Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification

Costabal, Francisco Sahli; Matsuno, Kristen; Jiang, Yao; Perdikaris, Paris; Kuhl, Ellen

doi:10.1016/j.cma.2019.01.033

Cited by 87 publications

(83 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These two channels have opposing effects: blocking I Kr can initiate and blocking I CaL can prevent early afterdepolarizations. In a recent study, we have found a similar trend at the QT interval level 24 , which is also considered in current regulations 5 . These results are in line with other studies that have highlighted the importance of altered calcium dynamics during early afterdepolarizations 14,28,29 , and, more recently, also during delayed afterdepolarizations 30 .…”

Section: Early Afterdepolarizations Are a Multiple-channel Phenomenonsupporting

confidence: 76%

“…Although our proposed method holds promise to rapidly assess the risk of a new drug, it has a few limitations: First, our major focus was on combining computational modeling and machine learning to create risk estimators; long term, more experiments will be needed to better validate the method and broaden its scope and use. Second, our model is only as good as its input, the concentration-block curves; we have addressed this limitation in a separate study 24 , similar to other groups 27,40 , and found that there is a mismatch between the drugs that have been well characterized experimentally 3 -the input of the classifier-and the drugs that we agree in their risk classification-the output of the classifier; to mitigate this limitation, we used a deterministic approach to classify the set of compounds. Third, our current work has mainly followed recommendations of the CiPA initiative 6 ; it will be important to validate our model against other cell and heart models, and, probably most importanly, against other compounds.…”

Section: Kr and I Cal Modulate The Onset Of Torsades De Pointesmentioning

confidence: 99%

“…Using machine learning tools to sample the parameter space. To quickly and efficiently sample the parameter space for a wide range of conditions and a wide variety of drugs we combine our computational models with machine learning techniques 18,24 .…”

Section: All Studies Were Approved By the Stanford Administrative Panmentioning

confidence: 99%

“…We have recently proposed a novel exposure-response simulator that allows us to quickly and reliably visualize how different drugs-either individually or in combination-modulate ion channel dynamics, cellular electrophysiology, and electrocardiogram recordings across ten orders of magnitude in space and time 22 . Combining this simulator with machine learning techniques 23 would allow us to seamlessly integrate experimental and computational data from the protein, cellular, tissue, and organ scales to assess cardiac toxicity during pharmacological profiling 24 . levels.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Classifying drugs by their arrhythmogenic risk using machine learning

Costabal¹,

Seo

Ashley

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their pro-arrhythmic potential. We capitalize on recent developments in machine learning and integrate information across ten orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 23 common drugs, exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the pro-arrhythmic potential of new drugs. Our study paves the way towards establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.

show abstract

Section: Early Afterdepolarizations Are a Multiple-channel Phenomenonsupporting

confidence: 76%

Section: Kr and I Cal Modulate The Onset Of Torsades De Pointesmentioning

confidence: 99%

Section: All Studies Were Approved By the Stanford Administrative Panmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classifying drugs by their arrhythmogenic risk using machine learning

Costabal¹,

Seo

Ashley

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Biomedicine has seen the first successful application of these techniques in cardiovascular flows modeling [50] or in cardiac activation mapping [109], where we already have a reasonable physical understanding of the system and can constrain the design space using the known underlying wave propagation dynamics. Another example where machine learning can immediately benefit from multiscale modeling and physics-based simulation is the generation of synthetic data [106], for example, to supplement sparse training sets. This raises the obvious question-especially within the computational mechanics communitywhere can physics-based simulations benefit from machine learning?…”

Section: Motivationmentioning

confidence: 99%

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Peng

Alber

Tepole³

et al. 2020

Arch Computat Methods Eng

Self Cite

222

103

View full text Add to dashboard Cite

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. This review serves as introduction to a special issue on Uncertainty Quantification, Machine Learning, and Data-Driven Modeling of Biological Systems that will help identify current roadblocks and areas where computational mechanics, as a discipline, can play a significant role. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

show abstract

3D‐Printed Functional Hydrogel by DNA‐Induced Biomineralization for Accelerated Diabetic Wound Healing

et al. 2023

View full text Add to dashboard Cite

Chronic wounds in diabetic patients are challenging because their prolonged inflammation makes healing difficult, thus burdening patients, society, and health care systems. Customized dressing materials are needed to effectively treat such wounds that vary in shape and depth. The continuous development of 3D‐printing technology along with artificial intelligence has increased the precision, versatility, and compatibility of various materials, thus providing the considerable potential to meet the abovementioned needs. Herein, functional 3D‐printing inks comprising DNA from salmon sperm and DNA‐induced biosilica inspired by marine sponges, are developed for the machine learning‐based 3D‐printing of wound dressings. The DNA and biomineralized silica are incorporated into hydrogel inks in a fast, facile manner. The 3D‐printed wound dressing thus generates provided appropriate porosity, characterized by effective exudate and blood absorption at wound sites, and mechanical tunability indicated by good shape fidelity and printability during optimized 3D printing. Moreover, the DNA and biomineralized silica act as nanotherapeutics, enhancing the biological activity of the dressings in terms of reactive oxygen species scavenging, angiogenesis, and anti‐inflammation activity, thereby accelerating acute and diabetic wound healing. These bioinspired 3D‐printed hydrogels produce using a DNA‐induced biomineralization strategy are an excellent functional platform for clinical applications in acute and chronic wound repair.

show abstract

Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification

Cited by 87 publications

References 76 publications

Classifying drugs by their arrhythmogenic risk using machine learning

Classifying drugs by their arrhythmogenic risk using machine learning

Multiscale Modeling Meets Machine Learning: What Can We Learn?

3D‐Printed Functional Hydrogel by DNA‐Induced Biomineralization for Accelerated Diabetic Wound Healing

Contact Info

Product

Resources

About