Named entity recognition of pharmacokinetic parameters in the scientific literature.

The development of accurate predictions for a new drug's absorption, distribution, metabolism, and excretion profiles in the early stages of drug development is crucial due to high candidate failure rates. The absence of comprehensive, standardised, and updated pharmacokinetic (PK) repositories limits pre-clinical predictions and often requires searching through the scientific literature for PK parameter estimates from similar compounds. While text mining offers promising advancements in automatic PK parameter extraction, accurate Named Entity Recognition (NER) of PK terms remains a bottleneck due to limited resources. This work addresses this gap by introducing novel corpora and language models specifically designed for effective NER of PK parameters. Leveraging active learning approaches, we developed an annotated corpus containing over 4000 entity mentions found across the PK literature on PubMed. To identify the most effective model for PK NER, we fine-tuned and evaluated different NER architectures on our corpus. Fine-tuning BioBERT exhibited the best results, achieving a strict F 1 score of 90.37% in recognising PK parameter mentions, significantly outperforming heuristic approaches and models trained on existing corpora. To accelerate the development of end-to-end PK information extraction pipelines and improve pre-clinical PK predictions, the PK NER models and the labelled corpus were released open source at https://github.com/PKPDAI/PKNER .

Pharmacokinetic Research in Pediatric Extracorporeal Therapies: Current State and Future Directions.

Extracorporeal life support (ECLS), including extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT), are life-saving therapies for critically ill children. Despite this, these modalities carry frustratingly high mortality rates. One driver of mortality may be altered drug disposition due to a combination of underlying illness, patient-circuit interactions, and drug-circuit interactions. Children receiving ECMO and/or CRRT routinely receive 20 or more drugs, and data supporting optimal dosing is lacking for most of these medications. The Pediatric Paracorporeal and Extracorporeal Therapies Summit (PPETS) gathered an international group of experts in the fields of ECMO, CRRT, and other ECLS modalities to discuss the current state of these therapies, disseminate innovative support strategies, share clinical experiences, and foster future collaborations. Here, we summarize the conclusions of PPETS and put forward a pathway to optimize pharmacokinetic (PK) research in this population. We must prioritize specific medications for in-depth study to improve drug use in ECLS and patient outcomes. Based on frequency of use, potential for adverse outcomes if dosed inappropriately, and lack of existing PK data, a list of high priority drugs was compiled for future research. Researchers must additionally reconsider study designs, emphasizing pooling of resources through multi-center studies and the use of innovative PK modeling techniques. Finally, the integration of validated PK models into clinical practice must be streamlined to deliver optimal medication use at the bedside. Focusing on the proposed list of highlighted medications and key methodological considerations will maximize the impact of future research.

Hierarchical deep compartment modeling: A workflow to leverage machine learning and Bayesian inference for hierarchical pharmacometric modeling.

Population pharmacokinetic (PK) modeling serves as the cornerstone for understanding drug behavior within a specific population. It integrates subject covariates to elucidate the variability in PK parameters, thus enhancing predictive accuracy. However, covariate modeling within this framework can be intricate and time-consuming due to the often obscure structural relationship between covariates and PK parameters. Previous attempts, such as deep compartment modeling (DCM), aimed to streamline this process using machine learning techniques. Nonetheless, DCM fell short in assessing residual errors and interindividual variability (IIV), potentially leading to model misspecification and overfitting. Furthermore, DCM lacked the ability to quantify model uncertainty. To address these limitations, we introduce hierarchical deep compartment modeling (HDCM) as an advancement of DCM. HDCM harnesses machine learning to discern the interplay between covariates and PK parameters while simultaneously evaluating diverse levels of random effects and quantifying uncertainty through Bayesian inference. This tutorial provides a comprehensive application of the HDCM workflow using open-source Julia tools.

PharmaBench: Enhancing ADMET benchmarks with large language models.

Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing ADMET-related benchmark sets are limited in utility due to their small dataset sizes and the lack of representation of compounds used in drug discovery projects. These shortcomings hinder their application in model building for drug discovery. To address this issue, we propose a multi-agent data mining system based on Large Language Models that effectively identifies experimental conditions within 14,401 bioassays. This approach facilitates merging entries from different sources, culminating in the creation of PharmaBench. Additionally, we have developed a data processing workflow to integrate data from various sources, resulting in 156,618 raw entries. Through this workflow, we constructed PharmaBench, a comprehensive benchmark set for ADMET properties, which comprises eleven ADMET datasets and 52,482 entries. This benchmark set is designed to serve as an open-source dataset for the development of AI models relevant to drug discovery projects.

Novel analytical solutions for convolution in compartmental pharmacokinetic models and application to non-bioequivalent formulations.

Deconvolution and convolution are powerful tools that allow decomposition and reconstruction, respectively, of plasma versus time profiles from input and impulse functions. While deconvolution have commonly used compartmental approaches (e.g., Wagner-Nelson or Loo-Riegelman), convolution most typically used the convolution integral which can be solved with numerical methods. In 2005, an analytical solution for one-compartment pharmacokinetic was proposed and has been widely used ever since. However, to the best of our knowledge, analytical solutions for drugs distributed in more than one compartment have not been reported yet. In this paper, analytical solutions for compartmental convolution from both original and exact Loo-Riegelman approaches were developed and evaluated for different scenarios. While convolution from original approach was slightly more precise than that from the exact Loo-Riegelman, both methods were extremely accurate for reconstruction of plasma profiles after respective deconvolutions. Nonetheless, convolution from exact Loo-Riegelman was easier to interpret and to be manipulated mathematically. In fact, convolution solutions for three and more compartments can be easily written with this approach. Finally, our convolution analytical solution was applied to predict the failure in bioequivalence for levonorgestrel, demonstrating that equations in this paper may be useful tools for pharmaceutical scientists.

Physiologically based pharmacokinetic modeling in obesity: applications and challenges.

INTRODUCTION: Rising global obesity rates pose a threat to people's health. Obesity causes a series of pathophysiologic changes, making the response of patients with obesity to drugs different from that of nonobese, thus affecting the treatment efficacy and even leading to adverse events. Therefore, understanding obesity's effects on pharmacokinetics is essential for the rational use of drugs in patients with obesity. AREAS COVERED: Articles related to physiologically based pharmacokinetic (PBPK) modeling in patients with obesity from inception to October 2023 were searched in PubMed, Embase, Web of Science and the Cochrane Library. This review outlines PBPK modeling applications in exploring factors influencing obesity's effects on pharmacokinetics, guiding clinical drug development and evaluating and optimizing clinical use of drugs in patients with obesity. EXPERT OPINION: Obesity-induced pathophysiologic alterations impact drug pharmacokinetics and drug-drug interactions (DDIs), altering drug exposure. However, there is a lack of universal body size indices or quantitative pharmacology models to predict the optimal for the patients with obesity. Therefore, dosage regimens for patients with obesity must consider individual physiological and biochemical information, and clinically individualize therapeutic drug monitoring for highly variable drugs to ensure effective drug dosing and avoid adverse effects.

Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage.

We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our study introduces and describes a large-scale data set of 11 clinical PK end points, encompassing over 2700 unique chemical structures to train machine learning models. To that end multiple advanced training strategies are compared, including the integration of in vitro data and a novel self-supervised pretraining task. In addition to the predictions, our final model provides meaningful epistemic uncertainties for every data point. This allows us to successfully identify regions of exceptional predictive performance, with an absolute average fold error (AAFE/geometric mean fold error) of less than 2.5 across multiple end points. Together, these advancements represent a significant leap toward actionable PK predictions, which can be utilized early on in the drug design process to expedite development and reduce reliance on nonclinical studies.

Application of Mechanistic Multiparameter Optimization and Large-Scale In Vitro to In Vivo Pharmacokinetics Correlations to Small-Molecule Therapeutic Projects.

Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g., minimizing dose, maximizing safety margins, and/or minimizing drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo pharmacokinetic (PK) properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small-molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top second percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared with the rest of the compounds being synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.

In vitro modelling of intramuscular injection site events.

INTRODUCTION: Intramuscular (IM) injections deliver a plethora of drugs. The majority of IM-related literature details dissolution and/or pharmacokinetic (PK) studies, using methods with limited assessments of post-injection events that can impact drug fate, and absorption parameters. Food and Drug Association guidelines no longer require preclinical in vivo modeling in the U.S.A. Preclinical animal models fail to correlate with clinical outcomes, highlighting the need to study, and understand, IM drug fate in vitro using bespoke models emulating human IM sites. Post-IM injection events, i.e. underlying processes that influence PK outcomes, remain unacknowledged, complicating the application of in vitro methods in preclinical drug development. Understanding such events could guide approaches to predict and modulate IM drug fate in humans. AREAS COVERED: This article reviews challenges in biorelevant IM site modeling (i.e. modeling drug fate outcomes), the value of technologies available for developing IM injectables, methods for studying drug fate, and technologies for training in performing IM administrations. PubMed, Web-of-Science, and Lens databases provided papers published between 2014 and 2024. EXPERT OPINION: IM drug research is expanding what injectable therapeutics can achieve. However, post-injection events that influence PK outcomes remain poorly understood. Until addressed, advances in IM drug development will not realize their full potential.

Analyzing parametric influences driving age-associated changes in absorption using a PBPK-GSA approach.

The advanced age population may be susceptible to an increased risk of adverse effects due to increased drug exposure after oral dosing. Factors such as high-interindividual variability and lack of data has led to poor characterization of absorption's role in pharmacokinetic changes in this population. Physiologically based pharmacokinetic (PBPK) models are increasingly being used during the drug development process, as their unique qualities are advantageous in atypical scenarios such as drug-drug interactions or special populations such as older people. Along with relying on various sources of data, auxiliary tools including parameter estimation and sensitivity analysis techniques are employed to support model development and other applications. However, sensitivity analyses have mostly been limited to localized techniques in the majority of reported PBPK models using them. This is disadvantageous, since local sensitivity analyses are unsuitable for risk analysis, which require assessment of parametric interactions and proper coverage of the input space to better estimate and subsequently mitigate the effects of the phenomenon of interest. For this reason, this study seeks to integrate a global sensitivity analysis screening method with PBPK models based in PK-Sim® to characterize the consequences of potential changes in absorption that are often associated with advanced age. The Elementary Effects (Morris) method and visualization of the results are implemented in R and three model drugs representing Biopharmaceutical Classification System classes I-III that are expected to exhibit some sensitivity to three age-associated hypotheses were successfully tested.

Extracellular vesicles as the next-generation modulators of pharmacokinetics and pharmacodynamics of medications and their potential as adjuvant therapeutics.

BACKGROUND AND MAIN BODY: Pharmacokinetics (PK) and pharmacodynamics (PD) are central concepts to guide the dosage and administration of drug therapies and are essential to consider for both healthcare professionals and researchers in therapeutic planning and drug discovery. PK/PD properties of a drug significantly influence variability in response to treatment, including therapeutic failure or excessive medication-related harm. Furthermore, suboptimal PK properties constitute a significant barrier to further development for some candidate treatments in drug discovery. This article describes how extracellular vesicles (EVs) affect different aspects of PK and PD of medications and their potential to modulate PK and PD properties to address problematic PK/PD profiles of drugs. We reviewed EVs' intrinsic effects on cell behaviours and medication responses. We also described how surface and cargo modifications can enhance EV functionalities and enable them as adjuvants to optimise the PK/PD profile of conventional medications. Furthermore, we demonstrated that various bioengineering strategies can be used to modify the properties of EVs, hence enhancing their potential to modulate PK and PD profile of medications. CONCLUSION: This review uncovers the critical role of EVs in PK and PD modulation and motivates further research and the development of assays to unfold EVs' full potential in solving PK and PD-related problems. However, while we have shown that EVs play a vital role in modulating PK and PD properties of medications, we postulated that it is essential to define the context of use when designing and utilising EVs in pharmaceutical and medical applications. HIGHLIGHTS: Existing solutions for pharmacokinetics and pharmacodynamics modulation are limited. Extracellular vesicles can optimise pharmacokinetics as a drug delivery vehicle. Biogenesis and administration of extracellular vesicles can signal cell response. The pharmaceutical potential of extracellular vesicles can be enhanced by surface and cargo bioengineering. When using extracellular vesicles as modulators of pharmacokinetics and pharmacodynamics, the 'context of use' must be considered.

Perspectives on the use of machine learning for ADME prediction at AstraZeneca.

A drug's pharmacokinetic (PK) profile will determine its dose and the frequency of administration as well as the likelihood of observing any adverse drug reactions.It is important to understand these PK properties as early as possible in the drug discovery process, ideally, to accurately predict these prior to synthesising the molecule leading to significant improvements in efficiency.In this paper, we describe the approaches used within AstraZeneca to improve our ability of predicting the preclinical and human pharmacokinetic profiles of novel molecules using machine learning and artificial intelligence.We will show how combining chemical structure-based approaches with experimentally derived properties enables improved predictions of in vivo pharmacokinetics and can be extended to molecules that go beyond the classical Lipinski's rule-of-five space.We will also discuss how combining these in vitro and in vivo predictive models could ultimately improve our ability to predict the human outcome at the point of chemical design.

A Data Driven Strategy and Case Study for Implementation of Singlicate Analysis in Ligand Binding Assays Used for PK Quantitation.

Duplicate analysis has been a conventional practice in the industry for ligand-binding assays (LBA), particularly for plate-based platforms like Enzyme-linked immunosorbent assay (ELISA) and Meso Scale Discovery (MSD) assays. Recent whitepapers and guidance have opened a door to exploring the implementation of single-well (singlicate) analysis approach for LBAs. Although the bioanalytical industry has actively investigated the suitability of singlicate analysis, applications in supporting regulated LBA bioanalysis are limited. The primary reason for this limitation is the absence of appropriate strategy to facilitate the transition from duplicate to singlicate analysis. In this paper we present the first case study with our data-driven approach to implement singlicate analysis in a clinical pharmacokinetics (PK) plate based LBA assay with ISR data. The central aspect of this strategy is a head-to-head comparison with Precision and Accuracy assessment in both duplicate and singlicate formats as the initial stage of assay validation. Subsequently, statistical analysis is conducted to evaluate method variability in both precision and accuracy. The results of our study indicated that there was no impactful difference between duplicate vs singlicate, affirming the suitability of singlicate analysis for the remaining steps of PK assay validation. The validation results obtained through singlicate analysis demonstrated acceptable assay performance characteristics across all validation parameters, aligning with regulatory guidance. The validated PK assay in singlicate has been employed to support a Phase I study. The appropriateness of singlicate analyses is further supported by initial Incurred Sample Reanalysis (ISR) data in which 90.1% of ISR samples fall within the acceptable criteria.

Predicting Clearance with Simple and Permeability-Limited Physiologically Based Pharmacokinetic Frameworks: Comparison of Well-Stirred, Dispersion, and Parallel-Tube Liver Models.

One-compartment (1C) and permeability-limited models were used to evaluate the ability of microsomal and hepatocyte intrinsic clearances to predict hepatic clearance. Well-stirred (WSM), parallel-tube (PTM), and dispersion (DM) models were evaluated within the liver as well as within whole-body physiologically based pharmacokinetic frameworks. It was shown that a linear combination of well-stirred and parallel-tube average liver blood concentrations accurately approximates dispersion model blood concentrations. Using a flow/permeability-limited model, a large systematic error was observed for acids and no systematic error for bases. A scaling factor that reduced interstitial fluid (ISF) plasma protein binding could greatly decrease the absolute average fold error (AAFE) for acids. Using a 1C model, a scalar to reduce plasma protein binding decreased the microsomal clearance AAFE for both acids and bases. With a permeability-limited model, only acids required this scalar. The mechanism of the apparent increased cytosolic concentrations for acids remains unknown. We also show that for hepatocyte intrinsic clearance in vitro-in vivo correlations (IVIVCs), a 1C model is mechanistically appropriate since hepatocyte clearance should represent the net clearance from ISF to elimination. A relationship was derived that uses microsomal and hepatocyte intrinsic clearance to solve for an active hepatic uptake clearance, but the results were inconclusive. Finally, the PTM model generally performed better than the WSM or DM models, with no clear advantage between microsomes and hepatocytes. SIGNIFICANCE STATEMENT: Prediction of drug clearance from microsomes or hepatocytes remains challenging. Various liver models (e.g., well-stirred, parallel-tube, and dispersion) have been mathematically incorporated into liver as well as whole-body physiologically based pharmacokinetic frameworks. Although the resulting models allow incorporation of pH partitioning, permeability, and active uptake for prediction of drug clearance, including these processes did not improve clearance predictions for both microsomes and hepatocytes.

Multi-Task ADME/PK prediction at industrial scale: leveraging large and diverse experimental datasets.

ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi-task machine learning (ML) models to predict ADME and animal PK endpoints trained on in-house data generated at Boehringer Ingelheim. Models were evaluated both at the design stage of a compound (i. e., no experimental data of test compounds available) and at testing stage when a particular assay would be conducted (i. e., experimental data of earlier conducted assays may be available). Using realistic time-splits, we found a clear benefit in performance of multi-task graph-based neural network models over single-task model, which was even stronger when experimental data of earlier assays is available. In an attempt to explain the success of multi-task models, we found that especially endpoints with the largest numbers of data points (physicochemical endpoints, clearance in microsomes) are responsible for increased predictivity in more complex ADME and PK endpoints. In summary, our study provides insight into how data for multiple ADME/PK endpoints in a pharmaceutical company can be best leveraged to optimize predictivity of ML models.

Distribution Clearance: Significance and Underlying Mechanisms.

PURPOSE: Evaluation of distribution kinetics is a neglected aspect of pharmacokinetics. This study examines the utility of the model-independent parameter whole body distribution clearance (CLD) in this respect. METHODS: Since mammillary compartmental models are widely used, CLD was calculated in terms of parameters of this model for 15 drugs. The underlying distribution processes were explored by assessment of relationships to pharmacokinetic parameters and covariates. RESULTS: The model-independence of the definition of the parameter CLD allowed a comparison of distributional properties of different drugs and provided physiological insight. Significant changes in CLD were observed as a result of drug-drug interactions, transporter polymorphisms and a diseased state. CONCLUSION: Total distribution clearance CLD is a useful parameter to evaluate distribution kinetics of drugs. Its estimation as an adjunct to the model-independent parameters clearance and steady-state volume of distribution is advocated.

Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.

CONTEXT: Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achieved good results in prediction accuracy, they often suffer from insufficient accuracy when dealing with data with irregular topological structures. METHODS: In view of this, this study proposes a pharmacokinetic parameter prediction framework based on graph convolutional networks (GCN), which predicts the PPBR and OBA of small molecule drugs. In the framework, GCN is first used to extract spatial feature information on the topological structure of drug molecules, in order to better learn node features and association information between nodes. Then, based on the principle of drug similarity, this study calculates the similarity between small molecule drugs, selects different thresholds to construct datasets, and establishes a prediction model centered on the GCN algorithm. The experimental results show that compared with traditional machine learning prediction models, the prediction model constructed based on the GCN method performs best on PPBR and OBA datasets with an inter-molecular similarity threshold of 0.25, with MAE of 0.155 and 0.167, respectively. In addition, in order to further improve the accuracy of the prediction model, GCN is combined with other algorithms. Compared to using a single GCN method, the distribution of the predicted values obtained by the combined model is highly consistent with the true values. In summary, this work provides a new method for improving the rate of early drug screening in the future.

Predicting the Intravenous Pharmacokinetics of Covalent Drugs in Animals and Humans.

30 covalent drugs were used to assess clearance (CL) prediction reliability in animals and humans. In animals, marked CL underprediction was observed using cryopreserved hepatocytes or liver microsomes (LMs) supplemented for cytochrome P450 activity. Improved quantitative performance was observed by combining metabolic stability data from LMs and liver S9 fractions, the latter supplemented with reduced glutathione for glutathione transferase activity. While human LMs provided reliable human CL predictions, prediction statistics were improved further by incorporating S9 stability data. CL predictions with allometric scaling were less robust compared to in vitro drug metabolism methods; the best results were obtained using the fu-corrected intercept model. Human volume of distribution (Vd) was well predicted using allometric scaling of animal pharmacokinetic data; the most reliable results were achieved using simple allometric scaling of unbound Vd values. These results provide a quantitative framework to guide appropriate method selection for human PK prediction with covalent drugs.