovery ◽  
Development Strategy ◽  
The Novel ◽  
Drug Discovery And Development

scholarly journals The Emergence of Imaging MS in Drug Discovery and Development: Making a Difference by Driving Decision Making

2021 ◽  
Author(s):  
Stephen Castellino ◽  
Nichole M. Lareau ◽  
M. Reid Groseclose
Keyword(s):  
Decision Making ◽  
Drug Discovery ◽  
Drug Discovery And Development ◽  
Making A Difference ◽  
Imaging Ms

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.

scholarly journals In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression

2020 ◽  
Vol 21 (10) ◽  
pp. 3582
Author(s):  
Ming-Han Lee ◽  
Giang Huong Ta ◽  
Ching-Feng Weng ◽  
Max K. Leong
Keyword(s):  
Drug Discovery ◽  
Support Vector Regression ◽  
Intestinal Permeability ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
The Novel ◽  
Drug Discovery And Development ◽  
Important Drug ◽  
Good Agreement

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure–activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75–0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78–0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

Transforming Translation Through Quantitative Pharmacology for High‐Impact Decision Making in Drug Discovery and Development

2019 ◽  
Vol 107 (6) ◽  
pp. 1285-1289 ◽  
Author(s):  
Neeraj Gupta ◽  
Dean Bottino ◽  
Ulrika S.H. Simonsson ◽  
Cynthia J. Musante ◽  
Tjerk Bueters ◽  
...  
Keyword(s):  
Decision Making ◽  
Drug Discovery ◽  
High Impact ◽  
Drug Discovery And Development ◽  
Quantitative Pharmacology

scholarly journals Importance of scientific collaboration in contemporary drug discovery and development: a detailed network analysis

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Feixiong Cheng ◽  
Yifang Ma ◽  
Brian Uzzi ◽  
Joseph Loscalzo
Keyword(s):  
Decision Making ◽  
Drug Discovery ◽  
Network Analysis ◽  
Pharmaceutical Industry ◽  
Health Care Providers ◽  
Scientific Collaboration ◽  
Knowledge Flows ◽  
Public Understanding ◽  
Care Providers ◽  
Drug Discovery And Development

Abstract Background Growing evidence shows that scientific collaboration plays a crucial role in transformative innovation in the life sciences. For example, contemporary drug discovery and development reflects the work of teams of individuals from academic centers, the pharmaceutical industry, the regulatory science community, health care providers, and patients. However, public understanding of how collaborations between academia and industry catalyze novel target identification and first-in-class drug discovery is limited. Results We perform a comprehensive network analysis on a large scientific corpus of collaboration and citations (97,688 papers with 1,862,500 citations from 170 million scientific records) to quantify the success trajectory of innovative drug development. By focusing on four types of cardiovascular drugs, we demonstrate how knowledge flows between institutions to highlight the underlying contributions of many different institutions in the development of a new drug. We highlight how such network analysis could help to increase industrial and governmental support, and improve the efficiency or accelerate decision-making in drug discovery and development. Conclusion We demonstrate that network analysis of large public databases can identify and quantify investigator and institutional relationships in drug discovery and development. If broadly applied, this type of network analysis may help to enhance public understanding of and support for biomedical research, and could identify factors that facilitate decision-making in first-in-class drug discovery among academia, the pharmaceutical industry, and healthcare systems.

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 Ethnomedicinal herbs in African traditional medicine with potential activity for the prevention, treatment, and management of coronavirus disease 2019

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Olutayo Ademola Adeleye ◽  
Mbang Nyong Femi-Oyewo ◽  
Oluyemisi Adebowale Bamiro ◽  
Lateef Gbenga Bakre ◽  
Akinyinka Alabi ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Traditional Medicine ◽  
Literature Search ◽  
Symptomatic Treatment ◽  
The Novel ◽  
Mortality And Morbidity ◽  
Drug Discovery And Development ◽  
African Traditional Medicine ◽  
Potential Activity ◽  
Novel Coronavirus

Abstract Background Ethnomedicine, a study of traditional medicine, is significant in drug discovery and development. African traditional medicine has been in existence for several thousands of years, and several drugs have been discovered and developed from it. Main text The deadly coronavirus disease 2019 (COVID-19) caused by a novel coronavirus known as SARS-CoV-2 has widely spread globally with high mortality and morbidity. Its prevention, treatment and management still pose a serious challenge. A drug for the cure of this disease is yet to be developed. The clinical management at present is based on symptomatic treatment as presented by individuals infected and this is by combination of more than two drugs such as antioxidants, anti-inflammatory, anti-pyretic, and anti-microbials. Literature search was performed through electronic searches of PubMed, Google Scholar, and several research reports including WHO technical documents and monographs. Conclusion Drug discovery from herbs is essential and should be exploited for the discovery of drugs for the management of COVID-19. This review is aimed at identifying ethnomedicinal herbs available in Africa that could be used for the discovery and development of a drug for the prevention, treatment, and management of the novel coronavirus disease 2019.

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