Research Progress of Machine Learning in Clinical Drug Therapy

With the advancement and development of concepts such as real-world research and precision treatment, the demand of researchers for medical big data processing keeps increasing. Because machine learning technology has unique advantages in processing massive, high-dimensional data and conducting predictive research, it has been deeply applied in the medical field in recent years. In addition to the application in disease diagnosis, image recognition and risk prediction, more and more studies have proved that machine learning can be applied to the decision support related research of clinical drug treatment. This article reviews the research progress of machine learning in clinical drug therapy

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D).Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People’s Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set.Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A ≥7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 ± 0.040, 0.859 ± 0.050, 0.889 ± 0.059, 0.832 ± 0.086, and 0.825 ± 0.092, respectively.Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care

Role of histone methyltransferase SETDB1 in regulation of tumourigenesis and immune response

Epigenetic alterations are implicated in tumour immune evasion and immune checkpoint blockade (ICB) resistance. SET domain bifurcated histone methyltransferase 1 (SETDB1) is a histone lysine methyltransferase that catalyses histone H3K9 di- and tri-methylation on euchromatin, and growing evidence indicates that SETDB1 amplification and abnormal activation are significantly correlated with the unfavourable prognosis of multiple malignant tumours and contribute to tumourigenesis and progression, immune evasion and ICB resistance. The main underlying mechanism is H3K9me3 deposition by SETDB1 on tumour-suppressive genes, retrotransposons, and immune genes. SETDB1 targeting is a promising approach to cancer therapy, particularly immunotherapy, because of its regulatory effects on endogenous retroviruses. However, SETDB1-targeted therapy remains challenging due to potential side effects and the lack of antagonists with high selectivity and potency. Here, we review the role of SETDB1 in tumourigenesis and immune regulation and present the current challenges and future perspectives of SETDB1 targeted therapy

Decoding signaling mechanisms: unraveling the targets of guanylate cyclase agonists in cardiovascular and digestive diseases

Soluble guanylate cyclase agonists and guanylate cyclase C agonists are two popular drugs for diseases of the cardiovascular system and digestive systems. The common denominator in these conditions is the potential therapeutic target of guanylate cyclase. Thanks to in-depth explorations of their underlying signaling mechanisms, the targets of these drugs are becoming clearer. This review explains the recent research progress regarding potential drugs in this class by introducing representative drugs and current findings on them

Adverse drug events in Chinese elder inpatients: a retrospective review for evaluating the efficiency of the Global Trigger Tool

BackgroundElderly patients frequently experience a high incidence of adverse drug events (ADEs) due to the coexistence of multiple diseases, the combination of various medications, poor medication compliance, and other factors. Global Trigger Tool (GTT) is a new method for identifying ADEs, introducing the concept of a trigger, that is, clues including abnormal laboratory values, reversal drugs, and clinical symptoms that may suggest ADEs, and specifically locating information related to ADEs in the medical record to identify ADEs. The aim of this study was to establish a GTT-based trigger tool for adverse medication events in elderly patients and to investigate the risk variables associated with such events.MethodsThe triggers were identified by reviewing the frequency of ADEs in elderly patients in Sichuan, China, retrieving relevant literature, and consulting experts. A retrospective analysis was carried out to identify adverse medication occurrences among 480 elderly inpatients in Sichuan People’s Hospital.ResultsA total of 56 ADEs were detected in 51 patients (10.62%), 13.04 per 1,000 patient days, and 11.67 per 100 admissions. The overall positive predictive value (PPV) of the triggers was 23.84, and 94.64% of ADEs caused temporary injury. Gastrointestinal system injury (27.87%) and metabolic and nutritional disorders (24.53%) were the primary organ-systems affected by ADEs. The majority of ADEs were caused by drugs used to treat cardiovascular diseases. 71.43% of ADE occurred within 2 days of administration and the risk factor analysis of ADE revealed that the number of medicines had a significant correlation.ConclusionThis study demonstrated GTT’s value as a tool for ADEs detection in elderly inpatients in China. It enhances the level of medication management and comprehensively reflects the situation of ADE of the elderly

Developing a Machine Learning Model to Predict 180-day Readmission for Elderly Patients with Angina

Background: Readmission of elderly angina patients has become a serious problem, with a dearth of available prediction tools for readmission assessment. The objective of this study was to develop a machine learning (ML) model that can predict 180-day all-cause readmission for elderly angina patients. Methods: The clinical data for elderly angina patients was retrospectively collected. Five ML algorithms were used to develop prediction models. Area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), and the Brier score were applied to assess predictive performance. Analysis by Shapley additive explanations (SHAP) was performed to evaluate the contribution of each variable. Results: A total of 1502 elderly angina patients (45.74% female) were enrolled in the study. The extreme gradient boosting (XGB) model showed good predictive performance for 180-day readmission (AUROC = 0.89; AUPRC = 0.91; Brier score = 0.21). SHAP analysis revealed that the number of medications, hematocrit, and chronic obstructive pulmonary disease were important variables associated with 180-day readmission. Conclusions: An ML model can accurately identify elderly angina patients with a high risk of 180-day readmission. The model used to identify individual risk factors can also serve to remind clinicians of appropriate interventions that may help to prevent the readmission of patients

Sialylation: An alternative to designing long-acting and targeted drug delivery system

Long-acting and specific targeting are two important properties of excellent drug delivery systems. Currently, the long-acting strategies based on polyethylene glycol (PEG) are controversial, and PEGylation is incapable of simultaneously possessing targeting ability. Thus, it is crucial to identify and develop approaches to produce long-acting and targeted drug delivery systems. Sialic acid (SA) is an endogenous, negatively charged, nine-carbon monosaccharide. SA not only mediates immune escape in the body but also binds to numerous disease related targets. This suggests a potential strategy, namely “sialylation,” for preparing long-acting and targeted drug delivery systems. This review focuses on the application status of SA-based long-acting and targeted agents as a reference for subsequent research

Advanced subunit vaccine delivery technologies: From vaccine cascade obstacles to design strategies

Designing and manufacturing safe and effective vaccines is a crucial challenge for human health worldwide. Research on adjuvant-based subunit vaccines is increasingly being explored to meet clinical needs. Nevertheless, the adaptive immune responses of subunit vaccines are still unfavorable, which may partially be attributed to the immune cascade obstacles and unsatisfactory vaccine design. An extended understanding of the crosstalk between vaccine delivery strategies and immunological mechanisms could provide scientific insight to optimize antigen delivery and improve vaccination efficacy. In this review, we summarized the advanced subunit vaccine delivery technologies from the perspective of vaccine cascade obstacles after administration. The engineered subunit vaccines with lymph node and specific cell targeting ability, antigen cross-presentation, T cell activation properties, and tailorable antigen release patterns may achieve effective immune protection with high precision, efficiency, and stability. We hope this review can provide rational design principles and inspire the exploitation of future subunit vaccines

A Facile Synthesis of Functionalized Dispirooxindole Derivatives via a Three-Component 1,3-Dipolar Cycloaddition Reaction

An efficient synthesis of novel dispirooxindoles has been achieved through three-component 1,3-dipolar cycloaddition of azomethine ylides generated in situ by the decarboxylative condensation of isatin and an α-amino acid with the dipolarophile 5-benzylideneimidazolidine-2,4-dione. The improved procedure features mild reaction conditions, high yields, high diastereoselectivities, a one-pot procedure and operational simplicity

Construction of an early alert system for intradialytic hypotension before initiating hemodialysis based on machine learning

Abstract Introduction: Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH. Materials and methods: We obtained data on 314534 hemodialysis sessions conducted at Sichuan Provincial People's Hospital from the Renal Disease Treatment Information System. IDH was defined as a systolic blood pressure drop≥20 mmHg, a mean arterial pressure drop≥10 mmHg during dialysis, or the occurrence of clinical hypotensive events requiring nursing intervention. After pre-processing, the data were randomly divided into training (80%) and testing (20%) sets. Four interpolation methods, three feature selection methods, and 18 machine learning algorithms were used to construct predictive models. The area under the receiver operating characteristic curve (AUC) was the main indicator for evaluating the performance of the models, while Shapley Additive ExPlanation (SHAP) was used to explain the contribution of each variable to the best predictive model. Results: A total of 3906 patients and 314534 dialysis sessions were included, of which 142237 cases showed IDH (incidence rate, 45.2%). Nineteen parameters were identified through artificial intelligence feature screening. They included age, pre-dialysis weight, dry weight, pre-dialysis blood pressure, heart rate, prescribed ultrafiltration, blood cell counts (neutrophil, lymphocyte, monocyte, eosinophil, lymphocyte, and platelet counts), hematocrit, serum calcium, creatinine, urea, glucose, and uric acid. Random forest, gradient boosting, and logistic regression were the three best models, and the AUCs were 0.812 (95% confidence interval[CI], 0.811-0.813), 0.748 (95% CI, 0.747-0.749), and 0.743 (95% CI, 0.742-0.744), respectively. Conclusion: Our dialysis software-based artificial intelligence alert system can be used to predict IDH occurrence, enabling the initiation of relevant interventions