Effect of Genetic Polymorphisms on the Pharmacokinetics of Deferasirox in Healthy Chinese Subjects and an Artificial Neural Networks Model for Pharmacokinetic Prediction

Application of Northern Goshawk Back-Propagation Artificial Neural Network in the Prediction of Monohydroxycarbazepine Concentration in Patients with Epilepsy

INTRODUCTION

A northern goshawk back-propagation artificial neural network (NGO-BPANN) model was established to predict monohydroxycarbazepine (MHD) concentration in patients with epilepsy.

METHODS

The data were collected from 108 Han Chinese patients with epilepsy on oxcarbazepine monotherapy. The results of 14 genotype variates were selected as the input layer in the first BPANN model, and the variables that had a more significant impact on the plasma concentration of MHD were retained. With demographic characteristics and clinical laboratory test results, the genotypes of SCN1A rs2298771 and SCN2A rs17183814 were used to construct the BPANN model. The BPANN model was comprehensively validated and used to predict the MHD plasma concentration of five patients with epilepsy in our hospital.

RESULTS

The model demonstrated favorable fitness metrics, including a mean squared error of 0.00662, a gradient magnitude of 0.00753, an absence of validation tests amounting to zero, and a correlation coefficient of 0.980. Sex, BMI, and the genotype SCN1A rs2298771 were ranked highest by the absolute mean impact value (MIV), which is primarily associated with the concentration of MHD. The test group exhibited a range of - 20.84% to 31.03% bias between the predicted and measured values, with a correlation coefficient of 0.941 between the two. With BPANN, the MHD nadir concentration could be predicted precisely.

CONCLUSION

The NGO-BPANN model exhibits exceptional predictive capability and can be a practical instrument for forecasting MHD concentration in patients with epilepsy.

CLINICAL TRIAL REGISTRATION

www.chiCTR-OOC-17012141 .

Genetic polymorphisms influencing deferasirox pharmacokinetics, efficacy, and adverse drug reactions: a systematic review and meta-analysis

Objective: Deferasirox is an iron-chelating agent prescribed to patients with iron overload. Due to the interindividual variability of deferasirox responses reported in various populations, this study aims to determine the genetic polymorphisms that influence drug responses. Methods: A systematic search was performed from inception to March 2022 on electronic databases. All studies investigating genetic associations of deferasirox in humans were included, and the outcomes of interest included pharmacokinetics, efficacy, and adverse drug reactions. Fixed- and random-effects model meta-analyses using the ratio of means (ROM) were performed. Results: Seven studies involving 367 participants were included in a meta-analysis. The results showed that subjects carrying the A allele (AG/AA) of ABCC2 rs2273697 had a 1.23-fold increase in deferasirox C_max (ROM = 1.23; 95% confidence interval [CI]:1.06-1.43; p = 0.007) and a lower Vd (ROM = 0.48; 95% CI: 0.36-0.63; p < 0.00001), compared to those with GG. A significant attenuated area under the curve of deferasirox was observed in the subjects with UGT1A3 rs3806596 AG/GG by 1.28-fold (ROM = 0.78; 95% CI: 0.60-0.99; p = 0.04). In addition, two SNPs of CYP24A1 were also associated with the decreased C_trough: rs2248359 CC (ROM = 0.50; 95% CI: 0.29-0.87; p = 0.01) and rs2585428 GG (ROM = 0.47; 95% CI: 0.35-0.63; p < 0.00001). Only rs2248359 CC was associated with decreased C_min (ROM = 0.26; 95% CI: 0.08-0.93; p = 0.04), while rs2585428 GG was associated with a shorter half-life (ROM = 0.44; 95% CI: 0.23-0.83; p = 0.01). Conclusion: This research summarizes the current evidence supporting the influence of variations in genes involved with drug transporters, drug-metabolizing enzymes, and vitamin D metabolism on deferasirox responses.

Development and validation of a new LC–MS/MS method for the determination of mefatinib in human plasma and its first application in pharmacokinetic studies

Mefatinib (MET306) is a novel second-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) designed to address the highly unmet clinical need of gefitinib-induced resistance and irreversibly bind to mutated tyrosine kinase domain of EGFR and human epidermal growth factor receptor 2 (HER2). In this study, a liquid chromatography–tandem mass spectrometry method was established and validated for determining MET306 in non-small cell lung cancer patients and a backpropagation artificial neural network was developed and constructed to predict the pharmacokinetic process. The mobile phase was water containing 5 mM ammonium acetate and acetonitrile at a flow rate of 0.3 mL min−1, within a 4.5 min run time. MET306 was separated on a Hypersil Gold-C18 at 40 °C and subjected to mass analysis using positive electrospray ionization. A total of 524 data were used as development groups and 145 data were used as testing groups. The final established Northern Goshawk Optimization-Backpropagation Artificial Neural Network (NGO-BPANN) model consisted of one input layer with 6 neurons, 1 hidden layer with 10 nodes, and 1 output layer with one node processed by MATLAB2021a.The calibration range of MET306 was 0.5–200 ng mL−1 with the correlation coefficient r ≥ 0.99. Accuracies ranged from 97.20 to 110.80% and the inter- and intra-assay precision were less than 15%. The ranges of extraction recoveries were 104.95% to 112.09% for analyte and internal standard and there was no significant matrix effect. The storage stability under different conditions was in accordance with the bioanalytical guidelines. The time-concentration profiles of the measured and predicted concentrations of MET306 by NGO-BPANN agree well. An NGO-BPANN model was developed to predict the plasma concentration and pharmacokinetic parameters of MET306 in the first time.

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.

Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

Pharmacokinetics of Eltrombopag in Healthy Chinese Subjects and Effect of Sex and Genetic Polymorphism on its Pharmacokinetic and Pharmacodynamic Variability

BACKGROUND AND OBJECTIVE

Eltrombopag is the first oral, small-molecule, non-peptide thrombopoietin receptor agonist for the treatment of idiopathic thrombocytopenic purpura. This study investigated the pharmacokinetics of eltrombopag in healthy Chinese subjects and evaluated the effect of sex and genetic polymorphisms on its variability.

METHODS

Forty-eight healthy subjects were administered a single dose of eltrombopag (25 mg). Plasma concentrations of eltrombopag were determined using a validated liquid chromatography-tandem mass spectrometry method, and platelet counts were determined by blood tests. CYP1A2 rs762551, CYP2C8*3 rs10509681, CYP2C8*3 rs11572080, UGT1A1 rs887829, UGT1A3 rs3806596, and BCRP rs2231142 polymorphisms were genotyped by Sanger sequencing. A back-propagation artificial neural network (BP-ANN) model was constructed to predict pharmacokinetics based on physiological factors and genetic polymorphism data.

RESULTS

Compared with male subjects, female subjects who received a single 25-mg dose of eltrombopag exhibited a significantly increased mean maximum plasma concentration (C_max) and significantly decreased apparent clearance. Additionally, CYP1A2 rs762551 C>A single nucleotide polymorphism influenced distribution and elimination. C-allele carriers exhibited 30% higher systemic exposure and 20% lower apparent clearance compared with homozygous A-allele carriers. Mean percentage increases in platelet counts from baseline to Day 5 were 9.38% and 17.06% in male and female subjects, respectively. The BP-ANN model had a high goodness-of-fit index and good coherence between predicted and measured concentrations (R = 0.98979).

CONCLUSION

Sex and CYP1A2 rs762551 C>A were associated with the pharmacokinetic variability of eltrombopag in healthy Chinese subjects. Females exhibited a better platelet-elevating effect compared with males administered the same dosage. The developed BP-ANN model based on physiological factors and genetic polymorphism data could be promising for applications in pharmacokinetic studies.

TRIAL REGISTRATIONS

https://www.Chinadrugtrials.org.cn CTR20190898.