Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas

Implementing machine learning to predict survival outcomes in patients with resected pulmonary large cell neuroendocrine carcinoma

BACKGROUND

The post-surgical prognosis for Pulmonary Large Cell Neuroendocrine Carcinoma (PLCNEC) patients remains largely unexplored. Developing a precise prognostic model is vital to assist clinicians in patient counseling and creating effective treatment strategies.

RESEARCH DESIGN AND METHODS

This retrospective study utilized the Surveillance, Epidemiology, and End Results database from 2000 to 2018 to identify key prognostic features for Overall Survival (OS) in PLCNEC using Boruta analysis. Predictive models employing XGBoost, Random Forest, Decision Trees, Elastic Net, and Support Vector Machine were constructed and evaluated based on Area Under the Receiver Operating Characteristic Curve (AUC), calibration plots, Brier scores, and Decision Curve Analysis (DCA).

RESULTS

Analysis of 604 patients revealed eight significant predictors of OS. The Random Forest model outperformed others, with AUC values of 0.765 and 0.756 for 3 and 5-year survival predictions in the training set, and 0.739 and 0.706 in the validation set, respectively. Its superior validation cohort performance was confirmed by its AUC, calibration, and DCA metrics.

CONCLUSIONS

This study introduces a novel machine learning-based prognostic model with a supportive web-based platform, offering valuable tools for healthcare professionals. These advancements facilitate more personalized clinical decision-making for PLCNEC patients following primary tumor resection.

A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data

Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the diagnosis, prognosis and prediction of the response to therapy has aroused the interest of researchers in their use together with machine learning and deep learning models. Most of the research in this field has been model-centric, meaning it has been based on finding better performing algorithms. However, in practice, improving data quality can result in a better model. This study investigates a data-centric machine learning approach to determine their potential benefits in predicting glioma grades. We report six performance metrics to provide a complete picture of model performance. Experimental results indicate that standardization and oversizing the minority class increase the prediction performance of four popular machine learning models and two classifier ensembles applied on a low-imbalanced data set consisting of clinical factors and molecular biomarkers. The experiments also show that the two classifier ensembles significantly outperform three of the four standard prediction models. Furthermore, we conduct a comprehensive descriptive analysis of the glioma data set to identify relevant statistical characteristics and discover the most informative attributes using four feature ranking algorithms.

Noninvasive prediction of CCL2 expression level in high-grade glioma patients

BACKGROUND

Gliomas are recognized as the most frequent type of malignancies in the central nervous system, and efficacious prognostic indicators are essential to treat patients with gliomas and improve their clinical outcomes. The chemokine (C-C motif) ligand 2 (CCL2) is a promising predictor for glioma malignancy and progression. However, at present, the methods to evaluate CCL2 expression level are invasive and operator-dependent.

OBJECTIVE

It was expected to noninvasively predict CCL2 expression levels in malignant glioma tissues by magnetic resonance imaging (MRI)-based radiomics and assess the association between the developed radiomics model and prognostic indicators and related genes.

METHODS

MRI-based radiomics was used to predict CCL2 expression level using data obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) databases. A support vector machine (SVM)-based radiomics model and a logistic regression (LR)-based radiomics model were used to predict the radiomics score, and its correlation with CCL2 expression level was analyzed.

RESULTS

The results revealed that there was an association between CCL2 expression level and the overall survival of cases with gliomas, and bioinformatics correlation analysis showed that CCL2 expression level was highly correlated with disease-related pathways, such as mTOR signaling pathway, cGMP-PKG signaling pathway, and MAPK signaling pathway. Both SVM- and LR-based radiomics data robustly predicted CCL2 expression level, and radiomics scores could also be used to predict the overall survival of patients. Moreover, the high/low radiomics scores were highly correlated with the known glioma-related genes, including CD70, CD27, and PDCD1.

CONCLUSION

An MRI-based radiomics model was successfully developed, and its clinical benefits were confirmed, including the prediction of CCL2 expression level and patients' prognosis.

Effect of Intraoperative Opioid Dose on Perioperative Neutrophil-to-Lymphocyte Ratio and Lymphocyte-to-Monocyte Ratio in Glioma

Background

The neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) are inflammatory biomarkers. Until now, it is unknown the impact of opioid dosage on perioperative immunity in glioma patients. The aim of this study was to explore the effect of intraoperative opioid dosage on perioperative immune perturbations using NLR and LMR as inflammatory biomarkers and evaluate the correlation between inflammatory biomarkers and pathological grade of glioma.

Methods

The study included 208 patients with primary glioma who underwent glioma resection from February 2012 to November 2019 at Harbin Medical University Cancer Hospital. Complete blood count (CBC) was collected at 3 time points: one week before surgery, and 24 hours and one week after surgery. Patients were divided into high-dose and low-dose groups, based on the median value of intraoperative opioid dose. The relationships between perioperative NLR, LMR and intraoperative opioid dosage were analyzed using repeated measurement analysis of variance (ANOVA). Correlations between preoperative various factors and pathological grade were analyzed by Spearman analysis. Receiver operating characteristic (ROC) curves were performed to assess the predictive performance of the NLR and LMR for pathological grade.

Results

The NLR (P=0.020) and lower LMR (P=0.037) were statistically significant different between high-dose and low-dose groups one week after surgery. The area under the curve (AUC) of the NLR to identify poor diagnosis was 0.685, which was superior to the LMR (AUC: 0.607) and indicated a correlation between the NLR with pathological grade. The preoperative NLR (P=0.000), LMR (P=0.009), age (P=0.000) and tumor size (P=0.001) exhibited a significant correlation with the pathological grade of glioma.

Conclusion

Intraoperative opioids in the high-dose group were associated with higher NLR and lower LMR in postoperative glioma patients. The preoperative NLR and LMR demonstrated predictive value for distinguishing between high-grade and low-grade gliomas.

Application of artificial intelligence in brain arteriovenous malformations: Angioarchitectures, clinical symptoms and prognosis prediction

BACKGROUND

Artificial intelligence (AI) has rapidly advanced in the medical field, leveraging its intelligence and automation for the management of various diseases. Brain arteriovenous malformations (AVM) are particularly noteworthy, experiencing rapid development in recent years and yielding remarkable results. This paper aims to summarize the applications of AI in the management of AVMs management.

METHODS

Literatures published in PubMed during 1999-2022, discussing AI application in AVMs management were reviewed.

RESULTS

AI algorithms have been applied in various aspects of AVM management, particularly in machine learning and deep learning models. Automatic lesion segmentation or delineation is a promising application that can be further developed and verified. Prognosis prediction using machine learning algorithms with radiomic-based analysis is another meaningful application.

CONCLUSIONS

AI has been widely used in AVMs management. This article summarizes the current research progress, limitations and future research directions.

Therapeutic Decision Making in Prevascular Mediastinal Tumors Using CT Radiomics and Clinical Features: Upfront Surgery or Pretreatment Needle Biopsy?

The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from patients who needed core needle biopsy among patients with prevascular mediastinal tumor (PMT). Patients with PMT who received a contrast-enhanced computed tomography (CECT) scan and initial management for PMT between January 2010 and December 2020 were included in this retrospective study. Fourteen ML algorithms were used to construct candidate classification models via the voting ensemble approach, based on preoperative clinical data and radiomic features extracted from the CECT. The classification accuracy of clinical diagnosis was 86.1%. The first ensemble learning model was built by randomly choosing seven ML models from a set of fourteen ML models and had a classification accuracy of 88.0% (95% CI = 85.8 to 90.3%). The second ensemble learning model was the combination of five ML models, including NeuralNetFastAI, NeuralNetTorch, RandomForest with Entropy, RandomForest with Gini, and XGBoost, and had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (p < 0.05). Due to the superior performance, the voting ensemble learning clinical-radiomic classification model may be used as a clinical decision support system to facilitate the selection of the initial management of PMT.

The evaluation of six genes combined value in glioma diagnosis and prognosis

PURPOSE

Glioma is the most common and fatal type of brain tumour. Owing to its aggressiveness and lethality, early diagnosis and prediction of patient survival are very important. This study aimed to identify key genes and biomarkers for glioma that can guide clinicians in making rapid diagnosis and prognostication.

METHODS

Data mining of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data, and Genotype-Tissue Expression Project brain expression data revealed significantly differentially expressed genes (DEGs), and the risk scores of individual patients were calculated. WGCNA was utilized to screen for genes most related to clinical diagnosis. Prognostic genes associated with glioma were selected via combining the LASSO regression with univariate and multivariate Cox regression and protein-protein interaction network analyses. Then, a nomogram was constructed. And CGGA dataset was utilized to validated. The protein expression levels of the signature were detected using the human protein atlas. Drug response prediction was carried out using the package "pRRophetic".

RESULTS

A six-gene signature (KLF6, CHI3L1, SERPINE1, ANGPT2, TGFBR1, and PTX3) was identified and used to stratify patients into low- and high-risk groups. Survival, ROC curve, and Cox analyses clarified that the six hub genes were a favourable independent prognostic factor for patients with glioma. A nomogram was set up by integrating clinical parameters with risk signatures, showing high precision for predicting 2-, 3-, 4-, 5-years survival. In addition, the expression of most genes was consistent with protein expression. Furthermore, the sensitivity to the top ten drugs in the GDSC database of the high-risk group was significantly higher than the low-risk group.

CONCLUSION

Based on genetic profiles and clinicopathological features, including age, grade, isocitrate dehydrogenase mutation status, we constructed a comprehensive prognostic model for patients with glioma. These signatures can be regarded as biomarkers to predict the prognosis of gliomas, possibly providing more therapeutic strategies for future clinical research.

A Clinic-Radiomics Model for Predicting the Incidence of Persistent Organ Failure in Patients with Acute Necrotizing Pancreatitis

Background

Persistent organ failure (POF) is the leading cause of death in patients with acute necrotizing pancreatitis (ANP). Although several risk factors have been identified, there remains a lack of efficient instruments to accurately predict the incidence of POF in ANP.

Methods

Retrospectively, the clinical and imaging data of 178 patients with ANP were collected from our database, and the patients were divided into training (n = 125) and validation (n = 53) cohorts. Through computed tomography image acquisition, the volume of interest segmentation, and feature extraction and selection, a pure radiomics model in terms of POF prediction was established. Then, a clinic-radiomics model integrating the pure radiomics model and clinical risk factors was constructed. Both primary and secondary endpoints were compared between the high- and low-risk groups stratified by the clinic-radiomics model.

Results

According to the 547 selected radiomics features, four models were derived from features. A clinic-radiomics model in the training and validation sets showed better predictive performance than pure radiomics and clinical models. The clinic-radiomics model was evaluated by the ratios of intervention and mechanical ventilation, intensive care unit (ICU) stays, and hospital stays. The results showed that the high-risk group had significantly higher intervention rates, ICU stays, and hospital stays than the low-risk group, with the confidence interval of 90% (p < 0.1 for all).

Conclusions

This clinic-radiomics model is a useful instrument for clinicians to evaluate the incidence of POF, facilitating patients' and their families' understanding of the ANP prognosis.

Grading the Severity of Damage to the Perijunctional Actomyosin Ring and Zonula Occludens-1 of the Corneal Endothelium by Ensemble Learning Methods

Purpose: In many epithelia, including the corneal endothelium, intracellular/extracellular stresses break down the perijunctional actomyosin ring (PAMR) and zonula occludens-1 (ZO-1) at the apical junctions. This study aims to grade the severity of damage to PAMR and ZO-1 through machine learning. Methods: Immunocytochemical images of PAMR and ZO-1 were drawn from recent studies on the corneal endothelium subjected to hypothermia and oxidative stress. The images were analyzed for their morphological (e.g., Hu moments) and textural features (based on gray-level co-occurrence matrix [GLCM] and Gabor filters). The extracted features were ranked by SHapley analysis and analysis of variance. Then top features were used to grade the severity of damage using a suite of ensemble classifiers, including random forest, bagging classifier (BC), AdaBoost, extreme gradient boosting, and stacking classifier. Results: A partial set of features from GLCM, along with Hu moments and the number of hexagons, enabled the classification of damage to PAMR into Control, Mild, Moderate, and Severe with the area under the receiver operating characteristics curve (AUC) = 0.92 and F1 score = 0.77 with BC. In contrast, a bank of Gabor filters provided a partial set of features that could be combined with Hu moments, branch length, and sharpness for the classification of ZO-1 images into four levels with AUC = 0.95 and F1 score of 0.8 with BC. Conclusions: We have developed a workflow that enables the stratification of damage to PAMR and ZO-1. The approach can be applied to similar data during drug discovery or pathophysiological studies of epithelia.

Accurate prediction of glioma grades from radiomics using a multi-filter and multi-objective-based method

Radiomics, providing quantitative data extracted from medical images, has emerged as a critical role in diagnosis and classification of diseases such as glioma. One main challenge is how to uncover key disease-relevant features from the large amount of extracted quantitative features. Many existing methods suffer from low accuracy or overfitting. We propose a new method, Multiple-Filter and Multi-Objective-based method (MFMO), to identify predictive and robust biomarkers for disease diagnosis and classification. This method combines a multi-filter feature extraction with a multi-objective optimization-based feature selection model, which identifies a small set of predictive radiomic biomarkers with less redundancy. Taking magnetic resonance imaging (MRI) images-based glioma grading as a case study, we identify 10 key radiomic biomarkers that can accurately distinguish low-grade glioma (LGG) from high-grade glioma (HGG) on both training and test datasets. Using these 10 signature features, the classification model reaches training Area Under the receiving operating characteristic Curve (AUC) of 0.96 and test AUC of 0.95, which shows superior performance over existing methods and previously identified biomarkers.

Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease

This study reviews the recent progress of explainable artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The source of data was eight original studies in PubMed. The search terms were "gastrointestinal" (title) together with "random forest" or "explainable artificial intelligence" (abstract). The eligibility criteria were the dependent variable of GID or a strongly associated disease, the intervention(s) of artificial intelligence, the outcome(s) of accuracy and/or the area under the receiver operating characteristic curve (AUC), the outcome(s) of variable importance and/or the Shapley additive explanations (SHAP), a publication year of 2020 or later, and the publication language of English. The ranges of performance measures were reported to be 0.70-0.98 for accuracy, 0.04-0.25 for sensitivity, and 0.54-0.94 for the AUC. The following factors were discovered to be top-10 predictors of gastrointestinal bleeding in the intensive care unit: mean arterial pressure (max), bicarbonate (min), creatinine (max), PMN, heart rate (mean), Glasgow Coma Scale, age, respiratory rate (mean), prothrombin time (max) and aminotransferase aspartate (max). In a similar vein, the following variables were found to be top-10 predictors for the intake of almond, avocado, broccoli, walnut, whole-grain barley, and/or whole-grain oat: Roseburia undefined, Lachnospira spp., Oscillibacter undefined, Subdoligranulum spp., Streptococcus salivarius subsp. thermophiles, Parabacteroides distasonis, Roseburia spp., Anaerostipes spp., Lachnospiraceae ND3007 group undefined, and Ruminiclostridium spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID.

Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review

Background and Objective

Machine learning (ML) models are increasingly being utilized in oncology research for use in the clinic. However, while more complicated models may provide improvements in predictive or prognostic power, a hurdle to their adoption are limits of model interpretability, wherein the inner workings can be perceived as a "black box". Explainable artificial intelligence (XAI) frameworks including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are novel, model-agnostic approaches that aim to provide insight into the inner workings of the "black box" by producing quantitative visualizations of how model predictions are calculated. In doing so, XAI can transform complicated ML models into easily understandable charts and interpretable sets of rules, which can give providers with an intuitive understanding of the knowledge generated, thus facilitating the deployment of such models in routine clinical workflows.

Methods

We performed a comprehensive, non-systematic review of the latest literature to define use cases of model-agnostic XAI frameworks in oncologic research. The examined database was PubMed/MEDLINE. The last search was run on May 1, 2022.

Key Content and Findings

In this review, we identified several fields in oncology research where ML models and XAI were utilized to improve interpretability, including prognostication, diagnosis, radiomics, pathology, treatment selection, radiation treatment workflows, and epidemiology. Within these fields, XAI facilitates determination of feature importance in the overall model, visualization of relationships and/or interactions, evaluation of how individual predictions are produced, feature selection, identification of prognostic and/or predictive thresholds, and overall confidence in the models, among other benefits. These examples provide a basis for future work to expand on, which can facilitate adoption in the clinic when the complexity of such modeling would otherwise be prohibitive.

Conclusions

Model-agnostic XAI frameworks offer an intuitive and effective means of describing oncology ML models, with applications including prognostication and determination of optimal treatment regimens. Using such frameworks presents an opportunity to improve understanding of ML models, which is a critical step to their adoption in the clinic.

A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas

Simple Summary

Lower-grade glioma (LGG) is a kind of center nervous system neoplasm that arises from the glial cells. Lower-grade glioma patients have a median survival time in the range of 1.5–8 years based on the tumor genotypes. In term of epidemiology, most of the lower-grade glioma patients are diagnosed at young adult of age, which led to an early age of death. For exact diagnosis and effective treatment, a pathological result from biopsy sample is required. However, it is long turnaround time. In this study, using pre-operative magnetic resonance images, we developed a non-invasive model to classify tumor mutational burden (TMB), a prognostic factor of treatment response in lower-grade glioma patients, with an accuracy of 0.7936. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present.

Abstract

Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients. However, the therapy is not effective for all types of LGGs, and tumor mutational burden (TMB) has been shown to be a potential biomarker for the susceptibility and prognosis of immunotherapy in lower-grade glioma patients. Hence, predicting TMB benefits brain cancer patients. In this study, we investigated the correlation between MRI (magnetic resonance imaging)-based radiomic features and TMB in LGG by applying machine learning methods. Six machine learning classifiers were examined on the features extracted from the genetic algorithm. Subsequently, a light gradient boosting machine (LightGBM) succeeded in selecting 11 radiomics signatures for TMB classification. Our LightGBM model resulted in high accuracy of 0.7936, and reached a balance between sensitivity and specificity, achieving 0.76 and 0.8107, respectively. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present.

A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas

Glioma is the most common primary malignant tumor of the adult central nervous system (CNS), which mostly shows invasive growth. In most cases, surgery is often difficult to completely remove, and the recurrence rate and mortality of patients are high. With the continuous development of molecular genetics and the great progress of molecular biology technology, more and more molecular biomarkers have been proved to have important guiding significance in the individualized diagnosis, treatment, and prognosis evaluation of glioma. With the updates of the World Health Organization (WHO) classification of tumors of the CNS in 2021, the diagnosis and treatment of glioma has entered the era of precision medicine in the true sense. Due to its ability to non-invasively achieve accurate identification of glioma from other intracranial tumors, and to predict the grade, genotyping, treatment response, and prognosis of glioma, which provides a scientific basis for the clinical application of individualized diagnosis and treatment model of glioma, radiomics has become a research hotspot in the field of precision medicine. This paper reviewed the research related to radiomics of adult gliomas published in recent years and summarized the research proceedings of radiomics in differential diagnosis, preoperative grading and genotyping, treatment and efficacy evaluation, and survival prediction of adult gliomas.

Predicting Kirsten Rat Sarcoma Virus Gene Mutation Status in Patients With Colorectal Cancer by Radiomics Models Based on Multiphasic CT

Objective

To develop and validate radiomics models based on multiphasic CT in predicting Kirsten rat sarcoma virus (KRAS) gene mutation status in patients with colorectal cancer (CRC).

Materials and Methods

A total of 231 patients with pathologically confirmed CRC were retrospectively enrolled and randomly divided into training(n=184) and test groups(n=47) in a ratio of 4:1. A total of 1316 quantitative radiomics features were extracted from non-contrast phase (NCP), arterial-phase (AP) and venous-phase (VP) CT for each patient. Four steps were applied for feature selection including Spearman correlation analysis, variance threshold, least absolute contraction and selection operator, and multivariate stepwise regression analysis. Clinical and pathological characteristics were also assessed. Subsequently, three classification methods, logistic regression (LR), support vector machine (SVM) and random tree (RT) algorithm, were applied to develop seven groups of prediction models (NCP, AP, VP, AP+VP, AP+VP+NCP, AP&amp;VP, AP&amp;VP&amp;NCP) for KRAS mutation prediction. The performance of these models was evaluated by receiver operating characteristics curve (ROC) analysis.

Results

Among the three groups of single-phase models, the AP model, developed by LR algorithm, showed the best prediction performance with an AUC value of 0.811 (95% CI:0.685–0.938) in the test cohort. Compared with the single-phase models, the dual-phase (AP+VP) model with the LR algorithm showed better prediction performance (AUC=0.826, 95% CI:0.700-0.952). The performance of multiphasic (AP+VP+NCP) model with the LR algorithm (AUC=0.811, 95%CI: 0.679-0.944) is comparable to the model with the SVM algorithm (AUC=0.811, 95%CI: 0.695-0.918) in the test cohort, but the sensitivity, specificity, and accuracy of the multiphasic (AP+VP+NCP) model with the LR algorithm were 0.810, 0.808, 0.809 respectively, which were highest among these seven groups of prediction models in the test cohort.

Conclusion

The CT radiomics models have the potential to predict KRAS mutation in patients with CRC; different phases may affect the predictive efficacy of radiomics model, of which arterial-phase CT is more informative. The combination of multiphasic CT images can further improve the performance of radiomics model.

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

The interpretation of imaging in medicine in general and in oncology specifically remains problematic due to several limitations which include the need to incorporate detailed clinical history, patient and disease-specific history, clinical exam features, previous and ongoing treatment, and account for the dependency on reproducible human interpretation of multiple factors with incomplete data linkage. To standardize reporting, minimize bias, expedite management, and improve outcomes, the use of Artificial Intelligence (AI) has gained significant prominence in imaging analysis. In oncology, AI methods have as a result been explored in most cancer types with ongoing progress in employing AI towards imaging for oncology treatment, assessing treatment response, and understanding and communicating prognosis. Challenges remain with limited available data sets, variability in imaging changes over time augmented by a growing heterogeneity in analysis approaches. We review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL) approaches, and hybrid analyses, are being employed in AI-driven imaging analysis in central nervous system tumors. ML, DL, and hybrid approaches coexist, and their combination may produce superior results although data in this space is as yet novel, and conclusions and pitfalls have yet to be fully explored. We note the growing technical complexities that may become increasingly separated from the clinic and enforce the acute need for clinician engagement to guide progress and ensure that conclusions derived from AI-driven imaging analysis reflect that same level of scrutiny lent to other avenues of clinical research.

Machine Learning in Drug Metabolism Study

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resourcedemanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-toactivity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.

Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features

The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for histamine antagonist drugs using simplified molecular-input line-entry systems (SMILES) combined with interaction features based on CYP450 group as inputs. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 26,344 DDI pairs from the DrugBank database. Various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost were used with 5-fold cross-validation to approach a large-scale DDIs prediction among histamine antagonist drugs. The prediction performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.788, a recall of 0.921, and an F1-score of 0.838 among 19 given DDIs types. An important finding of the study is that our prediction is based solely on the SMILES and CYP450 and thus can be applied at the early stage of drug development.