Clinical pharmacology in adolescent transgender medicine.

Adolescent transgender medicine is a growing clinical field. Gender-affirming medications for transgender youth may include gonadotropin-releasing hormone (GnRH) agonists, gender-affirming hormones or both. To evaluate the potential effects of GnRH agonists (puberty suppression) on pharmacokinetic processes for transgender youth, we searched PubMed from inception to May 2024 for publications on the effects of GnRH agonists on drug absorption, distribution, metabolism or excretion for transgender adolescents or effects on hormones (including gonadotropins, adrenal androgens, sex steroids) that are associated with changes in drug metabolism during puberty in the general adolescent population. No publications discussed the effects of GnRH agonist treatment on pharmacokinetic processes for adolescent transgender people. Sixteen publications observed marked decreases in gonadotropins and sex steroids for both adolescent transgender men and adolescent transgender women and slight effects on adrenal androgens. During GnRH agonist treatment, changes in body composition and body shape were greater for adolescent transgender people than for cisgender adolescent people. Further research is needed to better understand the effects of GnRH agonists on drug metabolism and other pharmacokinetic processes for transgender adolescents receiving GnRH agonists and other gender-affirming medications.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing.

BACKGROUND: Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have greatly benefitted from computational and mathematical advances over the past 60 years. Furthermore, the use of artificial intelligence (AI) and machine learning (ML) approaches for supporting clinical research and support is increasing. However, AI and ML applications for precision dosing have been evaluated only recently. Given the capability of ML to handle multidimensional data, such as from electronic health records, opportunities for AI and ML applications to facilitate TDM and MIPD may be advantageous. METHODS: This review summarizes relevant AI and ML approaches to support TDM and MIPD, with a specific focus on recent applications. The opportunities and challenges associated with this integration are also discussed. RESULTS: Various AI and ML applications have been evaluated for precision dosing, including those related to concentration or exposure prediction, dose optimization, population pharmacokinetics and pharmacodynamics, quantitative systems pharmacology, and MIPD system development and support. These applications provide an opportunity for ML and pharmacometrics to operate in an integrated manner to provide clinical decision support for precision dosing. CONCLUSIONS: Although the integration of AI with precision dosing is still in its early stages and is evolving, AI and ML have the potential to work harmoniously and synergistically with pharmacometric approaches to support TDM and MIPD. Because data are increasingly shared between institutions and clinical networks and aggregated into large databases, these applications will continue to grow. The successful implementation of these approaches will depend on cross-field collaborations among clinicians and experts in informatics, ML, pharmacometrics, clinical pharmacology, and TDM.

Sex-tailored pharmacology and COVID-19: Next steps towards appropriateness and health equity.

Making gender bias visible allows to fill the gaps in knowledge and understand health records and risks of women and men. The coronavirus disease 2019 (COVID-19) pandemic has shown a clear gender difference in health outcomes. The more severe symptoms and higher mortality in men as compared to women are likely due to sex and age differences in immune responses. Age-associated decline in sex steroid hormone levels may mediate proinflammatory reactions in older adults, thereby increasing their risk of adverse outcomes, whereas sex hormones and/or sex hormone receptor modulators may attenuate the inflammatory response and provide benefit to COVID-19 patients. While multiple pharmacological options including anticoagulants, glucocorticoids, antivirals, anti-inflammatory agents and traditional Chinese medicine preparations have been tested to treat COVID-19 patients with varied levels of evidence in terms of efficacy and safety, information on sex-targeted treatment strategies is currently limited. Women may have more benefit from COVID-19 vaccines than men, despite the occurrence of more frequent adverse effects, and long-term safety data with newly developed vectors are eagerly awaited. The prevalent inclusion of men in randomized clinical trials (RCTs) with subsequent extrapolation of results to women needs to be addressed, as reinforcing sex-neutral claims into COVID-19 research may insidiously lead to increased inequities in health care. The huge worldwide effort with over 3000 ongoing RCTs of pharmacological agents should focus on improving knowledge on sex, gender and age as pillars of individual variation in drug responses and enforce appropriateness.

GCN-BMP: Investigating graph representation learning for DDI prediction task.

One drug's pharmacological activity may be changed unexpectedly, owing to the concurrent administration of another drug. It is likely to cause unexpected drug-drug interactions (DDIs). Several machine learning approaches have been proposed to predict the occurrence of DDIs. However, existing approaches are almost dependent heavily on various drug-related features, which may incur noisy inductive bias. To alleviate this problem, we investigate the utilization of the end-to-end graph representation learning for the DDI prediction task. We establish a novel DDI prediction method named GCN-BMP (Graph Convolutional Network with Bond-aware Message Propagation) to conduct an accurate prediction for DDIs. Our experiments on two real-world datasets demonstrate that GCN-BMP can achieve higher performance compared to various baseline approaches. Moreover, in the light of the self-contained attention mechanism in our GCN-BMP, we could find the most vital local atoms that conform to domain knowledge with certain interpretability.

The message passing neural networks for chemical property prediction on SMILES.

Drug metabolism is determined by the biochemical and physiological properties of the drug molecule. To improve the performance of a drug property prediction model, it is important to extract complex molecular dynamics from limited data. Recent machine learning or deep learning based models have employed the atom- and bond-type information, as well as the structural information to predict drug properties. However, many of these methods can be used only for the graph representations. Message passing neural networks (MPNNs) (Gilmer et al., 2017) is a framework used to learn both local and global features from irregularly formed data, and is invariant to permutations. This network performs an iterative message passing (MP) operation on each object and its neighbors, and obtain the final output from all messages regardless of their order. In this study, we applied the MP-based attention network (Nikolentzos et al., 2019) originally developed for text learning to perform chemical classification tasks. Before training, we tokenized the characters, and obtained embeddings of each molecular sequence. We conducted various experiments to maximize the predictivity of the model. We trained and evaluated our model using various chemical classification benchmark tasks. Our results are comparable to previous state-of-the-art and baseline models or outperform. To the best of our knowledge, this is the first attempt to learn chemical strings using an MP-based algorithm. We will extend our work to more complex tasks such as regression or generation tasks in the future.

Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning.

Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused widespread concern in academia and industry. The existing computational DDI prediction methods are mainly divided into four categories: literature extraction-based, similarity-based, matrix operations-based and network-based. A number of recent studies have revealed that integrating heterogeneous drug features is of significant importance for developing high-accuracy prediction models. Meanwhile, drugs that lack certain features could utilize other features to learn representations. However, it also brings some new challenges such as incomplete data, non-linear relations and heterogeneous properties. In this paper, we propose a multi-modal deep auto-encoders based drug representation learning method named DDI-MDAE, to predict DDIs from large-scale, noisy and sparse data. Our method aims to learn unified drug representations from multiple drug feature networks simultaneously using multi-modal deep auto-encoders. Then, we apply four operators on the learned drug embeddings to represent drug-drug pairs and adopt the random forest classifier to train models for predicting DDIs. The experimental results demonstrate the effectiveness of our proposed method for DDI prediction and significant improvement compared to other state-of-the-art benchmark methods. Moreover, we apply a specialized random forest classifier in the positive-unlabeled (PU) learning setting to enhance the prediction accuracy. Experimental results reveal that the model improved by PU learning outperforms the original method DDI-MDAE by 7.1% and 6.2% improvement in AUPR metric respectively on 3-fold cross-validation (3-CV) and 5-fold cross-validation (5-CV). And in F-measure metric, the improved model gains 10.4% and 8.4% improvement over DDI-MDAE respectively on 3-CV and 5-CV. The usefulness of DDI-MDAE is further demonstrated by case studies.

TOP: A deep mixture representation learning method for boosting molecular toxicity prediction.

At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learning-based framework called TOP (the abbreviation of TOxicity Prediction). TOP integrates specifically designed data preprocessing methods, an RNN based on bidirectional gated recurrent unit (BiGRU), and fully connected neural networks for end-to-end molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction accuracy. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five state-of-the-art methods.

The application of extreme value theory to pharmacometrics.

Clinical trials are often analyzed by examining the means, e.g., what is the mean treatment effect or what is the mean treatment difference, but there are times when analysis of the maximums (or minimums) are of interest. For instance, what is the highest heart rate that could be observed or what the smallest treatment effect that could be expected? While inference on the means is based on the central limit theorem, the corresponding theorem for maximums or minimums is the Fisher-Tippett theorem, also called the extreme value theorem (EVT). This manuscript will introduce EVT to pharmacometricians, particularly block maxima analysis and peak over threshold analysis, and provide examples for how it can be applied to pharmacometric data, particularly the analysis of pharmacokinetics and ECG safety data, like QTcF intervals.

Drugs for Insomnia beyond Benzodiazepines: Pharmacology, Clinical Applications, and Discovery.

Although the GABAergic benzodiazepines (BZDs) and Z-drugs (zolpidem, zopiclone, and zaleplon) are FDA-approved for insomnia disorders with a strong evidence base, they have many side effects, including cognitive impairment, tolerance, rebound insomnia upon discontinuation, car accidents/falls, abuse, and dependence liability. Consequently, the clinical use of off-label drugs and novel drugs that do not target the GABAergic system is increasing. The purpose of this review is to analyze the neurobiological and clinical evidence of pharmacological treatments of insomnia, excluding the BZDs and Z-drugs. We analyzed the melatonergic agonist drugs, agomelatine, prolonged-release melatonin, ramelteon, and tasimelteon; the dual orexin receptor antagonist suvorexant; the modulators of the α2δ subunit of voltage-sensitive calcium channels, gabapentin and pregabalin; the H1 antagonist, low-dose doxepin; and the histamine and serotonin receptor antagonists, amitriptyline, mirtazapine, trazodone, olanzapine, and quetiapine. The pharmacology and mechanism of action of these treatments and the evidence-base for the use of these drugs in clinical practice is outlined along with novel pipelines. There is evidence to recommend suvorexant and low-dose doxepin for sleep maintenance insomnia; there is also sufficient evidence to recommend ramelteon for sleep onset insomnia. Although there is limited evidence for the use of the quetiapine, trazodone, mirtazapine, amitriptyline, pregabalin, gabapentin, agomelatine, and olanzapine as treatments for insomnia disorder, these drugs may improve sleep while successfully treating comorbid disorders, with a different side effect profile than the BZDs and Z-drugs. The unique mechanism of action of each drug allows for a more personalized and targeted medical management of insomnia.

Harnessing Big Data for Systems Pharmacology.

Systems pharmacology aims to holistically understand mechanisms of drug actions to support drug discovery and clinical practice. Systems pharmacology modeling (SPM) is data driven. It integrates an exponentially growing amount of data at multiple scales (genetic, molecular, cellular, organismal, and environmental). The goal of SPM is to develop mechanistic or predictive multiscale models that are interpretable and actionable. The current explosions in genomics and other omics data, as well as the tremendous advances in big data technologies, have already enabled biologists to generate novel hypotheses and gain new knowledge through computational models of genome-wide, heterogeneous, and dynamic data sets. More work is needed to interpret and predict a drug response phenotype, which is dependent on many known and unknown factors. To gain a comprehensive understanding of drug actions, SPM requires close collaborations between domain experts from diverse fields and integration of heterogeneous models from biophysics, mathematics, statistics, machine learning, and semantic webs. This creates challenges in model management, model integration, model translation, and knowledge integration. In this review, we discuss several emergent issues in SPM and potential solutions using big data technology and analytics. The concurrent development of high-throughput techniques, cloud computing, data science, and the semantic web will likely allow SPM to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.

Identification of Target Genes Related to Sulfasalazine in Triple-Negative Breast Cancer Through Network Pharmacology.

BACKGROUND The anti-inflammatory drug sulfasalazine (SAS) has been confirmed to inhibit the growth of triple-negative breast cancer (TNBC), but the mechanism is not clear. The aim of this study was to use network pharmacology to find relevant pathways of SAS in TNBC patients. MATERIAL AND METHODS Through screening of the GeneCards, CTD, and ParmMapper databases, potential genes related to SAS and TNBC were identified. In addition, gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed using the R programming language. Protein-protein interaction networks were constructed using Cytoscape. The Kaplan-Meier plotter screened genes related to TNBC prognosis. TNBC patient gene expression profiles and clinical data were downloaded from The Cancer Genome Atlas database. A heatmap was generated using the R programming language that presents the expression of potential target genes in patients with TNBC. RESULTS Eighty potential target genes were identified through multiple databases. The bioinformatical analyses predicted the interrelationships, potential pathways, and molecular functions of the genes from multiple aspects, which are associated with physiological processes such as the inflammatory response, metabolism of reactive oxygen species (ROS), and regulation of proteins in the matrix metalloproteinase (MMP) family. Survival analysis showed that 12 genes were correlated with TNBC prognosis. Heatmapping showed that genes such as those encoding members of the MMP family were differentially expressed in TNBC tissues and normal tissues. CONCLUSIONS Our analysis revealed that the main reasons for the inhibitory effect of SAS on TNBC cells may be inhibition of the inflammatory response and MMP family members and activation of ROS.

Assessing parameter uncertainty in small-n pharmacometric analyses: value of the log-likelihood profiling-based sampling importance resampling (LLP-SIR) technique.

Assessing parameter uncertainty is a crucial step in pharmacometric workflows. Small datasets with ten or fewer subjects appear regularly in drug development and therapeutic use, but it is unclear which method to assess parameter uncertainty is preferable in such situations. The aim of this study was to (i) systematically evaluate the performance of standard error (SE), bootstrap (BS), log-likelihood profiling (LLP), Bayesian approaches (BAY) and sampling importance resampling (SIR) to assess parameter uncertainty in small datasets and (ii) to evaluate methods to provide proposal distributions for the SIR. A simulation study was conducted and the 0-95% confidence interval (CI) and coverage for each parameter was evaluated and compared to reference CIs derived by stochastic simulation and estimation (SSE). A newly proposed LLP-SIR, combining the proposal distribution provided by LLP with SIR, was included in addition to conventional SE-SIR and BS-SIR. Additionally, the methods were applied to a clinical dataset. The determined CIs differed substantially across the methods. The CIs of SE, BS, LLP and BAY were not in line with the reference in datasets with ≤ 10 subjects. The best alignment was found for the LLP-SIR, which also provided the best coverage results among the SIR methods. The best overall results regarding the coverage were provided by LLP and BAY across all parameters and dataset sizes. To conclude, the popular SE and BS methods are not suitable to derive parameter uncertainty in small datasets containing ≤ 10 subjects, while best performances were observed with LLP, BAY and LLP-SIR.

Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine.

Population pharmacokinetic analysis is used to estimate pharmacokinetic parameters and their variability from concentration data. Due to data sparseness issues, available datasets often do not allow the estimation of all parameters of the suitable model. The PRIOR subroutine in NONMEM supports the estimation of some or all parameters with values from previous models, as an alternative to fixing them or adding data to the dataset. From a literature review, the best practices were compiled to provide a practical guidance for the use of the PRIOR subroutine in NONMEM. Thirty-three articles reported the use of the PRIOR subroutine in NONMEM, mostly in special populations. This approach allowed fast, stable and satisfying modelling. The guidance provides general advice on how to select the most appropriate reference model when there are several previous models available, and to implement and weight the selected parameter values in the PRIOR function. On the model built with PRIOR, the similarity of estimates with the ones of the reference model and the sensitivity of the model to the PRIOR values should be checked. Covariates could be implemented a priori (from the reference model) or a posteriori, only on parameters estimated without prior (search for new covariates).

Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Huashi Baidu formula in the treatment of COVID-19.

PURPOSE: Huashi Baidu formula (HSBDF) was developed to treat the patients with severe COVID-19 in China. The purpose of this study was to explore its active compounds and demonstrate its mechanisms against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through network pharmacology and molecular docking. METHODS: All the components of HSBDF were retrieved from the pharmacology database of TCM system. The genes corresponding to the targets were retrieved using UniProt and GeneCards database. The herb-compound-target network was constructed by Cytoscape. The target protein-protein interaction network was built using STRING database. The core targets of HSBDF were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The main active compounds of HSBDF were docked with SARS-CoV-2 and angiotensin converting enzyme II (ACE2). RESULTS: Compound-target network mainly contained 178 compounds and 272 corresponding targets. Key targets contained MAPK3, MAPK8, TP53, CASP3, IL6, TNF, MAPK1, CCL2, PTGS2, etc. There were 522 GO items in GO enrichment analysis (p < .05) and 168 signaling pathways (p < .05) in KEGG, mainly including TNF signaling pathway, PI3K-Akt signaling pathway, NOD-like receptor signaling pathway, MAPK signaling pathway, and HIF-1 signaling pathway. The results of molecular docking showed that baicalein and quercetin were the top two compounds of HSBDF, which had high affinity with ACE2. CONCLUSION: Baicalein and quercetin in HSBDF may regulate multiple signaling pathways through ACE2, which might play a therapeutic role on COVID-19.

Bioanalytical HPLC Applications of In-Tube Solid Phase Microextraction: A Two-Decade Overview.

In-tube solid phase microextraction is a cutting-edge sample treatment technique offering significant advantages in terms of miniaturization, green character, automation, and preconcentration prior to analysis. During the past years, there has been a considerable increase in the reported publications, as well as in the research groups focusing their activities on this technique. In the present review article, HPLC bioanalytical applications of in-tube SPME are discussed, covering a wide time frame of twenty years of research reports. Instrumental aspects towards the coupling of in-tube SPME and HPLC are also discussed, and detailed information on materials/coatings and applications in biological samples are provided.

[Principles of Bayesian statistics and its relationship with applied pharmacokinetics]. / Principios de estadística Bayesiana y su relación con la farmacocinética aplicada.

If one knows the probability of an event occurring in a population, Bayesian statistics allows mo difying its value when there is new individual information available. Although the Bayesian and frequentist (classical) methodologies have identical fields of application, the first one is increasin gly applied in scientific research and big data analysis. In modern pharmacotherapy, clinical phar macokinetics has been used for the expansion of monitoring, facilitated by technical-analytical and mathematical-statistical developments. Population pharmacokinetics has allowed the identification and quantification of pathophysiological and treatment characteristics in a specific patient popu lation, especially in the pediatric and neonatal population and other vulnerable groups, explaining interindividual variability. Likewise, Bayesian estimation is important as a statistical tool applied in pharmacotherapy optimization software when pharmacological monitoring is based on clinical phar macokinetic interpretation. With its advantages and despite its limitations, pharmacotherapeutic op timization based on Bayesian estimation is increasingly used, becoming the reference method today. This characteristic is particularly convenient for routine clinical practice due to the limited number of samples required from the patient and the flexibility it shows regarding blood sampling times for drug quantification. Therefore, the application of Bayesian principles to the practice of clinical phar macokinetics has led to the improvement of pharmacotherapeutic care.

Conference report: pharmacogenomics in special populations at WCP2018.

The 18th World Congress of Basic and Clinical Pharmacology (WCP2018), coordinated by IUPHAR and hosted by the Japanese Pharmacological Society and the Japanese Society of Clinical Pharmacology and Therapeutics, was held in July 2018 at the Kyoto International Conference Center, in Kyoto, Japan. Having as its main theme 'Pharmacology for the Future: Science, Drug Development and Therapeutics', WCP2018 was attended by over 4500 delegates, representing 78 countries. The present report is an overview of a symposium at WCP2018, entitled Pharmacogenomics in Special Populations, organized by IUPHAR´s Pharmacogenetics/Genomics (PGx) section. The PGx section congregates distinguished scientists from different continents, covering expertise from basic research, to clinical implementation and ethical aspects of PGx, and one of its major activities is the coordination of symposia and workshops to foster exchange of PGx knowledge (https://iuphar.org/sections-subcoms/pharmacogenetics-genomics/). The symposium attracted a large audience to listen to presentations covering various areas of research and clinical adoption of PGx in Oceania, Africa, Latin America and Asia.

Sources of Variability and Accuracy of Performance Assessment in the Clinical Pharmacology Quality Assurance Proficiency Testing Program for Antiretrovirals.

BACKGROUND: The Clinical Pharmacology Quality Assurance (CPQA) program provides semiannual proficiency testing (PT) of antiretroviral analytes for 11 US and international clinical pharmacology laboratories (CPLs) to ensure interlaboratory comparability. In this article, we provide estimates of the main sources of variability and assess the accuracy of the algorithm for the assessment of performance. METHODS: Descriptive statistics are reported from 13 PT rounds from 2010 to 2016. Eight of the most common antiretroviral analytes were examined. Variance components analysis was used to rank the relative contributions of CPLs, antiretroviral analyte, and concentration category (low, medium, and high) to bias and variability using mixed models. Binary classification metrics of the PT assessment algorithm are calculated in comparison with a model using 95% prediction limits around estimated regression equations. RESULTS: CPLs provided 4109 reported concentrations of 65 unique samples for each of the 8 antiretroviral analytes across 13 PT rounds. Individual CPL accounted for the greatest amount of total variability (4.4%). Individual CPL and analyte combination (interaction) accounted for the greatest amount of bias (8.1%). Analyte alone accounted for 0.5% or less for total variability and bias. Overall, using a ±20% acceptance window around the final target, 97% of individual reported concentrations were scored acceptable, and 96% of antiretroviral/round scores were deemed satisfactory. Comparison with the regression model gave 100% sensitivity but only 34.47% specificity. Narrowing the acceptance window to ±15% improved specificity to 84.47% while maintaining a 99.17% sensitivity. CONCLUSIONS: The current CPQA PT scoring algorithm that use a ±20% acceptance window seems to suffer from a low specificity and may be too lenient. A stricter ±15% acceptance window would increase specificity and overall accuracy while lowering the overall pass rate by only 3%.