Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

Comparison of population pharmacokinetic modeling and machine learning approaches for predicting voriconazole trough concentrations in critically ill patients

BACKGROUND

Despite the widespread use of voriconazole in antifungal treatment, its high pharmacokinetic and pharmacodynamic variability may lead to suboptimal efficacy, especially in intensive care unit (ICU) patients. Machine learning (ML), an artificial intelligence modeling approach, is increasingly being applied to personalized medicine. The effectiveness of ML models for predicting voriconazole blood concentrations in ICU patients, compared to traditional population pharmacokinetics (popPK) models, has been uncertain until now. This study aims to identify the most effective modeling strategy for voriconazole.

METHODS

We developed six ML models using 244 concentrations from 62 patients in our previous popPK dataset. Another additional dataset, consisting of 282 trough concentrations from 177 patients, was used to externally evaluate both ML models and five other published popPK models, utilizing prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting.

RESULTS

The XGBoost model exhibited superior predictive performance among the six ML models, achieving an R2 of 0.73. Its performance metrics (RMSE%: 127.21 %, median absolute prediction error: 29.65 %, median prediction error: 9.82 %, F20: 34.04 %, F30: 50.71 %) outperformed those of the best popPK model (RMSE%: 152.41 %, median absolute prediction error: 44.75 %, median prediction error: -0.99 %, F20: 23.40 %, F30: 36.88 %), suggesting greater accuracy and precision in predicting pharmacokinetics.

CONCLUSIONS

Both ML and popPK models can be utilized for individualized voriconazole therapy. Our comparative study provides insights into the most effective methods for modeling and predicting voriconazole concentrations.

Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques

OBJECTIVE

This study aimed to establish an optimal model based on machine learning (ML) to predict Valproic acid (VPA) trough concentrations in Chinese adult epilepsy patients.

METHODS

A single-center retrospective study was carried out at the Jinshan Hospital affiliated with Fudan University from January 2022 to December 2023. A total of 102 VPA trough concentrations were split into a derivation cohort and a validation cohort at a ratio of 8:2. Thirteen ML algorithms were developed using 27 variables in the derivation cohort and were filtered by the lowest mean absolute error (MAE) value. In addition, feature selection was applied to optimize the model.

RESULTS

Ultimately, the extra tree algorithm was chosen to establish the personalized VPA trough concentration prediction model due to its best performance (MAE = 13.08). The SHapley Additive exPlanations (SHAP) plots were used to visualize and rank the importance of features, providing insights into how each feature influences the model's predictions. After feature selection, we found that the model with the top 9 variables [including daily dose, last dose, uric acid (UA), platelet (PLT), combination, gender, weight, albumin (ALB), aspartate aminotransferase (AST)] outperformed the model with 27 variables, with MAE of 6.82, RMSE of 9.62, R2 value of 0.720, relative accuracy (±20%) of 61.90%, and absolute accuracy (±20 mg/L) of 90.48%.

CONCLUSION

In conclusion, the trough concentration prediction model for VPA in Chinese adult epileptic patients based on the extra tree algorithm demonstrated strong predictive ability which is valuable for clinicians in medication guidance.

Efficacy of mosapride combined with tacrolimus in treatment of gastrointestinal dysfunction secondary to nephrotic syndrome

BACKGROUND

Nephrotic syndrome (NS) is often accompanied by gastrointestinal hormone metabolic disorders, which can easily lead to gastrointestinal dysfunction. Tacrolimus capsules are commonly used in clinical treatment of gastrointestinal dysfunction, and mosapride has the effect of promoting gastrointestinal peristalsis. This study attempted to use mosapride combined with tacrolimus to treat patients with gastrointestinal dysfunction secondary to NS.

AIM

To explore the efficacy of mosapride combined with tacro-limus in the treatment of gastrointestinal dysfunction secondary to NS.

METHODS

A total of 128 patients with gastrointestinal dysfunction secondary to NS in our hospital from May 2021 to May 2023 were randomly divided into a control group and a study group, with 64 cases in each group. Both groups were given diet control, exercise intervention, and tacrolimus capsules. On this basis, the control group was given probiotics treatment, and the study group was given mosapride + probiotics treatment. Both groups were treated continuously for 8 weeks. The treatment effects and adverse reaction rates of the two groups were compared, as well as the renal function indicators [blood urea nitrogen (BUN), serum creatinine (Scr), and 24-hour quantitative proteinuria (TUP)], inflammatory factors [interleukin-6 (IL-6), interleukin-17 (IL-17), and tumor necrosis factor alpha (TNF-α)], typical intestinal flora indicators (Cocci, Lactobacilli, Bifidobacteria, and Enterobacterial colony count) before treatment, 4 weeks after treatment, and 8 weeks after treatment.

RESULTS

The total effective rate of the study group (93.75% vs 81.25%) was higher than that of the control group (P < 0.05). After 4 and 8 weeks of treatment, the plasma GAS level in the study group was lower than that of the control group, and the levels of SS and MTL were higher than those of the control group (P < 0.05). The levels of peripheral blood Scr, BUN, and 24 h urine TUP after 4 and 8 weeks of treatment were lower than those before treatment (P < 0.05). Serum levels of IL-6, IL-17, and TNF-α in the study group were lower than those of the control group after 4 and 8 weeks of treatment (P < 0.05). After 4 and 8 weeks of treatment, the number of colonies of Enterobacter and Coccibacterium was lower than that before treatment, and the number of colonies of Lactobacillus and Bifidobacterium was higher than that before treatment (P < 0.05). There was no significant difference in the incidence of adverse reactions between the study group and the control group (10.94% vs 6.25%) (P > 0.05).

CONCLUSION

Moxapride combined with tacrolimus capsules has a significant effect in the treatment of gastrointestinal dysfunction secondary to NS, which can effectively improve gastrointestinal hormones and renal function in patients, and is worthy of promotion and application.

[Translated article] Introducing artificial intelligence to hospital pharmacy departments

Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.

Approaching artificial intelligence to Hospital Pharmacy

Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.

Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients

Aims

The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients.

Methods

Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group.

Results

The final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance.

Conclusion

The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.

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.

Application of machine learning combined with population pharmacokinetics to improve individual prediction of vancomycin clearance in simulated adult patients

Background and aim

Vancomycin, a glycopeptide antimicrobial drug. PPK has problems such as difficulty in accurately reflecting inter-individual differences, and the PPK model may not be accurate enough to predict individual pharmacokinetic parameters. Therefore, the aim of this study is to investigate whether the application of machine learning combined with the PPK method can improve the prediction of vancomycin CL in adult Chinese patients.

Methods

In the first step, a vancomycin CL prediction model for Chinese adult patients is given by PPK and Hamilton Monte Carlo sampling is used to obtain the reference CL of 1,000 patients; the second step is to obtain the final prediction model by machine learning using an appropriate model for the predictive factor and the reference CL; and the third step is to randomly select, in the simulated data, a total of 250 patients for prediction effect evaluation.

Results

XGBoost model is selected as final machine learning model. More than four-fifths of the subjects' predictive values regarding vancomycin CL are improved by machine learning combined with PPK. Machine learning combined with PPK models is more stable in performance than the PPK method alone for predicting models.

Conclusion

The first combination of PPK and machine learning for predictive modeling of vancomycin clearance in adult patients. It provides a reference for clinical pharmacists or clinicians to optimize the initial dosage given to ensure the effectiveness and safety of drug therapy for each patient.

Weight, CYP3A5 Genotype, and Voriconazole Co-administration Influence Tacrolimus Initial Dosage in Pediatric Lung Transplantation Recipients with Low Hematocrit based on a Simulation Model

OBJECTIVE

The method of administering the initial doses of tacrolimus in recipients of pediatric lung transplantation, especially in patients with low hematocrit, is not clear. The present study aims to explore whether weight, CYP3A5 genotype, and voriconazole co-administration influence tacrolimus initial dosage in recipients of pediatric lung transplantation with low hematocrit based on safety and efficacy using a simulation model.

METHODS

The present study utilized the tacrolimus population pharmacokinetic model, which was employed in lung transplantation recipients with low hematocrit.

RESULTS

For pediatric lung transplantation recipients not carrying CYP3A5*1 and without voriconazole, the recommended tacrolimus doses for weights of 10-13, 13-19, 19-22, 22-35, 35-38, and 38-40 kg are 0.03, 0.04, 0.05, 0.06, 0.07, and 0.08 mg/kg/day, which are split into two doses, respectively. For pediatric lung transplantation recipients carrying CYP3A5*1 and without voriconazole, the recommended tacrolimus doses for weights of 10-18, 18-30, and 30-40 kg are 0.06, 0.08, 0.11 mg/kg/day, which are split into two doses, respectively. For pediatric lung transplantation recipients not carrying CYP3A5*1 and with voriconazole, the recommended tacrolimus doses for weights of 10-20 and 20-40 kg are 0.02 and 0.03 mg/kg/day, which are split into two doses, respectively. For pediatric lung transplantation recipients carrying CYP3A5*1 and with voriconazole, the recommended tacrolimus doses for weights of 10-20, 20-33, and 33-40 kg are 0.03, 0.04, and 0.05 mg/kg/day, which are split into two doses, respectively.

CONCLUSION

The present study is the first to recommend the initial dosages of tacrolimus in recipients of pediatric lung transplantation with low hematocrit using a simulation model.

Population pharmacokinetic analyses of tacrolimus in non-transplant patients: a systematic review

BACKGROUND AND OBJECTIVES

Tacrolimus (TAC) has been increasingly used in patients with non-transplant settings. Because of its large between-subject variability, several population pharmacokinetic (PPK) studies have been performed to facilitate individualized therapy. This review summarized published PPK models of TAC in non-transplant patients, aiming to clarify factors affecting PKs of TAC and identify the knowledge gap that may require further research.

METHODS

The PubMed, Embase databases, and Cochrane Library, as well as related references, were searched from the time of inception of the databases to February 2023, to identify TAC population pharmacokinetic studies modeled in non-transplant patients using a non-linear mixed-effects modeling approach.

RESULTS

Sixteen studies, all from Asian countries (China and Korea), were included in this study. Of these studies, eleven and four were carried out in pediatric and adult patients, respectively. One-compartment models were the commonly used structural models for TAC. The apparent clearance (CL/F) of TAC ranged from 2.05 to 30.9 L·h^-1 (median of 14.9 L·h^-1). Coadministered medication, genetic factors, and weight were the most common covariates affecting TAC-CL/F, and variability in the apparent volume of distribution (V/F) was largely explained by weight. Coadministration with Wuzhi capsules reduced CL/F by about 19 to 43%. For patients with CYP3A5*1*1 and *1*3 genotypes, the CL/F was 39-149% higher CL/F than patients with CYP3A5*1*1.

CONCLUSION

The optimal TAC dosage should be adjusted based on the patient's co-administration, body weight, and genetic information (especially CYP3A5 genotype). Further studies are needed to assess the generalizability of the published models to other ethnic groups. Moreover, external validation should be frequently performed to improve the clinical practicality of the models.