Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine

Johnson, Kipp W.; Shameer, Khader; Glicksberg, Benjamin S.; Readhead, Ben; Sengupta, Partho P.; Björkegren, Johan; Kovacic, Jason C.; Dudley, Joel T.

doi:10.1016/j.jacbts.2016.11.010

Cited by 62 publications

(42 citation statements)

References 153 publications

(167 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are already several large-scale biobanks and population-based cohorts including the UK Biobank (500,000 participants), the All of Us initiative (1000,000+ participants), and the Million Veterans Program, now include linkage to EHRs [73]. Deep phenotyping by way of using EHR-linked biobank data has been used as a resource for the discovery of novel drug targets [74][75][76][77]. For example, Dewey et al [78] used the exome sequencing data linked to the EHR from 58,000 patients collected at the Geisinger Health System and found that loss-of-function mutations in ANGPTL3 were correlated with the development of coronary artery disease.…”

Section: Suggestions To Improve Translational Researchmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

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 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.

show abstract

Section: Suggestions To Improve Translational Researchmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

show abstract

“…The same machine learning algorithms have been used widely in both pharmaceutical and toxicological research 30 (Table 1). Statistical machine learning methods have also been used to interrogate, model, and learn from complex multi-omics data to help to address uncertainties about the connections between different types of data 39 . For example, machine learning methods have been applied to electronic health records to accurately predict multiple medical events from different centers without site-specific data harmonization, with recent data suggesting that deep learning was comparable to regularized logistic regression in this case 40 .…”

Section: Machine Learning Models In Actionmentioning

confidence: 99%

Exploiting machine learning for end-to-end drug discovery and development

et al. 2019

View full text Add to dashboard Cite

A variety of machine learning methods such as Naïve Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger data sets created from high throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting. Learning from history 'Those who do not remember the past are condemned to repeat it' (Santayana). This observation applies as much to drug discovery as it does to other aspects of human endeavor 1. The history of drug discovery is a prelude to the emerging potential of computerassisted data exploration. One constant in drug discovery is that every few years the estimated cost to develop drugs rises further. Less than 20 years ago, developing a drug took ~12 years, cost under a billion dollars, and the biggest challenges were failures due to efficacy or toxicity-induced attrition 2. in vitro pharmacological profiling implemented earlier in the drug discovery process helped to identify some predictable undesirable off-*

show abstract

“…173,174 Similarly, but at the level of whole physiological systems or organs, one can turn to models of the heart, 164 of the liver, 175 and of the skeletal system 176 as valid examples of multiscale systems. Further examples include multiscale modeling approaches aiming at predicting tumor evolution, 177 the modeling of angiogenesis, 178 studying the signaling pathways that are relevant for specific kinds of cancer, 74 predicting cardiotoxicity, 179 and introducing so-called precision cardiology, 180 just to name a few examples. Also, the reader is referred to the numerous pertinent review articles (see, for instance, Refs.…”

Section: Limitations Of Modeling Approaches At Different Biological Smentioning

confidence: 99%

Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Stalidzāns

Zanin

Tieri

et al. 2020

Network and Systems Medicine

View full text Add to dashboard Cite

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body.

show abstract

Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine

Cited by 62 publications

References 153 publications

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

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

Exploiting machine learning for end-to-end drug discovery and development

Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Contact Info

Product

Resources

About