A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform

Gim, Jeong‐An; Kwon, Yonghan; Lee, Hyun A.; Lee, Kyeong‐Ryoon; Kim, Soohyun; Choi, Yuni; Kim, Yu Kyong; Lee, Howard

doi:10.3390/ijms21072517

Cited by 12 publications

(9 citation statements)

References 41 publications

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“…89 These results are promising but research is needed before the broader utility of ML for antibiotic discovery via molecule repurposing can be considered established. 91 Their results…”

Section: Studies Of the Pk/pd And Poppk Of Antibioticsmentioning

confidence: 99%

“…CART outperformed all other ML methods with a mean absolute error of 0.73 (95% CI: 0.63–0.82). Gim et al compared CART, RFR, and least absolute shrinkage and selection operator for determining the influence of single nucleotide polymorphism (SNP) for predicting of tacrolimus Cmax and AUC in healthy Korean males 91 . Their results showed that all ML methods identified CYP3A5 SNP, rs 776746 as the best predictor of tacrolimus exposure.…”

Section: Applications Of ML In Pharmaceutical Sciencesmentioning

confidence: 99%

“…Population PK data for tacrolimus, an immunosuppressive drug used in organ transplantation that requires dose optimization because of its narrow therapeutic index and large IIV, has been investigated in several ML studies 90–93 . Tang et al performed a head‐to‐head comparison of 8 different ML methods: NN; multiple linear regression; CART; multivariate adaptive regression splines (MARS); boosted regression tree; SVR; RFR; lasso regression; and Bayesian additive regression trees for tacrolimus dose optimization 90 .…”

Section: Applications Of ML In Pharmaceutical Sciencesmentioning

confidence: 99%

“…9.1.2 | ML studies of the PK/PD and PopPK of other drug classesPopulation PK data for tacrolimus, an immunosuppressive drug used in organ transplantation that requires dose optimization because of its narrow therapeutic index and large IIV, has been investigated in several ML studies [90][91][92][93]. Tang et al performed a head-to-head compari-…”

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confidence: 99%

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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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“…89 These results are promising but research is needed before the broader utility of ML for antibiotic discovery via molecule repurposing can be considered established. 91 Their results…”

Section: Studies Of the Pk/pd And Poppk Of Antibioticsmentioning

confidence: 99%

Section: Applications Of ML In Pharmaceutical Sciencesmentioning

confidence: 99%

Section: Applications Of ML In Pharmaceutical Sciencesmentioning

confidence: 99%

mentioning

confidence: 99%

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Machine learning in pharmacometrics: Opportunities and challenges

McComb

Bies

Ramanathan

2021

Brit J Clinical Pharma

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“…Artificial intelligence (AI) has been used to assist the discovery of a range of different genetic factors in humans and has helped the identification of novel SNPs associated with drug response when treating cancer, psychiatric disease, and cardiovascular disease [ 25 , 26 ]. In a previous study, AI machine learning failed to predict the platelet reactivity since very complex non-linear phenomena of platelet reactivity [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial-Intelligence-Assisted Discovery of Genetic Factors for Precision Medicine of Antiplatelet Therapy in Diabetic Peripheral Artery Disease

et al. 2022

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An increased risk of cardiovascular events was identified in patients with peripheral artery disease (PAD). Clopidogrel is one of the most widely used antiplatelet medications. However, there are heterogeneous outcomes when clopidogrel is used to prevent cardiovascular events in PAD patients. Here, we use an artificial intelligence (AI)-assisted methodology to identify genetic factors potentially involved in the clopidogrel-resistant mechanism, which is currently unclear. Several discoveries can be pinpointed. Firstly, a high proportion (>50%) of clopidogrel resistance was found among diabetic PAD patients in Taiwan. Interestingly, our result suggests that platelet function test-guided antiplatelet therapy appears to reduce the post-interventional occurrence of major adverse cerebrovascular and cardiac events in diabetic PAD patients. Secondly, AI-assisted genome-wide association study of a single-nucleotide polymorphism (SNP) database identified a SNP signature composed of 20 SNPs, which are mapped into 9 protein-coding genes (SLC37A2, IQSEC1, WASHC3, PSD3, BTBD7, GLIS3, PRDM11, LRBA1, and CNR1). Finally, analysis of the protein connectivity map revealed that LRBA, GLIS3, BTBD7, IQSEC1, and PSD3 appear to form a protein interaction network. Intriguingly, the genetic factors seem to pinpoint a pathway related to endocytosis and recycling of P2Y12 receptor, which is the drug target of clopidogrel. Our findings reveal that a combination of AI-assisted discovery of SNP signatures and clinical parameters has the potential to develop an ethnic-specific precision medicine for antiplatelet therapy in diabetic PAD patients.

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Computational design of clinical trials using a combination of simulation and the genetic algorithm

Tsuchiwata

Tsuji

2023

CPT Pharmacom & Syst Pharma

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Artificial intelligence (AI) has come to be used in various technological fields in recent years. However, there have been no reports of AI-designed clinical trials. In this study, we tried to develop study designs by a genetic algorithm (GA), which is an AI solution for combination optimization problems. Specifically, the computational design approach was applied to optimize the blood sampling schedule for a bioequivalence (BE) study in pediatrics and optimize the allocation of dose groups for a dose-finding study. The GA could reduce the number of blood collection points from 15 (typical standard) to seven points without meaningful impact on the accuracy and precision of the pharmacokinetic estimation for the pediatric BE study. For the dose-finding study, up to 10% reduction of the total number of required subjects from the standard design could be achieved. The GA also created a design that would lead to a drastic reduction of the required number of subjects in the placebo arm while keeping the total number of subjects at a minimum level. These results indicated the potential usefulness of the computational clinical study design approach for innovative drug development. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?The quality of a clinical study design is a key component of the success of the clinical development of new medicine. WHAT QUESTION DID THIS STUDY ADDRESS?This study assessed the possibility of creating a novel and innovative clinical study design using artificial intelligence (AI)-based approaches like genetic algorithm, to overcome the limitations of traditional standard and empirical choices. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?The results of this study suggest that an AI-based approach could help the development of a clinical study design. HOW MIGHT THIS CHANGE DRUG DISCOVERY, DEVELOPMENT, AND/OR THERAPEUTICS?The findings suggest that computer recommendations of a study design may result in opportunities to input novel and innovative ideas into model-informed drug development.

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A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform

Cited by 12 publications

References 41 publications

Machine learning in pharmacometrics: Opportunities and challenges

Machine learning in pharmacometrics: Opportunities and challenges

Artificial-Intelligence-Assisted Discovery of Genetic Factors for Precision Medicine of Antiplatelet Therapy in Diabetic Peripheral Artery Disease

Computational design of clinical trials using a combination of simulation and the genetic algorithm

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