A novel integrated action crossing method for drug-drug interaction prediction in non-communicable diseases

Hunta, Sathien; Yooyativong, Thongchai; Aunsri, Nattapol

doi:10.1016/j.cmpb.2018.06.013

Cited by 13 publications

(10 citation statements)

References 25 publications

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“…127 ML approaches are well suited for identifying complex associations from large databases and have been used for predicting DDIs. 135 NB and CART, 136 GBM, 137 and RFR. 138 While the FAERS data sets are large, the utility of these approaches is limited by data quality.…”

Section: For Ddismentioning

confidence: 99%

Machine learning in pharmacometrics: Opportunities and challenges

McComb

Bies

Ramanathan

2021

Brit J Clinical Pharma

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The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.

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Section: For Ddismentioning

confidence: 99%

Machine learning in pharmacometrics: Opportunities and challenges

McComb

Bies

Ramanathan

2021

Brit J Clinical Pharma

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“…Thus, an improvement in F1 score as high as 38% was achieved in comparison to the method that randomly selected unlabeled data points as likely negatives. Remarkably, Hunta et al 115 developed three ML approaches to predict DDIs in noncommunicable diseases (NCDs) based on the SVM, kNNs, and neural networks. Taking data from DrugBank, they crossed and integrated actions of enzymes and transporter proteins, comparing performance for different methodologies through five‐fold cross‐validation, achieving the best performance for the two NN layers, capable of predicting the NCD DDIs based on pharmacokinetic mechanisms with an accuracy of 83% (F‐Measure 85.23% and AUC 90%).…”

Section: Ai‐based Toxicity Predictionmentioning

confidence: 99%

“…The simultaneous administration of several drugs is increasingly common, raising the probability of DDIs, which can have multiple effects with positive or negative impacts on the expected therapeutic results. 61,114,115 According to the manner in which the data were obtained, there are two large groups of AI-based DDIs prediction models: (i) those using structured databases with molecular and/or pharmacological properties and (ii) those using unstructured information from a variety of sources, such as scientific publications and medical reports. In relation to the first group, many databases were initially only accessible to those involved in creating them, a perspective that is changing toward the use of open databases.…”

Section: Toxicity Due To Ddismentioning

confidence: 99%

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

Pérez‐Santín

Rodríguez‐Solana

García

et al. 2021

WIREs Comput Mol Sci

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The use and production of chemical compounds are subjected to strong legislative pressure. Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory aspects for a multitude of industries, such as chemical, pharmaceutical, or food, due to direct harm to humans, animals, plants, or the environment. Simultaneously, there are growing demands on the authorities to replace traditional in vivo toxicity tests carried out on laboratory animals (e.g., European Union REACH/3R principles, Tox21 and ToxCast by the U.S. government, etc.) with in silica computational models. This is not only for ethical aspects, but also because of its greater economic and time efficiency, as well as more recently because of their superior reliability and robustness than in vivo tests, mainly since the entry into the scene of artificial intelligence (AI)‐based models, promoting and setting the necessary requirements that these new in silico methodologies must meet. This review offers a multidisciplinary overview of the state of the art in the application of AI‐based methodologies for the fulfillment of regulatory‐related toxicological issues. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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“…For example, Sathien et al proposed a method named Integrated Action Crossing (IAC) to create the attributes of the substrate, inhibitor and inducer for the prediction of possible metabolic DDI. Among the three conducted machine learning approaches of the SVM, k‐nearest neighbours and neural networks, the SVM achieved the best result on the simvastatin data set with an accuracy of 76.32% (AUC = 0.776) . Daberdaku et al used the SVM as a classifier to distinguish the interface local surface patches from non‐interface ones in the investigation of the potential of 3D Zernike descriptors for protein‐protein interface predictions and achieved satisfactory performances .…”

Section: What Is Known and Objectivesmentioning

confidence: 99%

“…Among the three conducted machine learning approaches of the SVM, k-nearest neighbours and neural networks, the SVM achieved the best result on the simvastatin data set with an accuracy of 76.32% (AUC = 0.776). 10 Daberdaku et al used the SVM as a classifier to distinguish the interface local surface patches from non-interface ones in the investigation of the potential of 3D Zernike descriptors for protein-protein interface predictions and achieved satisfactory performances. 11 When combined with kernel-based methods, the SVM can also be used to solve nonlinear problems, which widely broadens its applications for real-world affairs.…”

Section: What Is K Nown and Objec Tive Smentioning

confidence: 99%

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies

et al. 2018

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Summary What is known and objective Drug‐drug interactions (DDI) are frequent causes of adverse clinical drug reactions. Efforts have been directed at the early stage to achieve accurate identification of DDI for drug safety assessments, including the development of in silico predictive methods. In particular, similarity‐based in silico methods have been developed to assess DDI with good accuracies, and machine learning methods have been employed to further extend the predictive range of similarity‐based approaches. However, the performance of a developed machine learning method is lower than expectations partly because of the use of less diverse DDI training data sets and a less optimal set of similarity measures. Method In this work, we developed a machine learning model using support vector machines (SVMs) based on the literature‐reported established set of similarity measures and comprehensive training data sets. The established similarity measures include the 2D molecular structure similarity, 3D pharmacophoric similarity, interaction profile fingerprint (IPF) similarity, target similarity and adverse drug effect (ADE) similarity, which were extracted from well‐known databases, such as DrugBank and Side Effect Resource (SIDER). A pairwise kernel was constructed for the known and possible drug pairs based on the five established similarity measures and then used as the input vector of the SVM. Result The 10‐fold cross‐validation studies showed a predictive performance of AUROC >0.97, which is significantly improved compared with the AUROC of 0.67 of an analogously developed machine learning model. Our study suggested that a similarity‐based SVM prediction is highly useful for identifying DDI. Conclusion in silico methods based on multifarious drug similarities have been suggested to be feasible for DDI prediction in various studies. In this way, our pairwise kernel SVM model had better accuracies than some previous works, which can be used as a pharmacovigilance tool to detect potential DDI.

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A novel integrated action crossing method for drug-drug interaction prediction in non-communicable diseases

Cited by 13 publications

References 25 publications

Machine learning in pharmacometrics: Opportunities and challenges

Machine learning in pharmacometrics: Opportunities and challenges

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies

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