Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling
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ABSTRACT:
SUBMITTER: Wu K
PROVIDER: S-EPMC10904617 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:One of the main obstacles in neurological disease treatment is the presence of the blood-brain barrier. New predictive high-throughput screening tools are essential to avoid costly failures in the advanced phases of development and to contribute to the 3 Rs policy. The objective of this work was to jointly develop a new in vitro system coupled with a physiological-based pharmacokinetic (PBPK) model able to predict brain concentration levels of different drugs in rats. Data from in vitro tests with three different cells lines (MDCK, MDCK-MDR1 and hCMEC/D3) were used together with PK parameters and three scaling factors for adjusting the model predictions to the brain and plasma profiles of six model drugs. Later, preliminary quantitative structure-property relationships (QSPRs) were constructed between the scaling factors and the lipophilicity of drugs. The predictability of the model was evaluated by internal validation. It was concluded that the PBPK model, incorporating the barrier resistance to transport, the disposition within the brain and the drug-brain binding combined with MDCK data, provided the best predictions for passive diffusion and carrier-mediated transported drugs, while in the other cell lines, active transport influence can bias predictions.
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Project description:The effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro, and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low (> 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to changes in the gastrointestinal luminal fluids or physiology, including fluid volume, motility, pH, micellar entrapment, and bile salts. Considering these findings, it is recommended that appropriately verified mechanistic PBPK modeling can be leveraged with high to moderate confidence as a key approach to predicting potential food effect, especially related to mechanisms highlighted here.
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Project description:The volume of distribution at steady state (Vss) in neonates is still often estimated through isometric scaling from adult values, disregarding developmental changes beyond body weight. This study aimed to compare the accuracy of two physiologically based pharmacokinetic (PBPK) Vss prediction methods in neonates (Poulin & Theil with Berezhkovskiy correction (P&T+) and Rodgers & Rowland (R&R)) with isometrical scaling. PBPK models were developed for 24 drugs using in-vitro and in-silico data. Simulations were done in Simcyp (V22) using predefined populations. Clinical data from 86 studies in neonates (including preterms) were used for comparison, and accuracy was assessed using (absolute) average fold errors ((A)AFEs). Isometric scaling resulted in underestimated Vss values in neonates (AFE: 0.61), and both PBPK methods reduced the magnitude of underprediction (AFE: 0.82-0.83). The P&T+ method demonstrated superior overall accuracy compared to isometric scaling (AAFE of 1.68 and 1.77, respectively), while the R&R method exhibited lower overall accuracy (AAFE: 2.03). Drug characteristics (LogP and ionization type) and inclusion of preterm neonates did not significantly impact the magnitude of error associated with isometric scaling or PBPK modeling. These results highlight both the limitations and the applicability of PBPK methods for the prediction of Vss in the absence of clinical data.
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Project description:Long-acting (LA) administration using a subcutaneous (s.c.) implant presents opportunities to simplify administration of antiretroviral drugs, improve pharmacological profiles, and overcome suboptimal adherence associated with daily oral formulations. Tenofovir alafenamide (TAF) is a highly potent nucleoside reverse transcriptase inhibitor (NRTI) and an attractive agent for LA delivery, with a high potency and long intracellular half-life. The aim of this study was to predict minimum TAF doses required to achieve concentrations effective for HIV preexposure prophylaxis (PrEP). Daily drug release requirements were then ascertained by averaging across the dosing interval. A TAF physiologically based pharmacokinetic (PBPK) model was developed and partially qualified against available oral single- and multiple-dose pharmacokinetics. The models were assumed to be qualified when simulated values were within 2-fold of the observed mean. TAF s.c. implants were simulated in five hundred individuals, reporting predicted TAF plasma and tenofovir (TFV) plasma concentrations for various release rates. Intracellular TFV diphosphate (TFV-DP) concentrations were also simulated in peripheral blood cells and cervical and rectal tissues. The minimum dose predicted to achieve intracellular TFV-DP levels above a target concentration of 48 fmol/106 cells for a month was identified. TAF, TFV, and TFV-DP concentrations for release rates between 1.0 and 1.6 mg/day were simulated. The PBPK model indicated that a minimum release of 1.4 mg/day TAF is necessary to achieve TFV-DP concentrations above the identified target in peripheral blood mononuclear cells (PBMCs). TFV-DP cervical and rectal tissue concentrations were predicted to be between 1.5 and 2.0 fmol/106 cells and 0.9 and 1.1 fmol/106 cells, respectively, for release rates between 1.3 and 1.6 mg/day. These simulations provide target minimum doses for LA TAF PrEP in humans. Based on the generated results, multiple implants delivering a total of 1.4 mg/day of TAF subcutaneously could provide protection levels for approximately 6 months to 1 year. This modeling may inform future design of s.c. implants to mitigate adherence issues for effective PrEP applications.
| S-EPMC7526822 | biostudies-literature
Project description:Minimizing in vitro and in vivo testing in early drug discovery with the use of physiologically based pharmacokinetic (PBPK) modeling and machine learning (ML) approaches has the potential to reduce discovery cycle times and animal experimentation. However, the prediction success of such an approach has not been shown for a larger and diverse set of compounds representative of a lead optimization pipeline. In this study, the prediction success of the oral (PO) and intravenous (IV) pharmacokinetics (PK) parameters in rats was assessed using a "bottom-up" approach, combining in vitro and ML inputs with a PBPK model. More than 240 compounds for which all of the necessary inputs and PK data were available were used for this assessment. Different clearance scaling approaches were assessed, using hepatocyte intrinsic clearance and protein binding as inputs. In addition, a novel high-throughput PBPK (HT-PBPK) approach was evaluated to assess the scalability of PBPK predictions for a larger number of compounds in drug discovery. The results showed that bottom-up PBPK modeling was able to predict the rat IV and PO PK parameters for the majority of compounds within a 2- to 3-fold error range, using both direct scaling and dilution methods for clearance predictions. The use of only ML-predicted inputs from the structure did not perform well when using in vitro inputs, likely due to clearance miss predictions. The HT-PBPK approach produced comparable results to the full PBPK modeling approach but reduced the simulation time from hours to seconds. In conclusion, a bottom-up PBPK and HT-PBPK approach can successfully predict the PK parameters and guide early discovery by informing compound prioritization, provided that good in vitro assays are in place for key parameters such as clearance.
| S-EPMC9257750 | biostudies-literature
Project description:Humans are exposed to persistent organic pollutants, such as dioxin-like compounds (DLCs), as mixtures. Understanding and predicting the toxicokinetics and thus internal burden of major constituents of a DLC mixture is important for assessing their contributions to health risks. PBPK models, including dioxin models, traditionally focus on one or a small number of compounds; developing new or extending existing models for mixtures often requires tedious, error-prone coding work. This lack of efficiency to scale up for multi-compound exposures is a major technical barrier toward large-scale mixture PBPK simulations. Congeners in the DLC family, including 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), share similar albeit quantitatively different toxicokinetic and toxicodynamic properties. Taking advantage of these similarities, here we reported the development of a human PBPK modeling framework for DLC mixtures that can flexibly accommodate an arbitrary number of congeners. Adapted from existing TCDD models, our mixture model contains the blood and three diffusion-limited compartments-liver, fat, and rest of the body. Depending on the number of congeners in a mixture, varying-length vectors of ordinary differential equations (ODEs) are automatically generated to track the tissue concentrations of the congeners. Shared ODEs are used to account for common variables, including the aryl hydrocarbon receptor (AHR) and CYP1A2, to which the congeners compete for binding. Binary and multi-congener mixture simulations showed that the AHR-mediated cross-induction of CYP1A2 accelerates the sequestration and metabolism of DLC congeners, resulting in consistently lower tissue burdens than in single exposure, except for the liver. Using dietary intake data to simulate lifetime exposures to DLC mixtures, the model demonstrated that the relative contributions of individual congeners to blood or tissue toxic equivalency (TEQ) values are markedly different than those to intake TEQ. In summary, we developed a mixture PBPK modeling framework for DLCs that may be utilized upon further improvement as a quantitative tool to estimate tissue dosimetry and health risks of DLC mixtures.
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Project description:Physiologically based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are designed to elucidate the mechanism of chemical compound action in organisms based on the physiological, biochemical, anatomical, and thermodynamic properties of organisms. After nearly a century of research and practice, good results have been achieved in the fields of medicine, environmental science, and ecology. However, there is currently a lack of a more systematic review of progress in the main research directions of PBPK models, especially a more comprehensive understanding of the application in aquatic environmental research. In this review, a total of 3974 articles related to PBPK models from 1996 to 24 March 2024 were collected. Then, the main research areas of the PBPK model were categorized based on the keyword co-occurrence maps and cluster maps obtained by CiteSpace. The results showed that research related to medicine is the main application area of PBPK. Four major research directions included in the medical field were "drug assessment", "cross-species prediction", "drug-drug interactions", and "pediatrics and pregnancy drug development", in which "drug assessment" accounted for 55% of the total publication volume. In addition, bibliometric analyses indicated a rapid growth trend in the application in the field of environmental research, especially in predicting the residual levels in organisms and revealing the relationship between internal and external exposure. Despite facing the limitation of insufficient species-specific parameters, the PBPK model is still an effective tool for improving the understanding of chemical-biological effectiveness and will provide a theoretical basis for accurately assessing potential risks to ecosystems and human health. The combination with the quantitative structure-activity relationship model, Bayesian method, and machine learning technology are potential solutions to the previous research gaps.
| S-EPMC11209072 | biostudies-literature
Project description:Buprenorphine plays a crucial role in the therapeutic management of pain in adults, adolescents and pediatric subpopulations. However, only few pharmacokinetic studies of buprenorphine in children, particularly neonates, are available as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling allows the prediction of drug exposure in pediatrics based on age-related physiological differences. The aim of this study was to predict the pharmacokinetics of buprenorphine in pediatrics with PBPK modeling. Moreover, the drug-drug interaction (DDI) potential of buprenorphine with CYP3A4 and P-glycoprotein perpetrator drugs should be elucidated. A PBPK model of buprenorphine and norbuprenorphine in adults has been developed and scaled to children and preterm neonates, accounting for age-related changes. One-hundred-percent of the predicted AUClast values in adults (geometric mean fold error (GMFE): 1.22), 90% of individual AUClast predictions in children (GMFE: 1.54) and 75% in preterm neonates (GMFE: 1.57) met the 2-fold acceptance criterion. Moreover, the adult model was used to simulate DDI scenarios with clarithromycin, itraconazole and rifampicin. We demonstrate the applicability of scaling adult PBPK models to pediatrics for the prediction of individual plasma profiles. The novel PBPK models could be helpful to further investigate buprenorphine pharmacokinetics in various populations, particularly pediatric subgroups.
| S-EPMC7355427 | biostudies-literature
Project description:AimsThis study aims to develop and verify a physiologically based pharmacokinetic (PBPK) population model for the Chinese geriatric population in Simcyp.MethodsFirstly, physiological information for the Chinese geriatric population was collected and later employed to develop the Chinese geriatric population model by recalibration of corresponding physiological parameters in the Chinese adult population model available in Simcyp (i.e., Chinese healthy volunteer model). Secondly, drug-dependent parameters were collected for six drugs with different elimination pathways (i.e., metabolized by CYP1A2, CYP3A4 or renal excretion). The drug models were then developed and verified by clinical data from Chinese adults, Caucasian adults and Caucasian elderly subjects to ensure that drug-dependent parameters are correctly inputted. Finally, the tested drug models in combination with the newly developed Chinese geriatric population model were applied to simulate drug concentration in Chinese elderly subjects. The predicted results were then compared with the observations to evaluate model prediction performance.ResultsNinety-eight per cent of predicted AUC, 95% of predicted Cmax , and 100% of predicted CL values were within two-fold of the observed values, indicating all drug models were properly developed. The drug models, combined with the newly developed population model, were then used to predict pharmacokinetics in Chinese elderly subjects aged 60-93. The predicted AUC, Cmax , and CL values were all within two-fold of the observed values.ConclusionThe population model for the Chinese elderly subjects appears to adequately predict the concentration of the drug that was metabolized by CYP1A2, CYP3A4 or eliminated by renal clearance.
| S-EPMC8359847 | biostudies-literature