TY - JOUR
T1 - Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug–Food Interactions from Chemical Structures
AU - Kha, Quang Hien
AU - Le, Viet Huan
AU - Hung, Truong Nguyen Khanh
AU - Nguyen, Ngan Thi Kim
AU - Le, Nguyen Quoc Khanh
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/4
Y1 - 2023/4
N2 - Possible drug–food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug–drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament’s effect, the withdrawals of various medications, and harmful impacts on the patients’ health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug–food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.
AB - Possible drug–food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug–drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament’s effect, the withdrawals of various medications, and harmful impacts on the patients’ health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug–food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.
KW - adverse food reaction
KW - chemical informatics
KW - DrugBank
KW - drug–food interactions
KW - drug–nutrient interactions
KW - explainable artificial intelligence
KW - FooDB
KW - machine learning
KW - precision medicine
KW - simplified molecular-input line-entry system
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U2 - 10.3390/s23083962
DO - 10.3390/s23083962
M3 - Article
C2 - 37112302
AN - SCOPUS:85153951370
SN - 1424-8220
VL - 23
JO - Sensors
JF - Sensors
IS - 8
M1 - 3962
ER -