Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation

Predicting high-flow arteriovenous fistulas and cardiac outcomes in hemodialysis patients

BACKGROUND

Heart failure is common in patients receiving hemodialysis. A high-flow arteriovenous fistula (AVF) may represent a modifiable risk factor for heart failure and death. Currently, no tools exist to assess the risk of developing a high-flow AVF (>2000 mL/min). The aim of this study was to use machine learning to develop a predictive model identifying patients at risk of developing a high-flow AVF and to examine the relationship between blood flow, heart failure, and death.

METHODS

Between 2011 and 2020, serial AVF blood flows were measured in 366 prevalent hemodialysis patients at two tertiary hospitals in Australia. Four prediction models (deep neural network and three separate tree-based algorithms) using age, first AVF flow, diabetes, and dyslipidemia were compared to predict high-flow AVF development. Logistic regression was used to assess the relationship between AVF blood flow, heart failure, and death.

RESULTS

High-flow AVFs were present in 31.4% of patients. The bootstrap forest predictive model performed best in identifying those at risk of a high-flow AVF (under the curve, 0.94; sensitivity 86%; specificity 83%). Heart failure before vascular access creation was identified in 10.2% of patients with an additional 24.9% of patients developing heart failure after AVF creation. Long-term mortality after access formation was 27%, with an average time to death after AVF creation of 307.5 ± 185.6 weeks. No univariable relationship using logistic regression was noted between AVF flow and incident heart failure after AVF creation or death. Age, flow at first measurement of >1000 mL/min, time to highest AVF flow, and heart failure predicted death after AVF creation using a general linear model.

CONCLUSIONS

Predictive modelling techniques can identify patients at risk of developing high-flow AVF. No association was seen between AVF blood flow rate and incident heart failure after AVF creation. In those patients who died, time to highest AVF flow was the most important predictor of death after AVF creation.

Tacrolimus trough level and oxidative stress in Tunisian kidney transplanted patients

BACKGROUND

The effect of tacrolimus (TAC) on oxidative stress after kidney transplantation (KT) is unclear. This study aimed to evaluate the influence of TAC trough levels of oxidative stress status in Tunisian KT patients during the post-transplantation period (PTP).

METHODS

A prospective study including 90 KT patients was performed. TAC whole-blood concentrations were measured by the microparticle enzyme immunoassay method and adjusted according to the target range. Plasma levels of oxidants (malondialdehyde (MDA) and advanced oxidation protein products (AOPP)) and antioxidants (ascorbic acid, glutathione (GSH), glutathione peroxidase (GPx), and superoxide dismutase (SOD)) were measured using spectrophotometry. The subjects were subdivided according to PTP into three groups: patients with early, intermediate, and late PT. According to the TAC level, they were subdivided into LL-TAC, NL-TAC, and HL-TAC groups.

RESULTS

A decrease in MDA levels, SOD activity, and an increase in GSH levels and GPx activity were observed in patients with late PT compared to those with early and intermediate PT (p < 0.05). Patients with LL-TAC had lower MDA levels and higher GSH levels and GPx activity compared with the NL-TAC and HL-TAC groups (p < 0.05).

CONCLUSION

Our results have shown that in KT patients, despite the recovery of kidney function, the TAC reduced but did not normalize oxidative stress levels in long-term therapy, and the TAC effect significantly depends on the concentration used.

The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications

Kidney allograft rejection is one of the main limitations to long-term kidney transplant survival. The diagnostic gold standard for detecting rejection is a kidney biopsy, an invasive procedure that can often give imprecise results due to complex diagnostic criteria and high interobserver variability. In recent years, several additional diagnostic approaches to rejection have been investigated, some of them with the aid of machine learning (ML). In this review, we addressed studies that investigated the detection of kidney allograft rejection over the last decade using various ML algorithms. Various ML techniques were used in three main categories: (a) histopathologic assessment of kidney tissue with the aim to improve the diagnostic accuracy of a kidney biopsy, (b) assessment of gene expression in rejected kidney tissue or peripheral blood and the development of diagnostic classifiers based on these data, (c) radiologic assessment of kidney tissue using diffusion-weighted magnetic resonance imaging and the construction of a computer-aided diagnostic system. In histopathology, ML algorithms could serve as a support to the pathologist to avoid misclassifications and overcome interobserver variability. Diagnostic platforms based on biopsy-based transcripts serve as a supplement to a kidney biopsy, especially in cases where histopathologic diagnosis is inconclusive. ML models based on radiologic evaluation or gene signature in peripheral blood may be useful in cases where kidney biopsy is contraindicated in addition to other non-invasive biomarkers. The implementation of ML-based diagnostic methods is usually slow and undertaken with caution considering ethical and legal issues. In summary, the approach to the diagnosis of rejection should be individualized and based on all available diagnostic tools (including ML-based), leaving the responsibility for over- and under-treatment in the hands of the clinician.

Predicting prognostic factors in kidney transplantation using a machine learning approach to enhance outcome predictions: a retrospective cohort study

BACKGROUND

Accurate forecasting of clinical outcomes after kidney transplantation is essential for improving patient care and increasing the success rates of transplants. The authors' study employs advanced machine learning (ML) algorithms to identify crucial prognostic indicators for kidney transplantation. By analyzing complex datasets with ML models, the authors aim to enhance prediction accuracy and provide valuable insights to support clinical decision-making.

MATERIALS AND METHODS

Analyzing data from 4077 KT patients (June 1990-May 2015) at a single center, this research included 27 features encompassing recipient/donor traits and peri-transplant data. The dataset was divided into training (80%) and testing (20%) sets. Four ML models-eXtreme Gradient Boosting (XGBoost), Feedforward Neural Network, Logistic Regression, And Support Vector Machine-were trained on carefully selected features to predict the success of graft survival. Performance was assessed by precision, sensitivity, F1 score, area under the receiver operating characteristic (AUROC), and area under the precision-recall curve.

RESULTS

XGBoost emerged as the best model, with an AUROC of 0.828, identifying key survival predictors like T-cell flow crossmatch positivity, creatinine levels two years post-transplant and human leukocyte antigen mismatch. The study also examined the prognostic importance of histological features identified by the Banff criteria for renal biopsy, emphasizing the significance of intimal arteritis, interstitial inflammation, and chronic glomerulopathy.

CONCLUSION

The study developed ML models that pinpoint clinical factors crucial for KT graft survival, aiding clinicians in making informed post-transplant care decisions. Incorporating these findings with the Banff classification could improve renal pathology diagnosis and treatment, offering a data-driven approach to prioritizing pathology scores.

Present and Future Applications of Artificial Intelligence in Kidney Transplantation

Artificial intelligence (AI) has a wide and increasing range of applications across various sectors. In medicine, AI has already made an impact in numerous fields, rapidly transforming healthcare delivery through its growing applications in diagnosis, treatment and overall patient care. Equally, AI is swiftly and essentially transforming the landscape of kidney transplantation (KT), offering innovative solutions for longstanding problems that have eluded resolution through traditional approaches outside its spectrum. The purpose of this review is to explore the present and future applications of artificial intelligence in KT, with a focus on pre-transplant evaluation, surgical assistance, outcomes and post-transplant care. We discuss its great potential and the inevitable limitations that accompany these technologies. We conclude that by fostering collaboration between AI technologies and medical practitioners, we can pave the way for a future where advanced, personalised care becomes the standard in KT and beyond.

Prospectively investigating the impact of AI onshared decision-making in post kidney transplant care (PRIMA-AI): protocol for a longitudinal qualitative study among patients, their support persons and treating physicians at a tertiary care centre

INTRODUCTION

As healthcare is shifting from a paternalistic to a patient-centred approach, medical decision making becomes more collaborative involving patients, their support persons (SPs) and physicians. Implementing shared decision-making (SDM) into clinical practice can be challenging and becomes even more complex with the introduction of artificial intelligence (AI) as a potential actant in the communicative network. Although there is more empirical research on patients' and physicians' perceptions of AI, little is known about the impact of AI on SDM. This study will help to fill this gap. To the best of our knowledge, this is the first systematic empirical investigation to prospectively assess the views of patients, their SPs and physicians on how AI affects SDM in physician-patient communication after kidney transplantation. Using a transdisciplinary approach, this study will explore the role and impact of an AI-decision support system (DSS) designed to assist with medical decision making in the clinical encounter.

METHODS AND ANALYSIS

This is a plan to roll out a 2 year, longitudinal qualitative interview study in a German kidney transplant centre. Semi-structured interviews with patients, SPs and physicians will be conducted at baseline and in 3-, 6-, 12- and 24-month follow-up. A total of 50 patient-SP dyads and their treating physicians will be recruited at baseline. Assuming a dropout rate of 20% per year, it is anticipated that 30 patient-SP dyads will be included in the last follow-up with the aim of achieving data saturation. Interviews will be audio-recorded and transcribed verbatim. Transcripts will be analysed using framework analysis. Participants will be asked to report on their (a) communication experiences and preferences, (b) views on the influence of the AI-based DSS on the normative foundations of the use of AI in medical decision-making, focusing on agency along with trustworthiness, transparency and responsibility and (c) perceptions of the use of the AI-based DSS, as well as barriers and facilitators to its implementation into routine care.

ETHICS AND DISSEMINATION

Approval has been granted by the local ethics committee of Charité-Universitätsmedizin Berlin (EA1/177/23 on 08 August 2023). This research will be conducted in accordance with the principles of the Declaration of Helsinki (1996). The study findings will be used to develop communication guidance for physicians on how to introduce and sustainably implement AI-assisted SDM. The study results will also be used to develop lay language patient information on AI-assisted SDM. A broad dissemination strategy will help communicate the results of this research to a variety of target groups, including scientific and non-scientific audiences, to allow for a more informed discourse among different actors from policy, science and society on the role and impact of AI in physician-patient communication.

Machine Learning: A New Approach for Dose Individualization

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N = 23, 35.9%) and anti-infectives (N = 21, 32.8%), and there is currently only very limited attention for special populations, such as children (N = 22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

Artificial intelligence: a new field of knowledge for nephrologists?

Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.

Beyond Human Limits: Harnessing Artificial Intelligence to Optimize Immunosuppression in Kidney Transplantation

The field of kidney transplantation is being revolutionized by the integration of artificial intelligence (AI) and machine learning (ML) techniques. AI equips machines with human-like cognitive abilities, while ML enables computers to learn from data. Challenges in transplantation, such as organ allocation and prediction of allograft function or rejection, can be addressed through AI-powered algorithms. These algorithms can optimize immunosuppression protocols and improve patient care. This comprehensive literature review provides an overview of all the recent studies on the utilization of AI and ML techniques in the optimization of immunosuppression in kidney transplantation. By developing personalized and data-driven immunosuppression protocols, clinicians can make informed decisions and enhance patient care. However, there are limitations, such as data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Future research should validate and refine AI models for different populations and treatment durations. AI and ML have the potential to revolutionize kidney transplantation by optimizing immunosuppression and improving outcomes. AI-powered algorithms enable personalized and data-driven immunosuppression protocols, enhancing patient care and decision-making. Limitations include data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Further research is needed to validate and enhance AI models for different populations and longer-term dosing decisions.

Population Pharmacokinetics of Tacrolimus in Pediatric Patients With Umbilical Cord Blood Transplant

Tacrolimus was frequently used in pediatric patients with umbilical cord blood transplant for the prevention of graft-versus-host disease. The aim of the present study was to evaluate the population pharmacokinetics of tacrolimus among pediatric patients with umbilical cord blood transplant and find potential influenced factors. A total of 275 concentrations from 13 pediatric patients were used to build a polulation pharmacokinetic model using a nonlinear mixed-effects modeling approach. The impact of demographic features, biological characteristics, and concomitant medications, including sex, age, body weight, postoperative day, white blood cell count, red blood cell count, hemoglobin, platelets, hematocrit, blood urea nitrogen, creatinine, aspartate transaminase, alanine transaminase, total bilirubin, albumin, and total protein were investigated. The pharmacokinetics of tacrolimus were best described by a 1-compartment model with first- and zero-order mixed absorption and first-order elimination. The clearance and volume of distribution of tacrolimus were 1.93 L/h and 75.1 L, respectively. A covariate analysis identified that postoperative day and co-administration with trimethoprim-sulfamethoxazole were significant covariates influencing clearance of tacrolimus. Frequent blood monitoring and dose adjustment might be needed with the prolongation of postoperative day and coadministration with trimethoprim-sulfamethoxazole.

Developing supervised machine learning algorithms to evaluate the therapeutic effect and laboratory-related adverse events of cyclosporine and tacrolimus in renal transplants

BACKGROUND

Single nucleotide polymorphisms influence the effects of tacrolimus and cyclosporine in renal transplants.

AIM

We set out to use machine learning algorithms (MLAs) to identify variables that predict the therapeutic effects and adverse events following tacrolimus and cyclosporine administration in renal transplant patients.

METHOD

We sampled 120 adult renal transplant patients (on cyclosporine or tacrolimus). Generalized linear model (GLM), support vector machine (SVM), artificial neural network (ANN), Chi-square automatic interaction detection, classification and regression tree, and K-nearest neighbors were the chosen MLAs. The mean absolute error (MAE), relative mean square error (RMSE), and regression coefficient (β) with a 95% confidence interval (CI) were used as the model parameters.

RESULTS

For a stable dose of tacrolimus, the MAEs (RMSEs) of GLM, SVM, and ANN were 1.3 (1.5), 1.3 (1.8), and 1.7 (2.3) mg/day, respectively. GLM revealed that the POR*28 genotype and age significantly predicted the stable dose of tacrolimus as follows: POR*28 (β -1.8; 95% CI -3, -0.5; p = 0.006), and age (β -0.04; 95% CI -0.1, -0.006; p = 0.02). For a stable dose of cyclosporine, MAEs (RMSEs) of 93.2 (103.4), 79.1 (115.2), and 73.7 (91.7) mg/day were observed with GLM, SVM, and ANN, respectively. GLM revealed the following predictors of a stable dose of cyclosporine: CYP3A5*3 (β -80.8; 95% CI -130.3, -31.2; p = 0.001), and age (β -3.4; 95% CI -5.9, -0.9; p = 0.007).

CONCLUSION

We observed that various MLAs could identify significant predictors that were useful to optimize tacrolimus and cyclosporine dosing regimens; yet, the findings must be externally validated.

Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation

With its robust ability to integrate and learn from large sets of clinical data, artificial intelligence (AI) can now play a role in diagnosis, clinical decision making, and personalized medicine. It is probably the natural progression of traditional statistical techniques. Currently, there are many unmet needs in nephrology and, more particularly, in the kidney transplantation (KT) field. The complexity and increase in the amount of data, and the multitude of nephrology registries worldwide have enabled the explosive use of AI within the field. Nephrologists in many countries are already at the center of experiments and advances in this cutting-edge technology and our aim is to generalize the use of AI among nephrologists worldwide. In this paper, we provide an overview of AI from a medical perspective. We cover the core concepts of AI relevant to the practicing nephrologist in a consistent and simple way to help them get started, and we discuss the technical challenges. Finally, we focus on the KT field: the unmet needs and the potential role that AI can play to fill these gaps, then we summarize the published KT-related studies, including predictive factors used in each study, which will allow researchers to quickly focus on the most relevant issues.

Machine learning in lung transplantation: Where are we?

Lung transplantation has been accepted as a viable treatment for end-stage respiratory failure. While regression models continue to be a standard approach for attempting to predict patients' outcomes after lung transplantation, more sophisticated supervised machine learning (ML) techniques are being developed and show encouraging results. Transplant clinicians could utilize ML as a decision-support tool in a variety of situations (e.g. waiting list mortality, donor selection, immunosuppression, rejection prediction). Although for some topics ML is at an advanced stage of research (i.e. imaging and pathology) there are certain topics in lung transplantation that needs to be aware of the benefits it could provide.

Estimation of Mycophenolic Acid Exposure in Chinese Renal Transplant Patients by a Joint Deep Learning Model

BACKGROUND

To predict mycophenolic acid (MPA) exposure in renal transplant recipients using a deep learning model based on a convolutional neural network with bilateral long short-term memory and attention methods.

METHODS

A total of 172 Chinese renal transplant patients were enrolled in this study. The patients were divided into a training group (n = 138, Ruijin Hospital) and a validation group (n = 34, Zhongshan Hospital). Fourteen days after renal transplantation, rich blood samples were collected 0-12 hours after MPA administration. The plasma concentration of total MPA was measured using an enzyme-multiplied immunoassay technique. A limited sampling strategy based on a convolutional neural network-long short-term memory with attention (CALS) model for the prediction of the area under the concentration curve (AUC) of MPA was established. The established model was verified using the data from the validation group. The model performance was compared with that obtained from multiple linear regression (MLR) and maximum a posteriori (MAP) methods.

RESULTS

The MPA AUC 0-12 of the training and validation groups was 54.28 ± 18.42 and 41.25 ± 14.53 µg·ml -1 ·h, respectively. MPA plasma concentration after 2 (C 2 ), 6 (C 6 ), and 8 (C 8 ) hours of administration was the most significant factor for MPA AUC 0-12 . The predictive performance of AUC 0-12 estimated using the CALS model of the validation group was better than the MLR and MAP methods in previous studies (r 2 = 0.71, mean prediction error = 4.79, and mean absolute prediction error = 14.60).

CONCLUSIONS

The CALS model established in this study was reliable for predicting MPA AUC 0-12 in Chinese renal transplant patients administered mycophenolate mofetil and enteric-coated mycophenolic acid sodium and may have good generalization ability for application in other data sets.

Development of a particle swarm optimization-backpropagation artificial neural network model and effects of age and gender on pharmacokinetics study of omeprazole enteric-coated tablets in Chinese population

Background

The effects of age and gender were explored on pharmacokinetics study of omeprazole enteric-coated tablets in Chinese population and a plasma concentration prediction model was developed. All the data (demographic characteristics and results of clinical laboratory tests) were collected from healthy Chinese subjects in pharmacokinetics study using 20 mg omeprazole enteric-coated tablets. A noncompartmental method was used to calculate pharmacokinetic parameters, and 47 subjects were divided into two groups based on the calculation of the median age. Pharmacokinetic data from the low-age and high-age groups or male and female groups were compared by Student t-test. After a total of 12 variables were reconstruct and convert into independent or irrelative variables by principal component analysis, particle swarm optimization (PSO) was used to construct a backpropagation artificial neural network (BPANN) model.

Result

The model was fully validated and used to predict the plasma concentration in Chinese population. It was noticed that the C_max, AUC_0-t, AUC_0-∞ and t_1/2 values have significant differences when omeprazole was administered by low-age groups or high-age groups while there were slight or no significant differences of pharmacokinetic data were found between male and female subjects. The PSO-BPANN model was fully validated and there was 0.000355 for MSE, 0.000133 for the magnitude of the gradient, 50 for the number of validation checks. The correlation coefficient of training, validation, test groups were 0.949, 0.903 and 0.874.

Conclusion

It is necessary to pay attention to the age and gender effects on omeprazole and PSO-BPANN model could be used to predict omeprazole concentration in Chinese subjects to minimize the associated morbidity and mortality with peptic ulcer.

Trial registration

The study was registered in China Drug Clinical Trial Registration and Information Publicity Platform (http://www.chinadrugtrials.org.cn), the registration number was CTR20170876, and the full date of registration was 04/AUG/2017.

Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation

Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually require human intelligence. Typical examples include complex decision-making and- image or speech analysis. AI application in healthcare is rapidly evolving and it undoubtedly holds an enormous potential for the field of solid organ transplantation. In this review, we provide an overview of AI-based approaches in solid organ transplantation. Particularly, we identified four key areas of transplantation which could be facilitated by AI: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology. The potential implementations are vast—from improved allocation algorithms, smart donor-recipient matching and dynamic adaptation of immunosuppression to automated analysis of transplant pathology. We are convinced that we are at the beginning of a new digital era in transplantation, and that AI has the potential to improve graft and patient survival. This manuscript provides a glimpse into how AI innovations could shape an exciting future for the transplantation community.

Machine Learning for Renal Pathologies: An Updated Survey

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

Machine learning-based method for tacrolimus dose predictions in Chinese kidney transplant perioperative patients

WHAT IS KNOWN AND OBJECTIVES

Tacrolimus (TAC), a first-line immunosuppressant in solid-organ transplant, has a narrow therapeutic window and large inter-individual variability, which affects its use in clinical practice. Successful predictions using machine learning algorithms have been reported in several fields. However, a comparison of 10 machine learning model-based TAC pharmacogenetic and pharmacokinetic dosing algorithms for kidney transplant perioperative patients of Chinese descent has not been reported. The objective of this study was to screen and establish an appropriate machine learning method to predict the individualized dosages of TAC for perioperative kidney transplant patients.

METHODS

The records of 2551 patients were collected from three transplant centres, 80% of which were randomly selected as a 'derivation cohort' to develop the dose prediction algorithm, while the remaining 20% constituted a 'validation cohort' to validate the final algorithm selected. Important features were screened according to our previously established population pharmacokinetic model of tacrolimus. The performances of the algorithms were evaluated and compared using R-squared and the mean percentage in the remaining 20% of patients.

RESULTS AND DISCUSSION

This study identified several factors influencing TAC dosage, including CYP3A5 rs776746, CYP3A4 rs4646437, haematocrit, Wuzhi capsules, TAC daily dose, age, height, weight, post-operative time, nifedipine and the medication history of the patient. According to our results, among the 10 machine learning models, the extra trees regressor (ETR) algorithm showed the best performance in the training set (R-squared: 1, mean percentage within 20%: 100%) and test set (R-squared: 0.85, mean percentage within 20%: 92.77%) of the derivation cohort. The ETR model successfully predicted the ideal TAC dosage in 97.73% of patients, especially in the intermediate dosage range (>5 mg/day to <8 mg/day), whereby the ideal TAC dosage could be successfully predicted in 99% of the patients.

WHAT IS NEW AND CONCLUSION

The results indicated that the ETR algorithm, which was chosen to establish the dose prediction model, performed better than the other nine machine learning models. This study is the first to establish ETR algorithms to predict TAC dosage. This study will further promote the individualized medication of TAC in kidney transplant patients in the future, which has great significance in ensuring the safety and effectiveness of drug use.

Machine Learning Applications in Solid Organ Transplantation and Related Complications

The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.

The Future Role of Machine Learning in Clinical Transplantation

The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.

Artificial intelligence and kidney transplantation

Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.

Artificial Intelligence in Chemistry: Current Trends and Future Directions

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.

An artificial neural network-pharmacokinetic model and its interpretation using Shapley additive explanations

We developed a method to apply artificial neural networks (ANNs) for predicting time‐series pharmacokinetics (PKs), and an interpretable the ANN‐PK model, which can explain the evidence of prediction by applying Shapley additive explanations (SHAP). A previous population PK (PopPK) model of cyclosporin A was used as the comparison model. The patients’ data were used for the ANN‐PK model input, and the output by ANN was the clearance (CL). The estimated CL value from the ANN were substituted into the one‐compartment with one‐order absorption model, the concentrations were calculated, and the parameters of ANN were updated by the back‐propagation method. Kernel SHAP was applied to the trained model and the SHAP value of each input was calculated. The root mean squared error for the PopPK model and the ANN‐PK model were 41.1 and 31.0 ng/ml, respectively. The goodness of fit plots for the ANN‐PK model represented more convergence to y = x compared with that for the PopPK model, with good model performance for the ANN‐PK model. The most influential factors on CL output were age and body weight from the evaluation using Kernel SHAP, and these factors were incorporated into the PopPK model as the significant covariates of CL. The ANN‐PK model could handle time‐series data and showed higher prediction accuracy then the conventional PopPK model, and the scientific validity for the model could be evaluated by applying SHAP.

Machine learning in pharmacometrics: Opportunities and challenges

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.

Machine learning in liver transplantation: a tool for some unsolved questions?

Machine learning has recently been proposed as a useful tool in many fields of Medicine, with the aim of increasing diagnostic and prognostic accuracy. Models based on machine learning have been introduced in the setting of solid organ transplantation too, where prognosis depends on a complex, multidimensional and nonlinear relationship between variables pertaining to the donor, the recipient and the surgical procedure. In the setting of liver transplantation, machine learning models have been developed to predict pretransplant survival in patients with cirrhosis, to assess the best donor-to-recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a narrative review on the role of machine learning in the field of liver transplantation, highlighting strengths and pitfalls, and future perspectives.

Artificial neural network and bioavailability of the immunosuppression drug

PURPOSE OF REVIEW

The success of organ transplant is determined by number of demographic, clinical, immunological and genetic variables. Artificial intelligence tools, such as artificial neural networks (ANNs) or classification and regression trees (CART) can handle multiple independent variables and predict the dependent variables by deducing the complex nonlinear relationships between variables.

RECENT FINDINGS

In the last two decades, several researchers employed these tools to identify donor-recipient matching pairs, to optimize immunosuppressant doses, to predict allograft survival and to minimize adverse drug reactions. These models showed better performance characteristics than the empirical dosing strategies in terms of sensitivity, specificity, overall accuracy, or area under the curve of receiver-operating characteristic curves. The performance of the models was dependent directly on the input variables. Recent studies identified protein biomarkers and pharmacogenetic determinants of immunosuppressants as additional variables that increase the precision in prediction. Accessibility of medical records, proper follow-up of transplant cases, deep understanding of pharmacokinetic and pharmacodynamic pathways of immunosuppressant drugs coupled with genomic and proteomic markers are essential in developing an effective artificial intelligence platform for transplantation.

SUMMARY

Artificial intelligence has a greater clinical utility both in pretransplantation and posttransplantation periods to get favourable clinical outcomes, thus ensuring successful graft survival.

CYP3A5 Gene-Guided Tacrolimus Treatment of Living-Donor Egyptian Kidney Transplanted Patients

Background

Tacrolimus is an approved first-line immunosuppressive agent for kidney transplantations. Part of interindividual and interethnic differences in the response of patients to tacrolimus is attributed to polymorphisms at CYP3A5 metabolic enzyme. CYP3A5 gene expression status is associated with tacrolimus dose requirement in renal transplant recipients.

Materials and Methods

In this study, we determined the allelic frequency of CYP3A5*3 in 76 renal transplanted patients of Egyptian descent. Secondly, we evaluated the influence of the CYP3A5 gene variant on tacrolimus doses required for these patients as well on dose-adjusted tacrolimus trough-concentrations.

Results

The CYP3A5*3 variant was the most frequent allele detected at 85.53%. Additionally, our results showed that, mean tacrolimus daily requirements for heterozygous patients (CYP3A5*1/*3) were significantly higher compared to homozygous patients (CYP3A5*3/*3) during the first year after kidney transplantation.

Conclusion

This is the first study in Egypt contributing to the individualization of tacrolimus dosing in Egyptian patients, informed by the CYP3A5 genotype.

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Metabolic pathway modeling plays an increasing role in drug design by allowing better understanding of the underlying regulation and controlling networks in the metabolism of living organisms. However, despite rapid progress in this area, pathway modeling can become a real nightmare for researchers, notably when few experimental data are available or when the pathway is highly complex. Here, three different approaches were developed to model the second part of glycolysis of E. histolytica as an application example, and have succeeded in predicting the final pathway flux: one including detailed kinetic information (white-box), another with an added adjustment term (grey-box) and the last one using an artificial neural network method (black-box). Afterwards, each model was used for metabolic control analysis and flux control coefficient determination. The first two enzymes of this pathway are identified as the key enzymes playing a role in flux control. This study revealed the significance of the three methods for building suitable models adjusted to the available data in the field of metabolic pathway modeling, and could be useful to biologists and modelers.

Recent advances in precision medicine for individualized immunosuppression

PURPOSE OF REVIEW

The current tools to proactively guide and individualize immunosuppression in solid organ transplantation are limited. Despite continued improvements in posttransplant outcomes, the adverse effects of over-immunosuppression or under-immunosuppression are common. The present review is intended to highlight recent advances in individualized immunosuppression.

RECENT FINDINGS

There has been a great focus on genomic information to predict drug dose requirements, specifically on single nucleotide polymorphisms of CYP3A5 and ABCB1. Furthermore, biomarker studies have developed ways to better predict clinical outcomes, such as graft rejection.

SUMMARY

The integration of advanced computing tools, such as artificial neural networks and machine learning, with genome sequencing has led to intriguing findings on individual or group-specific dosing requirements. Rapid computing allows for processing of data and discovering otherwise undetected clinical patterns. Genetic polymorphisms of CYP3A5 and ABCB1 have yielded results to suggest varying dose requirements correlated with race and sex. Newly proposed biomarkers offer precise and noninvasive ways to monitor patient's status. Cell-free DNA quantitation is increasingly explored as an indicator of allograft injury and rejection, which can help avoid unneeded biopsies and more frequently monitor graft function.

Tacrolimus trough levels higher than 6 ng/mL might not be required after a year in stable kidney transplant recipients

Background

Little is known regarding optimal tacrolimus (TAC) trough levels after 1 year post-transplant in stable kidney transplant recipients (KTRs) who have not experienced renal or cardiovascular outcomes. This study aimed to investigate the effect of 1-year post-transplant TAC trough levels on long-term renal and cardiovascular outcomes and opportunistic infections in stable KTRs.

Methods

KTRs receiving TAC with mycophenolate-based immunosuppression who did not experience renal or cardiovascular outcomes within 1 year post-transplant were enrolled from a multicenter observational cohort study. Renal outcome was defined as a composite of biopsy-proven acute rejection, interstitial fibrosis and tubular atrophy, and death-censored graft loss. Cardiovascular outcome was defined as a composite of de novo cardiomegaly, left ventricular hypertrophy, and cardiovascular events. Opportunistic infections were defined as the occurrence of BK virus or cytomegalovirus infections.

Results

A total of 603 eligible KTRs were divided into the low-level TAC (LL-TAC) and high-level TAC (HL-TAC) groups based on a median TAC level of 5.9 ng/mL (range 1.3–14.3) at 1 year post-transplant. The HL-TAC group had significantly higher TAC trough levels at 2, 3, 4, and 5 years compared with the levels of the LL-TAC group. During the mean follow-up of 63.7 ± 13.0 months, there were 121 renal outcomes and 224 cardiovascular outcomes. In multivariate Cox regression analysis, LL-TAC and HL-TAC were not independent risk factors for renal and cardiovascular outcomes, respectively. No significant differences in the development of opportunistic infections and de novo donor-specific anti-human leukocyte antigen antibodies and renal allograft function were observed between the two groups.

Conclusions

TAC trough levels after 1 year post-transplant remained at a similar level until the fifth year after kidney transplantation and were not directly associated with long-term outcomes in stable Korean KTRs who did not experience renal or cardiovascular outcomes. Therefore, in Asian KTRs with a stable clinical course, TAC trough levels higher than approximately 6 ng/mL might not be required after a year of kidney transplantation.

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

BACKGROUND

The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used?

METHODS

We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm.

RESULTS

AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues.

CONCLUSIONS

Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.

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

Tacrolimus is an immunosuppressive drug with a narrow therapeutic index and larger interindividual variability. We identified genetic variants to predict tacrolimus exposure in healthy Korean males using machine learning algorithms such as decision tree, random forest, and least absolute shrinkage and selection operator (LASSO) regression. rs776746 (CYP3A5) and rs1137115 (CYP2A6) are single nucleotide polymorphisms (SNPs) that can affect exposure to tacrolimus. A decision tree, when coupled with random forest analysis, is an efficient tool for predicting the exposure to tacrolimus based on genotype. These tools are helpful to determine an individualized dose of tacrolimus.

Influence of Germline Genetics on Tacrolimus Pharmacokinetics and Pharmacodynamics in Allogeneic Hematopoietic Stem Cell Transplant Patients

Tacrolimus exhibits high inter-patient pharmacokinetics (PK) variability, as well as a narrow therapeutic index, and therefore requires therapeutic drug monitoring. Germline mutations in cytochrome P450 isoforms 4 and 5 genes (CYP3A4/5) and the ATP-binding cassette B1 gene (ABCB1) may contribute to interindividual tacrolimus PK variability, which may impact clinical outcomes among allogeneic hematopoietic stem cell transplantation (HSCT) patients. In this study, 252 adult patients who received tacrolimus for acute graft versus host disease (aGVHD) prophylaxis after allogeneic HSCT were genotyped to evaluate if germline genetic variants associated with tacrolimus PK and pharmacodynamic (PD) variability. Significant associations were detected between germline variants in CYP3A4/5 and ABCB1 and PK endpoints (e.g., median steady-state tacrolimus concentrations and time to goal tacrolimus concentration). However, significant associations were not observed between CYP3A4/5 or ABCB1 germline variants and PD endpoints (e.g., aGVHD and treatment-emergent nephrotoxicity). Decreased age and CYP3A5*1/*1 genotype were independently associated with subtherapeutic tacrolimus trough concentrations while CYP3A5*1*3 or CYP3A5*3/*3 genotypes, myeloablative allogeneic HSCT conditioning regimen (MAC) and increased weight were independently associated with supratherapeutic tacrolimus trough concentrations. Future lines of prospective research inquiry are warranted to use both germline genetic and clinical data to develop precision dosing tools that will optimize both tacrolimus dosing and clinical outcomes among adult HSCT patients.

Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives

Artificial intelligence is playing an increasingly important role in many fields of medicine, assisting physicians in most steps of patient management. In nephrology, artificial intelligence can already be used to improve clinical care, hemodialysis prescriptions, and follow-up of transplant recipients. However, many nephrologists are still unfamiliar with the basic principles of medical artificial intelligence. This review seeks to provide an overview of medical artificial intelligence relevant to the practicing nephrologist, in all fields of nephrology. We define the core concepts of artificial intelligence and machine learning and cover the basics of the functioning of neural networks and deep learning. We also discuss the most recent clinical applications of artificial intelligence in nephrology and medicine; as an example, we describe how artificial intelligence can predict the occurrence of progressive immunoglobulin A nephropathy. Finally, we consider the future of artificial intelligence in clinical nephrology and its impact on medical practice, and conclude with a discussion of the ethical issues that the use of artificial intelligence raises in terms of clinical decision making, physician-patient relationship, patient privacy, and data collection.